Keywords: Ecodesign, Multi-objective Optimization, Life-Cycle Assessment (LCA), Photovoltaic (PV) system, Genetic Algorithm (GA), Principal Component Analysis (PCA), Multiple Criteria Decision Optimización Multi-objetivo, Análisis de Ciclo de

Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)

Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multiobjective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated.

The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration.

A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.

Résumé

Écoconception de systèmes photovoltaïques (PV) à grande échelle par optimisation multi-objectif et Analyse du Cycle de Vie (ACV)

En raison de la demande croissante d'énergie dans le monde et des nombreux dommages causés par l'utilisation des énergies fossiles, la contribution des énergies renouvelables a augmenté de manière significative dans le mix énergétique global dans le but de progresser vers un développement plus durable. Dans ce contexte, ce travail vise à l'élaboration d'une méthodologie générale pour la conception de systèmes photovoltaïques, basée sur les principes d'écoconception, en tenant compte simultanément des considérations technico-économiques et environnementales. Afin d'évaluer la performance environnementale des systèmes PV, une technique d'évaluation environnementale basée sur l'Analyse du Cycle de Vie (ACV) a été utilisée. Le modèle environnemental a été couplé d'une manière satisfaisante avec le modèle de conception d'un système PV connecté au réseau pour obtenir un modèle global, apte à un traitement par optimisation. Le modèle de conception du système PV résultant a été développé en faisant intervenir l'estimation du rayonnement solaire reçu dans une zone géographique concernée, le calcul de la quantité annuelle d'énergie produite à partir du rayonnement solaire reçu, les caractéristiques des différents composants et l'évaluation des critères technicoéconomiques à travers le temps de retour énergétique et le temps de retour sur investissement. Le modèle a ensuite été intégré dans une boucle d'optimisation multi-objectif externe basée sur une variante de l'algorithme génétique NSGA-II. Un ensemble de solutions du Pareto a été généré représentant le compromis optimal entre les différents objectifs considérés dans l'analyse. Une méthode basée sur une Analyse en Composantes Principales (ACP) est appliquée pour détecter et enlever les objectifs redondants de l'analyse sans perturber les caractéristiques principales de l'espace des solutions. Enfin, un outil d'aide à la décision basé sur M-TOPSIS a été utilisé pour sélectionner l'option qui offre un meilleur compromis entre toutes les fonctions objectifs considérées et étudiées.

Bien que les modules photovoltaïques à base de silicium cristallin (c-Si) ont une meilleure performance vis-à-vis de la production d'énergie, les résultats ont montré que leur impact environnement est le plus élevé des filières technologiques de production de panneaux. Les technologies en « couches minces » présentent quant à elles le meilleur compromis dans tous les scénarios étudiés.

Une attention particulière a été accordée aux processus de recyclage des modules PV, en dépit du peu d'informations disponibles pour toutes les technologies évaluées. La cause majeure de ce manque d'information est la durée de vie relativement élevée des modules photovoltaïques. Les données relatives aux procédés de recyclage pour les technologies basées sur CdTe et m-Si sont introduites dans la procédure d'optimisation par l'écoconception. En tenant compte de la production d'énergie et du temps de retour sur énergie comme critères d'optimisation, l'avantage de la gestion de fin de vie des modules PV a été confirmé. Une étude économique de la stratégie de recyclage doit être considérée et étudiée afin d'avoir une vision plus globale pour la prise de décision.

Mots-clés: Écoconception, Optimisation Multi-objectif, Systèmes Photovoltaïques (PV), Algorithme Génétique (AG), Analyse en Composantes Principaux (ACP), Méthode d'aide à la décision multicritère (MADMC)

Resumen

Ecodiseño de sistemas fotovoltaicos (FV) a gran escala por optimización multiobjetivo y Análisis de Ciclo de Vida (ACV)

Debido a la creciente demanda de energía a nivel mundial y los numerosos daños causados por el uso de fuentes fósiles, la contribución de las energías renovables en el mix energético global se ha incrementado significativamente con el objetivo de avanzar hacia un desarrollo más sostenible. En ese contexto, el presente trabajo tiene como objetivo el desarrollo de una metodología general para el diseño de sistemas fotovoltaicos basados en los principios del ecodiseño considerando de manera simultánea los aspectos técnico-económicos y ambientales. Con el fin de evaluar el desempeño ambiental de los sistemas FV, una técnica de evaluación ambiental basada en el Análisis de Ciclo de Vida (ACV) fue utilizada. El modelo ambiental fue acoplado exitosamente con el modelo para el diseño de un sistema fotovoltaico conectado a la red eléctrica. El modelo para el diseño de un sistema fotovoltaico fue desarrollado a partir de la estimación de la radiación solar recibida en una ubicación geográfica específica, el cálculo de la energía anual generada a partir de la radiación solar recibida, las características de los diferentes componentes y la evaluación de los criterios tecno-económicos a través del tiempo de retorno energético (EPBT, en ingles) y el periodo de recuperacion de la inversion (PRI). En seguida, el modelo fue incrustado en un bucle externo destinado a la optimización multiobjetivo tomando como referencia una variante del algoritmo genético NSGA-II. Un conjunto de soluciones de Pareto fue generado, el cual representa el compromiso óptimo entre los objetivos considerados en el análisis. El método de Análsis de Componentes Principlaes (ACP) fue aplicado para detectar y eliminar los objetivos redundantes existentes sin alterar las principales características del espacio de soluciones. Finalmente, una herramienta de ayuda para toma de decisiones basado en M-TOPSIS fue utilizado para seleccionar la alternativa que ofrece un mejor compromiso entre todas las funciones objetivo consideradas y estudiadas.

Los resultados mostraron que los módulos fotovoltaicos basados en silicio cristalino (c-Si) tienen un mejor desempeño en la generación de energía, sin embargo el impacto ambiental que generan es el más elevado de entre todas las tecnologías de paneles solares consideradas. Los módulos fotovoltaicos fabricados a partir de TF presentan el mejor compromiso en todos los escenarios estudiados.

Una atención especial fue puesta a los procesos de reciclaje de módulos fotovoltaicos, a pesar de que actualmente no existe suficiente información disponible para todas las tecnologías evaluadas. La principal causa de esta falta de información es la vida útil de los módulos fotovoltaicos. Los datos relativos a los procesos de reciclado para las tecnologías de CdTe y m-Si fueron introducidos en el procedimiento de optimización basado en el ecodiseño. La importancia de los beneficios que tiene la gestión de los módulos fotovoltaicos al final de su vida útil fue puesta en evidencia al considerar la producción de energía y el tiempo de retorno energético como criterios de optimización. Un estudio económico de las estrategias de reciclage debe ser considerado e investigado con el fin de tener una visión más integral para la toma de decisiones futura.

INTRODUCTION GÉNÉRALE

L'énergie du rayonnement solaire reçue sur la Terre constitue le seul véritable apport renouvelable héliostats offrent la possibilité de stocker l'énergie solaire sous forme thermique avant conversion en électricité, ce qui permet de pallier l'intermittence de la production. On peut ainsi obtenir des températures élevées nécessaires à la production de chaleur, d'électricité ou d'hydrogène [cf thèse d'Alaric Montenon [START_REF] Montenon | Analyse, mutualisation et optimisation par la commande de la consommation énergétique des héliostats autonomes des centrales à concentration solaire[END_REF]]. D'autre part les générateurs photovoltaïques implantés en toitures ou en en plein champ, fixes ou montés sur des suiveurs solaires, débitant au fil du soleil leur production dans le réseau électrique.

L'étude présentée ici ne s'intéresse qu'à l'implantation de panneaux fixes d'un parc photovoltaïque de production d'électricité connecté au réseau.

La conception de ces systèmes photovoltaïques à grande échelle est encore actuellement surtout basée sur une approche technico-économique qui a comme objectif de maximiser la production d'énergie.

Mais certains éléments, tels que le niveau d'émissions globales, notamment en gaz à effet de serre doivent être pris en compte pour renforcer l'intérêt de la filière et lui assurer un caractère effectivement durable. En fonction des technologies et de l'implantation des modules, il s'agit ici de concevoir des champs de panneaux solaires de façon optimale en combinant des critères de production et d'impact environnemental, afin de développer une méthodologie d'écoconception. Il est donc important également de considérer le recyclage des panneaux afin de régénérer les matériaux qui les constituent. Une analyse du cycle de vie complet a donc constitué une étape préliminaire afin d'évaluer le coût écologique des panneaux et du câblage associé à intégrer dès la conception du système photovoltaïque. Compte tenu du nombre de paramètres et de critères à traiter, le coeur de l'étude vise à proposer une méthode de conception par optimisation qui sélectionne les solutions les plus durables parmi un très grand nombre de choix possibles.

Le mémoire de thèse est organisé en sept chapitres dont nous ne donnons ci-dessous que les titres, la présentation de leur contenu étant donnée à la fin du premier chapitre qui permet de poser les éléments

MOTIVATION FOR THE STUDY AND STATE-OF-THE ART REVIEW

Ce chapitre d'introduction vise à définir le cadre de cette étude et justifie les objectifs généraux qui ont guidé ces travaux. La partie 1 présente brièvement le contexte énergétique général. Le cas de l'énergie solaire, sur laquelle est centrée l'étude est analysé en détail dans la partie 2. Les caractéristiques techniques des systèmes photovoltaïques sont présentées dans la partie 3 et le développement du marché photovoltaïque est positionné dans la partie 4. Les méthodes traditionnelles de conception et de dimensionnement du système photovoltaïque décrites dans la littérature spécialisée sont ensuite proposées, ce qui justifie l'intérêt de développer une méthode d'éco-conception combinant analyse de cycle de vie, optimisation multiobjectif et procédures multicritères d'aide à la décision pour les systèmes photovoltaïques à grande échelle, ce qui est la base de cette étude. L'organisation du manuscrit est présentée à la fin de ce chapitre.

Chapter 1

Introduction

This introduction chapter aims at defining the context of this study and justifies the general objectives that have guided this work. It is divided into 7 sections. Section 1 presents briefly the general energy context. The case of solar energy, that constitutes the centre of this study is thoroughly analysed in section 2. The technical features of PV systems are presented in section 3 and the PV market development is positioned in section 4. The traditional PV System design and sizing methods reported in the dedicated literature are then proposed, which justifies the interest to develop an ecodesign method combining Life Cycle Assessment, Multi-Objective Optimization and Multiple Criteria Decision-Making procedures for large-scale PV systems which is the core of this study and which has received little attention till now to our knowledge. The organization of the manuscript ends this chapter.

General context

During the last decades, the new technological advances have drastically changed our lifestyle. These changes try to satisfy our primary needs as human beings but equally they intended to provide comfort by eliminating repetitive tasks and facilitating our daily life. To achieve these objectives, the generation and supply of energy has become a crucial element for the sustainability of modern society.

The demand for the provision of energy is increasing rapidly worldwide and the trend is likely to continue in future. Increase in its production translates into better quality of life and creation of wealth.

Electricity producing systems presently in use across the world can be classified into three main categories: fossil fuels, nuclear power and renewables (Prakash & Bhat, 2009). Fossil fuels in their crude form, i.e. wood, coal and oil is traditionally the most extensive energy resource used. Nuclear power has been only accessible within developed countries. Renewable energy resources are abundant in nature and easily accessible around the world. Renewable energy sector is now growing faster than the growth in overall energy market. Solar, wind, geothermal, modern biomass, as well as hydro are some of the sources used in this category.

In 2011, the worldwide electricity generation was 21,964 TWh which 67.9% was originated from fossil fuels, 11.7% from nuclear, and 20.2% from renewable sources [START_REF] Observ | Worldwide electricity production from renewable energy sources[END_REF]. The graphic in Figure 1-1 represents the allocation of each of the three systems in global electricity production by 2011. Likewise, the emphasis is on the distribution of power generation of the six main sources of renewable energy. Hydroelectricity is the main source for renewable energy with a share of 80.5%.

Nowadays it is clearer that fossil fuel-based energy sources are damaging the environment and human life. Environmental pollution (of air, water, etc.) is largely linked to the increasing use of energy.

Climate change due to use of fossil fuel with emissions of sulphur dioxide (SO 2 ), nitrogen oxide (NO X ) and carbon dioxide (CO 2 ) is a worldwide problem that has a big impact in the future of all the species living in the Earth. The Kyoto Protocol (United Nations, 2013), an international environmental treaty, sets the obligations for industrialised countries to reduce overall emissions from six greenhouse [START_REF] Observ | Worldwide electricity production from renewable energy sources[END_REF] gases (GHG): carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), sulphur hexafluoride (SF 6 ),hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs). Because of this situation, the development of renewable energy systems is a current international priority for response to global warming. Some long-term scenarios postulate a rapidly increasing share of renewable technologies.

Under these scenarios, in the second half of the 21st century, renewable source could satisfy between 20% to 50% of world's total energy demand with the right policies in place and new technology developments [START_REF] Akella | Social, economical and environmental impacts of renewable energy systems[END_REF]. Table 1-1 shows the evolution from 2001 to 2011 of world electricity production by source. From this information wind (28.3%) and solar (45.8%) sources have considerably increased their contribution among renewable sources.

Several problems and disadvantages of the use of renewable energy can be yet highlighted:

A first apparent drawback, often cited is related to the low efficiency of the transformation of the initial energy provided by the source into electricity. But, it is important to underline that an usual 33% efficiency of conversion of traditional fossil or nuclear plants implies the dramatic waste of the two third of a precious natural reserve of energy, definitely lost for the future generations, while the typical 14% efficiency of a photovoltaic conversion simply means that only this proportion is extracted out of a permanently renewable source otherwise 100% available for the local environment for natural biosynthesis or local heating. Thence and moreover, such low conversion efficiency can augur a low local environmental impact.

However and on another hand, as renewable sources are generally available with low space densities, a true difficulty is to harvest enough final energy required by supplied applications while not using a very large land space. This latter one is of course especially larger with lower efficiency conversion devices. Furthermore, improved devices with higher conversion efficiency are often much more expensive with these very new technologies still in early development. So, the main consequence of this situation is on one hand a larger spreading on land space which may modify the natural landscapes in a non-friendly way and on the other hand a high cost of the generated electricity (see following point). This may lead to search an optimum compromise between cost and occupied land space. So, in our opinion, a low conversion efficiency of a renewable energy should not be directly considered and cited as an obvious drawback by itself. The current cost of renewable energy technology is an impediment for its development. The establishment of government policies that subsidize the implementation of these facilities as well as investment in research of materials and mechanisms to increase processing efficiency and reduce manufacturing cost are necessary to achieve its growth and consolidate its position as the main source of replacing traditional methods of energy generation. Particularly, these technologies require expensive installation investments with long payback times.

It must be also said that an enormous amount of fossil energy is required to manufacture, install and operate all forms of renewable energy systems. Without the input of fossil fuel the existing renewable energy projects probably could never have been built and could not be maintained in operation actually. The raw materials and components used require energy intensive extraction and fabrication techniques to be produced, and along with the finished products, also have to be transported across substantial distances. But, in most cases with the present improved technologies, the assessment on energy on the total life cycle is now positive which augurs of a sustainable development.

A main drawback which becomes a very strong impediment for a large development of renewable sources of electricity is the dependency to geographic and meteorological conditions, making them sometimes very variable along different time scales (night and day, different seasons) and even sometimes and somewhere unpredictable and inconsistent. As the usual electric grid reliable work requires the very good knowledge of consumptions and productions and very good regulations often based on well controlled sources of electricity these properties set a new crucial problem to be solved by means of the so-called new "smart-grids" with new architectures and technologies, for example larger grid connected storage unities. Besides the necessary breakthroughs, this situation generates an increasing of the costs.

In that context, it is an imperative that the use of renewable energy must be efficiently integrated with the natural environment during its whole lifecycle following ecological design. Ecodesign is the use of the ecological design principles and strategies to design products, processes and systems that take into account their impact on the environment at all stages of their life cycle, so that they integrate benignly and seamlessly with the natural environment that includes the biosphere, which contains all the forms of life that exist on earth. This goal must be the fundamental basis for the design of all our human-made environments. The PhD thesis focuses exclusively on solar energy with ecodesign guidelines in mind.

Solar energy

Solar energy is the renewable source that has the most important growth rate (see Table 1-1). Solar irradiation available is more than enough to satisfy the world's energy demands. The total solar energy that reaches the Earth's surface could meet global energy needs 10,000 times over [START_REF] Epia | Solar generation 6. Solar photovoltaic electricity[END_REF].

Where there is more Sun, more power can be generated that is why the sub-tropical areas of the world offer some of the best locations for solar power generation. Figure 1-2 compares the potential solar irradiation with existing energy sources. As it can be seen in this representation, maximizing the use of solar energy can meet the annual energy consumption across the planet.

The main advantages for solar energy are on the one hand:

the power source, the Sun, is totally free.

does not emit any GHG during the energy generation phase.

can be used in any area on Earth, especially remote areas where it is too expensive to extend the electricity power grid. It can be on or off the grid.

a very high reliability and a very low maintenance during their 20 -30 years lifespan despite it is very new and sophisticated technologies.

On the other hand, the main disadvantages for solar energy are:

the biggest disadvantage is the fact that it is not constant. Solar energy is harnessed when it is daytime. But also, beyond normal daily fluctuations, solar production largely varies with seasons outside the tropical latitudes and everywhere with meteorological conditions. large areas of land can be required to harness enough energy for aplications.

solar systems, made with recent and sophisticated technologies are relatively expensive although prices are falling very rapidly and strongly with the market development. low conversion efficiency is often cited as a drawback but refer to 1.1 above.

Solar energy can be converted directly into other forms of energy, such as heat and electricity. Heat can be directly used for industrial or domestic use (hot washing water). Electricity can be generated by means of different ways as:

Solar thermal energy (STE) is a technology for harnessing solar energy for thermal energy (heat). In STE, the light from the sun is concentrated to create heat, and that heat is used to run a heat engine, which turns a generator to make electricity. Water, oil, salts, air, nitrogen, helium are used as the fluid heated by the concentrated sunlight. Currently, there are three types of solar thermal power systems in use: the solar dish, solar power tower and parabolic trough (Solar Thermal, 2008).

Photovoltaic energy conversion (PV) directly converts the light of Sun into electricity. Some materials that are sensitive to the solar radiation react in such a way that they can produce electricity. The conversion is generqlly accomplished through a thin plate of light sensitive material called solar cell or PV cell.

This work will address PV energy conversion.

PV System

PV technology has shown the potential to become a major source of power generation for the world.

Proof of this is the fact that at the end of 2009 the PV cumulative installed capacity in the world was approaching 24 GW and in 2012, more than 100 GW are installed globally and they can produce at least 110 TWh of electricity every year [START_REF] Epia | Global Market Outlook For Photovoltaics 2012-2017[END_REF]. This represents a growth of capacity of three times.

PV power generation employs PV modules composed of a number of solar cells containing a photovoltaic material that converts sunlight into electricity (see Figure 1-3 for a diagram of the photovoltaic effect). A typical PV system is basically made up of one or more photovoltaic PV modules, a mounting system that holds the PV modules and electrical interconnections, a DC/AC power converter (also known as inverter) which can deliver standard alternating voltage and current. A battery system for electricity storage may be included.

PV systems are classified in either off-grid systems or grid-connected systems (see Figure 1-4) [START_REF] Epia | Solar generation 6. Solar photovoltaic electricity[END_REF][START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF][START_REF] Markvart | Recycling paths for thin-film chalcogenide photovoltaic waste -Current feasible processes[END_REF]. Off-grid systems, also known as standalone systems, have no connection to an electricity grid. That is why a battery is required to deliver the electricity needed at anytime especially during night or after several days of low irradiation. Standalone systems fall into one of three main groups:

Off-grid industrial applications. To power repeater station for mobile telephones, traffic signals, remote lighting, highways signs, marine navigation aids among others.

Off-grid systems for electrification. To bring electricity to remote areas or developing countries Consumer goods. Like those found in several electrical applications such as calculators, toys, watches, etc.

Grid-connected systems (PVGCS) are the most popular type of solar PV system and will be the core of this study. Connection to the local electricity network allows any excess power produced to be sold.

PVGCSs are classified in two main groups: residential and commercial systems and, industrial and utility-scale power plants. Residential and commercial systems are the most extensible used PVGCS because they can be installed on homes and businesses. By connecting to the local electricity network, owners can sell their excess power, but, when solar energy is not available, electricity can be drawn from the grid. This type of PVGCS generates up to 100 kWp (kilo Watt-peak). It must be said at this level of the presentation that kilo Watt-peak stands for peak power. This value specifies the output power delivered by a photovoltaic device (cell, module or system) working at its maximum power under set Standard Test Conditions i.e. a solar radiation of 1,000 watts per square meter, a cell temperature of 25°C and an Air Mass of 1.5.

Industrial and utility-scale power plants produce enormous quantities of electricity (>1 MWp). They need a large space to be installed. The solar panels are usually mounted on frames on the ground.

However, they can also be installed on large industrial buildings such as warehouses, airport terminals or railway stations.

A PVGCS is integrated through the following key elements: PV modules, DC/AC inverter and mounting system.

PV module 1.4.1 PV modules are made of PV cells incorporated into a unit, usually by soldering them together under a sheet of glass. Module producers usually guarantee a power output of 80% of the nominal power even after 20-25 years. Modules can be connected to each other in series (known as an array) to increase the total voltage produced by the system. The arrays are connected in parallel to increase the system current.

PV modules are grouped as first, second or third generation according the technology uses for manufacturing the solar cell [START_REF] Lund | Solar Cells[END_REF][START_REF] Petter Jelle | Building integrated photovoltaic products: A stateof-the-art review and future research opportunities[END_REF]. The first generation includes modules made by silicon cells. Silicon cells have a quite high efficiency, but very pure silicon is needed so the manufacturing process requires a big amount of energy. Efficiencies of more than 20% have been obtained with silicon cells already produced in mass production [START_REF] Epia | Solar generation 6. Solar photovoltaic electricity[END_REF]. Mono-crystalline (m-Si), poly-crystalline (p-Si) and silicon sheet-defined film growth (ribbon-Si) are considered in this generation. These technologies are named crystalline-Silicon technology (c-Si). Silicon-based modules dominate the current market [START_REF] Epia | Global Market Outlook For Photovoltaics 2012-2017[END_REF].

The so-called thin film (TF) PV modules are considered as second-generation PV technologies. It includes three main families: amorphous silicon (a-Si), Cadmium-Telluride (CdTe) and Copper-Indium-Selenide (CIS). TF solar cells are comprised of successive thin layers, just 1 to 4 μm thick [START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF]. The combination of using less material and lower cost manufacturing processes allow the manufacturers to produce and sell PV modules at a much lower cost. In addition, TFs can be packaged into flexible and lightweight structures. The main disadvantage is the lower efficiency (7-12%) (EPIA, 2011).

Third-generation PV modules include technologies that are still under demonstration or have not yet been widely commercialised. There are four types of third-generation PV technologies: concentrating PV (CPV), dry-sensitized solar cells (DSSC), organic solar cells and, novel and emerging solar cell concepts. The goal of these technologies is to improve on the solar cells already commercially by growing the conversion efficiency, make them less expensive, and to develop more and different uses.

In laboratory tests, they had reached an efficiency of 30% [START_REF] Epia | Solar generation 6. Solar photovoltaic electricity[END_REF].

According to EPIA (EPIA, 2013), c-Si technology has currently the highest market share (more than 80%) and is expected to maintain it in the future. TF technologies represented about 15% of the The DC/AC inverter is the second most important component. PV modules produce direct current (DC). However, most appliances run on alternating current (AC). Consequently, an inverter must be used to convert the DC into AC. Inverters are widely used for many industrial applications. The PV inverter has another very important role in PV systems achieving a Maximum Power Point Function (MPPT). This MPPT function consists in varying the electrical operating point of the PV array in order to maintain its output power at the maximum value possible which mainly depends on the environmental conditions: solar irradiation and temperature, that is, the variable bias point at which the PV array produces highest power extraction. Changes of temperature and insolation change the voltage where maximum power extraction occurs. Today, intelligent inverter control includes very effective maximum power point tracking systems (MPPT).

Inverters have often been the source of poor reliability in early systems. Feedback to manufacturers and more robust components has greatly reduced these problems, taking benefit of the tremendous development of power electronics and of the PV systems market.

Today most inverter models are additionally equipped with data loggers and measurement computers, which allow the power, voltage, current and other operating parameters to be recorded continuously and often available by an internet link.

Mounting system 1.4.3

The structures of mounting system are typically pre-engineered systems of aluminium or steel racks.

Mounting structures vary depending on where the PV systems are sited, with different solutions of ground-mounted systems. PV modules must be mounted such that they face the best angle. Because of their low value and substantial weight, mounting and racking structures are generally assembled locally.

Simple fixed platforms are commonly the most used, due their very high reliability. It is possible to install tracking platforms that can tilt the PV sensors surface along one or two axis by means of electric motors and a control device that determines the actual position of the sun. Not surprisingly, tracking can provide a significant energy boost so long that it is reliable. However, this comes at a cost and reduced reliability, as the tracking mechanics are more complicated and expensive. EPIA 2012 (EPIA, 2013) report indicates that no major changes should be expected in the main PV technologies, crystalline silicon (c-Si) and TF in the next five years. A slightly higher growth rate is expected for c-Si (6.34%) mainly due to the uncertainty of amorphous silicon (a-Si) technologies, for which the growth rate might be reduced by around 3% until 2017. The reason is the lower module efficiency of a-Si in comparison with the rapid evolution of CdTe and Copper Indium Gallium Selenide (CIGS) with efficiencies below 10% on module level. It is expected that by 2017 CdTe has a 5.95% growth while for CIGS growth will be 8.70%.

Historical PV market development

Moreover, the permanent decreasing of PV crystalline silicon (c-Si) due to a fast growing of production unities and market, particularly in China, slows down the diffusion of theoretically less In 2012 utility-scale applications reached more than 9 GW. EPIA expects utility-scale plants to grow much faster than rooftop applications. In the Policy-Driven scenario, utility-scale market could quadruple from 9 to 37 GW. This can be explained by the nature of the investors in the most promising markets and the reduced opposition to ground-mounted PV systems (Figure 1 -11). At the regional level, the utility-scale segment is expected to at best stagnate in Europe even as it booms in the Americas and Asia including China. In both scenarios, the APAC region including China should see the largest share of new utility-scale applications, ahead of the Americas.

The design and sizing of large-scale PV plants with more efficient energy production are then needed.

PV System design

Several works have been devoted to the optimized design of PV systems, mainly from a technoeconomic viewpoint. The majority of the reported works in the dedicated literature is related either to the minimization of an economic criterion or to the maximization of annual energy produced. However, these studies have adopted mainly simulation approaches to evaluate the system performance and are exclusively devoted to the electrical performance. An optimal unit sizing method has not been established to rationally determine device capacities in consideration of device operational strategies for seasonal and hourly variations of solar insolation and electricity demand.

Generally, two approaches have been adopted. The former one is a deterministic approach where the system performance is evaluated on the basis of original data on solar isolation and electricity demand obtained through measurement. The latter is a probabilistic approach which is based on probability distributions of solar insolation and electricity demand assumed from their original data.

The performance of the PV system depends upon several factors, especially the meteorological conditions such as solar radiation, ambient temperature and wind speed. Normally, the information provided about the PV module and other components from the manufacturers is used for sizing the PV system by a rough estimation of the system output based on average values of daily meteorological data inputs. The parameters that are most used for sizing a PVGCS are field surface, tilt angle and array size. A summary of some works in this field is proposed in Table 123From the abovementioned works, it is possible to establish a general scheme for the configuration of a PVGCS, as shown in Figure 1-12.

It must be yet emphasized that even if power generation from PV systems is free from fossil fue1 use and greenhouse gas (GHG) emissions, a considerable amount of energy is consumed in the manufacturing and transport of the elements of the system. Besides, the amount of energy and emissions from a decommissioning phase of the system must not be neglected. Moreover, any artificial installation implies an ecological impact on the local or even the global environment. For any energy source versus the aim of sustainable development, if to be "renewable" is an obvious "necessary condition", it is not a "sufficient condition"! Indeed, many other impact factors than energy resource exhaustion can be considered to be taken into account.

Ecodesign methods are thus necessary to check whether renewable energy systems as PV systems are truly environment-friendly (green). Generally the environmental assessment is performed as a postdesign stage of the PV systems. The objective of this work is to integrate the environmental assessment from the design stage. Table 1-4 displays some of the works that have evaluated environmental impacts generated by PV systems.

Configuration of PVGCS Evaluation of criteria PVGCS sizing Solar irradiance estimation

Organization of the manuscript

This PhD work aims at determining a general methodology for designing PVGCS, taking into account simultaneously both techno-economic and environmental considerations.

The manuscript consists of six chapters that are organized as follows:

Chapter 1 is focused on the presentation of the general context of PV systems as well as on the literature review for designing and sizing PV systems and justifies the scientific objectives of this work.

In Chapter 2, the methodology chosen for the assessment of environmental impacts asscoiated with PVGCS based on Life Cycle Assessment methodoloy is presented.

Chapter 3 is dedicated to the presentation of the model that has been developed for sizing a largescale PV system.

Chapter 4 discusses the methods and tools that are the support of the methodological study for ecodesign. They combine multi-objective optimization, principal component analysis and multiple criteria decision-making

The integration of the environmental and sizing PVGCS models in the multi-objective optimization framework is presented in Chapter 5. Different examples serve as an illutrsation of the performances of the proposed methodology for sizing a PV system taking into account simultaneously technoeconomic and environmental criteria. Particular emphasis is devoted to the reduction of the objectives in the multi-objective approach to make the analysis more consistent and facilitate result interpretation.

Chapter 6 presents a review of current recycling processes of PV modules. In addition, two examples of integration of the recycling process in the environmental assessment model developed in Chapter 2 will show the importance of recycling in the ecodesign procedures. Finally, the manuscript ends with conclusions and perspectives in Chapter 7. A vision of the report structure is presented in Figure 1-13:

Chapter 5: Multi-objective optimization

Chapter 2:

Environmental assessement

Chapter 3:

PVGCS sizing model

Chapter 4:

Auxiliary methods

Chapter 6: Reference value for the impact or damage categories k

Introduction

During the last years, climate change and other environmental threats have come more into focus by government and enterprises. Nowadays, environmental considerations are integrated as an important element in the evaluation of projects and other decision made by business, individuals, and public administrations. For this purpose, the development and use of environmental assessment and management techniques to better understand the environmental impacts are thus required. These techniques aim at identifying opportunities for reducing the environmental impacts and risks of projects, processes, products, and services.

Among the environmental assessment techniques, the methodological development in Life Cycle Assessment (LCA) technique has been strong, and LCA is now broadly applied in practice in several fields such as energy production.

LCA provides a well-established and comprehensive framework to compare renewable energy technologies with fossil-based and nuclear energy technologies [START_REF] Akella | Social, economical and environmental impacts of renewable energy systems[END_REF]Bhat & Prakash, 2009;World Energy Council, 2004). The improvement among renewable energy technologies can also be compared by LCA [START_REF] Akella | Social, economical and environmental impacts of renewable energy systems[END_REF]Bhat & Prakash, 2009). Even if renewable energy technologies are free of fossil fuel use and greenhouse gas (GHG) emissions during the energy generation phase, a considerable amount of energy and resources are generally consumed for the manufacturing of the different elements required to achieve the energy generation but also in the disposal of these elements at their end-of-life.

This chapter first discusses the environmental assessment of manufacturing processes used for PV modules by use of the LCA technique that will be further used to perform the environmental assessment of a PVGCS. Then, the manufacturing processes of the currently five most sold PV technologies (m-Si, p-Si, a-Si, CdTe and CIS) are described. Process knowledge is indeed considered as a cornerstone to properly apply the LCA methodology, since silicon production is highly energy intensive. To streamline the presentation of some processes which be exhaustive, some explanations are as an additional focus. It is necessary to understand the bottlenecks of the manufacturing processes that are involved in the various technologies.

Subsequently, a literature review of some of the most common techniques for environmental assessment will be presented in order to better position the LCA technique. The fundamentals and principles of LCA will be thus described.

This chapter concludes with three examples of LCA studies. The first example assesses the manufacturing process of m-Si PV module from solar grade silicon production to the PV module assembly. In this example, the influence of the energy mix will be analyzed. A comparison of environmental impacts between the three crystalline silicon-based technologies (m-Si, p-Si and ribbon-Si) is proposed as a second example of application. Finally, the evaluation and comparison of five configurations of PVGCS are presented. The environmental model resulting will be then considered as the basis for the integration of environmental analysis in multi-objective optimization for sizing a large-scale PV system.

Manufacturing processes for PV technologies

The manufacturing process of the product or system under study is a key element of the environmental study in order to determine to the system boundaries and to identify the material and energy requirements and the associated emissions. In this section, the manufacturing processes of the main commercial PV modules technologies are described. Figure 2-1 contains the main stages of the manufacturing process for the three types of crystalline modules. The three technologies share the same process both at the beginning and end. The difference, as mentioned above, corresponds to wafer manufacturing process. Each step is described in detail [START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF][START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]:

As mentioned in

Mining and refining of silica

Silicon is the second most abundant element in the Earth's crust. Quartz and sand are the raw materials for the production of silica (SiO 2 ). The mining of quartz or sand is a widely established technology. In this study, the process characteristics for this step are assumed identical for all three cases. After mining, the sand is transported, classified, scrubbed, conditioned, floated and deslimed.

Reduction of silica to Metallurgical Grade silicon

Silica is reduced to silicon with carbon by a thermal reaction according to:

(2.1)

The carbon used in the reduction is supplied by cokes, low ash coal and wood scrap. The reaction is made in an arc furnace at temperature of more than 1,600°C. The resulting silicon is primarily used in the metallurgical industry and is thus called metallurgical grade silicon (MG-Si). The MG-Si is 98% pure.

Production of Solar Grade silicon

MG-Si still contains too many impurities to be used in solar cell manufacturing. The polysilicon required for solar cells can be up to 99.999999% pure. This polysilicon is named Solar Grade silicon (SoG-Si). SoG-Si is usually produced by ether the Siemens process or fluidized-bed process. It important to highlight that less than 5% of worldwide MG-Si produced is used in making SoG-Si.

Figure 2-2 summarizes the SoG-Si operations.

Production of wafers

The arrangement of Si atoms in SoG-Si and the size are yet not adequate. An atomic arrangement is needed to give a defined shape (circular or square) but also the final characteristics of PV module. The manufacturing process for each of the three crystalline silicon-based PV technologies is presented in

Focus on SoG-Si solidification processes

First, a High Purity Si Containing Gases is needed for both processes. MG-Si is pulverized in fine power and reacted with anhydrous hydrogen chloride in a fluidized-bed reactor at 300°C in the presence of catalyst. During the process, trichlorosilane (SiHCl 3 ) and several other unwanted chlorides are formed, following an exothermic reaction:

(2.2)

In the next step, using a fractional distillation, SiHCl 3 is easily separated from the other impurities.

a/ Siemens process

The high purity trichlorosilane is converted in solid SoG-Si by chemical vapor deposition (CVD) process. Solidification of Si is done using a Siemens type reactor. In a Siemens reactor, a thin Si rod is heated at more than 1,100°C. A mixture of SiHCl 3 and H 2 is introduced in the chamber and the SiHCl 3 is reduced following the equation:

(2.3)

As the process continues, the Si rod becomes thicker and thicker. The rod rises to 30 cm in diameter and 2 m in length. The deposited Si is of polycrystalline type.

b/ Fluidized-bed process

In fluidized-bed process, the SiHCl 3 is decomposed in silane according to:

(2.4)

Using a CVD in a fluidized-bed reactor silane is converted into solid SoG-Si. In this process, silane is solidified using Si seed particles. At 600°C, the gas phase decomposition of silane takes place by reaction (see Equation (2.5)) and the Si atoms get deposited on the floating seed particles.

(2.5)

The particles grow up to 2 mm in size. When the weight of the Si particles is high enough, they fall on the bottom of the reactor, where they are collected for further use.

Focus on CZ/FZ processes

In CZ process, the SoG-Si is placed in a quartz crucible. Si is melted by induction heating and then cooled to form a long solid block called an ingot. A seed crystal gives the arrangement of Si atoms. Melts attain a temperature or more than 1,400°C. The ingot's diameter can reach be up to 300 mm. The length of ingot is 1 or 2 m. CZ process is the most commonly used process for ingot pulling.

In FZ process, the contact of melt with any crucible is avoided. The melt zone is a float zone. A seed crystal is melted with polysilicon rod using induction heating. As the process proceeds, the heated zone is moved upwards. The left behind melted zone solidifies in the form of m-Si ingot.

The m-Si wafers have a regular, perfectly-ordered crystal structure. To achieve this configuration, two processes are generally used: Czochralski process (CZ process) and float zone process (FZ process).

The p-Si wafers have square shape. This allows higher packing density of cells in the module. In p-Si ingots manufacturing process, SoG-Si is melted and poured into a square-shaped SiO-SiN-coated graphite crucible. The controlled directional solidification of the crucible results in p-Si block consisting of several smaller crystallites of varying sizes and orientation.

The m-Si and p-Si ingots need to be diced in order to obtain Si wafer. A wire saw is used to slice the wafer from the ingot. The saw is about the same thickness as the wafer. This method of slicing produces significant wastage up to 40% of the silicon (known as kerf loss).

Ribbon/sheet-Si produce wafer equivalent sheets directly from high purity polysilicon (without growing ingots and then sawing). The main problems found in this process are as follows: the required purity level could not be achieved and many defects are created in the crystal during the crystallization process.

The edge defined film-fed growth (EFG) technique is the most advanced for producing thin sheets of Si. Here a thin sheet is pulled from molten Si. The sheet is formed by the capillarity action of molten Si, and capillarity is defined by a graphite die. The material quality obtained is similar to p-Si. The Si sheet thickness is about 250 m. The Si wafers are cut using laser scribing. The use of a laser cutter reduces kerf loss.

Focus on Transforming the wafer into a solar cell

The solar cell is the unit that produces electricity. It is created using four main steps:

a. Surface treatment: The wafer's top layer is removed to make it perfectly flat.

b. Creation of the potential difference (p-n) junction.

c. Deposition of an anti-reflective coating.

d. Add metal grid (metallization)

Focus on Module manufacturing

The solar cells are placed between layers of coating material to protect them from the environment and breakage. Transparent glass is used for the front, while a weatherproof backing (typically a thin polymer) is applied to the back of the module. The cover is attached using thin sheets of ethylene vinyl acetate (EVA). Frames can be placed around the modules to increase their strength.

Amorphous silicon thin-film 2.2.2

The amorphous silicon or a-Si material has become an interesting material when it was discovered that its conductivity can be changed. The term "amorphous" is given to non-crystalline materials prepared by deposition from gases. The a-Si alloyed with hydrogen (a-Si:H) shows a very high absorption coefficient in the visible range and requires only about a micron thick layer. The manufacturing process of a-Si-based PV modules has a similar beginning to PV modules based crystalline silicon (Section 2.2.1). Figure 2-4 represents the manufacturing process flow for a-Si:H-based PV.

Mining and refining of silica and MG-Si process are the same as used for obtaining PV modules of c-Si. Silane (SiH 4 ) is produced in a fluidized-bed process according to the reaction (2.4). The remaining steps are detailed in the boxes [START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF][START_REF] Markvart | Recycling paths for thin-film chalcogenide photovoltaic waste -Current feasible processes[END_REF][START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF].

Focus on thin film single junction a-Si:H

The key component is deposition of a-Si:H layer with desired composition and thickness.

Radio frequency plasma enhanced chemical vapor deposition (RE-PECVD) technique is the most commonly used to deposit the a-Si:H film. Figure 2-5 shows a typical RE-PECVD chamber. In this process, a mixture of SiH 4 and H 2 flows into a vacuum chamber that is evacuated by a pump. Two electrode plates are installed inside, and a radio frequency power is applied between them in which plasma will occur. The plasma excites and decomposes the gas and generates radicals and ions in the chamber. Various substrates may be mounted on one or both of the electrodes, and thin hydrogenated silicon films grow on the substrates as these radicals diffuse into them. The substrates are heated to achieve optimum film quality.

The typical parameters for obtaining high quality a-Si:H using RE-PECVD are the following one: silane flow of 20 sccm to 50 sccm (standard cubic centimeters per minute), chamber pressure of 0.5 to 1 Torr, substrate temperature of 150 -350°C, RF power should be 20 -50 μW/cm 2 , electrode to substrate distance between 1-3 cm. A typical deposition rate is 0.1-0.2 nm per second. About 300 nm thickness of absorbed layer is required.

Focus on module manufacturing

The process consists of four steps: substrate washing, sputter deposition of the back reflector, a-Si semiconductor deposition, and the transparent conducting oxide (TCO) deposition. At the end of TCO deposition process, the a-Si solar cell is cut by a slab cutter. It is then covered with EVA and Tefzel (a modified ethylene-tetrafluoroethylene fluoroplastic), and vulcanized in a furnace for lamination. This is then followed by selected module framing. The typical structure of a.Si:H solar cell is given in Figure 23456.

Light incidence intrinsic a-Si:H-film (300 nm) N-doped a-Si:H-film (20 nm)

Al back (300 nm) TCO-film (900 nm)

Glass subtrate (2 nm) P-doped a-Si:H-film (10 nm) CdTe is manufactured from pure Cd and Te, both of which are by-products of smelting prime metals (e.g. Cu, Zn, Pb, and Au). Figure 2-7 shows a flow diagram from raw material acquisition to manufacturing stage of CdTe-based PV module. First cadmium production process will be described from Zn production. The different steps are detailed below (V. M. [START_REF] Fthenakis | Life cycle impact analysis of cadmium in CdTe PV production[END_REF][START_REF] Fthenakis | Life cycle inventory analysis of the production of metals used in photovoltaics[END_REF][START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF][START_REF] Markvart | Recycling paths for thin-film chalcogenide photovoltaic waste -Current feasible processes[END_REF][START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF].

Mining zinc ores

Cadmium minerals are not found alone. They are mainly generated as a by-product of smelting zinc ores. Zinc is found in the earth's crust as zinc sulfide (ZnS). Zinc ore contains, beside Zn, Cd, Cu, Pb, Ag and Fe. The ore is excavated by drilling machines, processed through a primary crusher, and then conveyed to surface where is screening and milling to reduce the ore to powder. The particles are separated from the gangue and concentrated in a liquid medium by gravitation and/or selective flotation, followed by cleaning, thickening, and filtering.

Cadmium production

Zinc concentrate is transferred to smelters/refiners to produce the primary metals. Sulfuric acid and other metals, e.g. Cd, are frequent by-products from most smelters. Zn can be refined by either pyrometallurgical or hydrometallurgical treatment. The process consists of five steps but only the first three are important for Cd production (see Figure 2345678).

Tellurium minerals, as Cd, are not found alone. It is a rare metal than can be extracted as by-product of processing copper ores. The production process for Cu is described to explain Te production.

Mining copper ores

Primary Cu is obtained mainly from sulfide ores. Cu ores contain, beside Cu, Fe, Te, Se, Mo, Ag and other metals. The ore is mined then it is crushed, ground and concentrated. In concentrated process, ground ore is slurred with water. The process continues as described in Figure 23456789.

Purification of Cd and Te

Metallurgical grade Cd and Te (i.e. 99.99% pure) metal is used in current applications except for semiconductor materials that require higher purity.

Focus on thin-film CdTe/CdS

CdTe is commonly deposed using Closed Space Sublimation (CSS) process, also known as Closed Space Vapour Transport (CSVT). A schematic CSS process is shown in Figure 2-10. It contains a CdTe plate (source plate) which is transported to the substrate in vapour form. The driving force for transfer is the temperature difference between the source (650-750°C) and the substrate (600°C). The pressure is about 10 Torr. The space between both plates lies between 1-15 μm and the growth rate is 1-5 μm/min. An inert gas such as N 2 , Ar, or He is used for CCS. CdS film can be deposited by the same process as CdTe.

Focus on module manufacturing

The cells are deposited in superstrate arrangement (see Figure 2 -11). The process starts with transparent conducting oxide (TCO) coated glass layer, such as SnO 2 at around 250°C. After cleaning the TCO layer, the area of the cell is defined by laser scribing.

Then, an n-CdS film is deposited, followed by the p-conducting CdTe film at about Copper Indium Selenide (CIS) thin-film 2.2.4 CuInSe 2 -based solar cells is a promising solar cell technologies due to its low-cost, high-rate semiconductor deposition over large areas using layers only a few microns thick and for fabrication of monolithically interconnected modules. Perhaps more importantly, very high efficiencies have been demonstrated with CIS at both the cell and the module levels in laboratory. The performance is increased by adding gallium (Ga) to the compound, thus making it Cu(In,Ga)Se 2 or CIGS.

The CIS manufacturing process is summarized in Figure 2-12. Indium (In) and Ga can be acquired as by-products of the production of Zn. About 5% of the global production of gallium is obtained from residues in zinc processing but 95% of the global supply is obtained as a by-product of alumina production from bauxite. Selenium (Se) is obtained as by-product from Cu ores. Processes for Zn and Cu were described in the previous section. In Figure 2-8, In and Ga production starts during the purification process of Zn. As indicated in Figure 2-9, Se is obtained during the electrolytic refining process for Cu. The next steps for the manufacture of CIS-based PV module are described in respective boxes (V. M. [START_REF] Fthenakis | Life cycle impact analysis of cadmium in CdTe PV production[END_REF][START_REF] Fthenakis | Life cycle inventory analysis of the production of metals used in photovoltaics[END_REF][START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF][START_REF] Markvart | Recycling paths for thin-film chalcogenide photovoltaic waste -Current feasible processes[END_REF][START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]. The copper production process was described in the previous section and the process flow is given in Figure 23456789.

Focus on indium production

Soda is added to the residue resulted in the purification process of Zn that contains particles of In in order to precipitate it. About 10% of In remains in the residue, which is leached with sodium hydroxide to create crude indium hydroxide. The crude indium hydroxide is leached with dilute hydrochloric acid. The solution is purified by cementation of copper and arsenic with iron, followed by cementation of tin and lead with indium. Finally, In is removed by adding aluminium to create indium cement.

Further purification is done by electrolysis to produce high purity grade (99.9999%).

Focus on selenium production

It is recovered as a by-product, mostly from the anode slimes in the electrolytic refining of copper. Two major processes of extracting selenium from copper refinery slime include roasting with soda ash and roasting with sulphuric acid. Soda ash roasting is a traditional method to recover selenium. This method is described in Figure 2-15.

Electrolytic copper refinery slimes are intensely mixed with soda ash binder and water to form a paste which is roasted at 530-650°C. Then the paste is leached in water to dissolve sodium selenate. Residues are separated from the selenate with filtration.

Sulphuric acid is used to remove the impurities in hydrolysis. Hydrochloric acid or ferrous iron salt is used for the reduction of hexavalent selenium. Iron chloride is discarded, which contain small amounts of selenium but is also extremely corrosive and creates problems for disposal. The remaining solution is precipitated with sulphur dioxide and then filtrated. The final steps are melting and shooting to produce selenium metal.

After Se is extracted from copper refinery slimes, the average purity is approximately 99%. For photovoltaic, the simplest and the most common method of achieving 99.99% pure selenium is vacuum distillation.

Mixing

Focus on thin-film CIS/CIGS

The CIGS absorbed layer is commonly deposited with co-evaporation techniques (see

Focus on module manufacturing

The process starts with the deposition of the absorber layer on the molybdenum coated glass substrate. A soda-lime substrate composed of about 70% silica, 15% sodium oxide, 9% calcium oxide, and 6% of others compounds is chosen due to the importance of Na. The CIGS layer is deposited. After the absorbed layer, the buffer layer of CdS is deposited using chemical bath deposition (CBD) method. On the top of CdS buffer layer, a TCO layer is added. The ZnO is typically used as TCO.

Finally, the electrical wire and buss bars are attached. These are metal stripes that can be soldered, welded, or glued to contact areas near the edges of the substrate plates.

Lamination with a front cover glass, which is usually EVA is next. Edge sealing and framing finished the product. The arrangement of CIS-based solar cell is found in Figure 2-17.

CdS buffer layer (50 nm)

Mo back contact (500 nm) TCO-film (500 nm)

Soda-limo glass substrate

Cu(In,Ga)Se2-film (2 μm) A valid question raised in scrutinizing technologies regarded as environmentally friendly is whether they are truly "sustainable" or not. For alternative energy systems in particular, this query translates in one key sense to whether they represent a net gaindo they generate more energy than was used to create them in the first place and if so to what extent?

Light incidence

From the description of the manufacturing processes for the top five PV modules technologies describe above is possible to note the large amount of energy that is required for both the production and purification of the raw material to obtain the degree of purity that allows them an efficient conversion energy, and the high temperatures required for each one of the processes for the manufacture of the module.

When comparing the embodied primary energy of the five production processes from the information above, it is emphasized that technologies based on c-Si (m-Si and p-Si mainly) are those that require the highest amount of material and energy on the one hand by the dimensions and thickness of the module and on the other hand by the purification process and growth of Si to form the ingot that subsequently will be transformed into the wafer.

The main advantage presented by the TF PV modules is related, because of their thickness, to the smaller amount of material and energy needed for manufacturing them. As mentioned in the previous chapter, the combination of less materials and energy needed to manufacture a PV module results in a much lower cost on manufacturing and therefore a lower price compared to c-Si PV modules. Some works try to answer if indeed the energy produced by a PV module during its lifetime is enough to offset the amount of energy they consume during the manufacturing process [START_REF] Ayompe | Projected costs of a grid-connected domestic PV system under different scenarios in Ireland, using measured data from a trial installation[END_REF][START_REF] Dale | The Energy Balance of the Photovoltaic (PV) Industry: Is the PV industry a net electricity producer?[END_REF][START_REF] Knapp | An Empirical Perspective on the Energy Payback Time for Photovoltaic Modules[END_REF][START_REF] Lloyd | The transition to renewables: Can PV provide an answer to the peak oil and climate change challenges?[END_REF][START_REF] Nawaz | Embodied energy analysis of photovoltaic (PV) system based on macro-and micro-level[END_REF]. They conclude that not all financial cost reductions lead to reductions in embodied energy, an economic analysis should be supplemented with energy analysis. Also PV systems with lower energy costs provide more net energy.

The net gain concept must be extended as well to pollutants (e.g. SOx, NOx, particulates) or global greenhouse gas emissions (e.g. CO 2 ). A truly sustainable technology should represent a net gain to the humanity that wish to continue its standard of living, historically correlated with energy use. The presentation of environmental assessment techniques is then proposed in what follows to identify the method that will be selected.

Environmental assessment techniques

According to Sadler [START_REF] Sadler | Strategic Environmental Assessment. Status, challenges and future directions[END_REF], environmental assessment is defined as "a systematic process for evaluating and documenting information on the potentials, capacities and functions of natural systems and resources in order to facilitate sustainable development planning and decision making in general, and to anticipate and manage the adverse effects and consequences of proposed undertakings in particular". There are many different procedures and methods to assess the environmental issues or impacts of plans, projects and programmes. Table 2-1 summarizes some of these techniques.

Among the techniques mentioned in Table 2-1, LCA is the most well-known and powerful tool [START_REF] Finnveden | Recent developments in Life Cycle Assessment[END_REF][START_REF] Heijungs | Life Cycle Assessment: Past, Present, and Future[END_REF][START_REF] Manuilova | Should Life Cycle Assessment be part of the Environmental Impact Assessment? Case study: EIA of CO2 Capture and Storage in Canada[END_REF]. Studies have shown that LCA can complement and add value to the other techniques [START_REF] Finnveden | Recent developments in Life Cycle Assessment[END_REF][START_REF] Manuilova | Should Life Cycle Assessment be part of the Environmental Impact Assessment? Case study: EIA of CO2 Capture and Storage in Canada[END_REF]. There is no single tool or approach to address all the problems of environmental management. The Society of Environmental Toxicology and Chemistry (SETAC) conducted a discussion within a working group in order to define the relationship of LCA with the others techniques [START_REF] Heijungs | Life Cycle Assessment: Past, Present, and Future[END_REF].

In addition to the advantages described, LCA employs many of the principles of the other techniques, e.g. LCA always requires to establish the inventory of flow of materials and substances as MFA, some of the LCA methods to assess the human health impacts use ERA principles, LCA shares with the EIA and MILP the use of characterization indices and impact factors.

Furthermore, LCA allows the comparison between different environmental impacts through design of alternative scenarios or making the comparison of different product's processes that perform the same function. These reasons explain why LCA has been selected as an environmental assessment technique in this work. A more detailed description of the LCA technique will be discussed in the following section of this chapter.

LCA Methodology

As abovementioned, Life cycle assessment (LCA) evaluates the environmental impacts of products, processes and services. The results of LCA can identify major emissions, thereby enabling consideration of measures for their reduction.

LCA evaluates the material and energy flows involved in the whole life cycle of the product as it is represented in Figure 2-18. It is possible to classify them in:

Elementary flows: consist of flows that each process exchanged with the ecosphere: primary resources as water, fuels, minerals..., and waste emissions as solid waste, effluents and gaseous emissions.

Intermediate flows: material or energy flows between the different stages of the life cycle.

For an adequate interpretation of the results that will be generated by an LCA, the goal must be appropriately defined and will guide the LCA operator to manage and focus the efforts to collect the information that best suit the purpose and interpret the outcomes appropriately.

For Jolliet et al. [START_REF] Jolliet | Analyse du Cycle de Vie: Comprendre et réaliser un écobilan[END_REF], LCA evaluates the environmental impact of a product, service or system related to a particular function, considering all stages of its life cycle. It identifies all the points on which a product can be improved and it contributes to the development of new products. From the LCA investigations that were already implemented and mentioned in numerous studies, [START_REF] Jolliet | Analyse du Cycle de Vie: Comprendre et réaliser un écobilan[END_REF] for instance, several strengths of the LCA methodology can be highlighted.

In eco-design, LCA can help to take into account environmental criteria during the design phase of a new product or product improvement already created. This is typically one of the first motivations of this work.

In the evaluation and improvement of product, LCA can identify critical areas on which it is possible to focus to optimize the environmental performance and to compare different manufacturing processes.

LCA can also be useful to obtain elements of decision support for the implementation of industrial policy (choice of design, product improvement, selection of procedures, etc..) or public policies (choice of recovery processes, eco-labelling criteria, etc..).

The objective of this study is to develop an environmental module for PV modules based on LCA that reflects the different options in PV manufacturing, based on existing data.

Two main advantages can be found by using LCA for PV systems:

1. When using LCA, the system can be optimized from an environmental viewpoint taking into account CO 2 emissions, human health impacts and effects on the local fauna and flora ….

2. The second advantage is comparability. When comparing energy generation technologies (e.g., when searching for the installation of a PV system as a supply of alternative energy as opposed to other generation systems, or when installing energy supply systems based on multiple generation technologies), LCA can provide quantitative results, thereby enabling comparison of each technology on an equal footing.

The application of LCA requires a protocol defined by the International Organization for Standardization (ISO) that has developed and formalized a series of standards for the Environmental Management. These standards include the ISO-14040 (International Standard Organization, 1997), which describes the principles and framework for LCA and ISO-14044 (International Standard Organization, 2000), which explains the requirements and guidelines of LCA. The research and analysis scheme for LCA consists in four stages as shown in Figure 1 Goal and scope definition 2.4.1 Defining the objectives and scope of the study is the first and essential step to guarantee the quality of the study. The definition of the problem establishes a rigorous framework for the study. It involves an accurate description of the study to be performed and the identification of the purpose, to whom it may concern and the possible applications.

The scope determines which product system or process will be analysed, the unit processes evaluated, functional unit, system boundaries, impact categories, data requirements, and limitations. Some important concepts in the LCA are: A unit process describes a stage within the life cycle of a product and serves as the basic element of analysis in the LCA. The identification of unit processes facilitates the quantification of the inputs and outputs flows at each phase of the life cycle. The set of unit processes gives the product system. It involves the production, use, and disposal of a product or service throughout its life cycle.

System boundaries specify the unit processes, defined at the scope of the analysis, to be included in the LCA. The accurate description of the system and of its boundaries has strong implications for the results of the assessment.

It necessary to define a reference unit to quantify the inputs and outputs flows. This unit is called the functional unit (FU). The FU must be fully specified and measurable. It also serves as the basis for comparison when considering the environmental impacts of multiple product systems.

Inventory analysis 2.4.2

Life-cycle inventory (LCI) analysis involves data collection and calculation procedures to quantify relevant input and output flows of the product system(s). The LCI phase requires the highest efforts and resources of an LCA. Data collection consists in the identification and quantification of relevant inputs and outputs for each unit process of a specific product system taking into account the FU. Data for each unit process within the system boundary include energy and raw material flows, products and co-products, waste and emissions to air, water, and soil (Figure 1-10).

Data for each unit process are either provided directly from industry or using an LCI database, such as Ecoinvent, European Life Cycle Database (ELCD) or US life cycle inventory database. Databases provide industrial data on energy supply, resource extraction, material supply, chemicals, metals, agriculture, waste management services, and transport services for a variety of generic unit processes that allow for the development of more complex product systems [START_REF] Center | The life cycle inventory data v2[END_REF].

The LCI must be done from the process tree in which the reference flows were defined and related to

Selection of impact categories and characterization models

The selection of impact categories must be comprehensive in the sense that they cover all relevant environmental issues related to the analysed system. Two main schools of methods have been developed depending on the level of analysis along the cause-effect chain [START_REF] Finnveden | Recent developments in Life Cycle Assessment[END_REF][START_REF] Jolliet | Presenting a New Method IMPACT 2002 + : A New Life Cycle Impact Assessment Methodology[END_REF]. The primary effects represent the direct result of activities studied e.g. the greenhouse gas emissions. They can be distinguished from side effects, which are the consequences of primary effects. For example, the ozone layer depletion generates the growth of UV radiation that reaches the ground, this situation increasing the human health problems.

Problem-oriented methods will model the relatively early stages in the cause-effect chain to limit uncertainties. These methods are known as midpoint method. Damage-oriented methods, the so-called endpoint methods, try to consolidate the impact on the final results, as far as possible in the causeeffect chain. They provide more concrete information, but they remain more uncertain. The methods for analysis of the impacts have been widely described in the literature. These methods are the result of several years of work and each has their specificities. In 2010, the European Commission (European Commission, Joint Research Centre, & Institute for Environment and Sustainability, 2010) published a guide with the description of some of these methods. In this publication, an analysis of the strengths, particularities, methodology used and impact categories preselected of each method was made. Table 2-2 presents some of the most used LCIA methodologies described by the European Commission.

In this work, the IMPACT 2002+ approach was selected as LCIA method. IMPACT 2002+ (IMPact Assessment of Chemical Toxics) proposes a feasible implementation of a combined midpoint/damage approach, linking all types of life cycle inventory results via 14 midpoint categories to four damage categories [START_REF] Jolliet | Presenting a New Method IMPACT 2002 + : A New Life Cycle Impact Assessment Methodology[END_REF]. IMPACT 2002 + combines New concepts and methods were developed, especially for the comparative assessment of human toxicity and ecotoxicity [START_REF] Frischknecht | Implementation of Life Cycle Impact Assessment Methods[END_REF].

Impacts and damages classification

In classification phase, emissions and extractions flows obtained in the LCI are assigned to the impact categories selected, some emissions or extractions can contribute to several categories. According to the characterization method selected, the classification of impacts is different because of the impact categories pre-selected. Impact categories include climate change, stratospheric ozone depletion, photooxidant formation (smog), eutrophication, acidification, water use, noise, etc. [START_REF] Pennington | Life cycle assessment part 2: current impact assessment practice[END_REF] It is possible to elaborate a damage classification (endpoint). Three major groups, commonly referred to as areas of protection (European Commission et al., 2010;[START_REF] Pennington | Life cycle assessment part 2: current impact assessment practice[END_REF], are considered for the classification of damages: resource use, human health consequences and ecological consequences.

Characterization of impacts and damages

This step consists of modelling, by using factors, the classified LCI flow data for each of the impact categories. To all classified flows a quantitative characterization factor shall be assigned for each category to which the flow relevantly contributes. This factor expresses how much that flow contributes to the impact category indicator (at midpoint level) or damage category indicator (at endpoint level).

For midpoint level indicators, this relative factor typically relates to a reference flow, e.g. kg CO 2equivalents per kg elementary flow in case of Global Warming Potential. For endpoint level indicators, it typically relates to a specific damage that relates to the broader area of protection, e.g. for species loss measured the potentially displaced fraction of species for an affected area and duration (PDF*m²*a) is used (European Commission et al., 2010).

The characterization of each impact categories is the sum of the product of the mass of the substances listed at the LCI classified by impact category and their own characterization factor. (Equation (2.6))

(2.6)

where SI i represents the characterization score for the impact category i, FI s,i is the characterization factor for the substance S in the impact category i, and M s is the mass of substance s from the LCI.

The impact categories can be grouped into the damage categories. Each impact category has a higher or lower contribution for the selected damage category. Therefore, a damage characterization factor is needed. Where:

Kg eq Substance x (kg equivalent of a reference substance x) expresses the amount of a reference flow x that equals the impact of the considered pollutant.

DALY (Disability Adjusted Life Years) characterizes the disease severity, accounting for both mortality and morbidity.

Optional elements: Normalisation, Grouping and Weighting

The purpose of the normalisation is to facilitate interpretation of the LCIA results by analyzing the importance of the respective contribution to the overall environmental impact. As impact or damage categories have different units, normalisation is used to make these categories dimensionless.

Normalized LCIA results are obtained by dividing the LCIA results by the reference value, separately for each impact category (Equation (2.8)). Each characterization method proposes its own reference value. There are numerous methods of selecting a reference value, including the total emissions or resource use for a given area that may be global, regional or local in a given period of time, or the total emissions or resource use for a given area in a per capita basis in a given period of time. Normalisation results can provide input to grouping or weighting.

(2.8)

N k represents the normalised score of the impact or damage categories k, S k is the characterization or damage score of the impact or damage categories k, and VR k is the reference value for the impact or damage categories k.

Grouping is a qualitative or semi-qualitative process that involves sorting and / or ranking among normalised scores. Grouping may result in a broad ranking, or hierarchy, of impact categories with respect to their importance. Such a ranking can provide structure to help draw conclusions on the relative importance of different impact or damage categories [START_REF] Pennington | Life cycle assessment part 2: current impact assessment practice[END_REF]. For example, categories could be grouped in terms of high importance, moderate importance and low priority issues.

Weighting involves assigning distinct quantitative weights to all impact categories expressing their relative importance. A weighting of the normalised indicator results may be performed. This can include aggregation to a single indicator. It is often applied in the form of linear weighting factors:

(2.9)

where EI is the overall environmental impact indicator, PF k is the weighting factor for impact category k.

Methods for weighting can be based on [START_REF] Pennington | Life cycle assessment part 2: current impact assessment practice[END_REF]:

A distinction between impact indicators defined early (midpoints) or late (endpoints) in the impact chain.

The expressed preference. People are asked the relative importance of damages or impact categories.

Distance to target, where characterization results are related to target levels.

Monetization. These monetized weighting factors are derived from reactions to different situations, such as insurance payouts, health care expenditures, fines, costs incurred.

Interpretation of results

2.4.4

The interpretation of LCIA results is the last phase. It analyzes the results provides in the phase above based on the objectives and scope of the study previously defined. Conclusions are thus made, and areas for improvement can be detected in order to start looking for possible alternatives of solutions to finally take a decision.

The interpretation proceeds through three main activities:

Identify the significant issues. An analysis and organization of the results must be done to identify the main contributors to the LCIA results (processes and elementary flows) and the most relevant impact categories. Significant choices as assumptions, foreground and background data used for deriving the process inventories, LCIA methods used, as well as the normalisation and weighting factors must also be identified because of the potential influence in the precision of the final results of the LCA.

Determine the influence of significant issues on the overall results of the LCA. The evaluation is performed in close interaction with the identification of significant issues in order to determine the reliability and robustness of the results. The evaluation involves completeness Σ Σ check, sensitivity check in combination with scenario analysis and potentially uncertainty analysis and consistency check.

Formulate the conclusions and recommendations of the LCA study. Recommendations based on the final conclusions of the LCA study must be logical and be reasonable and plausible founded in the conclusions and strictly relate to the intended applications as defined in the goal of the study.

Limitations of LCA 2.4.5

LCA studies present various limitations like:

• A LCA study, because of its "holistic" nature, requires a lot of time and economical resources.

The more detailed a LCA is the more time-consuming and expensive it will be. High costs are partly caused by the need for professional consultation and expert knowledge in the stages of impact and improvement analyses.

• LCA is a tool based on linear modelling so it regards all processes as linear. Some progress is being made in reducing this limitation.

• There is not a unique LCA methodology even if the main steps are regulated and guided by the ISO norm. Each impact assessment method has its own impact and/or damage categories, characterization factors and references values for the normalisation. This situation makes difficult the comparison of LCA studies between products or processes if they were not made under the same impact assessment method.

• The assumptions made in such studies (for example the boundary determination, the source of data and the impact assessment choice) might be subjective.

• The accuracy of a LCA study depends on the quality and the availability of the relevant data, and if these data are not accurate enough, the accuracy of the study is limited. These facts affect the precision of the final results.

• Because LCA studies are focused on national and regional level, they might not be suitable for local applications.

• The availability, customization and updating of the database is another problem. Even if the databases are being developed for several countries, considering its particularities, and the format for databases is being standardised, data are frequently obsolete, incomparable, or of unknown quality. Some of the data are available in aggregated format.

• LCA approach cannot replace the decision making process. It only provides information for decision support.

LCA software tools 2.4.6 Nowadays, many LCA software tools have been developed based on the methodology of LCA. Most of them include a certain number of databases and impact assessment methods.

These tools facilitate the estimation of total emissions and extraction for the LCI as well as the calculation of characterization, damage and normalised score. Some of them generate a report with the To perform the LCA study for PVGCS, the SimaPro software tool with the EcoInvent database was selected. It is widely mentioned in the dedicated literature for this kind of study.

The environmental impact results are available through graphs or tables that can be exported. Several processes or scenarios can be compared.

LCA study for m-Si based PV module

The LCA methodology is first applied to a simple case, the production of a PV module, in order to fully understand each of the abovementioned steps. In this first example, a description of each step of the LCA applied to the manufacturing process of the m-Si based PV module is made. The data were collected from the literature, particularly from the work developed by Alsema et al (Alsema & Wildscholten, 2006;[START_REF] Wild-Scholten | Environmental life cycle inventory of crystalline silicon photovoltaic module production[END_REF].

Goal and scope definition 2.5.1 The PV module is assumed to be manufactured in Germany. The energy mix of Germany is then used in the computations.

As shown in

Inventory analysis 2.5.2

From the data given by Alsema et al. for each unit process involved in the system boundary considering the interconnexion of the units constituting the whole manufacturing process, the material requirements and the emissions per PV module of m-Si (LCI) are calculated. Table 2-5 shows input and output flows for each unit process.

As it can be seen, each unit process has its own reference flow (per kg feedstock, m² of wafer or per solar cell). Yet it must be kept in mind that finally all material must be estimated per functional unit (per piece of PV module). Table 2- 2-7 shows only some of the 890 emissions that are produced during the manufacture of m-Si based PV module.

Impact assessment 2.5.3

The third step in the LCA methodology involves the assessment of environmental allocation caused by the emissions that were listed in the previous step. One of the characterization methods listed in Table 2-2 is required. As mentioned in Section 2.4.3.1, IMPACT 2002+ was chosen because it allows the classification and characterization of environmental impacts (midpoint) and damage consequences (endpoint).

The selected LCIA method is found within Ecoinvent database, taking into account the classification and characterization of each of the 890 substances into the categories considered by IMPACT 2002+ (see Table 23). For the sake of illustration, Table 2-8 andTable 2- The characterization score of the emissions from the production of an m-Si PV module in all impact categories is shown in Table 2-10. Finally, the characterization scores obtained for the four damage categories are presented in Table 2-11.

Due to the complexity to understand the information provided in the tables above, the normalisation of results was performed. The reference values for both impact and damages categories are listed in

Interpretation of results

2.5.4

Figure 2-24 represents the normalised scores. Comparing among the impact categories, GW and NR are those with the highest values. This result indicates that on the one hand GW and NR have a major influence within the overall environmental assessment for a module of m-Si. On the other hand, half of the impact categories have a very small contribution.

GW and NR are mainly related to the energy requirements of the process. Looking at the process flow for manufacturing the m-Si PV module, a large amount of energy is consumed to achieve the temperatures required to get the degree of purity and uniformity needed by this type silicon module.

Because of this, a question arises: what would happen if the module was manufactured in another country with a different energy mix?. The Ecoinvent database has different energy mix. Five scenarios will be tested. USA, China, Spain and France energy mix are chosen. The composition of the five

LCA study for silicon-based PV modules

Another advantage of LCA technique is the possibility to carry out a comparative analysis between processes, products or services. In this second example, a comparison between three technologies of PV modules (m-Si, p-Si and ribbon-Si) is made. LCA is conducted following the data published by Alsema et al. [START_REF] Alsema | Environmental Impacts of Crystalline Silicon Photovoltaic Module Production System boundary LCA study New LCI data set[END_REF][START_REF] Wild-Scholten | Environmental life cycle inventory of crystalline silicon photovoltaic module production[END_REF] to manufacture PV modules based on silicon.

Goal and scope definition 2.6.1 More precisely, the aim of this study is to compare the environmental impact of some manufacturing processes of crystalline silicon-based PV module (m-Si. p-Si and ribbon-Si). The standard manufacturing processes for these PV modules were described Section 2.2.1. Figure 2-27 shows the system boundaries. Data on the assembly of all components were collected by choosing a functional unit 1 kW p of installed power2 , which is more convenient as several technologies have to be compared.

The manufacturing process for each of the three c-Si-based PV module technologies is divided into 4 main groups: SoG-Si, wafer, solar cell, and PV module (see Figure 2-27). In the case of m-Si and p-Si PV modules, the process of ingot growth and wafer sawing were grouped together. Technology assumptions, LCI and data collection 2.6.2

The wafer dimensions that are considered are set at 125 x 125 mm as the standard size for all wafer technologies (including ribbon). Wafer thickness lies in the range of 270-300 µm for m-Si and p-Si wafers and 300-330 µm for ribbon wafers. Only one standard module type with 72 cells (1.25 m 2 module area) and with glass/EVA/Tedlar lamination is considered. Glass thickness is set at 3.6 mm. LCI for the three technologies is established from data published by de Wild-Scholten and Alsema (de [START_REF] Wild-Scholten | Environmental life cycle inventory of crystalline silicon photovoltaic module production[END_REF]. Complementary data were acquired from Ecoinvent database implemented in SimaPro LCA software tool for all manufacturing processes. As previously mentioned, a German electricity mix is chosen.

LCIA results and interpretation 2.6.3 IMPACT 2002+ [START_REF] Jolliet | Presenting a New Method IMPACT 2002 + : A New Life Cycle Impact Assessment Methodology[END_REF] method is selected for LCIA. The same procedure used in Section 2.5.3 was followed to classify and characterize the emissions. As in the previous case, the score of both impact and damage categories are normalized using the same factors contained in Table 2-12. Figure 2-28 shows the normalised results of main midpoint categories and damages categories for the three types of PV modules in order to analyze the importance of the respective contribution of each category in the overall environmental impact. Not surprisingly, it is observed that m-Si technology has the highest score for all impact categories while the silicon ribbon modules have the lowest impact. When comparing the categories of damage (see Figure 2-28b), it can be seen that Resource depletion and Climate Change have the largest contributions in terms of global environmental impacts. Figure 2-28a indicates that the categories relating to Non-renewable energy and Global Warming lead to higher scores among the midpoint categories.

A more realistic comparison to evaluate different technologies for PV is to consider the number of panels required to meet a given amount of energy. Using the same considerations as above, the minimum number of panels required to meet a demand of 1 kWh (see Table 234567891011121314) with an average daily irradiance of 1 kWh / m² is computed. The new FU is the demand of 1kWh. This functional unit corresponds now to the service t hat is produced corresponding to energy production which is more A closer look can be made to identify the steps of the manufacturing process that generate the highest impact. Global warming midpoint category is selected for the analysis.

LCA study for PVGCS

Another LCA is implemented to compare 5 different PV modules technologies in a large-scale PVGCS. The five PV module technologies are the most commercialized ones: m-Si, p-Si, a-Si, CdTe and CIS. In this example all the components of a PV system (PV modules, BOS components and the mounting system) are analysed. The procedure followed in this example as well as the resulting flow inventory will provide the basis for the integration and evaluation of the environmental aspect into the main model for the dimensioning of a large-scale PV system.

A guide published by International Energy Agency (V. Fthenakis et al., 2011) for LCA of PV system determines four main aspects that must be taken into consideration: Goal and scope definition 2.7.1

This LCA study aims at comparing the environmental impact associated with electricity production with different PVGCS configurations using the 5 most commercial PV module technologies. As in previous LCA study, the functional unit is fixed as energy demand. It concerns here the demand of 5 MWh that must be supplied by the PV power plant each year during 20 years. System boundaries are represented in Figure 2-32. They include the manufacturing of core infrastructures (modules, mounting system, cabling, and AC/DC inverter), the plant installation (excavation and mounting system) and the energy generation for a 20 year period (including component replacement).

Recycling processes of the different components of PVGCS are not included in this study due to lack of reliable information for all PV modules technologies evaluated. The different recycling processes currently implemented for PV modules and their implementation in LCA will be discussed in Chapter 6.

Technology assumptions, LCI and data collection 2.7.2 A yearly irradiation 1200 kWh/m 2 on an inclined plane (30°) is considered. A fixed-mounting system with aluminium supports is used. A 10-years lifetime is considered for AC/DC inverter and 20-year life time is considered for the other components. The conversion efficiency of PV modules is constant over time and is based on the characteristic given by PV modules contained at Ecoinvet database. The five PV modules are found in Ecoinvent database. The datasheets contain the input and output flows in order to calculate the total emission flows. The reason why these PV modules were used was the lack of data for material and energy flow found in the literature for manufacturing process of thinfilm modules. Ecoinvent database is constantly under improvement to become a more accurate tool for create the LCI of total emission for a given process or product.

The 2.5kW inverter is selected from Ecoinvent database for the five PV installations. The number of PV modules and DC / AC inverters needed to supply the energy demanded during the evaluation period as well as the main features are summarized in LCIA results and interpretation 2.7.3 IMPACT 2002+ method was applied for evaluating the environmental impacts. The characterization scores were obtained as in previous examples. The environmental assessment was carried out both in the main midpoint impact categories as in the four damage categories. The total score for each category was separate in order to compare the different elements that compose a PVGCS. A more detailed analysis, focusing into the components of a PVGCS, shows that the most influential process is PV module manufacturing for the total impact scores in all the categories. Among the five PV technologies under analysis, m-Si PV module leads in almost all the categories. CdTe PV modules had interesting results: it has the lowest total scores. The LCI as well as the procedure followed in this example will be used in the latter chapter when the evaluation of environmental impacts will be included and taken into consideration as criteria for the design of large-scale PV power plants.

Conclusion

The analysis of the manufacturing processes of the five main modules PV technology highlights that it is necessary to quantify the embodied primary energy required for their manufacture, especially with c-Si based technologies to guarantee the sustainable nature of a technology. An environmental assessment is required to confirm that, indeed, PV systems really help to reduce and / or prevent pollution. There are many techniques developed for environmental assessment and among them Life Cycle Assessment (LCA) which is particularly interesting for energy production. Three cases have been tackled with LCA. The results show that the PV modules are the elements of a PVGCS that contribute most to the overall environmental impacts. Another aspect to mention is the influence of the composition of the electricity mix in the assessment of environmental impacts generated by a PV module.

This discussion reinforces the need for a multi-criteria study that allows establishing a methodology that conciliates both the technical-economic and environmental criteria. The procedure of LCA applied in a PVGCS will serve to integrate the environment criterion into the proposed study. The results indicate that even if variation between the two configurations is not so high at design stage, the electrical grid heliostat generates the most important impacts to the environment after 20 years of operation. The energy supplied for operating the grid-connected heliostat is the main element that affects the different categories analyzed in LCA. It also depends on the energy mix of the country in which the power station will be built. This work was presented at First International Conference on Renewable Energies and Vehicular Technology (REVET) (see Appendix A).

A MODELLING AND SIMULATION FRAMEWORK FOR SIZING LARGE-SCALE PHOTOVOLTAIC POWER PLANTS

Introduction

System modelling forms a key part of the PV system design. It can provide answers to a number of important issues such as the overall array size, orientation and tilt, and the electrical configuration. The design criteria depend generally on the nature of the application. The applications of PVGCS vary from small building integrated systems to PV power plants. The performance of PVGCS depends upon solar resource at site, system configuration and load parameters. Modelling tools are available to provide solar radiation data, assess possible shading effects and produce the resulting electrical layout of the array.

The objective of this chapter is to present the modelling approach that will be used in this work to represent the performance of a PV power plant, taking into account its mains features.

This chapter is dived into seven sections. Section 2 first presents an overview of the available software tools that can be used to design and evaluate the performance of large-scale PVGCS. The list of software tools is not exhaustive but includes the most reported ones in the dedicated literature. The analysis of the reported contributions led to the development of a specific framework for PV modelling and simulation purpose that is proposed in section 3. The objective is then to couple it with an outer optimization module for generating optimal configuration alternatives. The system implies a three-step methodology:

(1). The estimated solar radiation received by the system according to the geographic location is the core of section 4.

(2). A mathematical model for PVGCS sizing is presented in section 5: it provides the annual energy generated from the characteristics of the system components and limitations on the design of the installation.

(3). The evaluation of techno-economic (PayBack Time and Energy PayBack) and environmental (IMPACT 2002+ midpoint categories) criteria is then presented in section 6.

The approach is then validated with a reference example from the literature (Weinstock and

Appelbaum [START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF]).

Finally, the integration of the proposed model in order to compute evaluation functions, based on techno-economic and environmental aspects, in an optimization loop is a natural extension of this work.

Literature review on PV System design tools and work objective

Following the guidelines presented in Chapter 1 (Figure 1-12) for PVGCS design, the first step is the estimation of the radiation received at the site as well as the amount of energy supplied to the utility grid.

Power generation through photovoltaic conversion is very difficult to make an accurate assessment when designing a PVGCS because it depends upon incident solar radiation and PV module performance which are affected by uncertain parameters such as daily weather conditions or ambient air temperature. These parameters change all the time and they are not the same every year. The hourly, daily, monthly or yearly mean value is considered for PVGCS design.

Numerous commercial and academic computer models using different algorithms for modeling, analyzing, simulating PV systems are available. These tools present different degrees of complexity and accuracy depending on the specific tasks that each tool has been developed for. It is usual to distinguish between sizing tools (which determine the component size and the corresponding configuration) and simulation/modelling tools, which analyse the system output and performance once its specifications are known. Examples of these sizing and simulation tools are given in Table 3-1.

They involve generally the estimation of solar radiation (using meteorological databases or mathematical models) and/or the estimation of the energy generated by the system taking into account the characteristics and location on the field of PV components (e.g. modules, the balance of system), weather consideration and solar radiation.

The main problem that can be encountered when using one of the available tools as those presented in Table 3-1 is the lack of an approach that allows the optimization of the sizing of a PVGCS considering economic and environmental criteria. Sizing is made taking into account technical objectives. In addition, the coupling of all the components via an external program to optimize the PV plant taking into consideration the three main criteria is difficult due to the closed structure used in each tool.

To overcome the problem of interoperability, the design of a simulator for received solar radiation coupled with a sizing module constitutes the most suitable option. The simulator must be designed in an open manner so that it can be interfaced easily with an outer optimization loop. The estimation of solar radiation and the output energy of the system are the two most critical aspects of any PV System design and sizing tools.

Development of the simulation tool

Most of the reported studied [START_REF] Gong | Design optimization of a large scale rooftop photovoltaic system[END_REF][START_REF] Kornelakis | Contribution for optimal sizing of grid-connected PV-systems using PSO[END_REF][START_REF] Mondol | Optimal sizing of array and inverter for grid-connected photovoltaic systems[END_REF][START_REF] Notton | Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations[END_REF][START_REF] Weinstock | Optimization of Economic Solar Field Design of Stationary Thermal Collectors[END_REF], 2009) involve PVGCS optimization considering only one criterion. Other investigations [START_REF] Dones | Life-cycle Assessment of Photovoltaic Systems: Results of Swiss Studies on Energy Chains[END_REF][START_REF] Ito | A Comparative Study on Cost and Lifecycle Analysis for 100 MW Very Large-scale PV (VLS-PV) System in Deserts Usinf m-Si, a-Si, CdTe, and CIS Module[END_REF][START_REF] Kannan | Life cycle assessment study of solar PV systems: An example of a 2.7kWp distributed solar PV system in Singapore[END_REF][START_REF] Pacca | Life Cycle Assessment of the 33 kW Photovoltaic System on the Dana Building at the University of Michigan[END_REF] address only the issue of the environmental impact assessment of the components of a PV system with emphasis on PV module technology. The main purpose consists in generating alternatives of optimal PVGCS configurations taking into account technical and economic aspects as well as their environmental impact.

The closed structure of the tools listed in Table 3-1 makes complicated to couple them with an environmental module and with an outer optimization loop to solve the resulting optimization problem. This explains why a dedicated simulation tool was developed in order to develop the proposed methodology.

Figure 3-1 illustrates the system flow diagram for modeling solar radiation and estimating the output energy of a PVGCS. The proposed modeling framework will then coupled with an optimization module for generating optimal configuration alternatives. The system is based on the following models:

(a). The estimated solar radiation received by the system according to the geographic location.

(b). The PVGCS sizing based on a mathematical model that provides the annual energy generated from the characteristics of the system components and limitations on the design of the installation.

(c). The evaluation of economic, technical and environmental criteria.

Solar radiation model

Solar radiation 3.4.1 Solar radiation on tilted planes is very important to design flat plate PV collectors for power plants.

When radiation passes through Earth's atmosphere, it suffers changes in its trajectory by the elements present in the atmosphere. Elements such as ozone, oxygen, carbon dioxide and water vapor absorb the radiation; some are reflected by the clouds. The dust and water droplets also cause disturbances.

To eliminate the effects of local features, solar radiation is measured on horizontal surfaces free of obstacles. The result is the decomposition of the incident solar radiation into a receiver placed on the surface in different components [START_REF] Lorenzo | Energy Collected and Delivered by PV Modules[END_REF]. Consequently, solar radiation data are most often given in the form of global radiation on a horizontal surface. Since PV modules are usually positioned at an angle to the horizontal plane, the radiation input to the system must be calculated from this data.

Global radiation on a tilted plane consists of three components (Figure 3-2):

Beam radiation. The radiation received from the sun without having been scattered or reflected that reaches the surface. It is also known as direct radiation.

Diffuse radiation. The radiation received from the sun after its direction has been changed by scattering by the atmosphere.

Reflected radiation. The radiation received from the sun that is reflected by the ground.

Albedo radiation is another name.

Global radiation. The total radiation falling on a surface is the sum of beam, diffuse and reflected radiation.

The calculation of irradiance arriving on a tilted surface, using as input global horizontal data, raises two main problems related on the one hand to the separation of the global horizontal radiation into its direct and diffuse components and, from them, on the other hand to the estimation of the irradiance components falling on an inclined surface. Over the years, different models have been developed to estimate solar radiation over tilted planes [START_REF] Demain | Evaluation of different models to estimate the global solar radiation on inclined surfaces[END_REF][START_REF] Duffie | Solar Engenierring of Thermal Process[END_REF][START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF].

The models can be classified as isotropic or anisotropic models. Yet, almost all models use the same method of calculating beam and ground-reflected radiation, the main difference is the treatment of diffuse radiation.

Isotropic models assume that the intensity of sky diffuse radiation is uniform over the sky dome.

Hence, the diffuse radiation incident on a tilted surface depends on the fraction of the sky dome seen by it. The most widely used model belonging to this category is the one developed by Liu and Jordan [START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF]. Because of its simplicity, this model has achieved great popularity, despite the fact that it underestimates diffuse irradiance on surfaces tilted to the equator.

The second group of models assumes both the anisotropy of the sky diffuse radiation in the circumsolar region (sky near the solar disc) and an isotropically distributed diffuse component from the rest of the sky dome. Better results are obtained with this type of model.

Model Description

3.4.2

The solar radiation model computes the radiation received in the site where the future plant will be built. Figure 3-3 shows the input data necessary for the operation of the model, sub-models and the outputs.

Visual Basic for Applications (VBA) in Excel was used for simulation purpose. The main advantages include the automation of repetitive tasks and calculations, the easy creation of macros in a friendly programming language, the possibility to use existing Excel functions and formulas, ability to import and export data and the classification and management of results.

It is relevant to make a difference between power and energy when considering PV systems. The radiation term is used as a general one for referring both aspects. The following concepts are used to distinguish between:

Irradiance. The density of power falling on a surface per unit area of surface at a specific time.

The SI units is watt per square meter (W/m 2 ); others units are MJ/m 2 /day or kWh/m 2 /day.

INPUT DATA OUTPUT DATA SUBMODEL

Solar position -Solar azimuth angle (ϒ S ) -Solar elevation angle (D)

Hourly solar irradiance The inputs for this module are classified into two groups. The former group is composed of climatological data of the studied site. The average hourly temperature is obtained from collected information that is available in various databases. Another important element to establish the relationship between solar radiation on the surface of the Earth and the extraterrestrial radiation is the index of transparency of the atmosphere or clearness index (Kt). This index is the radio between the horizontal radiation of a particular hour and the extraterrestrial radiation for that hour, as expressed by:

-On horizontal surface (G, G b , G d ) -On inclined surface (G β , G β,b , G β,d , G β,
(3.1) Kt is imported from climatological databases.
The latter group is composed of all the data inherent to the geographic location of the site where the facility will be placed. This information allows us to estimate the position of the sun and the solar radiation that the facility will handle every hour.

Before making any estimation of the amount of energy generated by a PV system, it is necessary to understand how the energy radiated by the sun reaches the earth and the effect of the atmosphere in its way to impact the surface of the panel solar.

The Sun is a sphere composed of extremely hot gas with a diameter of 1.39 x 10 9 m. It acts as a perfect emitter of radiation (black body) at a temperature close to 5800° K. The sun is indeed a continuous fusion reactor, and these reactions are the cause of the energy radiated by this celestial body. When radiation passes through Earth's atmosphere, it suffers changes in its trajectory by the elements present in the atmosphere. The overall amount of global irradiation that reaches a receiver placed on the surface of the earth is extremely variable. On one hand extraterrestrial radiation experiences a daily variation due to the apparent motion of Sun. On the other hand, there is random variation caused by weather: clouds, rain, sandstorm, etc. Figure 3-5 shows the relations among the different levels of solar irradiance.

To estimate each of the components of the global irradiance is important to understand the relationship between a plane at any orientation at any given time and the incoming solar radiation due to the position of Sun with respect to the plane (see Appendix B). The equations employed to calculate the sun's position and their encoding were taken from the research work of Montenon [START_REF] Montenon | Analyse, mutualisation et optimisation par la commande de la consommation énergétique des héliostats autonomes des centrales à concentration solaire[END_REF] as part of the collaboration between the two laboratories.

Components of hourly irradiance on horizontal surface

Solar irradiance received onto a horizontal surface is split into its beam and diffuse components. These components provide the basis for estimating solar radiation on tilted surfaces. Hourly irradiance received on the horizontal surfaces may be expressed by:

(3.2) The estimation of diffuse irradiance is very complex because it depends on the composition, shape and position of the elements that cause the scattering of radiation and this may vary with time. Diffuse irradiance is essentially anisotropic. The amount of reflected radiation is affected by the nature of the ground and by a wide range of features (snow, vegetation, water, etc.).

Miguel et al. [START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF]) establish a correlation between the diffuse fraction of hourly global horizontal irradiance and the clearness index. This correlation is given by the following expressions:

(3.3)
Then, the beam irradiance can be calculated reformulating Equation (3.2) as follows:

(3.4)

Components of hourly irradiance on tilted surface

The global irradiance on an inclined surface, G β , is integrated by beam, diffuse and albedo irradiance (Equation (3.5)). The most adequate procedure to calculate the global irradiance on a tilted surface is to obtain separately the components.

(3.5)

Beam irradiance

The amount of beam irradiance on a tilted surface can be calculated by multiplying the beam horizontal irradiance by the beam ratio factor (r b ).

(3.6)

(3.7)

One consideration must be taken into account in calculating this component, when the sun shines on the back of the surface (cos θ < 0) the irradiance on the PV modules is normally not utilized, G β,b = 0.

A factor max (0, cos θ) is introduced in Equation (3.7).

(3.8)

Reflected irradiance

The reflectivity of most types of ground is rather low [START_REF] Lorenzo | Energy Collected and Delivered by PV Modules[END_REF] except snow and ice.

Consequently, the contribution of this type of irradiance falling on a receiver is low. Equation (3.9) computes ground-reflected irradiance.

(3.9)

where ρ is the reflectivity of the ground and depends on the composition of the ground. A value of 0.2 is commonly adopted.

Diffuse irradiance

The methods used to estimate the diffuse irradiance on a titled surface are classified as either isotropic or anisotropic models. The isotropic models assume that the intensity of diffuse sky radiation is uniform over the sky dome. Hence, the diffuse irradiance incident depends on the fraction of the sky dome where the surface is located. A well-known isotropic model was proposed by Liu and Jordan (1963).

(3.10)

Better results are obtained with the so-called anisotropic models. This type of models includes a circumsolar brightening, which assumes that the highest intensity is found at the periphery of the solar disk and falls off with increasing angular distance from the periphery. (3.16)

(3.17)
where is the mean of the measured values. The results can be observed in Table 3-2 andTable 3-3.

Units of RMSE and MBE are kW/m².

The difference found was minimal especially when the model uses the formulation of Hay et al. [START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF] for the calculation of diffuse irradiance.

PVGCS sizing model

The second model of the system aims at calculating annual energy generated by the system from the radiation computed by the first model and the characteristics of the electrical components. This model considers the following aspects:

a) The field dimension where the PVGCS will be installed; b) Technical aspects of the different elements of the PVGCS.

c) Design restrictions due to maintenance and safety purposes. These restrictions concern not only the maximum weight that the structure where the PV modules will be placed support but also the standards and best practices to ensure appropriate maintenance in case of problems during operation of PVGCS.

Figure 3-6 describes the main elements of this model. VBA in Excel was used to encode this model.

Output energy estimation 3.5.1

The design of PVGCS must take into account the dimensions of the field, solar radiation data and the so-called balance of system components (BOS). The BOS encompasses all the components of a photovoltaic system other than the photovoltaic panels. In addition, shading and the effect of mask (i.e. corresponding to shades on a solar panel caused by obstacles such as buildings, vegetation or relief for example) affect the collector deployment by decreasing the incident energy on collector plane of the field.

In a solar field, collectors, an array of PV modules, are deployed in different sheds with spacing allowing tilting and being useful for maintenance purpose. In this arrangement, a collector may cast a shadow on the adjacent row during the day, thus decreasing the amount of collected energy. This shading effect depends on the spacing between the collector rows, the collector height, and the tilt The spacing and, consequently, shading have also and influence on local environmental not allowing grass or culture to grow between and enter the PV panels. This environmental consequence was not evaluated in this work.

The balance of system (BOS) also influences the estimate of annual energy generated by the facility because of the efficiency of electrical components.

Techniques for sizing PV systems 3.5.2

In any PVGCS, sizing represents an important part of the design that must satisfy techno-economic requirements. Undoubtedly, at the current stage of development of PV technology, the major impediment to a wider market penetration is the high investment costs of the PV systems [START_REF] Epia | Solar Photovoltaic on the Road to Large Scale Grid Integration[END_REF].

The solar field design problem may be described by a mathematical formulation, usually multivariable and nonlinear in both the objective and constraint functions.

In the literature, the configuration of PV is made following only one objective such as the minimum field area required for producing a given amount of energy, the maximum energy generated from a given field or minimum cost of investment.

Mathematical sizing model 3.5.3

Weinstock and Appelbaum [START_REF] Weinstock | Optimization of Economic Solar Field Design of Stationary Thermal Collectors[END_REF] formulated the PVGCS sizing problem as a mathematical problem. The optimal design parameters of the solar field were determined to obtain the maximum annual incident energy on the collector planes from a given field size.

The improvements that were implemented relative to the model presented in [START_REF] Weinstock | Optimization of Economic Solar Field Design of Stationary Thermal Collectors[END_REF] concern the computation of the output power of the system, mainly in the following aspects:

• The equation used for calculating the diffuse irradiance received by the collector is replaced by the anisotropic model of Hay et al. [START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF].

• The reflected irradiance is included in the calculation of the radiation received by the installation.

• The method used to calculate energy loss caused by the shadow generated by adjacent collectors is changed. An array indicating the number of panels covered in a collector is created following the method proposed by Ziar et al. [START_REF] Ziar | Analysis of shadow effect in photovoltaic array using binary coding method[END_REF] The model considers a horizontal field without elevations with a fixed length L and a fixed width W. It comprises K rows of solar collectors with a horizontal distance D between the rows; each collector has a length L C , a height H , and a tilted with an angle β with respect to the horizontal Figure 3 (3.20)

The space between collector rows D is at least equal to a distance D min , i.e.:

(3.21)

Maintenance and installation constraints are required to limit the height of collector above the ground E max , i.e.:

(3.22)

The collector height H itself can be limited by the solar field construction, maintenance and by PV module manufacturer, i.e.:

(3.23)

The collector tilt angle may vary in the range of 0° to 90°:

(3.24)

The number of PV shed of the final array is a least equal to 2 and takes a discrete value:

(3.25)

Direct shading

They can be due to trees, posts, nearby buildings, etc. placed between the sun and the panels during the day, or if there are several sheds of panels arranged in the same horizontal plane. Losses can be important, because of that, the location must be carefully chosen to avoid shading as much as possible.

In the case of large-scale solar plants, collectors are set in several sheds and shading by neighbors may become inevitable. The shadow that is projected from a shed to another one varies throughout the day and can be determined geometrically (Weinstock & Appelbaum, 2004a) (Appendix D).

The amount of shading depends on the distance between the collector rows D, their height H, the row length L c , the tilt angle β and the latitude (see Figure 345678).

A status matrix is defined, M(j, k, t, n), as follows in order to determine the shaded modules of the collector in a specific hour t and in a specific day n [START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF].

(3.26)

This matrix makes it possible to determine if a module receives solar irradiation during the whole day or only at given hours of the day. In addition, the status matrix assumes that any partially shaded module at a given time is considered as a fully shaded module. The modelling of a partially shaded module represents a complex situation. A solution found in the literature is to consider that a module is "shaded" once a shadow is cast on it even on a smallest part of its area. This is of course the extreme case because some power may be delivered by the module when partially shaded. This hypothesis is supported by the fact that a matrix of shaded/unshaded modules simplifies the algorithmic aspect and reduces the computational time while the results are not so different to those obtained by using PVsyst.

Output energy of solar field

The output power of the modules in a row connected in series depends on three main factors: module efficiency (η), module temperature (T m ), and the number of shaded modules at a given time. The meteorological data at the specific site together with the geographical coordinates of the site allow calculating the power delivered by a module as a function of time.

(3.27)

The module temperature was calculated according to Van Overstraeten et al. [START_REF] Weinstock | Optimal Solar Field Design of Stationary Collectors[END_REF], Equation (3.28)), and the loss of power due to temperature rise above 25°C is taken into account in Equation (3.29)) for the power delivered by a module in time t at day n:

(3.28)

(3.29)

The integration of Equation (3.29) over a year predicts the annual energy produced of a module. The yearly incident solar energy of the PV collectors placed in the field is given by:

(3.30)

The first part of the Equation (3.30) represents the energy produced by the unshaded first shed and the second part comprises the energy produced by the K-1 shaded sheds. This value represents the maximum amount of energy that is sent to the DC / AC inverter.

Energy losses

The losses inherent in any energy conversion process are numerous. These losses have different origins. In this chapter, we have already mentioned some of them.

Evaluation model

The third model of the integrated system is dedicated to the evolution of the three criteria. For each criterion, a performance index was selected. These indexes will allow the evaluation and comparison of the resulting options. [START_REF] Boulanger | Guide de rédaction du cahier des charges techniques des générateurs photovoltaïques connectés au réseau[END_REF]. Appendix D shows in detail the techniques used for designing the mounting structure and electrical components.

Techno-economic criteria 3.6.1

The techno-economic criteria chosen in this study concern the payback time of investment and energy payback time, respectively. These are classical criteria when energy production is involved. Their choice is summarized in what follows.

In project evaluation and capital budgeting, the payback time (PBT) is an estimation of the time that will be necessary for an investor to recover the initial investment. It is used to compare investments that might have different initial capital requirements. It is calculated by the following expression:

(3.31)

The cost of project considers the considering all the components that make up the installation purchasing (PV modules, cables, mounting system ...), the construction and the edification cost as well as the cost of connection to grid. Annual cash flow represents the incomes by selling all energy production.

Table 3-5 summarizes the main elements of economic evaluation considered in the model. The price of electrical components (PV modules and DC / AC inverters mainly) is set by the manufacturer and the price often depends on the volume purchased. Solarbuzz (Solarbuzz, 2012a, 2012b) together with PV magazine [START_REF] Schachinger | Module price index. PV-magazine[END_REF]) make regularly a study of PV modules technologies and DC/AC inverters market price. They proposed a price by W p and W AC respectively. The others costs were taken from [START_REF] Hayoun | Les installations photovoltaïques: Conception et dimensionnement des installations raccordées au réseau[END_REF], (Di Dio, Miceli, Rando, & Zizzo, 2010) and, (A. [START_REF] Kornelakis | Methodology for the design optimisation and the economic analysis of grid-connected photovoltaic systems[END_REF] works. The average sell price for the electricity generated by the PVGCS was obtained from the reports of the European Renewable Energies Federation [START_REF] Fouquet | Prices for Renewable Energies in Europe[END_REF][START_REF] Fouquet | Prices for Renewable Energies in Europe[END_REF].

Energy payback time (EPBT) is the time in which the input energy during the PV system life-cycle (which includes the energy requirement for manufacturing, installation, energy use during operation, and energy needed for decommissioning) is compensated by electricity generated by the PV system.

(3.32)

Primary energy required for manufacturing is obtained as a result of the Life Cycle Assessment, identified here in the Non-renewable energy category. A conversion factor of 2.58 is used to transform 1 kWh electricity into primary energy (ADEME, n.d.).

Environmental criteria 3.6.2

As it was mentioned in Chapter 2, the environmental assessment is performed following the methodology of Life Cycle Assessment (LCA). Following the guidelines indicated in the LCA study described in Section 2.7, the system boundaries are kept without considering recycling processes (see Chapter 5). PV modules and BOS component characteristics will be modified in accordance with the data and characteristics of each situation under evaluation.

IMPACT 2002+ [START_REF] Jolliet | Presenting a New Method IMPACT 2002 + : A New Life Cycle Impact Assessment Methodology[END_REF], included in SimaPro 7.3, was selected as a method for evaluating the environmental impacts. Only the midpoint categories are considered.

Validation of the model

Comparison with Weinstock and Appelbaum's model performances 3.7.1

The example given by Weinstock and Appelbaum [START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF]) (referred as WAP in the following) is used to validate the proposed model.

It must be kept in mind at this level that the objective is to verify the validity of the proposed modelling and simulation approach without considering optimization, that will be the core of the subsequent chapter. The maximization of the annual energy generation by the facility is used the reference objective. In order to check the relevance of the model, the same scenarios as those used in the WAP approach were used. WAP offers the best configuration of a PV power plant for the maximum energy generation under three different scenarios:

-in the first scenario, PV power plant is sized when maximizing the incident energy on to the total surface of PV modules without any type of energy losses; -the second one maximizes the output energy when only considering the module efficiency and shading;

-the third one maximizes the output energy of the PVGCS with accounting of all possible energy losses (shading, temperature and interconnections losses).

The PV power plant is located in Tel Aviv, Israel (32.0°N, 34.8°E, altitude 4 m, GMT +2). The PV module has a length L m = 1.293 m and a height H m = 0.33 m. The technology of the PV module used by WAP is not mentioned explicitly but the computation is performed with the assumption of an efficiency of 12.42%.

The field and PV collector parameters are the following ones: W = 150 m, L = 12.93 m, and N r = 6, N c = 10.

The simulation of each configuration calculated by WAP for each of the three proposed scenarios was performed with the model proposed in this work. The goal is to compare the amount of energy generated between both approaches. The site data, the dimension of the field and PV module characteristics were used in the simulation runs. It must be yet emphasized that because each configuration in WAP example was obtained by optimizing a mathematical model, the value of K, β and D are not the same in each run. Table 3-6 contains the values of these parameters for each scenario.

Table 3-7 shows the comparison between the results obtained by our approach and the WAP example.

The results estimated by the proposed simulator tool have a difference of about 20% with respect to the estimation of WAP. As mentioned in Section 3.5.3, this difference can be attributed to the modifications made in the model for predicting the annual amount of the energy generated.

Comparison with PVsyst

3.7.2

To verify this assumption, a second set of simulation runs is performed with PVsyst software (mentioned in Table 3-1) taking into account the technology type. For the simulations, the location of PV plant, the field dimensions and the parameter values for the three WAP cases (see Table 3456) are used again.

The objective is to study the five main PV modules technologies available in the market: monocrystalline silicon (m-Si), polycrystalline silicon (p-Si), amorphous silicon (a-Si), cadmium telluride (CdTe) and copper indium diselenide (CIS). Table 3-8 shows the main characteristics for each of the 5 PV modules technologies used in the simulations. The values of N c and N r for each of the technologies are shown in Table 3-9. In the simulations, only one technology is assumed per field which means that no mixed technologies are allowed. In order to compare the results, each of the 5 configurations is modelled with PVsyst software.

It must be highlighted that PVsyst is a software tool developed by the University of Geneva, Switzerland. This architect-and engineer-oriented tool is suitable for working in the field of renewable energy but is also for education. This program has three modules:

-a preliminary design, which allows making a quick evaluation of a grid-connected installation, a stand-alone installation or a pumping system; -a project design, it allows sizing an installation connected to the DC network using detailed hourly simulations; -a tool module, in which it possible to adjust certain parameters of the software.

PVsyst includes a database of around 330 sites in the world and it possible to import weather data from many popular meteorological sources. The component database holds over 1,750 PV modules from all common commercial technologies, 650 inverters and dozens of batteries or regulator models.

The Preliminary design module permits the definition of the plane orientation, PV components and location. It offers detailed parameters allowing fine effects analysis, including thermal behaviour, wiring and mismatch losses, and module quality loss. A detailed economic evaluation could be performed. A final report summarises all system parameters and the most significant result plots and tables for one given simulation.

PVsyst includes a 3-D CAD tool to draw the geometry of the system. It computes a shading factor for beam component as a function of the sun's position. Animation over a whole chosen day could be made for clarify the shading impact of a given situation. It must be highlighted that a good agreement is obtained between the prediction of the proposed tool and those of PVsyst. The deviation that is observed may be due to the precision adopted in PVsyst for Zenith South some parameters required for the estimation of the output energy of the system such as PV collector inclination and position of the sun throughout the evaluation period work only: integer numbers are used in PVSyst while the proposed simulation tool uses decimal numbers. This situation concerns in particular the amount of irradiance that PV power plant can receive and the energy loss due to the shadows cast by the PV shed.

The main disadvantage of using PVsyst, as presented in Table 3-1, is its closed structure, not allowing it to be embedded in an outer optimization loop. It is then difficult to achieve the main objective of this work, which consists in the development of an ecodesign tool for PV panels.

Another disadvantage of this software tool is that, although involving a 3-D CAD tool, the use of this tool requires the configuration and arrangement of all the elements of the system to be evaluated. A change in the dimensions of the PV collector or in the number of PV sheds placed in the field cannot be performed automatically by the program. A trial and error procedure must thus be implemented.

Additional information can also be obtained with the proposed simulation tool. The result of the evaluation of PBT and EPBT for each configuration is shown in Table 3-11.

The results are presented through radar charts normalised to unity. Figure 3-11 presents the radar chart of the results of the environmental impact assessment (15 midpoint categories). To facilitate the comparison, normalisation was performed by assigning the value 1 to the maximum value of each category. The computed relative impacts represent the ratio between the environmental impact and this maximum value.

From Table 3-11, it can be seen that the choice of the PV power plant that uses PV modules of m-Si has a lower PBT as the revenue generated by the large amount of annual energy that can be injected into the grid is the highest one and compensates for the highest unit cost of all the technologies considered. Considering EPBT, the use of PV modules based on CdTe has the shortest time. This is due to the low amount of primary energy needed for manufacturing (see Chapter 2).

The graphics show that the configuration that uses technology based on m-Si has the highest impact in 13 of 15 categories, while in the other categories (Carcinogens and Mineral Extraction) the highest impacts are observed when using PV modules based on a-Si.

Another analysis is then performed taking into account the energy generated by each configuration.

This new analysis consists in assessing the environmental impact per kWh produced, as follows: The results are presented through radar charts normalized to unity (Figure 3-12). It emphasizes the substantial reduction of categories led by m-Si technology from 13 to just one. The opposite case is the technology based on a-Si that increases of 2 to 6 category. These changes highlight the influence of the energy generated in a PVGCS to assess the environmental impacts generated.

Another fact to note from the graphics presented is the similarity in behaviour between some of the categories of environmental assessment. In the next chapter, a methodology will be proposed to identify the correlated impacts and the antagonist behaviour of the criteria in order to reduce their number in the optimization step.

Conclusion

The chapter was dedicated to the model developed in this work to represent the performance of a PV power plant. The model involves a three-step framework:

(1). The estimated solar radiation received by the system according to the geographic location

(2). The model provides the annual energy generated from the characteristics of the system components and limitations on the design of the installation. The design of PVGCS must take into account the dimensions of the field, solar radiation data (see first item) and the so-called balance of system components (BOS). Let us recall that BOS encompasses all the components of a photovoltaic system other than the photovoltaic panels. WAP does not consider BOS.

(3). The model is then coupled with two modules for evaluation of techno-economic (PayBack Time and Energy PayBack) and environmental (IMPACT 2002+ midpoint categories) criteria.

From the technical viewpoint, the model performance was validated for a reference example taken from the literature [START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF] with a standard simulation tool and exhibited a good agreement.

A preliminary assessment of the economic and environmental performance of some typical technologies that can be used for PV modules shows that the proposed approach can predict with a good accuracy monocrystalline silicon (m-Si), polycrystalline silicon (p-Si), amorphous silicon (a-Si), cadmium telluride (CdTe) and copper indium diselenide (CIS).

This analysis then confirms the interest to use an optimization approach to search for the most interesting solution taking into account simultaneously techno-economic aspects and environmental concern.

The integration of the proposed model in an outer optimization loop is therefore a natural extension of this work and is the core of the following chapter.

METHODS AND TOOLS FOR ECODESIGN: COMBINING MULTI-OBJECTIVE OPTIMIZATION (MOO), PRINCIPAL COMPONENT ANALYSIS (PCA) AND MULTIPLE CRITERIA DECISION MAKING (MCDM)

Les problèmes d'écoconception nécessitent en général la considération d'un grand nombre d'objectifs, induit par le jeu de catégories d'impact, de l'ordre d'une dizaine, lors de l'application de l'analyse du cycle de vie. Les méthodes d'optimisation multi-objectif, quant à elles, impliquent d'un point de vue de leur mise en oeuvre pratique, des problèmes ayant un nombre plus réduit de fonctions objectifs: la résolution d'un problème bicritère ou tricritère peut se révéler complexe selon la nature des contraintes et des critères mis en jeu. Les principaux obstacles au traitement d'un grand nombre d'objectifs sont divers : stagnation possible du processus de recherche, augmentation de la dimension du front de Pareto, temps de calcul élevé, et enfin difficulté à visualiser et analyser les résultats. Par ailleurs, l'analyse des résultats de l'analyse du cycle de vie pour un produit, processus ou service montre que certains critères peuvent être redondants : les groupes de critères liés dépendent du problème à traiter. Il est donc important de bien formuler le problème pour identifier le choix de critères indépendants et mener le processus d'optimisation de façon rationnelle.

Ce chapitre, consacré aux outils et méthodes utilisés dans le cadre de ce travail, est divisé en trois parties principales. La première partie est consacrée à l'optimisation multi-objectif et le choix d'une variante de la méthode dite NSGA-II est justifié. La partie 2 présente une approche fondée sur une analyse en composantes principales (ACP) couplée avec la variante de NSGA-II sélectionnée. L'idée est d'identifier les objectifs redondants des solutions obtenues par NSGA-II et de les éliminer dans le processus d'optimisation proprement dit. La partie 3 concerne les outils d'aide à la décision multicritère mis en oeuvre afin de sélectionner le meilleur compromis parmi les critères

a ij D i + , D i - G j R i Q out v ij w j x + x - X ij

Introduction

Ecodesign problems involve a large number of objectives, generally more than ten when carrying out Life Cycle Assessment. Multi-objective optimization methods are yet applied only to problems having a lower number of objectives. Among these methods, existing evolutionary multi-objective optimization methods, which turned out to be very attractive due to their ability to lead to a wellrepresentative set of Pareto-optimal solutions in a single simulation run, are generally applied only to problems having about 5 objectives or so. The major impediments in handling a large number of objectives relate to stagnation of search process, increased dimensionality of Pareto-optimal front, large computational cost, and difficulty in visualization of the objective space. Furthermore, several objectives are redundant so that a multi-objective strategy is not, strictly speaking, necessary.

The methods and tools that are proposed in this chapter can be viewed as generic approaches for ecodesign problems. They are applied more particularly here to the PVGCS problem which is the subject of this PhD work. The PVGCS strategy and the results obtained will not be presented in this chapter. They will be deeply analysed in the following chapter.

This chapter is divided into three main sections. Section 1 is dedicated to multi-objective optimization and the choice of a variant of the so-called NSGA-II method is justified. Section 2 addresses a principal component based approach coupled with the variant of NSGA-II that is selected. The idea is to identify redundant objectives from the solutions obtained by NSGA-II and to eliminate them from further consideration. Section 3 concerns Multiple Criteria Decision Making, in order to select from the optimal Pareto front the best compromises among the antagonist criteria.

Multi-objective optimization for sizing PV systems

In any PVGCS, sizing represents an important part of the design that must satisfy techno-economic requirements. Undoubtedly, at the present stage of development of the PV technology, the major impediment to a wider market penetration is the high investment costs of the PV systems (EPIA,

2012).

The solar field design problem may be described by a mathematical formulation. The configuration of PV is based on criteria such as the minimum field area required for producing a given amount of energy, the maximum energy generated from a given field or minimum cost of investment.

Several methods for solving optimization problems have been developed. These methods can be grouped into two main groups (Figure 4-1). The former group follows a linear formulation for the constraints (either of equality or inequality type) and objective functions. In the latter group, the nonlinear formulation involves a set of non-linear constraints and/or objective functions.

There are recent methods developed for sizing the parameters for PVGCS based on Artificial intelligence (AI) and Genetic algorithm (GA) techniques [START_REF] Gong | Design optimization of a large scale rooftop photovoltaic system[END_REF][START_REF] Mellit | Artificial intelligence techniques for sizing photovoltaic systems: A review[END_REF][START_REF] Mellit | Sizing of stand-alone photovoltaic systems using neural network adaptive model[END_REF][START_REF] Mondol | Optimal sizing of array and inverter for grid-connected photovoltaic systems[END_REF][START_REF] Mondol | Optimising the economic viability of grid-connected photovoltaic systems[END_REF]. The literature review reveals that evolutionary or stochastic methods e.g. Genetic Algorithms (GAs), Ant Colonies Optimization (ACO), Neural Networks (NN), Particle Swarm Optimization (PSO) and Differential Evolution (DE) [START_REF] Mondol | Optimal sizing of array and inverter for grid-connected photovoltaic systems[END_REF][START_REF] Notton | Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations[END_REF] are particularly attractive for non-linear problems. These methods are suitable for "black box" problems where the mathematical properties (continuity, convexity, derivability …) of the problem are difficult to establish. The evaluation of the criteria and constraints of a set of values of independent variables is only required. These methods do evolve in one or more series of initial solutions supported by a set of probabilistic rules often imitating a process of nature.

The solar field design problem can be described by a mathematical formulation as it was explained in Chapter 3 and can be viewed as an optimization problem. Several works deal with the configuration of PVGCS based on criteria to optimize such as the field area required for producing a given amount of energy (minimization case), the energy generated from a given field (maximization case), or cost of investment (minimization case) (García-Valverde, Miguel, Martínez-Béjar, & Urbina, 2009; [START_REF] Kaushika | Solar PV design aid expert system[END_REF][START_REF] Kornelakis | Methodology for the design optimisation and the economic analysis of grid-connected photovoltaic systems[END_REF][START_REF] Mondol | Optimising the economic viability of grid-connected photovoltaic systems[END_REF][START_REF] Senjyu | Optimal configuration of power generating systems in isolated island with renewable energy[END_REF][START_REF] Weinstock | Optimal Solar Field Design of Stationary Collectors[END_REF], 2007).

From the mathematical formulation performed to describe the problem of sizing a PVGCS presented in Chapter 3, it is possible to identify that: the set of equations does not respect the principle of linearity; the relevant meteorological data, especially solar radiation, are estimated from a mathematical model.

From the abovementioned reasons, it can be deduced that the use of solution methods from linear formulation (first group) may be difficult and that an optimization method for the design of PV systems representing a comprehensive set of variables from the solar radiation estimation to PV system configuration is required.

In order to deal with this situation, several studies are reported in the dedicated literature in which PV Espín Estrella, 2012; Adel [START_REF] Mellit | Sizing of stand-alone photovoltaic systems using neural network adaptive model[END_REF]. Stochastic algorithms have proved to be particularly efficient for solving complex problems with either linear or non-linear functions (Gómez-

Lorente et al., 2012).

This study is carried out in the framework of a multi-objective problem where multiple antagonist objectives must be optimized simultaneously: an economic objective (PBT), a technical goal (EPBT)

and several environmental objectives. The selected method is based on Multi-objective Genetic Algorithms (MGA). A brief overview of the main features of a GA and how it works is presented below in order to understand the operation of MGA.

Genetic algorithms 4.2.1

Genetic algorithms (GA) are inspired by how the organisms are adapted to the harsh realities of life in a hostile world, i.e., by evolution and inheritance. The algorithm imitates in the process the evolution of population by selecting only fit individuals for reproduction.

GAs were proposed by Holland in the 1970s as an algorithmic concept based on a Darwinian-type survival-of-the-fittest strategy with sexual reproduction, where stronger individuals in the population have a higher chance of creating an off-spring. A genetic algorithm is implemented as a computerized search and optimization procedure that uses principles of natural genetics and natural selection. The basic approach is to model the possible solutions to the search problem as binary strings. Various portions of these bit-strings represent parameters in the search problem. If a problem-solving mechanism can be represented in a reasonably compact form, then GA techniques can be applied using procedures to maintain a population of knowledge structure that represent candidate solutions, and then let that population evolve over time through competition (survival of the fittest and controlled variation). A GA will generally include the three fundamental genetic operations of selection, crossover and mutation (see Figure 4-2). These operations are used to modify the chosen solutions and select the most appropriate off-spring to pass on to succeeding generations. GAs consider many points in the search space simultaneously and have been found to provide a rapid convergence to a near optimum solution in many types of problems: in other words, they usually exhibit a reduced chance of converging to local minima. The first step in any GA is to generate an initial population with a group of individuals randomly created. The individuals in the population are then evaluated and assigned a fitness value. The evaluation function is provided by the operator and gives the individuals a score based on how well they perform at the given task. The fitness value is always defined with respect to other members of the current population. Fitness can be assigned based on an individuals' rank in the population and forms the relation between the evaluation score and the average evaluation of all the individuals in the population. Two individuals are then selected based on their fitness, the higher the fitness, the higher the chance of being selected.

After selection has been carried out, crossover is applied to randomly paired individuals. The recombined individuals create one or more off-spring. This can be viewed as creating the new population. The randomly mutation of the offspring is then applied. In terms of GAs, mutation means a random change of the value or a gene in the population as shown in Figure 4-2.

After the process of selection, recombination and mutation, the next population can be evaluated. The process continues until a suitable solution has been found or when a given number of generations has been reached. Figure 4-3 represents the flow chart of the process described above. Multi-objective Genetic Algorithm 4.2.2 GAs are well suited to solve multi-objective optimization problems. The ability of GA to simultaneously search different regions of a solution space makes it possible to find a diverse set of solutions for difficult problems.

A multi-objective decision problem tries to find a vector x* that minimizes a given set of K objective functions given a n-dimensional decision variable vector . A set of constraints restricts the solution space [START_REF] Konak | Multi-objective optimization using genetic algorithms: A tutorial[END_REF].

In many real-life problems, objectives under consideration conflict with each other. Therefore, optimizing x with respect to a single objective often results in unacceptable results with respect to the other objectives. It is almost impossible to find the vector x* that simultaneously optimizes each objective function. A reasonable solution is to investigate a set of solutions which satisfies the objectives at an acceptable level without being dominated by any other solution.

A feasible solution x is said to dominate another feasible solution y, if and only if, for i = 1,…, K and for least one objective function j. Multi-objective optimization provides a set of non-dominated solutions in the solution space called Pareto optimal set. While moving from one Pareto solution to another, there is always a certain amount of sacrifice in one objective to achieve a certain amount of gain in the other. For a given Pareto optimal set, the corresponding objective function values in the objective space are called the Pareto front. The ultimate goal of a multiobjective optimization algorithm is to identify solutions in the Pareto optimal set. The size of Pareto optimal set is related with the number of objectives.

The crossover operator of GA allows creating new non-dominated solutions in unexplored parts of the Pareto front. Another important characteristic is that most multi-objective GA do not require the user to prioritize, scale, or weigh objectives, which constitutes a major asset for MOO methods.

Several survey papers [START_REF] Coello Coello | Evolutionary Multi-Objective Optimization in Materials Science and Engineering[END_REF][START_REF] Coello Coello | Recent Trends in Evolutionary Multiobjective Optimization[END_REF] have been published on evolutionary multi-objective optimization. A list of more than 2000 references was published by Coello Coello in his website [START_REF] Coello Coello | List of References on Evolutionary Multiobjective Optimization[END_REF]. Generally, MGAs differ according to their fitness assignment procedure, elitism, or diversification approaches. Table 4-1 highlights the advantages and disadvantages of some well-known MGA techniques found by Konak et al. [START_REF] Konak | Multi-objective optimization using genetic algorithms: A tutorial[END_REF].

PVGCS optimization approach 4.2.3 GA applications are appearing as alternatives to conventional deterministic approaches and in some cases are useful where other techniques have been completely unsuccessful. GAs are also used with intelligent technologies such as neural networks, expert systems, and case-based reasoning. GAs constitute a quite popular method used in engineering field.

It must be emphasized that GAs are not yet widely used for PV system sizing. A summary of applications of GAs in this field is proposed in Table 4-2. It must be highlighted that they concern mainly hybrid technologies involving either stand-alone or grid connected panels. For instance, proper design of standalone renewable energy power systems [START_REF] Xu | Optimal sizing of standalone hybrid wind/pv power systems using genetic algorithms[END_REF]) is a challenging task, as the coordination among renewable energy resources, generators, energy storages and loads is very complicated. The types and sizes of wind turbine generators (WTGs), the tilt angles and sizes of photovoltaic (PV) panels and the capacity of batteries must be optimized when sizing a standalone hybrid wind/PV power system, which may be defined as a mixed multiple-criteria integer programming problem. A GA with elitist strategy is investigated for optimally sizing a standalone hybrid wind/PV power system. The objective is selected as minimizing the total capital cost, subject to the constraint of the loss of power supply probability. The literature review reveals that PV planners are not quite familiar with GA optimization techniques for PV design.

As explained in the previous chapter, several programs and mathematical models have been developed

to calculate solar irradiance received at a given point of the planet and size a PVGCS separately. Most of the studies reviewed [START_REF] Gong | Design optimization of a large scale rooftop photovoltaic system[END_REF][START_REF] Kornelakis | Contribution for optimal sizing of grid-connected PV-systems using PSO[END_REF][START_REF] Mondol | Optimal sizing of array and inverter for grid-connected photovoltaic systems[END_REF][START_REF] Notton | Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations[END_REF][START_REF] Weinstock | Optimization of Economic Solar Field Design of Stationary Thermal Collectors[END_REF], 2009) consider exclusively PVGCS optimization with only one criterion. Other authors [START_REF] Dones | Life-cycle Assessment of Photovoltaic Systems: Results of Swiss Studies on Energy Chains[END_REF][START_REF] Ito | A Comparative Study on Cost and Lifecycle Analysis for 100 MW Very Large-scale PV (VLS-PV) System in Deserts Usinf m-Si, a-Si, CdTe, and CIS Module[END_REF][START_REF] Kannan | Life cycle assessment study of solar PV systems: An example of a 2.7kWp distributed solar PV system in Singapore[END_REF][START_REF] Pacca | Life Cycle Assessment of the 33 kW Photovoltaic System on the Dana Building at the University of Michigan[END_REF] address only the issue of the environmental impact assessment of the elements of a PV system with emphasis on PV module technology. Our main purpose consists in generating alternatives of optimal PVGCS configurations taking into account technical and economic aspects as well as their environmental impact.

The main problem found in the programs described in Table 3-1 is the lack of an integrated approach that allows the optimization of the sizing of a PVGCS. The coupling of all elements via an external program to optimize the model using a genetic algorithm is difficult due to the closed structure used.

To overcome the problem of interoperability, the design of a simulator for received solar radiation coupled with a sizing module constitutes the most suitable option. The simulator must be designed in an open manner so that it can be interfaced easily with an outer optimization loop.

The MULTIGEN environment previously developed in our research group [START_REF] Gomez | Multiobjective genetic algorithm strategies for electricity production from generation IV nuclear technology[END_REF] was selected as the genetic algorithm platform. A variant of NSGA-II developed for mixed problems and implemented in the MULTIGEN environment is selected. The stopping criterion proposed in MULTIGEN (in addition to the maximum number of generations) consists in comparing the Pareto fronts associated with non-dominated solutions for populations and + , where the period [10,20,30,40,50] for example. If the union of the two fronts provides a single non dominated front, the procedure stops; else the iterations continue.

It can treat either mono-and multi-objective problems. The potential of GAs to solve multi-objective problems serves as an incentive to use such an optimization strategy. This constitutes a natural way to extend this work. As it was initially developed in Visual Basic for Applications (VBA) in Excel, the same language is used for simulation purpose. The main advantages of VBA include the automation of repetitive tasks and calculations, the easy creation of macros in a friendly programming language.

NSGA-II was selected, as it is explained in [START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF] [START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF] Each individual is assigned a fitness (or rank) equal to its non-domination level (1 is the best level, 2 is the next-best level, and so on). Thus, the maximization of fitness can be performed. At first, the usual binary tournament selection, recombination and mutation operators are used to create an off-spring population Q t of size N (Figure 4-4). Since elitism is introduced by comparing the current population with the previously best found non-dominated solutions, the procedure is different after the initial generation.

First, a combined population is formed (Figure 45). The population R t is of size 2N.

Then, the population is sorted according to non-domination. If the size of F 1 (set of individuals of rank 1) is lower then N, all the members of the set F 1 for the new population P t +1 are definitely chosen.

The remaining members of the population P t +1 are chosen from subsequent non dominated fronts in the order of their ranking. Thus, solutions from the set F 2 are chosen next, followed by solutions from the set F 3 , and so on. This procedure continues until no more set can be accommodated. Let us consider that the set F 1 is the last non-dominated set beyond which no other set can be accommodated.

In general, the number of solutions in all sets from F 1 to F l is higher than the population size.

In order to choose exactly the population members, the solutions of the last front using the crowdedcomparison operator are sorted in descending order and the best solutions needed to fill all population slots are selected. The new population P t +1 of size N is now used for selection, crossover and mutation to create a new population Q t +1 of size N. It must be highlighted that a binary tournament selection operator is used but the selection criterion is now based on the crowded-comparison operator. Since this operator requires both the rank and crowded distance of each solution in the population, these quantities are calculated while forming the population P t +1, as shown in Figure 456.

The MULTIGEN library and NSGA-II are described in detail in [START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF].

Reduction of environmental objectives by Principal Component Analysis

(PCA) method.

LCA requires a large amount of data in its different phases and when the comparative analysis of products or processes is performed, the amount of data obtained as a result of the environmental impact assessment may be large and hard to interpret thus complicating the subsequent decisionmaking processes. One of the limitations of MGA when it is applied to environmental problems is that its computational burden grows rapidly in size with the number of environmental objectives.

The dimensionality of a data set can often be reduced easily without disturbing the main features of the whole data set by using multivariate reduction techniques such as Principal Component Analysis (PCA). The purpose is to apply PCA once the Pareto optimal set of the optimization for sizing the PVGCS considering both technical and economic criteria as well as environmental criterion is found in order to reduce the environmental categories.

The steps to apply PCA method are:

Step 1: Get the data. First, it is necessary to generate a Pareto optimal set of the original problem by using the selected multi-objective algorithm (NSGA-II in this work).

Step 2: Subtract the mean. The data set is standardized to make its centroid equal to zero. This is done by subtracting the mean of each column from each data point in the matrix for PCA to work properly.

Step 3: Calculate the correlation matrix. For reducing the environmental categories, the correlation matrix is the best option because of the different units which each category uses.

Step 4: Calculate the eigenvectors and eigenvalues of the correlation matrix. The eigenvector corresponding to the largest eigenvalue is referred as the first principal component; one corresponding to the second largest eigenvalue is called the second principal component and so on.

Step 5: Choosing components. Applying the Kaiser-Guttman rule [START_REF] Sabio | Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis[END_REF], the eigenvalues that are less or equal to 1 are excluded from the analysis. The cumulative explained variance (G j ) of remaining eigenvalues in descendant order is determined by the equation: The main advantage of applying PCA for environmental objectives is the elimination of redundant environmental categories, which reduces the objectives to consider in the multi-objective optimization, decreases the resolution time of the AG and makes easier the interpretation of results.

PCA for environmental categories 4.3.1 An example will illustrate the usefulness of PCA method for the reduction of environmental categories. The case reported by [START_REF] Sabio | Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis[END_REF] will be used to explain the method.

The goal of the problem addressed by Sabio et al. is The methodology described above was applied for reducing the environmental categories. After obtaining the Pareto set of solutions for the configuration of the hydrogen supply chain and standardizing the results (Steps 1 and 2), the correlation matrix is generated (See Table 4-3). It can be seen that CS, OLD and ES are highly correlated with DM, whereas AE is highly correlated with CC.

From the correlation matrix given above, the eigenvectors and eigenvalues matrix are calculated. The eigenvectors and eigenvalues of the correlation matrix are presented in Table 4-4, where the principal components are arranged in a descending order.

A graphical screen test was performed to decide the number of principal component to be kept for further analysis applying the Kaiser-Guttman rule. As observed in Figure 4-8, the fourth and subsequent components do not satisfy the Kaiser-Guttman rule. The three remain principal components represent almost the 100% of cumulative variability. Yoon [START_REF] Hwang | Multiple Attribute Decision Making: Methods and Applications[END_REF] and later by Lai et al. [START_REF] Lai | TOPSIS for MODM[END_REF]. Among the MCDM methods, TOPSIS is attractive since it requires limited subjective inputs from decision makers. The only subjective inputs needed are weights assigned to objectives. This may explain why TOPSIS [START_REF] Ren | Comparative Analysis of a Novel M-TOPSIS Method and TOPSIS[END_REF] is very popular in chemical engineering applications.

MCDM methods, especially TOPSIS, have often been used in multi-criteria optimization problem.

Boix [START_REF] Boix | Optimisation multicritère de réseaux d'eau[END_REF] used the TOPSIS method for selecting the best water network configuration involving three criteria: amount of fresh and treated water entering the network and the number of connections.

Ouattara [START_REF] Ouattara | Méthodologie d'éco-conception de procédés par optimisation multiobjectif et aide à la décision multicritère[END_REF] shows how the results obtained by a MGA (NSGA-II) can be connected to a MCDM method (TOPSIS) to solve an ecodesign process problem. The objective is to take into account simultaneously the ecological and economic considerations at the preliminary design phase of chemical processes.

A variant of TOPSIS (M-TOPSIS) has been adopted in this work, integrating the guidelines proposed in [START_REF] Ouattara | Méthodologie d'éco-conception de procédés par optimisation multiobjectif et aide à la décision multicritère[END_REF] M-TOPSIS method 4.4.1 M-TOPSIS method (Lifeng Ren et al., 2007) is an evaluation method that is often used to solve MCDM problems [START_REF] Pinter | Evaluating construction project success with use of the M-TOPSIS method[END_REF]. It is based on the concept of original TOPSIS [START_REF] Hwang | Multiple Attribute Decision Making: Methods and Applications[END_REF]. The basic idea of TOPSIS method is to choose a solution that is closest to the ideal solution (better on all criteria) and away the worst (which degrades all criteria) [START_REF] Markovic | Modification of TOPSIS method for solving of multicriteria tasks[END_REF][START_REF] Opricovic | Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS[END_REF][START_REF] Ren | Comparative Analysis of a NovelM-TOPSISMethod and TOPSIS[END_REF] The modification introduced by Ren et al. in M-TOPSIS method could avoid rank reversals and solve the problem on evaluation failure when alternatives are symmetrical that often occurs in original TOPSIS.

A specific module with M-TOPSIS has been implemented as a tool for multi-criteria decision, thus facilitating its use after obtaining Pareto fronts. Particular attention was paid to the simultaneous treatment of problems involving minimization and maximization criteria. The stages of the M-TOPSIS procedure are listed below. The normalisation of the matrix is performed according to the original work of [START_REF] Hwang | Multiple Attribute Decision Making: Methods and Applications[END_REF].

Step 1: Build the decision matrix. Establish a matrix which shows m alternatives evaluated by n criteria (see Figure 4-10).

Criteria Where J + = {i = 1, 2, …, m} when i is associated with benefit criteria ; J -= {i = 1, 2, …, m} when i is associated with cost criteria. j = 1, 2, …, n.

n 1 n 2 … n j Alternatives m 1 m 2 … X ij m i
Step Step 6: Calculate the relative closeness to the ideal solution. In M-TOPSIS, unlike TOPSIS, the positive ideal solution (D i + ) and negative ideal solution (D i -) in finite planes are found at first; and then, the D + D --plane is constructed and set the optimized ideal reference point.

Finally, the relative distance from each evaluated alternative to the ideal reference point is calculated with (Lifeng Ren et al., 2007). Set the point A in Figure 4 -11 [min

(D i + ), max (D i -)]
as the optimized ideal reference point because the aim is to have the lowest distance Ren et al., 2007) between the ideal criteria set values (A + ) and get away as much as possible of non-ideal criteria set values (A -). The ratio value of R i is calculated as follows:

(4.9)

Where i = 1, 2, …, m.

Step 7: Rank order. Rank alternatives in increasing order according to the ratio value of R i . The best alternative is the one that having the M-TOPSIS coefficient R i nearest to 0.

Example of application of M-TOPSIS method 4.4.2

The M-TOPSIS procedure described above is applied here on 15 points from a Pareto front obtained after a bi-objective optimization, each point representing a potential solution. The criteria involve the maximization Q out (kWh) and the minimization of EPBT (year). The different stages of the M-TOPSIS algorithm for this example are applied as follows:

From original data, the decision matrix is built (see Table 45). Because the EPBT criterion represents a cost criterion (minimization), it is transformed into benefit criterion (maximization) by Equation (4.2). The transformed values are displayed in Table 4-6. M-TOPSIS coefficient R i is calculated for each alternative by Equation (4.9) and the ranking is presented in Table 4-11. The Pareto front EPBT-Q out in Figure 4-13 indicates the position of the three best alternatives after applying the M-TOPSIS method. The best alternative selected by M-TOPSIS method is alternative 1. As it can be seen in Figure 4-13, the best alternatives are located at the upper corner of the curve representing the Pareto front. If these three alternatives are compared with some of the alternatives that are in the knee of the curve, e.g. alternative 4 as shown in Figure 4-14, although EPBT is reduced, the energy produced is also strongly reduced. EPBT reduction is approximately 0.05 year while annual energy produced suffers a reduction of about 30%. The result provided by M-TOPSIS indicates that the best compromise that can be found at equal weight to both objectives in this example is to produce the maximum amount of energy because the difference between the growth in EPBT value is minimal as compared to the gain of Q out .

Conclusion

In this chapter, three methods to be applied for sizing PVGCS were presented. The number of objectives and the characteristics of the model developed in Chapter 3 make attractive the use of a GA to obtain the best alternatives embodied through a Pareto front. A variant of NSGA-II, embedded in MULTIGEN library, is selected. It must be emphasized that most of the works reported for PVGCS sizing through AG only consider economic or technical aspects. The main contribution of this work will be to integrate the environmental aspect from earlier design stage and not at end-of-pipe stage as Figure 4-15 summarizes how the three methods will be integrated and applied for PVGCS, which constitutes the core of the following chapter.

ECODESIGN OF LARGE-SCALE PHOTOVOLTAIC POWER PLANTS

Ce chapitre concerne la mise en oeuvre du cadre d'écoconception d'un système photovoltaïque connecté au réseau basé sur le couplage du modèle de dimensionnement avec l'algorithme d'optimisation multiobjectif, suivi de l'utilisation d'un outil d'aide à la décision multicritère. Lorsque le nombre d'objectifs devient prohibitif, une méthode systématique d'identification des critères redondants est mise en jeu par analyse en composantes principales. L'ensemble des outils et méthodes utilisé dans le cadre de l'étude a fait l'objet du chapitre précédent. Des cas spécifiques d'optimisation technicoéconomique qui correspondent à différentes situations auxquelles le praticien est confronté sont traités : par exemple, la maximisation de la production d'énergie à surface de champ donnée, ou la minimisation de l'aire du champ garantissant une fourniture d'énergie annuelle fixée. Les temps de retour sur énergie et sur investissement font partie du jeu de critères considéré.

Des cas d'optimisation multiobjectif faisant intervenir l'ensemble des éléments de la méthodologie, ce qui constitue l'objectif de l'étude, sont également présentés.

Les résultats obtenus dans ce chapitre mettent clairement en évidence le gain environnemental de l'utilisation de modules photovoltaïques de deuxième génération (couche mince) sur les modules photovoltaïques à base de c-

Introduction

Chapter 1 discusses the importance that represents the use of alternative energy sources for electricity supply as part of the new global policies undertaken favoring the preservation of the environment.

Installing PVGCS is one of the most used alternatives globally. A contribution to the development of design procedure was proposed in Chapter 3. The objective of this chapter is to embed this design model into an optimization process. As it was indicated in Chapter 4, when dealing with optimization problems, particularly as far as engineering is concerned, it is very common to consider other objectives besides the traditional economic approach, e.g. risk or environmental assessment. In many real-life problems, objectives under consideration conflict with each other. Hence, optimizing one of them with respect to a single objective often leads to unacceptable results with respect to the other objectives. A perfect multi-objective solution that simultaneously optimizes each objective function is yet impossible. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Because of this situation, sizing a PVGCS can become a multi-objective optimization problem.

This chapter is dedicated to the coupling of the PVGCS design model with the three methods described in Chapter 3 at it was indicated in Figure 4-15. Specific cases that correspond to different situations that the practitioner has to cope with are treated: a set of solutions that meets the economic, technical and environmental criteria for designing a PV solar field is thus generated for each case.

The technical options that have been developed for solving the optimization problems of PVGCS design were justified in the previous chapter.

In this chapter, the proposed methodology is evaluated and validated by the treatment of some examples of optimization problems. First, two mono-objective problems taken from the literature [START_REF] Weinstock | Optimal Solar Field Design of Stationary Collectors[END_REF], 2009) are investigated. The former example maximizes the output energy of the plant in a specific place. The second example determines the minimum area required to install a solar field in order to supply a fixed amount of energy per year. PBT, EPBT and environmental impacts assessment are performed for each of the generated solutions.

Then, multi-objective problems that allow the correct integration of all the components of the methodology proposed in this work are solved. Due to the high number of environmental objectives, an analysis in order to eliminate the correlated objectives so that the optimization process performed becomes most efficient. For this purpose, Principal Component Analysis (PCA) is applied to achieve a considerable reduction in terms of the number of environmental impact categories to be considered in the analysis.

In all the optimization problems, M-TOPSIS is then used to select the alternative with the best tradeoff among the objectives considered.

Optimal design of photovoltaic solar fields

Figure 5-1 shows how the three methods described in Chapter 4 are integrated into the general approach proposed in Chapter 3. At first, the model proposed for sizing a PVGCS is coupled with the GA (NSGA-II). The results of a preliminary optimization process will provide the best alternatives in the Pareto front. Then, environmental categories will be reduced by applying the PCA method to the set of alternatives of the Pareto front. A new optimization scheme will be implemented from the categories highlighted by PCA that will be more systematically used in further optimization runs.

Finally, the best option is chosen by using the M-TOPSIS method among the solutions of the new Pareto front.

The common parameters of the GA that will be used in each problem resulting from a preliminary study are determined following the guidelines suggested by the MULTIGEN developer [START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF] . They are shown in Figure 5-1.

Mono-objective optimization cases

The proposed methodology is first implemented for single objective optimization. Weinstock and

Appelbaum [START_REF] Weinstock | Optimal Solar Field Design of Stationary Collectors[END_REF], 2009) treat two mono-objective cases to design a PV power plant. The cases are referred as WAP conditions as previously adopted in Chapter 3. The former case is to find the best design that maximizes annual energy output of the facility. This case was used in Section 3.7 for the validation of the simulation tool. The latter one is related to the minimization of the area required to supply a minimum amount of energy in a year. In both cases, the methodology described in Figure 5-1 is modified. PCA and M-TOPSIS method will not be used.

Another set of optimization runs for both cases is carried out considering the five different PV modules technologies presented in Table 3-8. A mix of PV technologies is not allowed to facilitate maintenance. The other objectives (PBT, EPBT and environmental categories) will be only evaluated considering the solution found after the optimization process. Of course, the PCA method will not be applied for mono-objective optimization. A weighted evaluation is proposed to select the best configuration among the five resulting power plant configurations.

Maximum annual output energy 5.3.1

As explained in Section 3.7, the example reproduces the WAP conditions for the configuration that maximizes PV power plant annual energy. The same conditions, PV module characteristics and location (Tel Aviv, Israel) used in Section 3.7 are considered as well as the three scenarios mentioned. Equation (3.30) in Section 3.5.3.2, represents the objective function to maximize for each of the three scenarios. The mathematical model and the constraints of the design problem were described by Equations (3.20) to (3.25) in Section 3.5.3.

The limit values for the involved constraints are the same as those used for WAP: minimum space between collector rows (D min ) equal to 0.80 m, maximum collector height (H max ) equal to 1.98 m and height of collector above the ground (E max ) equal to 1.80 m.

The decision variables that are used are the same as indicated in WAP mathematical model (β, D, K, N r , N c ).

Results

Table 5-2 shows the comparison between the results obtained by the approach proposed and WAP. A good agreement is obtained between both models. Not surprisingly, the difference in the amount of output power for the three cases is mainly due to the improvement in the computation of irradiance received at the facility as presented in Section 3.5.3.

It is worth mentioning that WAP used MATLAB optimization toolbox to solve the optimization problems. This toolbox uses a sequential quadratic programming method [START_REF] Weinstock | Optimal Solar Field Design of Stationary Collectors[END_REF].

A modification was done to the previous example. The PV modules considered by WAP do not consider explicitly a PV technology. Only an average efficiency, i.e., 12.4% is mentioned. In this study, the five different PV technologies presented in Table 3- 5-3 andTable 5-4. Table 5-4 also presents the results of the simulation made for each configuration in PVsyst for comparison purpose.

The output energy estimated by the model is on average 6.50% higher than that obtained after the simulation of each power plant configuration in PVsyst. The results suggest that the configuration using PV modules based on m-Si generates the highest amount of annual energy under the conditions given in the case study.

The result of the evaluation of PBT and EPBT for each configuration (see Table 5-5) shows that the lowest EPBT is achieved by using PV modules based on CdTe but this technology does not lead to the lowest PBT value. Even though the m-Si PV module generates the maximum output energy, its EPBT is high due to the amount of energy required during the manufacturing phase. The results of the environmental impact assessment (15 midpoint categories) for each configuration are shown in Figure 5-2 by the use of radar charts (Comparison 1). To facilitate the comparison, normalisation was performed by assigning the value 1 to the maximum value of each category. The computed relative impacts represent the ratio between the environmental impact and this maximum value.

The analysis of the result shows that in 9 of the 15 categories the highest impacts occur with m-Si technology: in Global Warming category, where CO 2 is the reference component, the installation with PV modules based on m-Si, generates a higher CO 2 amount after the characterization of all inventory flows. Likewise, for the Non-renewable Energy category, the highest amount of non-renewable primary energy consumed by all the processes evaluated within the boundaries set for the LCA study was found for m-Si based PV modules installation. In spite of its low EPBT, the solar plant with CdTe modules has a significant impact within the category of Non-carcinogens, i.e., the characterization of the different flows in the inventory for CdTe module installation results in a large amount of chloroethylene C 2 H 3 Cl into air, a substance that affects human health.

To select the best PV power plant among the five proposed alternatives, a weighted evaluation is performed for the 18 objectives (maximizing final energy generation output, minimizing PBT, minimizing EPBT and minimizing 15 environmental impacts). First, a ranking for each alternative of solar plant configuration was made giving a value of 1 to the alternative that best meets the objective and 5 to the worst one. The value assigned to each alternative in a given goal is then multiplied by a weighting factor. This factor may be of course subjective. An equal factor for the 18 objectives was assigned, thus giving importance to environmental impact. Then, the scores obtained by each alternative are added to give a cumulative score. As it can be seen in Table 5-6, the alternative with the lowest total score is the a-Si technology.

As in Section 3.7, another analysis is then performed taking into account the energy generated by each configuration. The results are presented through radar charts normalized to unity (Figure 5-2,

Comparison 2). It can be highlighted that the type of PV technology with the higher ratio is the one based on p-Si modules (8 of 15 categories). Although the environmental impacts of m-Si based technology are higher, these are offset by the large amount of energy generated annually.

The same weighted evaluation is made for this analysis and the results are reported in Table 5-7. The alternative of CIS PV module technology best meets the objectives.

Reviewing the results obtained from the weighted evaluation in Table 5-6 andTable 5-7, if all criteria have the same weights, the conversion efficiency of PV module takes an important role depending on the form of evaluation of environmental categories. It may serve as a mitigating circumstance to the values reported for the different environmental categories, e.g. the alternative based on a-Si PV module proved to be the best trade-off for all the objectives considered when only the results obtained from the LCA study are taken into account but it falls to fourth position if these values are divided by the amount of energy produced.

For example, Comparison 2 in Figure 5-2 shows that the configuration with m-Si has a better performance in almost all environmental categories than the results of Comparison 1. This alternative goes from the fifth place to the second one. It is important to keep in mind that the impact assessment does not consider the recycling of PV plant components. Chapter 5 will study the impact of recycling on the final result. Another possible objective function is to find the smallest area, W x L, of a solar field to generate a required yearly output energy Q min . Such a problem occurs where the ground is expensive or on rooftops where the available area for solar collector installation is limited. Some modifications were made to the mathematical model used until now to fit this case. The new constrains were taken from WAP model. Equation (2.6) represents the new objective function to minimize:

(5.1) Equations (3.20) to (3.25) of Section 3.5.3 are still considered as model constraints. Equation (5.2) to (5.4) are added as constraints in order to delineate the possible area of the solar field.

(5.2) (5.3) (5.4) Equation ( 5.4) represents a relationship between the length and width of the solar field.

The field should generate at least a required amount of yearly energy. Equation (5.5) represents this condition:

(5.5) For this second mono-objective case, the following example is proposed in order to test the new model. The main goal is to place a PV power plant capable to produce at least 1 GWh/year in the smallest area possible. The considered constrains are the following ones: D min = 0.80 m, H max = 4.00 m, E max = 3.00 m, W max = 150.00 m, L max = 100.00 m, a 1 = 0.5 and a 2 = 2.

In this second case, in addition to the variables corresponding to the first scenario (β, D, K, N r , N c ), the length and width of the solar field (W, L) are added. The parameters of NSGA-II were mentioned in Table 5-1.

Results

Five different PV module technologies are evaluated. As previously, no mix in the technologies is allowed. The resulting five configurations are shown in Table 5-8. For the sake of illustration, Figure 5-3 shows the 3D-perspective for each configuration that can be obtained from PVsyst. Each square represented 100 m² of land. Not surprisingly, on the one hand, the configuration using PV modules based on m-Si has the smallest surface under the given conditions in the case studied because this technology has the highest conversion rate among all the studied technologies. On the other hand, the configuration with a-Si PV modules which has the lowest efficiency needs the highest surface. As already carried out, the assessment of PBT, EPBT (Table 56789) and environmental impacts (Figure 5-4) was performed for each of the resulting combinations.

As in the examples discussed in Section 5.3.1.1, the technology based on m-Si has the lowest PBT while the lowest EPBT is found in PV module technology based on CdTe. Table 5-9 reveals that the values of PBT (respectively EPBT) exhibit the same order of magnitude for the five configurations, unlike the case of maximum output energy presented in Table 5-5. It must yet be emphasized that the cost of land is not included in the economic evaluation so there is no penalty for the plant size. Concerning EPBT values, the energy produced by each plant is practically the same, exceeding a minimal annual amount of 1 GWh. Even if the surface of the base configuration of CdTe modules is larger than the one with m-Si technology, the total primary energy for the production of CdTe panels is less than that required for the PV modules based m-Si.

Figure 5-4 shows the standardized radar charts of 15 midpoint categories. The analysis of the chart shows that in 10 of the 15 categories, the a-Si PV module has the highest impact even if it has an average value for EPBT and PBT.

As in previous examples, the weighted evaluation is performed for the 18 targets (minimizing the surface, minimizing PBT, minimizing EPBT and minimizing the 15 environmental impacts). Table 5-10 displays the score and ranking of the 5 configurations. The final classification reveals that PV power plant from m-Si technology corresponds to the best compromise while the a-Si PV modules exhibit the highest score.

As a conclusion of these mono-optimization cases corresponding to different scenarios, it can be said that optimization can be particularly useful as a decision-making aid tool for ecodesign purpose.

Due to the multi-objective nature of the problem, the problem is now treated with more appropriate multi-objective genetic algorithms.

Bi-objective optimization cases

Different cases of bi-objective optimization for sizing a large-scale photovoltaic power plant are discussed below.

The example which is treated here was part and parcel of a student group project at ENSIACET in the framework of the EcoEnergy programme.

In all cases, PV power plant is placed near the city of Toulouse, France (43.4° N, 1.2° E, elevation 152 m, +1 GMT). The following considerations are taken into account:

The dimensions of the field is W max = 150.00 m and L max = 100.00 m.

The minimum separation between each shed is D min = 1.00 m.

The dimensions of the PV collectors must respect: H max = 3.00 m y E max = 4.00 m.

Minimum number of shed (K) should be 2.

The percentage of the loss caused by the array module wiring and mismatch is 5%.

AC / DC invertor has a nominal power of 300 kW DC with an efficiency of 97.5% and a lifetime of 10 years.

The dimension and characteristics of PV modules used are the same as those indicated in Table 345678and no mix in technologies is allowed. The decision variables are the following ones: β, D, K, N r , N c .

NSGA-II with continuous-integer variables embedded in MULTIGEN library was used to perform the optimization runs. Each optimization case was run three times to guarantee the stochastic nature of the algorithm.

Five cases of multi-objective optimization are explored. A first set of five bi-objective optimization runs are first studied in order to verify that the objectives are antagonist. Besides, these preliminary examples allow assessing the correct coupling between the proposed model to size a PVGCS and the AG selected. Taking as references the mono-objective cases studied above, these bi-objective cases use the already studied objective (maximize annual Q out and minimize the area, respectively) and a second objective is added. The economic approach through PBT and an energetic criterion through EPBT are selected to take part in these first bi-objective problems. Due to the reduced number of objectives in these studies, PCA is not implemented here. A final bi-objective case will try to optimize EPBT and PBT. In all the multi-optimization runs, although not optimized, the environmental categories will be computed for information. The best alternative will be selected by application of the m-TOPSIS approach.

Q out -PBT 5.4.1
The first case of bi-objective optimization is based on the minimization of PBT while maximizing simultaneously the annual amount of energy produced. Figures 5-5 (a to e) show the resulting Pareto fronts for each of the PV technologies discussed. The average computation time for each optimization run is about 2.5 hours CPU on the same PC (Intel Core 2 Duo @ 3.00 GHz).

As already presented in Chapter 4, a choice must be made to select a PV technology among the options in the Pareto front. For this purpose, the decision support tool M-TOPSIS method is used through a prioritization of the objectives only at the end of the optimization process. The 18 objectives are considered even if only two objectives are used in the optimization process.

The purpose of using a decision support tool is to suggest to the decision-maker which configuration found by the GA for all the technologies that are evaluated is the one with the best compromise for the objectives under study. The selection of the best option will involve a two-step application of the M-TOPSIS method: first, the best alternative in each of the five technologies is chosen (M-TOPSIS application 1); then, from these results, the best compromise is selected (M-TOPSIS application 2).

To study the influence of the weight allocated to each objective under study for selecting the best alternative of PV power plant, four different sets of weights are proposed (see Table 5 -11). These weights will be applied only in the selection of the best option for each of the PV technologies considered. For the second phase, the weight is the same for all objectives. This consideration is taken into account because the alternatives were already evaluated and weighted in the first selection phase. Table 5-12 displays M-TOPSIS selected configurations for the four sets of weights. From all the evaluated technologies, Figures 5-5 (a to e) show that the M-TOPSIS selected alternatives for the proposal sets (2 to 4) of weights are located at the upper end of the Pareto front. This can be attributed to the scale difference in the coordinates: the range of PBT values is very narrow, only a few months difference while the energy range is quite large. It must be concluded that a higher PBT (a few months) is acceptable here to increase considerably the amount of generated energy. Almost no difference can be observed for TOPSIS-ranked top 1 solution for these three cases as it can be observed in Table 5-12 when varying the weight.

Top ranked solution for proposal 1 is located in the other extreme of Pareto front for all PV technologies. The reason for this trend is that, considering equal weights for all objectives, the compromise to do is higher given the number of objectives to be minimized. This corresponds to the lower amount of Q out for this scenario over the other scenarios.

The environmental impacts corresponding to the studied scenarios (see Figure 5-6) exhibit the same differences. PV power plant with CdTe PV modules is the best in almost all categories, for equal weights (proposal 1) are used whereas m-Si based configurations are the best for the other three scenarios. From the results of Table 5-12, if only the two objectives that were optimized are considered to select the best alternative among the five possible configurations, m-Si based PV power plant is the most suitable in all the scenarios.

As already mentioned, M-TOPSIS is once more applied among the set of the best compromises obtained for each technology. The final ranking for the five alternatives of PV power plant for each scenario highlights (see Table 5-13) that a-Si based configurations better fits all the objectives, i.e. the maximization of energy production and minimization of PBT, EBPT and environmental categories.

Table 5-12 and Table 5-13 exhibit the importance of the weight given to each objective. For the following cases where the maximization of the annual energy generation is one objective, only the weights of proposal 2 will be used. The proposal 1 is eliminated since the drastic reduction in annual energy produced (more than 50% in all technologies) while the results of proposals 3 and 4 do not differ from those of the proposal 2.

Q out -EPBT 5.4.2
This second optimization case deals with the simultaneous minimization of EPBT and maximization of the energy fed into the grid. The same process as in previous case was followed. The resulting Pareto fronts for each of the technologies are presented in Figures 567(a to e). The average computation time for complete each optimization problem was 2.51 hours CPU on the same PC. The M-TOPIS approach is implemented to select a configuration from the Pareto front. As it was indicated in previous case, only the set of weight of proposal 2 (Table 567891011) is used here. Table 5-14 shows the best alternative for each PV technology.

As in the previous case, it is observed that the range of EPBT values is very narrow and M-TOPSIS selected alternatives are also located in the upper end of the Pareto front. A slight increase in EPBT leads once more to a high gain in the energy injected into the grid. From the results of Table 5-14, it is worth mentioning that the configuration based on m-Si generates a larger amount of energy but does have the best EPBT. The CdTe based configuration is the best for the EPBT criterion. The ranking of the five PV technologies in Table 5-13 andTable 5-15 with the proposal 2 of set of weights shows that the PV power plant with a-Si-based PV modules has the best trade-off.

PBT -EPBT

5.4.3

The final bi-objective optimization run concerns the simultaneous minimization of PBT and EPBT.

The Pareto fronts of the 5 PV module technologies are presented in Figure 5-9 (a to e). The average time for each optimization problem is lower 2.96 hours CPU.

As previously, only the weights indicated in proposal 2 are taken into consideration for M-TOPSIS application. The solutions are also plotted in Figure 5-9 (a to e). Table 5-16 presents the alternatives that will be again evaluated by M-TOPSIS. Not surprisingly, the results confirm that the variation range is not significant in both axes. As compared to the previous cases, the selection of the best alternative is found in the central part of the Pareto front. The choice of the best configuration is obtained from M-TOPSIS. Table 5-17 shows the ranking of the alternatives selected: the CdTe-based configuration is the best candidate.

An examination of the radar graphs of environmental impacts (see Figure 5-10) shows that the a-Si based configuration for PV modules exhibits the highest values in most of the categories under evaluation.

One aspect that should be emphasized is the behavior of annual energy produced by any of the five configurations shown in Table 5-16. Not being considered into the objectives to optimize, when the annual energy produced from each of the combinations held after applying M-TOPSIS is compared with the results of the first two bi-objective optimization (Table 5-12 andTable 5-14), the reduction of the amount of energy generated is about 60% on average. This situation demonstrates the importance of considering the annual energy generated as an objective to optimize.

Area -PBT 5.4.4

The following case refers to the minimization of the area required for the installation of a PV power plant with a given amount of energy to provide together with the minimization of PBT. Following the model discussed in Section 5.3.2, the minimum Q out is set at 500MWh/year and the geometrical relationship between the length and width of the solar field constraints (a 1 and a 2 ) are not taken into account.

The Pareto fronts of the 5 PV module technologies are presented in Figure 5-11 (a to e). The same order of magnitude of CPU time is required in this case (about 2.61 hours CPU). Because this purpose is different from the previous ones, the four sets of weights (2 to 4) given in Table 5-11 for the selection of the best alternative in each of the five categories through M-TOPSIS are applied again in order to check the variations that may occur. Q out is replaced by the Area. The final ranking of the five PV technologies in each of the scenarios (Table 5678910111213141516171819) follows the same behavior regardless of the weight given to each of the objectives. Similarly, radar graphs that analyze environmental impacts have an identical behavior. As in the case for Q out maximization, the proposal 2 of weights for the objectives under study is adopted in further optimization runs.

Area -EPBT 5.4.5

A second bi-objective optimization run is performed by taking into account the minimization of both the area needed for a PV power plant to supply a certain amount of energy and EPBT. The same conditions as in the previous case are considered.

The alternatives represented in a Pareto front for the five PV module technologies are shown in Figure 5-13 (a to e). The same order of magnitude of CPU time is observed (2.54 h) for each optimization

Multi-objective optimization for the optimal design of PV power plant

The previous analysis confirms that the environmental impacts must be taken into account in the ecodesign process since they must drastically influence the final choice. Our experience in multiobjective optimization demonstrates that handling a large number of objectives may lead to a stagnation of the search process, an increased dimensionality of Pareto-optimal front, a large computational cost, and finally a difficulty in visualization of the objective space.

The analysis of the radar charts obtained with IMPACT 2002+ midpoint categories for environmental assessment in both mono-and bi-objectives optimization reveals that some of them exhibit a similar behavior which suggests that these categories may be correlated. For this purpose, a reduction in the number of objectives to be simultaneously optimized is required. Using the guidelines proposed in Chapter 4, the technique of Principal Component Analysis (PCA) was selected.

Let us recall that PCA is one of the extraction methods of factor analysis used to reduce the number of variables, relying on the linear algebra. Different methods based on PCA have been proposed so far for identifying a subset of uncorrelated variables from a wider set of correlated variables for identifying redundant environmental objectives in the multi-objective formulation [START_REF] Gutiérrez | Assessing relationships among life-cycle environmental impacts with dimension reduction techniques[END_REF][START_REF] Sabio | Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis[END_REF].

Application of PCA method 5.5.1

The first step for reducing the environmental categories is to perform a preliminary multi-objective optimization that includes the 18 objectives that have been used so far (Q out , PBT, EPBT and 15 environmental categories) to calibrate the optimization and identify the consistent set of objective functions. The model developed for maximizing annual energy produced by the PV solar field is A new set of optimizations is then carried out with this reduced set of objective functions and the obtained results are then compared with those of the previous case.

Each optimization run takes on average 10.2 hours CPU, which is reduced from one third as compared to the run with the total number of environmental categories.

Kaiser-Guttman criterion

The selection of the best alternative is performed again with M-TOPSIS. The weights used in the multi-objective problem with 18 objectives refers to proposal 2 in Table 5-11 while for the multiobjective problem with 6 objectives a weight of 1 is allocated to Q out , PBT and EPBT , and respectively 1/3 to RI, ME and OLD. Table 5-24 shows the obtained configurations. Only the 6 criteria are presented in both cases. No configuration dominates the others for both cases. It is necessary to use M-TOPSIS for selecting the best compromise (see Table 5-25). The a-Si-based configuration is the top-ranked one for 18 objectives and second for 6 objectives while the CdTe-based configuration the top-ranked one for 6 objectives and second for 18 objectives.

Multi-objective optimization case: Qout -PBT -RI -OLD 5.5.3

In order to continue reducing the number of objectives to be optimized, PCA was applied again to the results of 6 targets. This new analysis leads to suppress 2 criteria (EPBT and ME). The multi-objective analysis is then conducted with only PBT, RI, OLD and Q out with an average computation time of 8.76 hours CPU, thus reducing it of a 20% factor from the previous case. Table 5-26 shows the five configurations chosen by M-TOPSIS. The weighting for Q out and PBT is w 1 = w 2 = 1 and, for RI and OLD w 3 = w 4 = 1/2. Under these conditions, the best option among the five proposed configurations is a-Si based (see Table 5-27).

If the ranking of the five technologies of PV modules is now compared among the three multiobjective cases treated in this study, it can be said on the one hand that both a-Si and CdTe PV modules achieve a better compromise regardless of the objective under study. On the other hand, c-Si PV technologies have the lowest rank in all the three cases. Even if they are the ones that produce more energy, they are the less environment-friendly. It must be yet remembered that this analysis does not include the recycling process which can change this trend.

This study emphasizes the interest of PCA multi-objective problem with a second reduction of objectives is capable to find the same results as in the two previous cases in less time and with less information to process.

Conclusion

In this chapter, a multi-objective optimization procedure based on a variant of NSGA-II has been embedded in the mathematical model within the ecodesign framework that considers simultaneously several technical, economic and life cycle environmental criteria. The environmental performance of the PVGCS has been measured via 15 life cycle assessment metrics of IMPACT 2002+ that inform about the damage caused in different midpoint impact categories. The proposed methodology comprises three main steps: first, a multi-objective genetic algorithm is implemented and a set of Pareto solutions are generated that represent the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., PCA) is then applied to detect and omit redundant environmental indicators that can be left out of the analysis without disturbing the main features of the solution space. The capabilities of this technique have been demonstrated through a PVGCS case study. Finally, A decision-making tool made based on M-TOPSIS is used to select the alternative that provides a better compromise among all the objective functions that have been investigated.

The proposed methodology has been incrementally developed. Different optimization cases have been investigated to establish the approach developed for sizing PV systems. The examples discussed in this chapter correspond to two possible issues that arise when designing PV solar plants: what is the ideal configuration to produce the largest amount of energy? what is the minimum area needed to generate a given amount of energy? First, two examples of mono-objective optimization taken from the literature were used to calibrate the coupling between the model described in Chapter 3 and the optimization loop. Second, bi-objective cases were treated and M-TOPSIS was implemented for selecting the best option that satisfied the set of all criteria (18, i.e. 3 technico-economic and 15 environmental ones) taken into account in this work. The influence of the weights assigned to each goal in the final result must be highlighted.

This analysis shows that a similar behavior of some environmental categories plotted in radar charts for each of the bi-objective cases studied and emphasizes that the number of objectives can be reduced from an optimization viewpoint. The proposed approach enabled us to identify redundant environmental metrics making it easier to interpret and analyse the efficient solutions to the problem. This was carried out through PCA on a post analysis of the multi-objective cases that were treated.

Only four objectives (Q out , PBT, RI, and OLD) have been identified as significant to perform the multi-objective optimization for ecodesign of a PV power plant.

The results presented in this chapter highlight the advantage that the use of second-generation PV modules (thin film) over the c-Si based PV modules. While the latter ones have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. It is necessary to emphasize that recycling the elements of a PV system has not been considered so far. The next chapter addresses a special attention to the recycling phase of the PV modules.

RECYCLING OF PV MODULES

Dans les chapitres précédents, l'évaluation du cycle de vie a été utilisée comme un outil majeur pour l'évaluation des impacts environnementaux qui se produisent le long de la chaîne logistique de modules photovoltaïques. Pourtant, le démantèlement et le recyclage des modules PV n'ont pas été pris en compte car les données sur les impacts environnementaux associés à ces étapes de fin de vie sont relativement rares et ne figurent pas encore dans les bases de données d'inventaire des logiciels d'ACV classique (EcoInvent par exemple). Ce court chapitre vise à étendre la méthodologie proposée pour l'éco-conception du système PV reliée au réseau, élargissant ainsi les frontières en tenant compte du recyclage du module PV. L'idée est de démontrer que le cadre est suffisamment générique pour intégrer le cycle de vie des modules photovoltaïques. La première partie de ce chapitre traite de la mise en oeuvre d'un processus de recyclage dans une étude ACV. Ensuite, une brève description des procédés de recyclage existants correspondant aux différentes technologies de fabrication de modules photovoltaïques est proposée. Deux cas d'ACV extraites de la littérature pour le recyclage des modules PV à base de c-Si and CdTe renforcent l'intérêt de considérer le processus de

Symbols

Q out

Yearly output energy of the field, kWh

Introduction

In the previous chapters, Life Cycle Assessment was used as a major tool for the evaluation of the environmental impacts occurring along the supply chain of PV modules. Yet, the decommissioning and recycling of PV modules were not taken into account since data on the environmental impacts associated with these end-of-life steps were relatively scarce and not yet included in classical LCA database. This short chapter aims at extending the proposed methodology to the PVGCS ecodesign, thus broadening the boundaries by taking into account PV module recycling. The product life-cycle will encompass material production, manufacturing, use and service and end-of-life management. The underlying idea is to demonstrate that the framework is generic enough to embed the whole life cycle of PV modules.

Indeed, due to the increase in photovoltaic as a source for generating electricity, it is important not to lose sight of what happens to PV modules and electric components once they reach the end of their lifetime. PV recycling is still a young industry and only taking off [START_REF] Neidlein | PV module recycling mandatory for all EU members by Q1 2014[END_REF]. Many innovations have been made in the past years and the industry continues to heavily invest in this field.

From an environmental perspective, not only does the recycling of a product lead to waste reduction but also the use of recycled materials could contribute to energy saving and emission reductions in manufacturing processes.

The most significant aspect is that recycled materials substitute primary materials, which allows conserving materials (especially for rare materials), energy and land resources. This possible replacement significantly reduces materials and energy needs in the extraction processes of raw materials.

The WEEE (Waste Electrical and Electronic Equipment) Directive came into force on August 13, 2012: by Q1 2014 at the latest, all EU member countries must implement a national WEEE law, regulating for the first time PV modules. Responsible for the free take back and recycling of the photovoltaic modules are the producers (manufacturers or importers) [START_REF] Neidlein | PV module recycling mandatory for all EU members by Q1 2014[END_REF]. As any other waste, the disposal of end-of-life PV modules needs to comply with European, national and local waste legislation.

Nowadays, there are organizations such as PV Cycle and CERES, and companies like First Solar and PV Recycling that are engaged in the recycling of PV modules. These institutions offer different types of services for the collection, transportation, recycling and sale of material once treated for the main PV module technologies.

Due to the long life expectancy, take-back and recycling used PV modules are relatively low at present, but it is expected that there will be a significant increase from 2020 [START_REF] Larsen | End-of-life PV: then what?[END_REF]. Since the waste streams are very low, recycling is hardly visible today but in the future, with larger waste streams, it will be a must. According to some studies, PV recycling is not yet economically viable today, in the absence of a carbon pricing scheme [START_REF] Larsen | End-of-life PV: then what?[END_REF].

Unlike other industries, PV waste is unique because it has a long lag time from the time it is produced up to the time it is decommissioned. [START_REF] Mcdonald | Producer responsibility and recycling solar photovoltaic modules[END_REF]) estimated the expected waste until 2038 assuming the historical percentages and efficiencies of thin film and siliconbased technologies and an end-of-life matching the warranty lag (see Figure 6-1): the amount of PV modules created for any one of these years will correlate to the amount of PV waste that will exist assuming that modules are withdrawn after their warranty has expired.

According to PV Cycle annual report (European Association for the Recovery of Photovoltaic Modules, 2012), in 2012, 3,759 tons of end-of-life PV modules were collected by all the members of the organization. This situation represents an increase of over 160% as compared to the previous year.

Table 6-1 shows the percentage of recycled modules according to manufacturing technology. PV modules based on CdTe were not processed by the collective in 2012. These PV modules were collected and treated by the producers themselves as First Solar.

This situation shows that an assessment of the environmental benefits of material recycling must be taken into account for quantifying the environmental performance of PVCGS.

As it was mentioned before, LCA method is a suitable tool to estimate the impacts due to recycling processes of materials as well as to quantify the avoided impacts by returning materials to the value chain. The first part of this chapter addresses how to implement a recycling process in an LCA study. Then a brief description of the different existing recycling processes corresponding to the different manufacturing technologies of PV modules is provided. Two LCA cases from the literature for recycling c-Si and CdTe PV modules serve to explain the importance of considering the recycling process within the LCA. From the published results of these cases and following the guidelines of the procedure described in Chapter 3, an optimization run for both PV technologies is performed.

Recycling in LCA methodology

The manufacture of a product typically requires a mixture of primary resources and resources from the recycling phase of the same product or from another one. At end-of-life stage, several ways of treatments exist. The main difficulties for recycling process are related to the choice of the boundaries for the different flows that can end in different product systems and to the allocation of the resulting impacts.

To consider recycling process modeling within an LCA study, three schemes are generally reported [START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF]: closed loop, open loop and semi-closed loop recycling. Figure 6-2 illustrates these schemes differing from where and how the recycled material is used again.

Closed loop recycling. Materials associated with a product are recycled and used again in the same product system. The material properties are not changed in comparison to the original primary material. The so-called bottle-to-bottle recycling is an example of closed loop recycling [START_REF] Komly | Multiobjective waste management optimization strategy coupling life cycle assessment and genetic algorithms: Application to PET bottles[END_REF][START_REF] Mantia | Closed-loop recycling. A case study of films for greenhouses[END_REF][START_REF] Mcneil | Closed-loop wool carpet recycling[END_REF][START_REF] Palmer | Successful closed-loop recycling of thermoset composites[END_REF].

Open loop recycling. A recycled material goes to another product system and the initial material properties are changed. This material cannot be used in its original system. The recycled material does not yet replace all primary raw materials. Plastic recycling is a well-known open loop recycling example [START_REF] Ha | Open-loop recycling to apply refrigerator plastics from post-consumer waste polypropylene[END_REF][START_REF] Williams | A case study of the open-loop recycling of mixed plastic waste for use in a sports-field drainage system[END_REF].

P Product B

Closed loop recycling

Semi-closed loop recycling

Same inherent properties

Open loop recycling

Loss of inherent properties

Figure 6-2 Three recycling schemes (inspired from [START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF])

Semi-closed loop recycling. Recycled material is used in another product system without changing any material properties. This concerns the case of construction steel [START_REF] Chen | Life Cycle Assessment of Internal Recycling Options of Steel Slag in Chinese Iron and Steel Industry[END_REF][START_REF] Chong | Understanding transportation energy and technical metabolism of construction waste recycling[END_REF][START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF].

A product system does not fully recycle all materials that come available after use. To quantify the efficiency of an end-of-life system, the following indicators can be used:

Recycling efficiency (RE):

(6.1)
Recycling rate (RR):

(6.2)
Allocation methods 6.2.1

The allocation or partitioning of environmental burdens between various co-products or processes with multiple inflows is a discussed subject in LCA methodology. The allocation of the benefits obtained in the recycling stage within a LCA study is extremely important for the final result of the impacts caused by a particular product with open loop recycling [START_REF] Nicholson | End-of-life LCA allocation methods : open loop recycling impacts on robustness of material selection decisions[END_REF][START_REF] Vogtländer | Allocation in Recycling Systems An Integrated Model for the Analyses of Environmental Impact and Market Value[END_REF]. Currently, a diverse set of methods exists to address this challenge [START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF][START_REF] Nicholson | End-of-life LCA allocation methods : open loop recycling impacts on robustness of material selection decisions[END_REF][START_REF] Vogtländer | Allocation in Recycling Systems An Integrated Model for the Analyses of Environmental Impact and Market Value[END_REF]. The most common approaches are:

Cut-off method. All environmental impacts directly caused by the production of a product are assigned to that product. An eventual waste treatment, other than recycling, is allocated to the product as shown in Figure 6-3.

Closed-loop method. Each product is equally responsible for the environmental impacts associated with primary material production, recycling, and final waste treatment. The burden is therefore an average impact, equally distributed among products and depending on the number of life cycles studied. Figure 6-4 represents the product material flows and processes for two life cycles. The environmental impacts of each one, applying the closed loop approach, will be a half of the sum of the primary material 1, recycling 1 and final waste treatment.

Disposal

Recycling process

Recovery process Primary material

Recycled material

Production Use

Figure 6-3 Product system for the cut-off approach [START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF] components of crystalline silicon modules and the possible integration of recovered silicon in the production of new PV cells.

The first step consists to remove the aluminium frames and junction boxes in a manual process. A thermal process enabling the quick, simple and efficient disassembly of the module is the next stage in PV module recycling. The ethylene vinyl acetate (EVA) lamination layer is vaporized by the inert atmosphere pyrolysis at about 500ºC by using nitric acid.

The process continues with a chemical etching to remove metal coatings, antireflective coatings and diffusion layers. Common acidic chemical etching mixtures are based on HF-HNO 3 -H 2 O solutions.

The etching recipes have to be adapted to the different PV cell technologies (monocrystalline or polycrystalline). The chemical etching of semiconductors with this mixture is divided into two steps, oxidation and reduction, followed by dissolution of the oxidation products to form a soluble ion complex.

The recovered silicon could be utilised as a raw material in the photovoltaic industry, as an additive to alloy steel to alter its mechanical properties or as a material for ceramics, based on the manufacture of non-metal powders.

The main researches aim at: (1) -improving the pyrolysis process for separation of EVA [START_REF] Frisson | Recent improvements in industrial PV module recycling[END_REF]; ( 2 CdTe and CIS 6.3.2 Materials such as In, Ga and Te are regarded as critical because several studies show that the availability of them can, under certain conditions, limit the market growth of this kind of PV technologies (Marwede, Berger, Schlummer, Mäurer, & Reller, 2013;[START_REF] Mcdonald | Producer responsibility and recycling solar photovoltaic modules[END_REF].

Additionally, these PV module technologies contain hazardous materials such as cadmium, tellurium, lead and selenium. For example, cadmium compounds are currently regulated in many countries because of their toxicity to fish and wildlife. Cadmium has also been associated with numerous human illnesses [START_REF] Bernard | Cadmium and its adverse effects on human health[END_REF]V. M. Fthenakis, 2004).

Several recycling technologies for CdTe and CIS PV modules have already been developed by PV manufacturers, research institutions and enterprises [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF][START_REF] Huot | Recycling of solar Thin Film PV modules and scraps , and closed-loop use of metals[END_REF]Marwede et al., 2013;[START_REF] Sander | Study on the development of a take back and recovery system for photovoltaic products[END_REF]Sustainability EvaluatioN of Solar Energy Systems, 2007). It is possible to distinguish three main stages in all processes: delamination of the modules, decoating of the substrate and, extraction and refining of the metals and semiconductors. For each step, mechanical or chemical processes can be used (see Figure 67).

Delamination process consists to break laminated modules. One way of delamination is the physical disintegration of the modules by shredding and milling. During those processes, the modules are crushed and milled into small particles. However, it is not possible to fully liberate the semiconductor layer from the glass substrate. This explains why other separation processes are required after this step. For example, the use of micro-emulsion that contains tensides permits to detach all joined compounds (adhesion, encapsulation, sealing, and coatings).

Another type of delamination process tested in laboratory consists to dissolve the encapsulant in a solvent. The European project Sustainability Evaluation of Solar Energy Systems (SENSE) proved water jet cutting as one feasible option to delaminate the modules. 5N Plus also tested irradiation to decompose EVA through the glass.

De-coating and separation of non-metallic fractions from metal compound is the next step. In wetmechanical processes, broken PV modules are treated in an intensive batch mixer for the complete attrition of the semiconductor materials from the carrier glass. During the mixing process, the material is further crushed due to the strong forces. After the attrition process, the mixture of semiconductors, glass and EVA is rinsed and sieved into different fractions. The fraction <150 mm, containing a preconcentrate of semiconductor materials and glass dust, can be used for the subsequent flotation process. Leaching of the flotation products is carried out by adding acids (H 2 SO 4 or HCl) and hydrogen peroxide, thus leaving an inert material with Cd and Te.

A dry-mechanical method to remove the CIGS absorber layer can be also used (Marwede et al., 2013).

The CIGS dust is sucked off and collected by a vacuum dust remover. The very hard Mo electrode remains on the substrate and works as a solid lubricant for the metal blade. A diluted nitric acid is used to remove the Mo back contact from the soda-lime glass. The solution is neutralized and filtered which resulted in a Mo rich sludge. A dry etching process for recycling PV scrap is also used. The PV module fragments are exposed to a chlorine-containing and nitrogenous gas atmosphere at a temperature of more than 400°C, causing an etching process. Gaseous CdCl 2 and TeCl 4 that are generated in the etching process are made to condense (separately) and precipitate on cold surfaces (cooling traps). Resulting CdCl 2 and TeCl 4 are sent to a metal refinery where semiconductor grade Tellurium is extracted.

The final phase is the metal extraction and refining. For the production of thin-film PV modules, a high purity of the metals and semiconductors is required. Therefore the pre-processed metals have to be enriched, separated and purified.

Several chemical methods can be used to get the metals from acidic or other solutions: precipitation, liquid-liquid extraction, electro-winning, ion-exchange and oxidation/reduction. Important parameters to consider are the concentration of the target metals in the feed, their chemical form and other components.

a-Si 6.3.3

As the amount of a-Si material in a given module is minimal there is currently no literature explaining the recycling process of amorphous silicon solar cells [START_REF] Mcdonald | Producer responsibility and recycling solar photovoltaic modules[END_REF]): a-Si based solar cell recycling is likely to be primarily driven to reclaim the substrates using any of the techniques utilized by recycling CdTe or CIS based PV modules.

LCA of PV modules recycling process

At the beginning of this work, there is a lack of information related to LCA data in the dedicated database for the recycling processes of PV modules. This can be attributed to the fact that PV industry is relatively young and that a large number of PV modules has not yet reached their end-of-life. To our knowledge, in the dedicated literature, the two most reported PV recycling processes concern crystalline silicon and CdTe based modules. The description of these processes, their integration in the Life Cycle Assessment developed in Chapter 2 and the application of the ecodesign approach are presented below. The analysis of the literature survey showed that the results are generally given in terms of Global Warming Potential and Primary Energy Demand. A systematic assessment of all midpoint categories as previously carried out for PV panel production is not yet available.

Crystalline silicon 6.4.1 An analysis of the environmental impact of a recycling process for crystalline silicon PV modules is presented by [START_REF] Bombach | Technical experience during thermal and chemical recycling of a 23 year old PV generator formely installed on Pelllworm island[END_REF][START_REF] Müller | Life Cycle Analysis of Solar Module Recycling Process[END_REF]. The data are based on a PV module recycling process of Deutsche Solar AG.

Figure 6-8 displays the recycling process of Deutsche Solar AG. The process consists of two main steps. First, the laminate is burned off in a furnace at a temperature of 600°C to separate the module compound structure. This process makes easier the manual separation of solar cells, glass and metals.

The metals and the glass are given to recycling partners for integration in the adequate material loops.

During the thermal treatment, a significant amount of energy is consumed by the furnace and afterburner. The recovered cells are treated in the next step.

The metallization, antireflection coating and pn-junction of the cell are removed subsequently by etching. During the chemical process, different chemicals are required. The etching sequence involves removal metallization, removal antireflection layer, isotropic removal of pn-junction, surface finish, rinsing and drying.

Water and energy are consumed in the line and the gas washer. The chemicals used for etching are treated chemically and physically. The resulting sludge is disposed of. The resulting water is delivered to a treatment plant. Broken cells are also collected for reuse as raw materials for ingot growing after etching with a different technology. [START_REF] Müller | Life Cycle Analysis of Solar Module Recycling Process[END_REF] give an assessment of the total energy demand during the recycling process. They compare the energy consumption during production of a module with new wafers and a module with recycled wafers. They concluded that, due to the recycling process, almost 75% of the necessary primary energy for wafer production can be saved.

The LCA of recycling process of a standard crystalline PV module with 72 cells (12.5 x 12.5 cm), Tedlar as backside foil and an aluminium frame was also performed in [START_REF] Müller | Life Cycle Analysis of Solar Module Recycling Process[END_REF]. For the evaluation of the environmental impacts, the CML 2001 method of the institute of Environmental Science in Leiden (CML) was used (Centre of Enviromental Science, 2001). Calculations were based on Deutsche Solar data as well as data from the Ecoinvent database. Inflows and outflows including the treatment of wastewater and used chemicals were considered. Concerning the geographical scope, the recycling plant is considered to be in Germany. Therefore all datasets of used auxiliaries, energies and end-of-life processes like material recycling or disposal on landfill are country representative datasets. The environmental impact of recycling processes of glass and metals as well as the amount of recovered wafers are credited to the impacts of the recycling process, following the substitution method of allocation for environmental impacts.

The highest contribution to disburden of the environment is related to the substitution of new wafers by recycled ones. The burden of the environment is mainly related to the energy consumption during the thermal treatment and the use of chemicals in the etching line. In GWP category, they found a benefit of 59.2 kg CO 2 eq per module recycled used.

From these reported works, the recycling process is now embedded into the environmental model developed for m-Si in order to compute the environmental impact. A new LCA with recycling process is conducted. It must be yet kept in mind that the impact assessment method adopted in this work is not the same as the one mentioned in the reported work. This explains why only GWP and PE categories are used. We are aware that end-of-life assessment assumptions are not exactly the same as those used for PV processing.

The reference flow used in PV module processing was 1 m 2 of PV module but Müller's results are reported per module. An adjustment is done to have the same reference flow, i.e. 1 m 2 of PV module.

The m-Si based PV module taken as reference in Table 3-8 is kept. The GWP value found by Müller et al. is divided by the PV module surface referenced in their work. The new value results in a benefice of 52.62 kg CO 2 eq per m² of PV module. It is assumed that the PE demanded for a recycled module is 75% less than a PV module with new wafers as in Müller et al.'s work.The same efficiency as the value for the new panels has been considered for the recycled panels, i.e., 20%.

A new bi-objective optimization run is performed. The maximum energy produced and the minimum EPBT are searched. The bi-objective optimization is chosen because the analysis performed in Chapter 5 has identified these objectives as antagonist. A cost criterion, for instance, PBT is not considered because of the current lack of reliable information about the costs of recycling process of PV modules as well as the price of PV modules from recycled materials. EBPT is of course useful in order to detect changes due to reduction of primary energy for recycled PV modules. The same conditions and restrictions as those followed in Chapter 5 for bi-objectives case are taken into account.

Four scenarios are considered: (1) -PVGCS with 100% of PV modules with new wafers; (2) -PVGCS with 80% of PV modules with recycled wafers; (3) -PVGCS with 90% of PV modules with recycled wafers; and (4) -PVGCS with 100% of PV modules with recycled wafers. These recycling rates were considered from the information given by Solar World [START_REF] Wambach | Sources of secondary Silicon[END_REF] and PV CYCLE (PV CYCLE, n.d.). Figure 6-9 represents the system boundary under analysis for the scenarios. Table 6-2 shows the results of bi-objective optimization runs. The value of GWP is also presented in Table 6-2. The impact scores are divided by the energy produced during all the period of evaluation (20 years). As can be seen in Table 6-2, the use of recycled modules reduces significantly the EPBT (a factor of 1,8 is observed from the 100% new to the 100% recycled case). The yearly produced energy is approximately the same in all the optimization runs since the same efficiency has been considered for the recycled and new panels. Even if the GWP is not optimized, a significant reduction of its impact (per kWh of energy produced) is observed when recycling of PV modules is considered (a 20% reduction is observed in the more extreme case). These results justify and quantify the interest of m-Si PV panels recycling in the ecodesign strategy. Although PV recycling is energy intensive, its implementation compensates for the use of new produced panels. [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF].

Manufacturing of components

First Solar recycles spent CdTe PV modules by mechanical and hydrometallurgical process. The process is divided into five main steps:

1. Delamination by shredding and milling. The collected PV modules are reduced in a shredder and crushed in a hammer mill into small pieces from 4-5 mm. 2. Extraction. The semiconductors films are removed physically in a rotating leach drum. Sulphuric acid (H 2 SO 4 ) and hydrogen peroxide (H 2 O 2 ) are added throughout the leach cycle to form tellurous acid (H 2 TeO 3 ).

3. Solid-Liquid separation. After extracting the semiconductor materials, the liquids are separated from solid materials. A spiral classifier with an Archimedean screw allows the separation of the glass pieces from the liquid. The glass pieces are further treated to separate the laminate foil from the glass whereas the extracted liquor leaves to next step. 4. Precipitation and filtration. The extracted liquor is treated by a three-stage precipitation process with an increasing pH using sodium hydroxide for pH control. The precipitated solution is thickened, so the solids settle and increase in a solids loading. The thickened slurry is filtered and ends up in a semiconductor material enriched filter cake and a liquid solution. The filter cake is stored and sent to third party companies to recover the metals. The liquid solution is transferred to waste water treatment. 5. Laminate foil/glass separation and rinsing. In the milling and crushing process, most of the laminate foil is already separated in large pieces from glass. In a vibrating screen, the remaining laminate foil parts are separated from the glass cullet. The separated glass is then discharged and washed and sends to recycling.

Held [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF]) also follows a substitution approach and considers in his work the recycling and further treatment of clean glass cullet, lamination waste and liquid waste. The recycling process for filter cake is not considered in this study. However, it can be expected that the recovery of the metals provides a positive benefit.

The environmental benefits due to the glass cullet recycling are reflected by substituting primary material, which avoids environmental impacts and primary energy demand, and by a reduction of CO 2 emissions in the melting process.

The recycling process for junction box and lead wires is represented by material specific end-of-life treatments. Held [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF] assumed that all plastic material is burned in a waste incineration plat.

The recovery energy by the incineration is reflected as a credit for the substitution of electrical power and thermal energy from fossil fuels. Metal parts, mainly copper, are represented by a copper specific recycling process. Environmental benefits of secondary copper are accounted as a credit. In Held's work [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF], all primary data are based on industry data. Additional data are based on available GaBi 4 datasets. Life Cycle Inventory (LCI) is created in GaBi 4. The methodology for quantifying the environmental impact is CML2001 (Centre of Enviromental Science, 2001). Once more, the recycling plant is considered to be at Germany. The functional unit used is 1m² of spent CdTe modules.

The evaluation of LCIA results of the end-of-life phase CdTe modules is done for two cases: including and excluding environmental credits from material recycling (glass cullet and copper recycling).

In both cases, the same efficiency as the value for the new panels has been considered for the recycled panels, i.e., 11.5%. First Solar recycling process achieves a recycling rate of 95%

The results when environmental benefits from material recycling are included show that the benefits due to material recycling and energetic recovery outweigh the impacts of the recycling process and therefore would lead to a reduction of the environmental profile of the overall CdTe PV module life cycle. Table 6-3 displays the results of both cases. Negative values indicate that the environmental benefits constitute a credit within the life cycle assessment. As in m-Si recycling case, the impact assessment method adopted in this work is not the same as the one mentioned in Held's work. As previously, only GWP and PE categories are reported for further analysis.

From the data reported by Held, it is not possible to perform the same analysis carried out for the case of m-Si. Compared with m-Si case, Held just focus on performing the LCA for the recycling process of PV module based on CdTe and he does not perform the same analysis for the primary energy saved by the use of recycled material in the manufacture of new modules. However, Held performs two scenarios: allocate or not the benefits of recycling the material (glass cullet and copper) into recycling process. These data can complement the LCA already made to the PVGCS using CdTe PV modules described in Chapter 2.

The maximum energy produced and the minimum EPBT are optimized for the same raison as already explained in the previous example. The conditions and restrictions followed in Chapter 5 for biobjective case are kept. The two scenarios proposed by Held are investigated: including and excluding material recycling credits. Figure 6-11 shows the integration of recycling process within the system boundary for LCA. The results of both cases are presented in Table 6-4. The results confirm the benefit related to the overall environmental impacts when recycling of material (glass cullet and copper) is considered. The gain concerning EPBT and GWP is not so significant as in the m-Si case. It must be yet highlighted that these results do not consider recycling of cadmium and tellurium included in the filter cake due to a lack of information.

When comparing the two technologies m-Si and CdTe for PVGCS configuration, it must be said that recycling can significantly influence the choice of a technology: recycling will undoubtedly favour m-Si not only from the yearly produced energy (and consequently the economic criterion) but also from the EPBT and related GWP criteria points of view.

Conclusion

Even if the average lifetime of PV modules can be expected to be more than 25 years, the disposal of PV systems will become a problem in view of the continually increasing production of PV modules. In that context, a sustainable recycling of photovoltaic modules gains in importance due to the considerable growing of the PV market and the increasing scarcity of the resources for semiconductor materials.

In this chapter, an evaluation of photovoltaic recycling strategies showed that currently only two processes in the market are operated in an industrial scale. On the one hand, the company Deutsche Solar applies a treatment to recycle crystalline silicon modules [START_REF] Bombach | Technical experience during thermal and chemical recycling of a 23 year old PV generator formely installed on Pelllworm island[END_REF][START_REF] Müller | Life Cycle Analysis of Solar Module Recycling Process[END_REF]. On the second hand, CdTe thin film modules are recycled using a combination of mechanical and chemical process steps. This technology is established by the company First Solar [START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF].

Processes for other technologies are under development, mainly still in laboratory scale.

The data relative to these recycling processes are introduced in the optimization procedure for ecodesign. A bi-objective optimization is carried out in both cases, considering energy production and EPBT as optimization criteria. The results are largely influenced by the recycling strategy.

Concerning crystalline modules, the use of a recycling strategy reduces significantly the EPBT (a factor of 1,8 is observed from the 100% new to the 100% recycled case). It must be yet kept in mind that simplifying assumptions are considered in this preliminary study: the same efficiency has been considered for the recycled and new panels. This explains why the yearly produced energy is approximately the same in all the optimization runs. Even if the GWP is not optimized, a significant reduction of its impact (per kWh of energy produced) is observed when recycling of PV modules is considered (a 20% reduction is observed in the more extreme case). Although PV recycling is energy intensive, its implementation compensates for the use of new produced panels.

For CdTe thin film modules, the results confirm recycling benefit related to the overall environmental impacts when recycling of material (glass cullet and copper) is considered. Yet, the gain concerning EPBT and GWP is not so significant as in the m-Si case.

This study confirms that PV modules end-of-life management must be thoroughly studied not only from the viewpoint of the feasibility of the process but also from a more systemic way in order to assess the energy and global warming potential benefit of the recovery pathway.

Of course, we are aware that an economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making. This was not done due to the lack of reliable economic data, but this could be easily taken into account if they are available in the ecodesign procedure. These first results demonstrate the need to encourage producer responsibility not only in the PV manufacturing sector but also in the entire energy industry.

GENERAL CONCLUSION AND PERSPECTIVES

Chapter 7

General conclusions

Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the use of the so-called renewable energies has been increasing significantly with the aim at moving towards a more sustainable development. But, as any artificial installation implies an ecological impact, to be "renewable" is an obvious "necessary" condition but not a "sufficient" one! This explains the use of renewable energy must be efficiently integrated with the natural environment during its whole lifecycle following ecological design (ecodesign). Ecodesign methods are thus a necessary milestone to check whether renewable energy systems are truly sustainable. Now, even if the major renewable sources today are still biomass and electricity generation from hydraulics and wind, a very rapid growth of photovoltaic electricity has been observed in the last decade, logically because the main source that is truly external to the earth system is our Sun. In that context, despite its small use today, solar photovoltaic (PV) power has a particularly promising future.

This work aimed at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. Most of the works reported in the literature involve PVGCS optimization considering only one criterion or only address the issue of the environmental impact assessment of the components of a PV system with emphasis on PV module technology.

Generally, the environmental assessment is performed as a post-design stage of the PV systems.

In general terms, three main aspects can be highlighted in this work:

Environmental modelling

In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used. The well-known Life Cycle Assessment (LCA) method was chosen because it is the most reliable alternative for the type of evaluation proposed in this work: the rigorous framework based on ISO 14000 guidelines and the database that is implemented (i.e., Ecoinvent) for energy solutions guided this choice. From the reported literature and our own experience acquired through By itself, the development of renewable electricity will mechanically improve this point following a vertuous circle.

Techno-economic modelling

The analysis of the reported contributions led to the development of a specific framework for PV modelling and simulation purpose. The main problem that can be encountered when using one commercialized tools is the lack of an approach that allows the optimization of the sizing of a PVGCS considering economic and environmental criteria. Sizing is made taking into account technical objectives. In addition, the coupling of all the components via an external program to optimize the PV plant taking into consideration the three main criteria is difficult due to the closed structure used in each tool. To overcome the problem of interoperability, the design of a simulator for received solar radiation coupled with a sizing module constituted the chosen option. The simulator must be designed in an open manner so that it can be interfaced easily with an outer optimization loop. The estimation of solar radiation and the output energy of the system are the two most critical aspects of any PV system design and sizing tools.

This developed model consists of three stages: (1) the estimation of solar radiation received in a specific geographic location; (2) the design of the PV power plant; the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components that constitute the PV system (PV modules and BOS) and the constraints for the design of the plant; (3) the evaluation of the techno-economic criteria through EPBT and PBT as well as the environmental assessment through 15 midpoint categories. A good agreement between the "hand-made" model and the commercial codes was observed.

Ecodesign methodology

An ecodesign framework that considers simultaneously the technical, economic and life cycle environmental criteria was then established. The proposed methodology comprises three main steps:

first, a multi-objective optimization procedure based on a variant of NSGA-II was embedded in the mathematical model and a set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., PCA) is then applied to detect and omit redundant objectives that can be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS is used to select the alternative that provides a better compromise among all the objective functions that have been investigated. The capabilities of this technique have been demonstrated through several PVGCS case studies which aim at obtaining either the ideal configuration to produce the largest amount of energy or the minimum area needed to generate a given amount of energy. From the 18 objectives that were considered initially (i.e. 3 techno-economic and 15 environmental), only four objectives (Q out , PBT, RI, and OLD) have been identified as significant to perform the multi-objective optimization for ecodesign of a PV power plant. The results of the analyzed cases show that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all cases under consideration.

A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies are introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases for m-Si and CdTe PV modules, the importance of the benefits of PV modules end-of-life management was

confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.

Perspectives

After this work, many questions are still outstanding and could motivate other works in the future, from pratctical theoretical viewpoints.

An extension of this work would be in the optimization of PVGCS that are mounted in a single or twoaxis tracking system. The particularity of this type of structure is that the PV module is oriented following the movement of the sun in either a single axis or two axes. This type of system generates a greater amount of annual energy compared to traditional photovoltaic installations, even if a higher investment is required. The works that have been reported in the literature are limited to the optimum design of this type of PV systems from a techno-economic viewpoint.

More practically, it would be interesting to evaluate the performance of the methodology within the process of design and construction of PVGCS for a real new project with an industrial partner.

Considering the results obtained by the integration of technical, economic and environmental criteria in designing PVGCS and especially in the use and integration of the Principal Component Analysis (PCA) method to reduce the number of objectives followed by the decision-making tool (M-TOPSIS)

for the selection of alternatives generated after multi-objective optimization, the proposed methodology could be applied to the study of other ecodesign problems involving an important number of criteria that can be generated by the application of LCA: the ecodesign of chemical and dairy processes that is currently under investigation, the design of the hydrogen green supply chain are a consistent application of the proposed methodology.

Due to the significant changes in legislation relating to the treatment of electronic waste, including the elements of the PV systems, and considering the discussion in Chapter 6 related to the volume of PV modules that will arrive to their end-of-life in the coming years, the analysis of recycling processes will offer an overview of the implications that we can wait for the future development of these technologies.

The main interest is to know if, by integrating recycling processes within the environmental evaluation model proposed in this work for all technologies under study, the results will follow the same trend.

One of the interesting aspects to evaluate when an alternative source of energy generation is introduced is the social impact that can be generated within a specific area, i.e. changing habits of the people living in a certain region. In the case of large-scale photovoltaic systems, the use of large areas of land for the facilities' installation can lead that economic activities of the site where it is installed change affecting the population that lives near. Similarly it would be interesting to study how power generation from photovoltaic or other renewable energy sources can coexist with other economic activities. For example, it can be imagined that other economic activities, as agriculture or breeding, could be developed in the same area where the PV power plant is installed: the optimization process could help to determine the optimum solution for both activities together. Such a study would lead to introduce agricultural models.

All these questions emphasize that our current energy path is really changing: mixed energy vectors and sources will cohabit on the one hand; mixed approaches will be involved with different economic targets on the same area on the other hand. This issue opens up new fields for multi-optimization challenges.

LISTE OF FIGURES

D. SHADOW ESTIMATION ONTO A TILTED PV SHED

Appendix

D

The estimation of linear shading between two PV shed was based on Appelbaum and Bany work [START_REF] Appelbaum | Shadow Effect of Adjacent Solar Collectors in Large Scale System[END_REF] The shadow height is therefore:

(D.6)

As the pole OO' and shed MM'NN' have the same tilted angle β, the distance MF is equal to X E .

The energy produced by the PV shed field depends on the shaded area. A solar field is shown in by them is presented. A module is defined "shaded" once a shadow is cast on it even on a smallest part of its area. The power output of this module is considered to be zero as long as the shadow prevails on the module. This is the extreme case because some power may be delivered by the module when partially shaded but this case is most simple to program.

The length of the shadow projected from OLO'L' into MNM'N' is JN = L s and the height of the shadow ' JJ = H s . To predict the number of shaded PV modules, the following steps were established:

Step 1. Set the number of rows shaded. From the value, the number of rows shaded in the PV collector can be calculated (Equation (D.7)). The value must be rounded up to the nearest integer.

(D.7)

Where H m represent the PV module height. Care must be taken for determinate if there is a shadow on the PV collector MNM'N', i.e. , and .

E. BALANCE OF SYSTEM (BOS) SIZING

Appendix

E

The Balance of the System (BOS) is the term used to refer to all the other components in the PV installation besides the modules. The BOS includes the structure to support the modules and the electrical installation (inverters and cables).

E.1 Mounting structure

The PV modules are designed to be placed on the outside without protection, so it is fundamental to provide a medium that resists all meteorological conditions occurs. Manufacturers offer a variety of support structures for mounting the PV modules of PVCGS.

There are two types of mounting structures for PV modules. Fixed mounting structures are the most common for PVGCS. They are made principally in aluminium and stainless steel. The second type of structure, called tracking system, allows the PV collector to move either in one or two axes trying to follow the sun during the day. This type of structures permits to generate a greater amount of energy. Kornelakis and Koutroulis (Aris Kornelakis & Marinakis, 2010) propose a generalised model for the PV module fixed mounting structures in order to incorporate the cost of the PV module mounting structures in economic evaluation.

E.2 Inverter

PV inverter converts the direct current (DC) output of a PVGCS to deliver alternating current (AC), which can be fed into the electrical grid. Solar inverters have special functions adapted for use with photovoltaic arrays, including maximum power point tracking. Grid frequency and voltage are two important aspects to take into account for select an inverter. The lifetime of the inverter, as a rule, is 10 years. Manufacturers often offer in option an extension of the warranty for 20 years.

The Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME in french) [START_REF] Boulanger | Guide de rédaction du cahier des charges techniques des générateurs photovoltaïques connectés au réseau[END_REF] analyses three different types of integration of PV inverters in a PV power plant (see Figure E-2). The Table E-1 lists the characteristics, advantages and disadvantages of each of the three configurations. The cost of inverters varies strongly (depending on project size, supplier, promotional offers...). Solarbuzz (Solarbuzz, 2012a) periodically analyzes the price of inverters on the market and establish a price per kilowatt-pick, C kWp . The total cost for inverters, C INV , is given by the following expression:

(E.14)

Wiring

Since the system is composed of PV modules and DC/AC inverters, the most important section to wire is that connects the PV collectors and the DC/AC inverters. The length of the cable, Length Total , is estimated from the scheme shown in The total length is given by the Equation (E.18). (E.18)

  photovoltaïque exploitables dans une très large gamme de puissances. Les installations photovoltaïques ont ainsi connu une croissance récente vertigineuse, la puissance installée dans le Monde passant de 1,4 à 102 GW crête en 10 ans, notamment de 13 à 25,5 GW crête au cours de la seule année 2012 en Europe, leader dans cette avancée vers un Monde de l'énergie renouvelé et différent. Mais apparaissent dans le même temps plusieurs inconvénients dénoncés, tels que par exemple les besoins en eau des centrales thermosolaires installées en milieux ensoleillés arides, les impacts nocifs de la fabrication des matériaux photovoltaïques ou encore l'emprise au sol et sur les paysages de toutes ces installations artificielles nouvelles. Emblématique d'un développement durablela filière solaire se doit donc de veiller particulièrement à limiter son impact écologique et de maîtriser son développement de façon exemplaire.

  motivant cette étude et d'introduire de façon plus détaillée les chapitres de ce document : Chapitre Motivation de l'étude et présentation de l'état de l'art Chapitre Analyse du Cycle de Vie pour les systèmes photovoltaïques Chapitre Cadre de modélisation et de simulation pour les centrales photovoltaïques à grande échelle Chapitre Méthodes et outils pour l'écoconception : combiner optimisation multi-objectif, analyse en composantes principales et aide à la décision multicritère Chapitre Ecoconception de centrales photovoltaïques à grande échelle Chapitre Recyclage de modules de panneaux solaires Chapitre Conclusions et perspectives

Figure 1 - 1

 11 Figure 1-1 Structure of electricity production in 2011[START_REF] Observ | Worldwide electricity production from renewable energy sources[END_REF]

Figure 1 - 2

 12 Figure 1-2 Solar irradiation versus global energy resources (EPIA, 2011)

Figure 1 - 3 Figure 1 - 4

 1314 Figure 1-3 Photovoltaic effect (EPIA, 2011)

Figure 1 - 5

 15 Figure 1-5 PV module technology market share, based on (EPIA, 2013)

Figure 1 -

 1 Figure 1-6 exhibits the evolution of PV cumulative installed capacity in the world from 2000 to 2012.

Figure 1 -

 1 Figure 1-6 also displays the cumulative capacity by region. Europe leads with more than 70 GW installed about 70% of total, particularly thanks to a very strong policy of Germany, the far leader. Next in the ranking are Asia-Pacific region (APAC) with 12.4 GW installed, America with almost 8.7 GW and not far away China with 8.3 GW. Middle East and Africa (MEA) and the Rest of the World (ROW) represent about 3 GW of world's total PV capacity in 2012.

Figure 1 - 6

 16 Figure 1-6 Evolution of global cumulative installed capacity 2000-2012 (MW) (EPIA, 2013)

  Figure 1-8 shows the number of new power generation capacities by source added in 2012. It can be seen that for traditional sources (fossil fuel and nuclear) the installed capacity balance turned negative last year. A significant number of facilities were dismantled. Germany contributed to 44.31% of new PV installations that allow the European market to keep a reasonable level in relation to the other regions. Figure 1-9 indicates the percentage of new gridconnected PV capacities by country in Europe during 2012.

Figure 1 - 7 Figure 1 - 9

 1719 Figure 1-7 Evolution of global PV cumulative installed capacity per region until 2017 in MW (EPIA, 2013)

Figure 1 -

 1 Figure 1-10 Global PV production in 2012 by region (EPIA, 2013)

Figure 1 -

 1 Figure 1-11 Global utility-scale PV development scenarios until 2017 (MW) (EPIA, 2013)

Figure 1 -

 1 Figure 1-12 General scheme for the configuration of a PVGCS

Figure 2 - 1

 21 Figure 2-1 Production flow of crystalline silicon PV modules based on (de Wild-Scholten & Alsema, 2005)

FigureFigure 2 - 2 Figure 2 - 3

 2223 Figure 2-3.

Figure 2 - 4

 24 Figure 2-4 Production flow of a-Si PV modules

Figure 2 - 5

 25 Figure 2-5 Principle of a RF-PECVD deposition tool[START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF]

Figure 2 - 6

 26 Figure 2-6 Structure of a-Si:H-based solar cell[START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]

Figure 2 - 7

 27 Figure 2-7 Production flow of CdTe PV modules (V.[START_REF] Fthenakis | Life cycle inventory analysis of the production of metals used in photovoltaics[END_REF]

Figure 2 -

 2 Figure 2-10 Schematic CSS deposition tool[START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]

Figure 2 -

 2 Figure 2-11 Structure of CdTe-based solar cell[START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]

  Figure 2-12 Production flow of CIS PV module

Figure 2 -Figure 2 - 13 Figure 2 -

 22132 Figure 2-13 represents the production process of In.

Figure 2 -

 2 Figure 2-16). The substrate in the co-evaporation process reaches a temperature range between 400 and 600°C. In this process, all the elements are evaporated together on the substrate. The deposition of the material and formation of the compound happens together. This is achieved by thermal evaporation from elemental sources at temperatures greater than 1,000°C for Cu, In and Ga. A mass controller is used. The CIGS absorbed layer thickness is about 2 m.

Figure 2 -

 2 Figure 2-16 Deposition of CIGS layer using co-evaporation (Singh Solanki, 2011)

Figure 2 -

 2 Figure 2-17 Structure of CIS-based solar cell[START_REF] Singh Solanki | Solar Photovoltaics: Fundamentals, Technologies and Applications[END_REF]

  -8. Only the key points are briefly recalled in what follows.

Figure 2

 2 Figure 2-19 LCA framework(ISO 14040:1997)

Figure 2 -

 2 Figure 2-20 LCI schema

Figure 2 -

 2 Figure 2-21 Elements of LCIA(ISO 14042:2000)

  the advantages that different existing LCIA methods have as well as internal developments in various impacts categories. In IMPACT 2002 +, the characterization factors for Human Toxicity and Aquatic & Terrestrial Ecotoxicity from the methodology IMPACT 2002, the other characterization factors are adapted from existing

Σ

  To pass from the impact characterization through the damage evaluation and calculate the damage score, the characterization score should be multiplied by its damage characterization factor. SD d represents the damage score for the damage category d, FD i,d is the damage characterization factor for the impact category i in the damage category d. The characterization factors differ from one characterization method to another. They are available in the literature, in the form of databases, as well as in LCA support software tools. Table 2-3 and Figure 2-22 contain the midpoint and damage categories which IMPACT 2002+ works and the reference flow for each category.

FDFigure 2 - 22

 222 Figure 2-22 General approach of LCIA of emissions on the major categories of environmental damage PDF*m 2 *yr (Potentially Disappeared Fraction of species per m 2 per year) is the unit to "measure" the impacts on ecosystems. PDF*m 2 *yr represents the fraction of species disappeared on 1 m 2 of earth surface during one year.

Figure 2 - 23

 223 Figure 2-23 System boundaries for m-Si PV module LCA

  6 shows the amount of total inputs (intermediate flows) and direct emission for each process unit considering the FU. The next step is to calculate the total emissions and extraction flows. SimaPro and more precisely Ecoinvent database are particularly useful. The different unit processes considered within the boundaries of the system is created in SimaPro. Inputs and outputs as indicated in Figure 2-23 are introduced by using the processes that are then included into the Ecoinvent database. The program calculates both direct and indirect emissions and displays the total emissions. For the manufacture of the PV module, the program identifies 890 different types of substances that are released either into water, land and air. Table

  9 indicate the total characterization score for two midpoint categories, i.e. Global Warming and Respiratory Inorganic respectively.

Figure 2 -

 2 Figure 2-24 Normalised scores for both midpoint and damages categories. IMPACT 2002+

  Figure 2-28 Normalised results for the three PV module technologies. IMPACT 2002+

Figure 2 -

 2 Figure 2-29 shows the results of the LCA. A similar trend relative to the impact of each technology assessed is observed.

Figure 2 -

 2 Figure 2-30 demonstrates that the largest contribution comes from the first two stages of module manufacturing (i.e. silicon wafer and solar cell) for the three technologies. It can be clearly pointed out that for the m-Si module, the wafer manufacturing process generates more than half of the total impact of the category, because of the high energy consumption involved during CZ crystal growth. The main causes are related to the emissions corresponding to the energy needs of the process as it can be highlighted through the analysis of the resources involved in the manufacturing process of m-Si wafers (see Figure 2-31).

Figure 2 -

 2 Figure 2-29 Normalised results for the three PV module technologies. FU: 1 kWh demanded. IMPACT 2002+

  Technical characteristics related to PV systems. The life expectancy of PV components and systems is not the same. e.g. 20-30 years for PV modules or 10 year for AC/DC inverter. Depending on the goal of the study, the irradiation collected by modules or their degradation can be important. LCI/LCA modelling aspects. The appropriate system model depends on the goal of the LCA. It can have a short-term prospective as for the choice of a PV electricity-supplier or comparisons between PV systems or electricity-generating technologies; or long-term prospective as for comparison of future PV systems or of future electricity-generating technologies. The electricity mix must be considered carefully as well as the reference flow to enable comparisons. The reference flow can be expressed in kWh electricity produced (used for comparing PV technologies and modules), m² of module or kWp rated power. Discuss and interpretation of results. Beside the impact indicators used in LCIA it may be helpful use another indicator as energy payback time (EPBT)Reporting and communication. Some key parameters must be kept in mind : irradiance level and location; PV module efficiency; type of mounting system; components expected lifetime; system boundaries; technical and modelling assumptions; LCA tool and database used.

Figure 2 -

 2 Figure 2-32 Boundaries of the system examined to compare different PV technologies

  Figure 2-33 shows the normalized results. Looking at the total score for each of the five configurations in all categories shows that the highest environmental impacts in almost every category midpoint are obtained when a-Si PV modules are used. Only into the categories related with climate change and resources m-Si PV module configuration has the highest impacts.

Figure 2 -

 2 Figure 2-33 Normalized results for the five PV installation considering 1200 kWh/m 2 yr of irradiation on an inclined plane (30°). IMPACT 2002+

Figure 3 - 1

 31 Figure 3-1 Functional flow diagram of the proposed methodology

Figure 3 - 2

 32 Figure 3-2 Different components of solar radiation in a tilted surface

  Figure 3-3 Data flow diagram of solar irradiance estimation model

Figure 3 -

 3 Figure 3-4 shows the relationship between the Sun and Earth. Radiation emitted by the sun and its relation to the Earth gives an almost constant solar radiation outside the Earth's atmosphere.

FigureFigure 3 - 5

 35 Figure 3-4 Sun-Earth relationship

  Hay and Davies[START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF] propose a model based on the assumption that all of the diffuse can be represented by a circumsolar component coming directly from the sun and an isotropic component coming from the entire celestial hemisphere. The diffuse irradiance on a tilted surface is then.[START_REF] Noorian | Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces[END_REF] propose another model (Equation (3.13)).. This model extends the Hay and Davies model by adding the horizon brightening. The horizon brightening is assumed to be a linear source at the horizon and to be independent of azimuth. In fact, for clear skies, the horizon brightening is highest at the horizon and decreases in intensity away from the horizon. For overcast skies, the horizon brightening has a negative value.(3.13)3.4.2.3 ValidationThe simulator was used to estimate the annual radiation received in 4 different positions: Toulouse, France (43.4° N, 1.2°E,altitude 152 m), Sydney, Australia (33.5°S, 151.1°E, altitude 42 m), Mexico City, Mexico (19.2° N, 99.1°W, altitude 2277 m) and Singapore, Singapore (1.1° N, 104.1°E, altitude 5 m). The results were compared with those estimated for the same cities by MIDC SOLPOS Calculator 2.0 (National Renewable Energy Laboratory, 2000) for extraterrestrial irradiance and PVsyst software (University of Geneva, n.d.) for horizontal and tilted irradiance. MIDC SOLPOS Calculator 2.0 was developed by the National Renewable Energy Laboratory (NREL), a research laboratory for the U.S. Department of Energy. This software tool contains a Solar Position Algorithm (SPA) (Reda & Andreas, 2008) for solar radiation applications developed by the NREL. The algorithm can calculate the sun zenith and azimuth angle with uncertainties equal to ± 0.0003°. MIDC SOLPOS Calculator calculates the position of the sun in the sky and its intensity for any given location, day and time. It is valid from the year 1950 to 2050 and has an uncertainty of ±0.01° (National Renewable Energy Laboratory, n.d.).As mentioned in Table3-1, PVsyst is a PV simulation tool developed at the University of Geneva, Switzerland to be used by architects, engineers and researchers. In 2011, PVsyst got excellent results in the PHOTON Magazine evaluation[START_REF] Mermoud | Note sur le comparatif de programmes de simulation PV de PHOTON[END_REF]. The evaluation considered about 20 different PV simulation software available on the market for the study of PV systems yield and tried to assess the accuracy of irradiance data in the horizontal plane and ambient temperature, as well as horizon shading. Two statistical tests based on root mean square (RMSE) and mean bias error (MBE), were used to compare the results of the model developed in this work and the aforementioned software tools (Diez-Mediavilla, de Miguel, & Bilbao, 2005; Noorian et al., 2008). (3.14) (3.15) where C i is the i th calculated value, M i is the i th measured value of the radiation, and n is the total number of observations for a specified period of time. The lower the RMSE, the more accurate the model is. A positive MBE indicates an overestimation of the calculated values while a negative MBE indicates an underestimation. Dimensionless measures of RMSE and MBE, relative RMSE (% RMSE) and relative MBE (% MBE), were also used. They are defined as follows.

Figure 3 - 6

 36 Figure 3-6 Data flow diagram of PVGCS model

Figure 3 - 7

 37 Figure 3-7 Solar collector field reproduced from[START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF]

Figure 3 - 8

 38 Figure 3-8 Shading by collectors in a stationary solar field reproduced from[START_REF] Weinstock | Optimization of Solar Photovoltaic Fields[END_REF]

  Figure 3-9 summarizes the different elements required by this model.In order to determine the requirement of construction material and electric components necessary for the design of the PV power plant and, the cost incurred as well the associated environmental impacts, the scheme proposed by Kornelakis and Koutroulis (A.[START_REF] Kornelakis | Methodology for the design optimisation and the economic analysis of grid-connected photovoltaic systems[END_REF] is used. A fixed mounting structure is selected because of the simplicity. A centralized inverter zone is proposed as it is indicated by the guidelines published by the Agence de l'Environnement et de la Maîtrise de

Figure 3 - 9

 39 Figure 3-9 Evaluation of criteria model

Figure 3 -

 3 Figure 3-10 Field layout of PV power plant made in PVsyst

Figure 3 -

 3 Figure 3-12 Results of the environmental impacts per annual energy generated ratio normalized to unity

  e th eigenvalue remained in PCA method Ideal and non-ideal solution in M-TOPSIS method Normalized result of alternative i into the criterion j Euclidean distance for ideal and non-ideal solution for alternative i Cumulative explained variance M-TOPSIS ratio value for alternative i Yearly output energy of the field, kWh Weighted normalized result of alternative i into the criterion j Weight of the individual criterion j Most positive element of principal component Most negative element of principal component Value of alternative i into the criterion j

Figure 4 - 1

 41 Figure 4-1 Classification of optimization methods[START_REF] Garcia | Introduction to Optimization Methods : a Brief Survey of Methods[END_REF]

  systems are optimized based on stochastic algorithms(Gómez-Lorente, Triguero, Gil, Estrella, &

Figure 4 - 3

 43 Figure 4-2 Genetic Algorithm operators

  , because of the way to manage the diversity of populations. Algorithms based on the concept of niche as NPGA and MOGA do not ensure a proper convergence of the Pareto front. Algorithms such as SPEA or NSGA-II are based on the principle that single non-dominated individuals are better than individuals in dense areas. In SPEA, the probability of selection is based on the isolation of the individual, which implies a quantification of that probability, and therefore the implementation of more complex algorithms. NSGA-II opts for a simple elimination of individuals at dense areas after a sorting according to their density. In addition, NSGA-II needs low computational requirements. The step-by-step procedure is illustrated in Figure 4-4 to Figure 4-6. Initially, a random parent population P 0 of size N is created. The population is sorted based on the non-domination principle.

Figure 4 - 4

 44 Figure 4-4 Operating principle of NSGA-II (Part 1)[START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF]

Figure 4 - 5 Figure 4 - 6

 4546 Figure 4-5 Operating principle of NSGA-II (Part 2)[START_REF] Gomez | Optimisation technico-économique multiobjectif de systèmes de conversion d'énergie: cogénération électricité-hydrogène à partir d'un réacteur nucléaire de IVème génération[END_REF]

  e represents e th eigenvalue remained. A second reduction is made from G j values. A threshold cut value (CUT) must be established in order to keep for the PCA the eigenvalues with cumulative explained variance below this value (G j ≤ CUT).[START_REF] Deb | On Finding Pareto-Optimal Solutions Through Dimensionality Reduction for Certain Large-Dimensional Multi-Objective Optimization Problems EMO for Many Objectives[END_REF] suggest a CUT value of 0.95 (95%).Step 6: Selecting environmental impact categories. This is done by analyzing the eigenvectors to identify conflicts among the categories. The heuristic procedure suggested by Deb and Saxena is followed to identify conflicts and redundancies among all the environmental categories. Technical details about this strategy are summarized in Figure4-7. In this figure, x + denotes the most positive element of principal component and x -represent the most negative element of principal component.

Figure 4 - 7

 47 Figure 4-7 Scheme of the PCA procedure for selecting environmental impact categories[START_REF] Sabio | Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis[END_REF]

  to determine the optimal configuration of a threeechelon hydrogen supply chain for vehicle use (production-storage-market) in terms of cost and damage to the environment. A multi-objective mixed-integer linear program (MILP) formulation is employed to solve the problem. The environmental performance of the hydrogen supply chain is assessed by following 8 environmental LCA indicators: damage to human health caused by carcinogenic substances (CS), damage to human health caused by respiratory effects (RE), damage to human health caused by climate change (CC), damage to human health caused by ozone layer depletion (OLD), damage to ecosystem quality caused by ecotoxic substances (ES), damage to ecosystem quality caused by acidification and eutrophication (AE), damage to minerals (DM), and damage to fossil fuels (DFF).

Figure 4 - 9

 49 Figure 4-9 PCA results in the bi-and tri-dimensional spaces for Sabio et al. case

Figure 4 -

 4 Figure 4-10 Decision matrix

  5: Calculate Euclidean distance. Calculate the separation measures, using the n-dimensional Euclidean distance.[START_REF] García-Cascales | On rank reversal and TOPSIS method[END_REF][START_REF] Pinter | Evaluating construction project success with use of the M-TOPSIS method[END_REF]

Figure 4 -

 4 Figure 4-11 Example D + D --plane of M-TOPSIS method (LifengRen et al., 2007)

  (D i -)] as the optimized ideal reference point, the Figure4-12 displays the position of all the alternatives in D + D --plane.

Figure 4 -

 4 Figure 4-12 D + D -plane for M-TOPSIS example

Figure 4 -

 4 Figure 4-13 Pareto front EPBT-Q out with top 3 ranked alternatives

Figure 4 -

 4 Figure 4-14 Pareto front EPBT-Q out with top 3 ranked alternatives and alternative 4

Figure 4 - 15

 415 Figure 4-15 Integration of NSGA-II, PCA method and M-TOPSIS method

Figure 5 - 1

 51 Figure 5-1 Functional flow diagram of the proposed methodology

  8 are considered. Only the third scenario of previous case is considered, based on the maximization of the output energy with all possible energy losses. Equation (3.30) is kept as the objective function. The conditions and constraint boundaries are the same. The values of the decision variables and annual output energy for each of the resulting configurations are shown in Table

Figure 5 - 4

 54 Figure 5-4 Results of the environmental impacts normalized to unity. Minimum field area

  Pareto fronts with M-TOPSIS selected PV power plant configuration. Max Q out -Min PBT

  Figures 5-5 (a to e) shows the location of the resulting configurations in the graphs of the respective Pareto fronts.

Figure 5 - 6

 56 Figure 5-6 Results of the environmental impacts normalized to unity. Max Q out -Min PBT

  Figures 5-7 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Max Q out -Min EPBT

  Figure 5-8 represents the radar charts of environmental impacts and shows that the m-Si based configuration leads in almost all of the categories.M-TOPSIS application gives the ranking proposed inTable 5-15 for the 5 PV technologies: a-Si based configuration is best positioned.

Figure 5 - 8

 58 Figure 5-8 Results of the environmental impacts normalized to unity. Max Q out -Min EPBT

Figure 5

 5 Figure 5-9 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Min PBT-Min EPBT

Figure 5 -Figure 5

 55 Figure 5-10 Results of the environmental impacts normalized to unity. Min PBT -Min EPBT

Figure 5 - 12

 512 Figure 5-12 Results of the environmental impacts normalized to unity. Min Area -Min PBT

Figure 5

 5 Figure 5-13 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Min Area-Min EPBT

Figure 5 -

 5 Figure 5-14 Results of the environmental impacts normalized to unity. Min Area -Min EPBT

Figure 5 -

 5 Figure 5-15 Scree plot for the 15 environmental categories

Figure 6 - 1

 61 Figure 6-1 Global PV production and projected waste from 1998 to 2038 from (McDonald & Pearce, 2010)

  ) -choosing a suitable composition and concentration of the etching solution and the optimal temperature range for the chemical reaction (Kang, Yoo, Lee, Boo, & Ryu, 2012; Klugmannradziemska & Ostrowski, 2010); (3) -reducing the amount of energy required and the total cost of the recycling process.

Figure 6 - 6

 66 Figure 6-6 Recycling process for PV crystalline module from (Klugmann-Radziemska, 2012)

Figure 6 - 7

 67 Figure 6-7 Recycling process for CdTe and CIS PV modules from[START_REF] Marwede | Future recycling flows of tellurium from cadmium telluride photovoltaic waste[END_REF]

Figure 6 - 8

 68 Figure 6-8 Recycling Process of Deutsche Solar AG[START_REF] Bombach | Technical experience during thermal and chemical recycling of a 23 year old PV generator formely installed on Pelllworm island[END_REF]

Figure 6 - 9

 69 Figure 6-9 Process flow and system boundary for PVGCS with m-Si PV module

  the end-of-life phase of CdTe PV module was conducted by Held[START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF] following the recycling process established by First Solar. Figure6-10 illustrates the simplified process flow chart of First Solar CdTe PV module recycling process.

Figure 6 -

 6 Figure 6-10 Flow chart of First Solar's CdTe PV module recycling process from[START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF].

  this work, the classical LCA tools (SimaPro and other LCA software) turned out to be not flexible and suffer from a lack of interoperability. From a practical viewpoint, a specific environmental module was designed from extraction of the dedicated EcoInvent database and can be used for the studied technologies for PV systems. The environmental model was for successfully embedded in the design stage of a PVGCS.As part of the LCA guidelines, a description of the manufacturing process of the five PV module technologies that are the most commercialized ones was performed. Data collection highlighted the significant amount of the embodied primary energy required, especially for c-Si based PV module technologies. This situation was observed for three different LCA case studies. IMPACT 2002 + was selected to perform the environmental assessment of the flows and emissions generated during the development of the module. It is also important to highlight the influence of the composition of the energy mix used in the manufacture of the module in the characterization of environmental impacts.

Figure 1 - 1

 11 Figure 1-1 Structure of electricity production in 2011 (Observ'ER, 2012)............................................................

Figure 1 - 2

 12 Figure 1-2 Solar irradiation versus global energy resources (EPIA, 2011) ..........................................................

Figure 1 - 3

 13 Figure 1-3 Photovoltaic effect (EPIA, 2011) .......................................................................................................

Figure 1 - 4

 14 Figure 1-4 Different configurations of PV solar systems from (EPIA, 2011) ......................................................

Figure 1 - 5

 15 Figure 1-5 PV module technology market share, based on (EPIA, 2013) ............................................................

Figure 1 - 6

 16 Figure 1-6 Evolution of global cumulative installed capacity 2000-2012 (MW) (EPIA, 2013) ..........................

Figure 1 - 7

 17 Figure 1-7 Evolution of global PV cumulative installed capacity per region until 2017 in MW (EPIA, 2013) ...................................................................................................................................................

Figure 1 - 8

 18 Figure 1-8 Power generation capacities added in the EU 27 in 2012 (MW) (EPIA, 2013) .................................

Figure 1 - 9

 19 Figure 1-9 European new grid-connected PV capacities in 2012 (EPIA, 2013) ..................................................

Figure 1 -

 1 Figure 1-10 Global PV production in 2012 by region (EPIA, 2013) ...................................................................

Figure 1 -

 1 Figure 1-11 Global utility-scale PV development scenarios until 2017 (MW) (EPIA, 2013)..............................

Figure 1 -

 1 Figure 1-12 General scheme for the configuration of a PVGCS ..........................................................................

Figure 1 -

 1 Figure 1-13 Organization of manuscript ..............................................................................................................

Figure 2 - 1

 21 Figure 2-1 Production flow of crystalline silicon PV modules based on (de Wild-Scholten & Alsema, 2005) ...................................................................................................................................................

Figure 2 - 2

 22 Figure 2-2 Main manufacturing processes of SoG-Si ..........................................................................................

Figure 2 - 3

 23 Figure 2-3 Manufacturing processes of silicon wafer ..........................................................................................

Figure 2 - 4

 24 Figure 2-4 Production flow of a-Si PV modules ..................................................................................................

Figure 2 - 5

 25 Figure 2-5 Principle of a RF-PECVD deposition tool (Luque & Hegedus, 2003) ...............................................

Figure 2 - 6

 26 Figure 2-6 Structure of a-Si:H-based solar cell (Singh Solanki, 2011) ................................................................

Figure 2 - 7

 27 Figure 2-7 Production flow of CdTe PV modules (V. Fthenakis et al., 2009) .....................................................

Figure 2 - 8

 28 Figure 2-8 Flows in Zn refining (V. Fthenakis et al., 2009) .................................................................................

Figure 2 - 9

 29 Figure 2-9 Extractive metallurgy of Cu (V. Fthenakis et al., 2009) .....................................................................

Figure 2 -

 2 Figure 2-10 Schematic CSS deposition tool (Singh Solanki, 2011) .....................................................................

Figure 2 -

 2 Figure 2-11 Structure of CdTe-based solar cell (Singh Solanki, 2011) ...............................................................

Figure 2 -

 2 Figure 2-12 Production flow of CIS PV module ..................................................................................................

Figure 2 -

 2 Figure 2-13 Process flows for In production (V. Fthenakis et al., 2009) .............................................................

Figure 2 -

 2 Figure 2-14 Process flows for Ga production (V. Fthenakis et al., 2009) ............................................................

Figure 2 -

 2 Figure 2-15 Process flows for Se production (V. Fthenakis et al., 2009) .............................................................

Figure 2 -

 2 Figure 2-16 Deposition of CIGS layer using co-evaporation (Singh Solanki, 2011) ...........................................

Figure 2 -

 2 Figure 2-17 Structure of CIS-based solar cell (Singh Solanki, 2011) ..................................................................

Figure 2 -

 2 Figure 2-18 Schematic representation of the life cycle of a generic product (based on (Rebitzer et al., 2004)) ..................................................................................................................................................

Figure 2 -

 2 Figure 2-19 LCA framework (ISO 14040:1997) ..................................................................................................

Figure 2 -

 2 Figure 2-20 LCI schema .......................................................................................................................................

Figure 2 -

 2 Figure 2-21 Elements of LCIA (ISO 14042:2000) ...............................................................................................

Figure 2 -

 2 Figure 2-22 General approach of LCIA of emissions on the major categories of environmental damage ...........

Figure 2 -

 2 Figure 2-23 System boundaries for m-Si PV module LCA ..................................................................................

Figure 2 -

 2 Figure 2-24 Normalised scores for both mid-point and damages categories. IMPACT 2002+ ...........................

Figure 2 -

 2 Figure 2-25 Energy mix composition of Germany, USA, China, Spain and France ............................................

Figure 2 -

 2 Figure 2-26 Normalized scores both mid-point and damages categories of five scenarios. IMPACT 2002+ ..................................................................................................................................................

Figure 2 -

 2 Figure 2-27 Scheme of system boundaries ...........................................................................................................

Figure 2 -

 2 Figure 2-28 Normalised results for the three PV module technologies. IMPACT 2002+ ...................................

Figure 2 -

 2 Figure 2-29 Normalised results for the three PV module technologies. FU: 1 kWh demanded. IMPACT 2002+ ..................................................................................................................................................

Figure 2 -

 2 Figure 2-30 Result of main processes in Global Warming category for 3 PV module technologies ...................

Figure 2 -

 2 Figure 2-31 Score of the five main resources that contribute to GW characterization for m-Si PV module at wafer elaboration process. ...............................................................................................................

Figure 2 -

 2 Figure 2-32 Boundaries of the system examined to compare different PV technologies .....................................

Figure 2 -

 2 Figure 2-33 Normalized results for the five PV installation considering 1200 kWh/m 2 yr of irradiation on an inclined plane (30°). IMPACT 2002+ ............................................................................................

Figure 3 - 1

 31 Figure 3-1 Functional flow diagram of the proposed methodology .....................................................................

Figure 3 - 2

 32 Figure 3-2 Different components of solar radiation in a tilted surface .................................................................

Figure 3 - 3

 33 Figure 3-3 Data flow diagram of solar irradiance estimation model ....................................................................

Figure 3 - 4

 34 Figure 3-4 Sun-Earth relationship ........................................................................................................................

Figure 3 - 5

 35 Figure 3-5 Sequence for determination of hourly global tilted irradiance ...........................................................

Figure 3 - 6

 36 Figure 3-6 Data flow diagram of PVGCS model .................................................................................................

Figure 3 - 7

 37 Figure 3-7 Solar collector field reproduced from (Weinstock & Appelbaum, 2009) ..........................................

Figure 3 - 8

 38 Figure 3-8 Shading by collectors in a stationary solar field reproduced from (Weinstock & Appelbaum, 2009) ...................................................................................................................................................

Figure 3 - 9

 39 Figure 3-9 Evaluation of criteria model ...............................................................................................................

Figure 3 -

 3 Figure 3-10 Field layout of PV power plant made in PVsyst ...............................................................................

Figure 3 -

 3 Figure 3-11 Results of the environmental impacts normalized to unity ...............................................................

Figure 3 -

 3 Figure 3-12 Results of the environmental impacts per annual energy generated ratio normalized to unity .........

Figure 4 - 1

 41 Figure 4-1 Classification of optimization methods (Garcia, Avila, Carpes, & Avila, 2005) .............................

Figure 4 - 2

 42 Figure 4-2 Genetic Algorithm operators ............................................................................................................

Figure 4 - 3

 43 Figure 4-3 Genetic Algorithm flow chart ...........................................................................................................

Figure 4 - 4

 44 Figure 4-4 Operating principle of NSGA II (Part 1) (Gomez, 2008) .................................................................

Figure 4 - 5

 45 Figure 4-5 Operating principle of NSGA II (Part 2) (Gomez, 2008) .................................................................

Figure 4 - 6

 46 Figure 4-6 Operating principle of NSGA II (Part 3) (Gomez, 2008) .................................................................

Figure 4 - 7

 47 Figure 4-7 Scheme of the PCA procedure for selecting environmental impact categories (Sabio et al., 2012) .................................................................................................................................................

Figure 4 - 8

 48 Figure 4-8 Screen plot for Sabio et al. case ........................................................................................................

Figure 4 - 9

 49 Figure 4-9 PCA results in the bi-and tri-dimensional spaces for Sabio et al. case ............................................

Figure 4 -

 4 Figure 4-10 Decision matrix ..............................................................................................................................

Figure 4 -

 4 Figure 4-11 Example D + D --plane of M-TOPSIS method (Lifeng Ren et al., 2007) ......................................

Figure 4 -

 4 Figure 4-12 D + D -plane for M-TOPSIS example .............................................................................................

Figure 4 -

 4 Figure 4-13 Pareto front EPBT-Q out with top 3 ranked alternatives ...................................................................

Figure 4 -

 4 Figure 4-14 Pareto front EPBT-Q out with top 3 ranked alternatives and alternative 4 ........................................

Figure 4 -

 4 Figure 4-15 Integration of NSGA II, PCA method and M-TOPSIS method .....................................................

Figure 5 - 1

 51 Figure 5-1 Functional flow diagram of the proposed methodology ...................................................................

Figure 5 - 2

 52 Figure 5-2 Results of the environmental impacts normalized to unity. Maximum output energy .....................

Figure 5 -

 5 Figure 5-3 3-D perpectives of 5 configurations .................................................................................................

Figure 5 - 4

 54 Figure 5-4 Results of the environmental impacts normalized to unity. Minimum field area .............................

Figures

  Figures 5-5 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Max Q out -Min PBT ...................................................................................................................................................

Figure 5 - 6

 56 Figure 5-6 Results of the environmental impacts normalized to unity. Max Q out -Min PBT ...........................

Figures

  Figures 5-7 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Max Q out -Min EPBT .................................................................................................................................................

Figure 5 - 8

 58 Figure 5-8 Results of the environmental impacts normalized to unity. Max Q out -Min EPBT .........................

Figure 5 -

 5 Figure 5-9 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Min PBT-Min EPBT .................................................................................................................................................

Figure 5 -

 5 Figure 5-10 Results of the environmental impacts normalized to unity. Min PBT -Min EPBT .......................

Figure 5 -

 5 Figure 5-11 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Min Area -Min PBT ...................................................................................................................................................

Figure 5 -

 5 Figure 5-12 Results of the environmental impacts normalized to unity. Min Area -Min PBT .........................

Figure 5 -

 5 Figure 5-13 (a-e) Pareto fronts with M-TOPSIS selected PV power plant configuration. Min Area-Min EPBT .................................................................................................................................................

Figure 5 -

 5 Figure 5-14 Results of the environmental impacts normalized to unity. Min Area -Min EPBT ......................

Figure 5 -

 5 Figure 5-15 Scree plot for the 15 environmental categories ..............................................................................

Figure 5 -

 5 Figure 5-16 PCA results in the bi-dimensional spaces .......................................................................................

Figure 6 - 1

 61 Figure 6-1 Global PV production and projected waste from 1998 to 2038 from (McDonald & Pearce, 2010) .................................................................................................................................................

Figure 6 - 2

 62 Figure 6-2 Three recycling schemes (inspired from (Ligthart & Ansems, 2002)) .............................................

Figure 6 - 3

 63 Figure 6-3 Product system for the cut-off approach (Ligthart & Ansems, 2002)...............................................

Figure 6 - 4

 64 Figure 6-4 Product material flows and processes for 2 life cycles. Closed loop approach (Nicholson et al., 2009) .................................................................................................................................................

Figure 6 - 5

 65 Figure 6-5 Substitution allocation approach (Ligthart & Ansems, 2002) ..........................................................

Figure 6 - 6

 66 Figure 6-6 Recycling process for PV crystalline module from (Klugmann-Radziemska, 2012) .......................

Figure 6 - 7

 67 Figure 6-7 Recycling process for CdTe and CIS PV modules from (Marwede & Reller, 2012) ......................

Figure 6 - 8

 68 Figure 6-8 Recycling Process of Deutsche Solar AG (Bombach et al., 2006) ...................................................

Figure 6 - 9

 69 Figure 6-9 Process flow and system boundary for PVGCS with m-Si PV module ...........................................

Figure 6 -

 6 Figure 6-10 Flow chart of First Solar's CdTe PV module recycling process from (Held, 2009). ......................

Figure 6 -

 6 Figure 6-11 Process flow and system boundary for PVGCS with CdTe PV module .........................................

  . A pole OO' of length A in the ZY plane (Figure D.1), inclined at angle β, causes a shadow on the tilted shed MM'NN' placed at distance R OM R . The shed is perpendicular to the ZY plane inclined with respect to XY plane with the same angle β. The shadow length is and the components of the pole shadow on the XY plane are IC = P x (Equation (D.1)) and OI = P y (Equation (D.2)) and its shadow projection on the PV shed is EF .

(Figure D- 1

 1 Figure D-1 Shadow of an inclined pole on a tilted PV shed (with same angle β) from Appelbaum andBany[START_REF] Appelbaum | Shadow Effect of Adjacent Solar Collectors in Large Scale System[END_REF]

Figure D- 2 .

 2 Figure D-2. The position of two inclined PV shed OLO'L' and MNM'N' and the shadow shape caused

Figure D- 2

 2 Figure D-2 Shading by PV sheds in a solar field

  Figure E-1 corresponds to the schema of fixed mounting structure used by Kornelakis and Koutroulis. The mounting structures are constructed using metallic rods and the estimation of the corresponding cost is based on the calculation of the total length of the metallic rods required for the installation of the PVGCS. Each PVGCS row is comprised of multiple, identical mounting structures. The intermediate vertical rods are installed at each point that the row vertical height has been increased by 2 m. The PV modules metallic mounting frames are installed on concrete foundation bases. The total length in meters of the metallic rods, B, required for the installation of the entire PVGCS, is calculated as m n N c C o lu m n 2 C o lu m n 1 . . .

  Figure E-1 Schema of the mounting structures

  A3 is discarded due to size of the large-scale PVGCS and the maintenance cost of the number of inverters. The two remaining alternatives are appropriate for this type of facility and a combination of them can be used depending on the dimension of PVGCS.(E.10) P max represent the maximum input DC power of DC/AC inverter and P M,max is the maximum possible PV module output power at the MPP.The maximum number of parallel branches connected to a DC/AC converter, N p,max , is calculated using the following equation:(E.11)The maximum possible number of PV modules, N block , that the DC/AC inverter can support is: (E.12) Total DC/AC inverters N INV for the PVGCS is: (E.13)

  Figure E-3. It is observed that all DC/AC investors are placed in the same space. Length 1 and Length 3 are considered as constant values for all the PV collectors. Length 2 varies depending on the location of the PV collector with respect to where the DC/AC inverters are installed.The sum of all Length 2 can be defined as: number of PV collectors. The series above is simplified to obtain the following equation: (E.17

  )

Table 1 -1

 1 World electricity production by source inTWh (2001TWh ( -2011) ) [START_REF] Observ | Worldwide electricity production from renewable energy sources[END_REF]

							Variation
	Source	2001	2008	2009	2010	2011	2001-2011
	Renewable	2,862.4	3,812.5	3,951.1	4,225.2	4,447.5	4.50 %
	-Geothermal	51.7	65.3	67.3	68.5	69.9	3.10 %
	-Wind	37.9	219.6	276.4	351.5	459.9	28.30 %
	-Biomass	134.1	232.0	250.8	270.1	276.0	7.50 %
	-Solar	1.4	12.8	21.0	33.5	61.6	45.80 %
	-Hydraulic	2,636.8	3,282.3	3,335.2	3,501.1	3,579.5	3.10 %
	-Marine	0.575	0.546	0.527	0.558	0.555	-0.40 %
	Fossil	10,010.6	13,637.5	13,409.6	14,340.4	14,908.1	4.10 %
	Non-renewable waste	39.3	38.7	40.0	43.1	40.3	0.30 %
	Nuclear	2,637.7	2,730.8	2,696.4	2,755.1	2,568.2	-0.30 %

Total Production 15,55.,075 20,219.546 20,097.227 21,363.858 21,964.055

Table 1 -2

 1 Top 10 countries with the highest PV cumulative installed capacity in 2012[START_REF] Epia | Global Market Outlook For Photovoltaics 2012-2017[END_REF]

Table 1 -

 1 2 shows the top 10 countries with the highest PV cumulative installed capacity in 2012. Not surprisingly, Germany continues to be, and with a large difference, the world leader (32.4 GW).According to the predictions made by the European Photovoltaic Industry Association (EPIA) (EPIA, 2013), a fastest PV growth is expected to continue in China and India, followed by Southeast Asia, Latin America and the MEA countries. The projections for the growth of PV cumulative installed capacity in the world until the year 2017 by region are presented in Figure1-7 with two possible scenarios. The first called Business-as-Usual scenario assumes a pessimistic market with no major reinforcement or replacement of existing support mechanisms. This scenario also assumes that if the country is close to energy transition, markets are significantly slowing down because the policy mechanisms designed to accelerate investment in renewable energy technologies are phased out. The second scenario called Policy-Driven scenario assumes the continuation, adjustment or introduction of adequate support mechanisms with strong policies to allow considering PV as a major power source in the coming years. During 2012 in Europe around 17 GW of new PV installations were mounted. That is why PV became the number-one electricity source among the countries of European Union (EU) in terms of added installed capacity.

		Cumulative		Cumulative
	Country	in GW	Country	in GW
	1 Germany	32.4	6 Spain	5.2
	2 Italy	16.3	7 France	4.0
	3 China	8.3	8 Belgium	2.7
	4 United States	7.8	9 Australia	2.4
	5 Japan	6.9	10 Czech Republic	2.1

Table 1 -

 1

3 Summary of literature works for sizing PV systems

2. LIFE-CYCLE ASSESSMENT (LCA) FOR PV SYSTEMS

	Nomenclature
	Symbols
	η	PV module efficiency, %
	EI	Overall environmental impact indicator
	Acronyms FD i,d	Damage characterization factor for the impact category i in the damage category d
	AA AE AEU C FI s,i M s N k	Aquatic Acidification midpoint category Characterization factor for the substance s in the impact category i Aquatic Ecotoxicity midpoint category Mass of substance s Aquatic Eutrophication midpoint category Carcinogen midpoint category Normalised score of the impact or damage categories k
	CBA PF k	Cost-Benefit Analysis Weighting factor for impact category k
	CBD SD d	Chemical Bath Deposition Damage score for the damage category d
	CdTe SI i	Cadmium Telluride Characterization score for the impact category i
	CIS VR k	Copper indium diselenide
	CSS	Closed Space Sublimation
	CSVT	Closed Space Vapour Transport
	CVD	Chemical Vapor Deposition
	CZ	Czochralski process
	DC/AC	Direct Current / Alternative Current
	EIA	Environmental Impact Assessment
	ERA	Environmental Risk Assessment
	EVA	Ethylene Vinyl Acetate
	FU	Functional Unit
	GW	Global Warming midpoint category
	IO	Ionizing Radiation midpoint category
	LCA	Life-Cycle Assessment
	LCI	Life-Cycle Inventory
	LCIA	Life-Cycle Impact Assessment
	LO	Land Occupation midpoint category
	ME	Mineral Extraction midpoint category
	MFA	Material Flow Analysis
	MILP	Material Intensity Per unit Service
	NC	Non-Carcinogen midpoint category
	NR	Non-Renewable energy midpoint category
	OLD	Ozone Layer Depletion midpoint category
	Ce chapitre présente tout d'abord les procédés de fabrication utilisés pour cinq PV Photovoltaic
	technologies de modules PV (m-Si, p-Si, a-Si, CdTe et CIS). La connaissance du procédé PVGCS Photovoltaic Grid-Connected System
	est perçue comme un point fondamental pour comprendre les limitations liées à une RE-PECVD Radio Frequency Plasma Enhanced Chemical Vapor Deposition
	technologie d'un point de vue environnemental. Une analyse de la littérature dédiée des RI Respiratory Inorganic midpoint category
	approches d'évaluation environnementale est ensuite menée. Les principes RO Respiratory Organic midpoint category
	fondamentaux de l'ACV finalement retenue sont ainsi décrits. Trois exemples d'études de m-Si Monocrystalline silicon
	l'ACV sont proposées. Le premier exemple traite le cas du module PV basé sur la p-Si Polycrystalline silicon
	a-Si technologie m-Si, de la production du silicium de qualité solaire à l'assemblage du Amorphous silicon TAN Terrestrial Acidification/Nitrification midpoint category module PV. L'influence du « mix » énergétique est pris en compte. La deuxième TCO Transparent Conducting Oxide illustration est consacrée à la comparaison des impacts environnementaux de 3 TE Terrestrial Ecotoxicity midpoint category technologies (m-Si, p-Si, Si en ruban). En final, l'évaluation et la comparaison de 5 configurations de systèmes photovoltaïques connectés au réseau sont présentées. Le TF Thin Film PV technology
	modèle environnemental proposé ici sert de brique de base pour l'intégration de l'analyse
	environnementale dans le cadre d'une optimisation multiobjectif	pour le

L'objectif de ce chapitre est de présenter le modèle environnemental retenu dans le cadre de cette étude. L'approche par Analyse du Cycle de Vie (ACV) largement appliquée dans plusieurs domaines, notamment pour la production d'énergie, est utilisée pour mesurer la performance environnementale des systèmes photovoltaïques. dimensionnement de champs de panneaux solaires. Le cas du recyclage des panneaux fera l'objet d'un chapitre dédié en fin de manuscrit. Le manque de données lors du démarrage de ces travaux et qui perdure pour certaines technologiques n'a pas permis une vision holistique sur laquelle reposer certes une démarche ACV.

Chapter 2

Table 2 -1Continuation…

 2

Table 2 -2

 2 Methods for LCIA(European Commission et al., 2010)

				Impact modelling depth
	Methodology	Developed by	Midpoint Endpoint	Normalisation	Source
	CML 2002	CML (Netherlands)	X		X	(Guinée et al., 2002)
	Eco-indicator 99	PRé (Netherlands)		X	X	(M. Goedkoop & Spriensma, 2001)
	IMPACT 2002+	Ecole Polytechnique Fédérale de Lausanne (Switzerland)	X	X	X	(Jolliet et al., 2003)
	ReCiPe	Radboud University Nijmegen + PRé + CML + RIVM (Netherlands)	X	X	X	(Mark Goedkoop et al., 2009)

characterizing methods, i.e. Eco-indicator 99, CML 2001, IPCC and the Cumulative Energy Demand.

Table 2 -

 2

			Midpoint reference flow
	Midpoint category		(Kg eq Substance x)	Damage category	Reference flow
	Human Toxicity (carcinogens + non-carcinogens)	Kg eq chloroethylene into air
	Respiratory effects (inorganic)	Kg eq PM2.5 into air
	Ionizing radiation	Bq eq carbon-14 into air	Human health	DALY
	Ozone layer depletion	Kg eq CFC-11 into air
	Photochemical oxidation (Respiratory organics)	Kg eq ethylene into air
	Aquatic ecotoxicity	Kg eq triethylene glycol into water
	Terrestrial ecotoxicity	Kg eq triethylene glycol into soil	PDF*m²*a
	Terrestrial acid/nutri	Kg eq SO 2 into air
	Land occupation	M 2	eq organic arable land-year	Ecosystem quality
	Aquatic acidification	Kg eq SO 2 into air	Under development
	Aquatic eutrophication	Kg eq PO 4	3 into water	Under development
	Global warming	Kg eq CO 2 into air	Climate change	Kg eq CO 2 into air
	Non-renewable energy	MJ Total primary non-renewable or kg eq crude oil (860kg/m 3 )	Resources	MJ
	Mineral extraction	MJ additional energy or kg eq iron

3 Characterisation reference substances and reference flow used in IMPACT 2002+ (Based on (Margni, Jolliet, & Humbert, 2005)) Σ Damage characterization factor

Table 2 -4

 2 Main LCA software tools Evaluation of scenarios and sensitivity analysis are other optional features of these software tools. Table2-4 shows some of the LCA software tools currently available on the market.

	Software name	Supplier	Website
	TEAM	ECOBILAN-PricewaterhouseCoopers	http://ecobilan.pwc.fr/fr/boite-a-outils/team.jhtml
	GaBi Software	PE INTERNATIONAL	http://www.gabi-software.com/france/software/
	Umberto	ifu Hamburg GmbH	http://www.umberto.de/en/
	SimaPro	PRé Consultants	http://www.pre-sustainability.com/simapro-lca-software
	openLCA	GreenDelta GmbH	http://www.openlca.org/openlca
	results obtained through graphs.

Table 2 -6 LCI

 2 of total flows for production of 1 piece of m-Si PV module

Table 2 -8

 2 Characterization of LCI emission into GW impact category

						Score
		Emission to	Amount	Unit	FI s,i	kg CO 2 eq
	TOTAL					15,789.18
	Carbon dioxide	Air	1.89 x10 -2	kg	1.00	1.89 x10 -2
	Carbon dioxide. fossil	Air	1.52 x10 4	kg	1.00	1.52 x10 4
	Carbon dioxide. land transformation	Air	1.80	kg	1.00	1.80
	Carbon monoxide	Air	3.25 x10 -5	kg	1.57	5.11 x10 -5
	Carbon monoxide. fossil	Air	7.77	kg	1.57	1.22 x10 1
	Chloroform	Air	1.44 x10 -5	kg	9.00	1.30 x10 -4
	Dinitrogen monoxide	Air	7.21 x10 1	kg	156.00	1.13 x10 2
	Ethane. 1.1.1.2-tetrafluoro-. HFC-134a	Air	6.42 x10 -4	kg	400.00	2.57 x10 -1
	Ethane. 1.1.1-trichloro-. HCFC-140	Air	4.55 x10 -8	kg	42.00	1.91 x10 -6
	Ethane. 1.1.2-trichloro-1.2.2-trifluoro-. CFC-113	Air	1.00 x10 -5	kg	2,700.00	2.71 x10 -2
	Ethane. 1.1-difluoro-. HFC-152a	Air	1.38 x10 -6	kg	37.00	5.09 x10 -5
	Ethane. 1.2-dichloro-1.1.2.2-tetrafluoro-. CFC-114	Air	1.94 x10 -4	kg	8,700.00	1.68
	Ethane. hexafluoro-. HFC-116	Air	4.95 x10 -5	kg	18,000.00	8.90 x10 -1
	Methane	Air	3.41 x10 -3	kg	7.60	2.59 x10 -2
	Methane. biogenic	Air	1.86	kg	7.60	1.42 x10 1
	Methane. bromo-. Halon 1001	Air	5.61 x10 -14	kg	1.00	5.61 x10 -14
	Methane. bromochlorodifluoro-. Halon 1211	Air	1.72 x10 -4	kg	390.00	6.69 x10 -2
	Methane. bromotrifluoro-. Halon 1301	Air	1.92 x10 -5	kg	2,700.00	5.18 x10 -2
	Methane. chlorodifluoro-. HCFC-22	Air	6.69 x10 -4	kg	540.00	3.61 x10 -1
	Methane. chlorotrifluoro-. CFC-13	Air	5.20 x10 -10	kg	16,300.00	8.48 x10 -6
	Methane. dichloro-. HCC-30	Air	1.10 x10 -6	kg	3.00	3.30 x10 -6
	Methane. dichlorodifluoro-. CFC-12	Air	8.01 x10 -7	kg	5,200.00	4.16 x10 -3
	Methane. dichlorofluoro-. HCFC-21	Air	8.07 x10 -9	kg	65.00	5.24 x10 -7
	Methane. fossil	Air	3.43 x10 1	kg	10.35	3.55 x10 2
	Methane. monochloro-. R-40	Air	1.29 x10 -6	kg	5.00	6.44 x10 -6
	Methane. tetrachloro-. CFC-10	Air	1.10 x10 -4	kg	580.00	6.38 x10 -2
	Methane. tetrafluoro-. CFC-14	Air	4.30 x10 -4	kg	8,900.00	3.83
	Methane. trichlorofluoro-. CFC-11	Air	4.46 x10 -9	kg	1,600.00	7.14 x10 -6
	Methane. trifluoro-. HFC-23	Air	1.20 x10 -7	kg	10,000.00	1.20 x10 -3
	Sulfur hexafluoride	Air	1.19 x10 -3	kg	32,400.00	3.86 x10 1

Table 2 -9

 2 Characterization of LCI emission into RI impact category

						Score
		Emission to	Amount	Unit	FI s.i	kg PM2.5 eq
	TOTAL					5.1569
	Ammonia	Air	3.38 x10 -1	kg	0.121	4.10 x10 -2
	Carbon monoxide	Air	3.25 x10 -5	kg	0.001	3.40 x10 -8
	Nitrogen oxides	Air	1.70 x10 1	kg	0.127	2.16
	Particulates. < 10 um	Air	3.12 x10 -4	kg	0.536	1.67 x10 -4
	Particulates. < 10 um (mobile)	Air	1.82 x10 -6	kg	0.536	9.77 x10 -7
	Particulates. < 10 um (stationary)	Air	7.58 x10 -6	kg	0.536	4.06 x10 -6
	Particulates. < 2.5 um	Air	1.40	kg	1	1.40
	Sulfur dioxide	Air	1.99 x10 1	kg	0.078	1.55
	Sulfur oxides	Air	1.03 x10 -4	kg	0.078	8.07 x10 -6
	Sulfur trioxide	Air	1.31 x10 -7	kg	0.062	8.20 x10 -9

Table 2 -

 2 10 Characterization score of impact categories

	Impact category	Amount Unit
	Carcinogens	77.43 kg C 2 H 3 Cl eq
	Non-carcinogens	53.03 kg C 2 H 3 Cl eq
	Respiratory inorganics	5.16 kg PM2.5 eq
	Ionizing radiation	448,213.72 Bq C-14 eq
	Ozone layer depletion	0.0016 kg CFC-11 eq
	Respiratory organics	2.73 kg C 2 H 4 eq
	Aquatic ecotoxicity	499,953.26 kg TEG water
	Terrestrial ecotoxicity	103,003.74 kg TEG soil
	Terrestrial acid/nutri	118.31 kg SO 2 eq
	Land occupation	50.49 m 2 org.arable
	Aquatic acidification	33.29 kg SO 2 eq
	Aquatic eutrophication	4.16 kg PO 4 P-lim
	Global warming	15,789.18 kg CO 2 eq
	Non-renewable energy	289,745.99 MJ primary
	Mineral extraction	60.24 MJ surplus

Table 2 -

 2 11 Characterization score of damage categories

	Damage category	Amount Unit
	Human health	0.004 DALY
	Ecosystem quality	1,017.93 PDF*m2*yr
	Climate change	15,789.18 kg CO2 eq
	Resources	289,806.23 MJ primary

Table 2 -

 2

12. Table 2-13 shows the normalised values.

Table 2 -

 2 12 IMPACT 2002+ reference values for normalisation[START_REF] Margni | IMPACT 2002 + : User Guide[END_REF]

		Reference		Damage	Reference
	Midpoint category	value	Unit	category	value	Unit
	Carcinogens	2,533.00	kg C 2 H 3 Cl eq/ pers/year 1
	Non-carcinogens	2,533.00	kg C 2 H 3 Cl eq/ pers/year
	Respiratory inorganics Ionizing radiation	10.00 33,772,000	kg PM2.5 eq/ pers/year Bq C-14 eq/ pers/year	Human health	0.0071	DALY/ pers/year
	Ozone layer depletion	6.75	kg CFC-11 eq/ pers/year
	Respiratory organics	3,330.00	kg C 2 H 4 eq/ pers/year
	Aquatic ecotoxicity	272,881,000	kg TEG water/ pers/year
	Terrestrial ecotoxicity	1,732,000	kg TEG soil/ pers/year
	Terrestrial acid/nutri Land occupation	13,100.00 12,600.00	kg SO 2 eq/ pers/year m 2 org.arable/ pers/year	Ecosystem quality	13,700	PDF * m 2 * yr/pers/year
	Aquatic acidification	Under development
	Aquatic	Under
	eutrophication	development
	Global warming	9,900.00	kg CO 2 eq/ pers/year	Climate change	9,900	Kg eq CO 2 /pers/yr
	Non-renewable energy	151,975.00	MJ primary/ pers/year	Resources	152,000	MJ/ pers/yr
	Mineral extraction	150,600.00	MJ surplus/ pers/year

Table 2 -

 2 13 Normalised scores for both impact and damage categories

		Reference			Reference
	Midpoint category	value	Unit	Damage category	value	Unit
	Carcinogens	0.03057 pers/year
	Non-carcinogens	0.02094 pers/year
	Respiratory inorganics Ionizing radiation	0.50899 pers/year 0.01327 pers/year	Human health	0.57482	pers/year
	Ozone layer depletion	0.00023 pers/year
	Respiratory organics	0.00082 pers/year
	Aquatic ecotoxicity	0.00183 pers/year
	Terrestrial ecotoxicity	0.05948 pers/year
	Terrestrial acid/nutri Land occupation	0.00898 pers/year 0.00402 pers/year	Ecosystem quality	0.07431	pers/year
	Aquatic acidification	-----
	Aquatic eutrophication	-----
	Global warming	1.59471 pers/year	Climate change	1.59471	pers/yr
	Non-renewable energy Mineral extraction	1.90653 pers/year 0.00040 pers/year	Resources	1.90693	pers/yr

1 The reference value used by IMPACT 2002+ considered the European population in a year.

Table 2 -14 Efficiencies and number of PV modules required Ribbon-Si p-Si m-Si

 2

	Efficiency	11.5%	13.2%	14.0%
	No. PV module	7	7	6

(a) Midpoint categories (b) Damage categories

  Table 2-15. The calculation was made taking into account the amount of irradiation received, the PV module efficiency as well as the electrical characteristics of the DC / AC inverter. From the values shown in the Table 2-15 is noticed that the PV modules based on thin film technologies require a greater number of panels due to the low efficiency they have.

LCI of each technology under evaluation was performed from data displayed at the Table 2-15. The procedure followed was similar to the two last examples.

Table 2 -

 2 15 Key features for LCA study

	Module technology	m-Si	p-Si	a-Si	CdTe	CIS
	Module efficiency (%)	14.00	13.2	6.45	9.00	10.00
	Module surface (m²)	1.46	1.46	1.10	0.72	0.72
	Module (years)	life	expectancy	20	20	20	20	20
	No. PV modules		22	24	58	62	69
	No. AC/DC inverters	4	6	6	4	6

  To our knowledge, this kind of approach has not yet been implemented. The classical LCA tools (SimaPro and other LCA software) are generally not flexible and do not exchange with other programs. From a practical viewpoint, a specific environmental module was designed from extraction of the dedicated EcoInvent database that is used for PV systems.

It must be highlighted that this kind of study has been extended to other kinds of solar systems to compare two heliostat configurations (autonomous and classical heliostats) for heliothermodynamic power plants for concentrated solar power. This research was conducted within the OSSOLEMIO project in collaboration with the Laboratoire Plasma et Conversion d'Energie (LAPLACE) in the framework of Alaric Maintenon's PhD thesis

[START_REF] Montenon | Analyse, mutualisation et optimisation par la commande de la consommation énergétique des héliostats autonomes des centrales à concentration solaire[END_REF]

, under the supervision of Prof. Pascal Maussion.

Table 3 -

 3

1 (continued)

  Table 3-3 shows the mean of results exposed in Appendix C for estimate G β following Lu et al., Hay et al. and, Reindl et al. equation for calculate G β,d . This explains why the model of Hay et al. model was adopted in this work.

Table 3 -2

 3 Root mean square (RMSE) and mean bias errors (MBE) of proposed simulator

		Horizontal Global Irradiance	Diffusse Horizontal Irradiance	Beam Global Irradiance
			%	%		%	%	%	%
		RMSE MBE	RMSE	MBE RMSE MBE	RMSE	MBE RMSE MBE	RMSE	MBE
	Mexico City 39.523 -1.529 18.786 -0.727 23.401 1.706 24.345 1.774 25.065 -3.234 21.935 -2.830
	Singapore	39.523 -1.529 18.786 -0.727 24.780 0.345 23.749 0.331 21.503 -2.026 26.493 -2.497
	Sydney	34.634 -0.978 18.870 -0.533 23.683 4.028 28.928 4.920 25.450 -5.006 25.033 -4.924
	Toulouse	27.486 -1.283 17.943 -0.837 20.181 1.909 25.464 2.409 19.480 -3.192 26.348 -4.317

Table 3 -

 3

		Titl (°)	Lu et al.	Reindl et al.	Hay et al.
		RMSE	43.688	39.231	40.704
	Mexico City	MBE % RMSE	8.618 23.577	-1.343 20.861	0.913 21.838
		% MBE	2.597	-0.636	0.898
		RMSE	31.051	27.197	28.190
	Singapore	MBE % RMSE	2.414 22.933	-0.669 19.286	1.302 20.225
		% MBE	2.534	-0.391	1.576
		RMSE	46.319	35.474	47.361
	Sydney	MBE % RMSE	-5.318 26.714	-1.488 19.979	0.394 27.407
		% MBE	-3.032	-0.839	0.347
		RMSE	36.105	34.267	34.523
		MBE	-5.462	-1.099	0.522
	Toulouse
		% RMSE	24.285	23.019	23.222
		% MBE	-3.636	-0.704	0.512

3 Comparison of G β estimated

Table 3

 3

-4 summarizes the main

Σ Σ Σ Σ Σ Σ

Table 3 -4

 3 Summary of the main energy losses[START_REF] Brigand | Installations solaires photovoltaïques: Dimensionnement, Installation et mise en oeuvre, maintenance[END_REF][START_REF] Hayoun | Les installations photovoltaïques: Conception et dimensionnement des installations raccordées au réseau[END_REF]

	Origin of loss	Loss (%)	Observations
	Shading loss	Variable	Loss related to the geographic location of PVGCS in
			addition to the inclination, spacing and dimensions of the
			shed. (Section 3.5.3.2).
	PV conversion	Variable	Loss due to the type of technology of PV module (module
			efficiency). Value indicated in the technical data sheet of
			the PV module.
	Thermal loss	Approx. 0,5 %/°C	Loss caused by the temperature rise of the PV module.
			Value indicated in the technical data sheet of the PV
			module.
	Modules array mismatch	≤ 3%	Loss caused by the interconnection of PV module.
	loss
	Ohmic wiring loss	≤ 3%	Loss linked to the characteristics of the wiring that
			connects all electrical devices.
	Loss due to DC/AC	3-10%	Loss caused by the internal characteristics of the
	inverter		components of the DC / AC inverter. Value indicated in
			the specifications of the DC / AC inverter.

energetic losses to consider sizing a PVGCS, starting from available global horizontal irradiance until to obtain the total energy injected into the grid.

Table 3 -5 Main elements of economic evaluation

 3

	Description		Value
	PV module
	-	m-Si	0.85	€/W p
	-	p-Si	0.82	€/W p
	-	a-Si	0.74	€/W p
	--	CdTe CIS	0.77 0.86	€/W p €/W p
	DC/AC inverter		0.40 €/W AC
	Fixed support structure	33.00 €/m
	Cables		0.50 €/m
	Concrete basements	230.00 €/m 3
	Network connecting	0.05 €/W p
	Construction fee	0.39 €/W p
	Price of energy sold	0.276 €/kWh

Table 3 -6

 3 Parameter values for each scenario of WAP example

	Objective function	K	β (°)	D (m)
	Maximum incident energy on to the total surface of PV modules	58	24.62	0.80
	Maximum output energy with shading losses	58	24.62	0.80
	Maximum output energy of PV array (shading, temperature and interconnections losses)	57	21.23	0.80

Table 3 -7

 3 Comparison of output energy from the example of WAP and the proposed tool

	Objective function	Q out (kW h) Weinstock et al. This approach	Diff
	Maximum incident energy on to the total surface of PV modules	2 641 034	3 187 715	+ 20,70%
	Maximum output energy with shading losses	328 048	395 914	+ 20,69%
	Maximum output energy of PV array (shading, temperature and interconnections losses)	268 000	326 692	+ 21,90%

Table 3 -8

 3 Typical features of various commercial PV modules technologies

					Nominal
	Technology	H m (m)	W m (m)	(%)	power (Wp)
	m-Si	1.56	1.05	20.10	327.00
	p-Si	1.64	0.94	15.50	240.00
	a-Si	1.31	1.11	7.20	105.00
	CdTe	1.20	0.60	11.50	82.50
	CIS	1.26	0.98	12.20	150.00

Table 3 -9

 3 Values of Nc and Nr for simulation

	Technology	N r	N c
	m-Si	1	12
	p-Si	1	13
	a-Si	1	11
	CdTe	1	21
	CIS	1	13

Table 3 -

 3 10 Comparison of output energy from PVsyst and the proposed tool

			Q out (kW h)
	Objective function	Techno	PVSyst	Simulator	Deviation
		m-Si	2 497 636	2 558 693	+2,44%
		p-Si	2 723 105	2 631 590	-3,36%
	Maximum incident energy onto total surface of PV modules	a-Si	2 036 096	2 084 487	+2,38%
		CdTe	1 936 192	1 977 019	+2,11%
		CIS	2 043 586	2 084 206	+1,99%
	Mean				+1,11%
		m-Si	483 935	486	+0,62%
		p-Si	383 003	361	-5,54%
	Maximum output energy with shading losses	a-Si	145 525	145	+0,20%
		CdTe	216 193	221	+2,55%
		CIS	242 834	247	+1,93%
	Mean				-0,05%
		m-Si	420 604	430	+2,30%
		p-Si	319 830	314	-1,80%
	Maximum output energy of PV array (shading, temperature and interconnections losses)	a-Si	149 561	155	+3,89%
		CdTe	188 687	198	+5,19%
		CIS	210 516	218	+3,76%
	Mean				+2,67%

Table 3

 3

	-11 PBT and EPBT for each configuration
	PV module	PBT (yr)	EPBT (yr)
	m-Si	6,35	1,18
	p-Si	8,19	1,33
	a-Si	12,28	1,37
	CdTe	10,08	0,99
	CIS	8,71	1,24

Table 4 -1

 4 List of well-known MGA (based on[START_REF] Konak | Multi-objective optimization using genetic algorithms: A tutorial[END_REF]

	Algorithm	Fitness assignment	Elitism	Advantages	Disadvantages
	MOGA	Pareto ranking	No	Simple extension of single	Usually slow convergence
	(Fonseca &			objective GA	Problems related to niche
	Fleming,				size parameter
	1993)
	NSGA	Ranking based on non-	Yes	Fast convergence	Problems related to niche
	(Srinivas &	domination sorting			size parameter
	Deb, 1994)
	NSGA-II (K.	Ranking based on non-	Yes	Single parameter (N)	Crowding distance works in
	Deb, Pratap,	domination sorting		Well tested	objective space only
	Agarwal, &			Efficient
	Meyarivan,
	2002)
	NPGA (Horn,	No fitness assignment,	No	Very simple selection	Problems related to niche
	Nafpliotis, &	tournament selection		process with tournament	size parameter
	Goldberg,			selection	Extra parameter for
	1994)				tournament selection
	SPEA (Zitzler	Ranking based on the external	Yes	Well tested	Complex clustering
	& Thiele,	archive of non-dominated		No parameter for clustering	algorithm
	1999)	solutions
	VEGA	Each subpopulation is	No	Straightforward	Tend to converge at the
	(Schaffer,	evaluated with respect to a		implementation	extreme of each objective
	1985)	different objective

Table 4 -2

 4 Summary of applications of GAs for sizing PV systems

	Authors	Year	Subject
	(Dufo-López, Bernal-Agustín,	2007	Optimization of control strategies for stand-alone renewable energy
	& Contreras, 2007)		systems with hydrogen storage
	(Senjyu et al., 2007)	2007	Optimal configuration of power generating systems in isolated island with
			renewable energy
	(Koutroulis, Kolokotsa,	2006	Methodology for optimal sizing of stand-alone photovoltaic/wind-
	Potirakis, & Kalaitzakis, 2006)		generator systems
	(El-Hefnawi, 1998)	1998	Photovoltaic diesel-generator hybrid power system sizing
	(Yokoyama, Yuasa, & Ito,	1992	Multiobjective Optimal Unit Sizing of Hybrid Power Generation Systems
	1994)		Utilizing Photovoltaic and Wind Energy
	(Seeling-hochmuth, 1998)	1998	Optimisation of hybrid energy systems sizing and operation control
	(Xu, Kang, & Cao, 2006)	2006	Graph-Based Ant System for Optimal Sizing of stand-alone Hybrid
			Wind/PV Power Systems
	(Xu, Kang, Chang, & Cao,	2005	Optimal sizing of stand-alone hybrid wind/PV power systems using genetic
	2005)		algorithms

Table 4 -3

 4 Correlation matrix for Sabio et al. case

		CS	RE	CC	OLD	ES	AE	DM	DFF
	CS	1	0.4400	0.4007	0.9995	1.0000	0.6729	0.9988	-0.0375
	RE		1	0.0735	0.4517	0.4435	0.3886	0.4792	-0.8966
	CC			1	0.4235	0.4027	0.9237	0.377	0.2743
	OLD				1	0.9996	0.6938	0.9984	-0.0453
	ES					1	0.6753	0.9989	-0.0408
	AE						1	0.6621	0.0165
	DM							1	-0.0847
	DFF								1

Table 4 -5

 4 Decision data matrix

			Criteria				Criteria
			Q out	EPBT			Q out	EPBT
			max	min			max	min
		1	2,286,757.98	1.753		9	1,424,028.60	1.701
		2	1,072,808.71	1.699		10	2,088,618.68	1.735
	Alternatives	3 4 5 6	2,005,066.69 1,710,340.98 1,933,294.35 2,183,467.41	1.730 1.711 1.727 1.747	Alternatives	11 12 13 14	716,057.32 2,040,111.49 358,578.79 2,076,489.16	1.692 1.731 1.691 1.732
		7	2,253,731.29	1.749		15	1,760,111.18	1.718
		8	716,068.22	1.692

Table 4 -6

 4 Transformed values matrix

			Criteria				Criteria
			Q out	EPBT			Q out	EPBT
			max	max			max	max
		1	2,286,757.98	0.5705		9	1,710,340.98	0.5879
		2	1,072,808.71	0.5886		10	2,088,618.68	0.5764
	Alternatives	3 4 5 6	2,005,066.69 1,710,340.98 1,933,294.35 2,183,467.41	0.5780 0.5845 0.5790 0.5724	Alternatives	11 12 13 14	716,057.32 2,040,111.49 358,578.79 2,076,489.16	0.5910 0.5777 0.5914 0.5774
		7	2,253,731.29	0.5718		15	1,760,111.18	0.5821
		8	716,068.22	0.5910

Table 4 -7

 4 Normalized decision matrix

			Criteria			Criteria
			Q out	EPBT			Q out	EPBT
			max	max			max	max
		1	0.3338	0.2534		9	0.2497	0.2611
		2	0.1566	0.2614		10	0.3049	0.2560
	Alternatives	3 4 5 6	0.2927 0.2497 0.2822 0.3188	0.2567 0.2596 0.2572 0.2542	Alternatives	11 12 13 14	0.1045 0.2978 0.0523 0.3031	0.2625 0.2566 0.2627 0.2564
		7	0.3290	0.2539		15	0.2569	0.2585
		8	0.1045	0.2625

Table 4 -8 Weighted normalized matrix

 4

			Criteria			Criteria
			Q out	EPBT			Q out	EPBT
			max	max			max	max
		1	0.3338	0.2534		9	0.2497	0.2611
		2	0.1566	0.2614		10	0.3049	0.2560
	Alternatives	3 4 5 6	0.2927 0.2497 0.2822 0.3188	0.2567 0.2596 0.2572 0.2542	Alternatives	11 12 13 14	0.1045 0.2978 0.0523 0.3031	0.2625 0.2566 0.2627 0.2564
		7	0.3290	0.2539		15	0.2569	0.2585
		8	0.1045	0.2625
	The normalized decision matrix A is obtained using Equation (4.3) (see Table 4-7). None of the

criteria is preferred over the other, so the coefficient vector of importance W is equal to

[1, 1]

. The normalized weighted matrix is then represented in Table 4-8. The positive ideal (respectively negative ideal, i.e. non-ideal) solution is determined from the matrix A as well as the Euclidean distance matrix (Equations (4.5) to (4.8)). The obtained results are shown in Table 4-9 and Table 4-10. Considering

Table 4 -

 4 9 A + and A -values

		Criteria
		Q out	EPBT
	A +	0.3338	0.2627
	A -	0.0523	0.2534

Table 4

 4

				-10 Euclidean distance matrix (D i	+ and D i	-)
			D i	+	D i	-			D i	+	D i	-
		1	0.0093	0.2815		9	0.0842	0.1975
		2	0.1772	0.1046		10	0.0297	0.2526
	Alternatives	3 4 5 6	0.0415 0.0842 0.0519 0.0173	0.2404 0.1974 0.2299 0.2664	Alternatives	11 12 13 14	0.2293 0.0365 0.2815 0.0313	0.0530 0.2455 0.0093 0.2508
		7	0.0100	0.2767		15	0.0770	0.2047
		8	0.2293	0.0530

Table 4 -

 4 11 Rank alternatives by M-TOPSIS coefficient R i

			R i	Rank			R i	Rank
		1	0.0000	1		9	0.1125	10
		2	0.2439	12		10	0.0354	4
	Alternatives	3 4 5 6	0.0522 0.1126 0.0669 0.0171	7 11 8 3	Alternatives	11 12 13 14	0.3172 0.0451 0.3849 0.0378	14 6 15 5
		7	0.0049	2		15	0.1024	9
		8	0.3172	13

  Si. L'approche développée est suffisamment générique pour s'adapter à l'étude de différents scénarios.

	Nomenclature Abbreviations Symbols L m PV module length, m L max Maximum solar field length, m N c Number of PV modules columns in the collector AA AE AEU C Aquatic Acidification midpoint category N r Number of PV modules rows in the collector Aquatic Ecotoxicity midpoint category PC i i th Principal component Aquatic Eutrophication midpoint category Carcinogen midpoint category Q min Minimum yearly output energy of the field, kWh	Chapter	5
	CdTe Q out	Cadmium Telluride Yearly output energy of the field, kWh
	CIS W	Copper Indium Diselenide Solar field width, m
	EPBT W max	Energy PayBack Time Maximum solar field width, m
	GA	Genetic Algorithm
	GW	Global Warming midpoint category
	IO	Ionizing Radiation midpoint category
	LCA	Life Cycle Assessment
	LO	Land Occupation midpoint category
	M-TOPSIS	Technique for Order Preference by Similarity to Ideal Solution
	ME	Mineral Extraction midpoint category
	NC	Non-Carcinogen midpoint category
	NR	Non-Renewable energy midpoint category
	NSGA-II	Fast Non-dominated Sorting Genetic Algorithm
	OLD	Ozone Layer Depletion midpoint category
	PBT	PayBack Time
	PCA	Principal Component Analysis
	PV	Photovoltaic
	PVGCS	Photovoltaic Grid-Connected System
	RI	Respiratory Inorganic midpoint category
	RO	Respiratory Organic midpoint category
	a-Si	Amorphous silicon
	m-Si	Monocrystalline silicon
	p-Si	Polycrystalline silicon
	TAN	Terrestrial Acidification/Nitrification midpoint category
	TE	Terrestrial Ecotoxicity midpoint category
	WAP	Weinstock and Appelbaum approach
	Symbols
	β	PV collector inclination angle, degree
	η	PV module efficiency, %
	A	Surface of PVGCS, m²
	a 1	Lower limit of the ratio W/L
	a 2	Upper limit of the ratio W/L
	D	Distance between PV sheds, m
	D min	Minimum distance between PV sheds, m
	E max	Maximum PV collector height above ground, m
	H	PV collector height, m
	H m	PV module height, m
	H max	Maximum PV collector height, m
	K	Number of PV sheds
	L	Solar field length, m

Table 5 -1

 5 Parameter values of NSGA-II for different examples

		1 objective	2 objectives	3 or more objectives
	Population size	100	100	200
	No. generation	200	200	400
	Crossover rate (%)	90	90	90
	Mutation rate (%)	50	50	50

Table 5 -2

 5 Comparison between WAP and the proposed approach

						Q out
	Objective function		K	D(m)	β (°)	(kWh)
	Maximum incident energy onto total surface of	WAP	58	0.80	24.62	2,641,034
	PV modules	PA	58	0.80	24.62	3,201,915
		WAP	58	0.80	24.62	328,048
	Maximum output energy w shading losses
		PA	58	0.80	24.62	397,793
	Maximum output energy of PV array (shading,	WAP	57	0.80	21.23	268,000
	temperature and interconnections losses)	PA	57	0.80	21.26	327,338
	WAP = Results of Weinstock and Appelbaum	PA = Results of proposed approach

Table 5 -3

 5 Values of decision variables for the best configuration of each PV technology.

			Maximum output energy
	PV module	β (°)	K	D (m)	N r	N c
	m-Si	18.42	55	0.84	1	12
	p-Si	21.22	60	0.80	1	13
	a-Si	17.01	54	0.81	1	11
	CdTe	25.30	80	0.80	1	21
	CIS	22.68	77	0.80	1	13

Table 5 -4

 5 Output energy for the best configuration of each PV technology. Maximum output energy.

			Q out PVsyst
	PV module	Q out (kWh)	(kWh)	Gap
	m-Si	430 397	400 336	-7.50%
	p-Si	328 453	299 421	-8.84%
	a-Si	131 021	119 700	-8.64%
	CdTe	275 449	262 042	-4.87%
	CIS	293 904	283 558	-3.52%

Table 5 -

 5 5 PBT and EPBT for each configuration. Maximum output energy

	PV module	PBT (yr)	EPBT (yr)
	m-Si	5,90	2,36
	p-Si	7,59	2,67
	a-Si	7,59	2,04
	CdTe	9,23	1,77
	CIS	6,29	2,14

Table 5 -6

 5 Final ranking of alternatives. Maximum output energy

		Final weighted
	PV module	evaluation	Ranking
	m-Si	72	5
	p-Si	71	4
	a-Si	32	1
	CdTe	49	3
	CIS	46	2

Table 5

 5 Results of the environmental impacts normalized to unity. Maximum output energy

	-7 Final ranking of alternatives (environmental impact per kWh
	produced). Maximum output energy
		Final weighted
	PV module	evaluation	Ranking
	m-Si	48	2
	p-Si	74	5
	a-Si	59	4
	CdTe	52	3
	CIS	37	1

Table 5 -8

 5 Best configuration that minimizes the surface of PV system

	PV module	β (°)	K	D (m)	W(m)	L(m)	A(m²)
	m-Si	21.36	21	0.80	46.49	69.04	3,209.55
	p-Si	21.06	34	0.80	78.44	52.86	4,146.44
	a-Si	9.73	23	0.80	106.55	72.02	7,674.08
	CdTe	10.35	18	0.80	77.35	62.40	4,826.39
	CIS	9.88	17	0.80	75.86	59.58	4,520.79

Table 5 -

 5 9 PBT and EPBT for each configuration. Minimum field area.

	PV module	PBT (yr)	EPBT (yr)
	m-Si	6.34	1.18
	p-Si	7.68	1.32
	a-Si	7.34	1.18
	CdTe	7.43	0.88
	CIS	6.48	1.16

Table 5 -

 5 10 Final ranking of alternatives. Minimum field area

		Final weighted
	PV module	evaluation	Ranking
	m-Si	40	1
	p-Si	65	4
	a-Si	72	5
	CdTe	51	3
	CIS	41	2

Table 5 -11Weight proposals

 5

Table 5 -

 5 12 Configuration selected by applying M-TOPSIS. Max Q out -Min PBT

	PV							PBT	EPBT
	Techno Proposal	β (°)	K	D (m)	Nr	Nc	Q out (MW h)	(year)	(year)
	m-Si	27.71	9	16.15	1	94	358.05	8.76	1.69
		16.79	58	1.03	1	95	2 245.36	9.18	1.76
		16.79	58	1.03	1	95	2 245.36	9.18	1.76
		16.79	58	1.03	1	95	2 245.36	9.18	1.76
	p-Si	27.63	18	7.16	1	93	484.19	9.88	1.84
		15.00	54	1.21	1	105	1 590.26	10.34	1.90
		15.00	54	1.21	1	105	1 590.26	10.34	1.90
		15.00	54	1.21	1	105	1 590.26	10.34	1.90
	a-Si	24.69	15	8.07	2	88	364.01	10.85	1.73
		11.01	40	1.12	2	90	944.78	11.36	1.81
		11.01	40	1.12	2	90	944.78	11.36	1.81
		11.01	40	1.12	2	90	944.78	11.36	1.81
	CdTe	22.14	26	3.58	2	129	724.44	10.79	1.30
		12.65	45	1.01	2	166	1 526.37	11.47	1.36
		13.00	41	1.34	2	164	1 407.29	10.94	1.32
		12.65	45	1.01	2	166	1 526.37	11.47	1.36
	CIS	25.28	11	11.37	2	84	359.95	9.25	1.67
		11.08	41	1.23	2	102	1 558.88	9.71	1.74
		11.08	41	1.23	2	102	1 558.88	9.71	1.74
		11.08	41	1.23	2	102	1 558.88	9.71	1.74

Table 5 -

 5 14 Configurations selected by applying M-TOPSIS. Max Q out -Min EPBT

	PV							PBT	EPBT
	Techno	β (°)	K	D (m)	Nr	Nc	Q out (MW h)	(year)	(year)
	m-Si	14.32	59	1.04	1	95	2 286.76	9.12	1.75
	p-Si	15.71	58	1.02	1	105	1 694.19	11.16	1.94
	a-Si	10.84	41	1.06	2	90	965.20	11.34	1.81
	CdTe	11.64	44	1.08	2	166	1 512.99	11.34	1.35
	CIS	9.79	43	1.02	2	102	1 625.08	9.65	1.74

Table 5 -

 5 16 Configuration selected by applying M-TOPSIS. Min PBT-Min EPBT

	PV							PBT	EPBT
	Techno	β (°)	K	D (m)	Nr	Nc	Q out (MW h)	(year)	(year)
	m-Si	28.60	9	17.01	1	94	408.19	8.76	1.69
	p-Si	28.16	21	5.97	1	55	386.75	10.55	1.86
	a-Si	26.52	15	8.17	2	88	404.81	10.86	1.73
	CdTe	26.00	11	12.61	2	153	409.56	10.77	1.29
	CIS	27.91	9	16.19	2	102	404.16	9.27	1.67

Table 5 -

 5 17 Ranking of the resulting configurations after applying M-TOPSIS. Min PBT -Min EPBT

	PV Techno	Ranking
	m-Si	3
	p-Si	5
	a-Si	4
	CdTe	1
	CIS	2

Table 5 -

 5 18 Configuration selected by applying M-TOPSIS. Min Area -Min PBT

	PV								PBT	EPBT
	Techno Proposal	β (°)	K	D (m)	Nr	Nc	Area (m²)	(year)	(year)
	m-Si	1	5.99	6	1.00	3	71	2,444.14	8.04	1.74
		2	5.99	6	1.00	3	71	2,444.14	8.04	1.74
		3	5.38	6	1.02	3	71	2,453.02	8.03	1.73
		4	6.64	6	1.00	3	71	2,441.39	8.09	1.75
	p-Si	1	4.96	6	1.00	3	103	3,346.27	11.00	1.94
		2	4.96	6	1.00	3	103	3,346.27	11.00	1.94
		3	4.97	7	1.00	3	92	3,501.12	10.89	1.94
		4	4.96	6	1.00	3	103	3,346.27	11.00	1.94
	a-Si	1	6.08	15	1.00	3	86	6,911.30	11.62	1.81
		2	6.08	15	1.00	3	86	6,911.30	11.62	1.81
		3	6.07	16	1.01	3	85	7,300.96	11.47	1.80
		4	6.08	15	1.00	3	86	6,911.30	11.62	1.81
	CdTe	1	18.83	10	1.46	1	122	1,792.35	11.16	1.48
		2	18.83	10	1.46	1	122	1,792.35	11.16	1.48
		3	18.83	10	1.46	1	122	1,792.35	11.16	1.48
		4	18.83	10	1.46	1	122	1,792.35	11.16	1.48
	CIS	1	6.25	11	1.00	3	83	4,149.98	10.12	1.76
		2	6.25	11	1.00	3	83	4,149.98	10.12	1.76
		3	6.30	10	1.00	3	98	4,444.85	9.92	1.76
		4	7.41	11	1.00	3	83	4,141.65	10.16	1.77

Table 5 -

 5 19 Ranking of resulting configuration after applying M-TOPSIS. Min Area -Min PBT

	PV Techno Proposal 1 Proposal 2 Proposal 3 Proposal 4
	m-Si	2	2	2	2
	p-Si	4	4	4	4
	a-Si	5	5	5	5
	CdTe	1	1	1	1
	CIS	3	3	3	3

Table 5 -

 5 20 Configuration selected by applying M-TOPSIS. Min Area -Min EPBT

	PV							PBT	EPBT
	Techno	β (°)	K	D (m)	Nr	Nc	Area (m²)	(year)	(year)
	m-Si	5.81	10	1.00	3	43	2,497.77	8.03	1.74
	p-Si	4.56	6	1.00	3	103	3,349.21	11.01	1.94
	a-Si	6.53	15	1.00	3	87	6,986.62	11.61	1.81
	CdTe	8.20	8	1.43	4	153	4,410.29	10.04	1.29
	CIS	7.13	9	1.10	3	102	4,227.91	10.09	1.76

Table 5

 5

	-21 Ranking of resulting configuration after applying M-TOPSIS.
	Min Area -Min EPBT
	PV Techno	Ranking
	m-Si	3
	p-Si	5
	a-Si	4
	CdTe	1
	CIS	2

Table 5 -

 5 24 Comparison of results of two multi-objective optimization

		PV			EPBT	C (kg C 2 H 3 Cl	NC (kg	RI (kg PM2.5
	Case	Techno Q out (MW h) PBT (year)	(year)	eq)	C 2 H 3 Cl eq)	eq)
	18	m-Si	2,322.14	8.49	1.73	32,271.73	78,479.79	1,761.35
	objectives	p-Si	1,608.63	10.35	1.91	28,042.93	62,114.83	1,399.16
		a-Si	921.26	10.68	1.78	43,477.17	47,949.20	888.50
		CdTe	1,514.30	10.35	1.31	17,499.69	90,409.98	1,178.14
		CIS	1,539.23	9.62	1.79	15,643.13	50,288.20	1,117.40
	6	m-Si	2,250.96	8.50	1.73			1,704.13
	objectives	p-Si	1,615.37	10.34	1.90			1,401.71
		a-Si	947.38	10.59	1.78			909.19
		CdTe	1,384.24	10.49	1.31			1,079.72
		CIS	1,524.48	9.29	1.72			1,063.37

Table 5 -24 Continuation

 5

		PV	IO (Bq C-14	OLD (kg	RO (kg	AE (kg TEG	TE (kg TEG	TAN (kg
	Case	Techno	eq)	CFC-11 eq)	C 2 H 4 eq)	wáter)	soil)	SO 2 eq)
	18	m-Si	75,779,566.24	0.44	1,498.64	276,741,480.99 65,302,346.37	35,811.44
	objectives	p-Si	43,904,843.16	0.41	1,389.89	219,906,378.08 53,734,861.05	29,660.84
		a-Si	21,341,069.90	0.04	221.73	122,542,189.87 35,147,744.31	18,410.86
		CdTe	28,860,588.78	0.07	266.35	174,783,918.77 53,251,137.41	26,868.77
		CIS	51,030,788.15	0.08	289.24	152,631,961.03 47,778,106.21	24,811.18
	6	m-Si		0.43
	objectives	p-Si		0.41
		a-Si		0.04
		CdTe		0.06
		CIS		0.08

Table 5 -24 Continuation

 5

	Case	PV Techno	LO (m 2 org.arable)	AA (kg SO 2 eq)	AEU (kg PO 4 P-lim)	GW (kg CO 2 eq)	NR (MJ primary)	ME (MJ surplus)
	18	m-Si	30,228.04	10,941.96	1,369.57 2,423,794.25 37,329,219.80	79,624.96
	objectives	p-Si	28,204.34	8,662.39	1,023.96 1,979,693.26 28,473,388.21	69,630.03
		a-Si	20,785.77	6,376.01	484.76 1,243,123.69 15,142,258.88	129,037.18
		CdTe	27,035.49	7,803.03	1,048.94 1,561,837.48 18,383,990.24	52,248.61
		CIS	23,029.54	7,178.96	1,040.82 1,871,273.71 25,500,442.10	130,287.72
	6	m-Si						77,316.43
	objectives	p-Si						69,675.70
		a-Si						132,210.36
		CdTe						48,175.48
		CIS						124,433.07

Table 5 -

 5 25 Ranking of the configurations

	PV Techno	18 objectives	6 objectives
	m-Si	5	4
	p-Si	4	5
	a-Si	1	2
	CdTe	2	1
	CIS	3	3

Table 5 -

 5 26 Selected configurations by applying M-TOPSIS. Four objectives

										OLD (kg
	PV						Q out		RI (kg	CFC-11
	Techno	β (°)	K	D (m)	Nr	Nc	(MW h)	PBT (year)	PM2.5 eq)	eq)
	m-Si	14.14	60	1.00	1	95	2,323.27	8.46	1,759.37	0.44
	p-Si	13.15	57	1.04	1	105	1,668.83	10.28	1,448.41	0.42
	a-Si	11.09	40	1.14	2	90	945.45	10.59	908.41	0.04
	CdTe	12.39	43	1.17	2	166	1,483.10	10.40	1,156.00	0.06
	CIS	9.79	43	1.03	2	102	1,625.54	9.20	1,134.83	0.09

Table 5 -

 5 27 Ranking of the resulting configuration after applying M-TOPSIS.

	Four objectives
	PV Techno	Ranking
	m-Si	4
	p-Si	5
	a-Si	1
	CdTe	2
	CIS	3

  recyclage au sein d'une ACV. A partir des analyses du chapitre 3, une optimisation de technologies PV représentatives est effectuée. Les résultats confirment les avantages du recyclage, notamment par une réduction des impacts environnementaux globaux lorsqu' un recyclage matière est envisagé.

	Nomenclature Acronyms CdTe CIS EVA Cadmium Telluride Copper Indium Diselenide Ethylene Vinyl Acetate EPBT Energy PayBack TIme	Chapter	6
	GWP	Global Warming Potential
	LCA	Life Cycle Assessment
	LCI	Life Cycle Inventory
	LCIA	Life Cycle Inventory Assessment
	PV	Photovoltaic
	PVGCS	Photovoltaic Grid-Connected System
	a-Si	Amorphous silicon
	m-Si	Monocrystalline silicon
	p-Si	Polycrystalline silicon
	PE	Primary energy demand
	WEEE	Waste Electrical and Electronic Equipment

Table 6 -

 6 1 End-of-life PV modules collected and treated in 2012 (European Association for the Recovery of PhotovoltaicModules, 2012)

	Silicon based	Non-silicon based
	m-Si	51 %	CIS	16 %
	p-Si	26 %	CdTe	0 %
	a-Si	6 %	Flexible	1 %

Table 6 -2

 6 Results of four scenarios for m-Si based PVGCS configuration

		Yearly Q out	EPBT	GWP
	Scenario	(MW h)	(year)	(g CO 2 eq/kWh)
	100% new	2,286.76	1.753	52.621
	80% recycled -20% new	2,218.50	1.112	43.591
	90% recycled -10% new	2,253.12	1.036	42.604
	100% recycled	2,286.67	0.961	41.635

Table 6 -

 6 3 LCIA of CdTe PV module recycling per m²[START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF]

			Primary	GWP
			Energy (MJ)	(kg CO 2 eq)
	Without material recycling credits	81.03	6.03
	Including material recycling credits	-12.49	-2.50
	Energy
	Raw material	Manufacturing of components	Energy generation phase	PV module recycling
		System boundary

Figure 6-11 Process flow and system boundary for PVGCS with CdTe PV module

Table 6 -4

 6 Results of scenarios for CdTe based PVGCS configuration

		Yearly Q out	EPBT	GWP
	Scenario	(MW h)	(year)	(g CO 2 eq/kWh)
	Without material recycling credits	1,494.18	1.444	56.597
	Including material recycling credits	1,515.40	1.297	50.686
	Without recycling process	1,512.99	1.346	52.548

Table 4 -

 4 4 PCA results for Sabio et al. case. Eigenvalues and eigenvectors ....................................................... Decision data matrix .......................................................................................................................... Transformed values matrix ................................................................................................................ Normalized decision matrix ............................................................................................................... Weighted normalized matrix ..............................................................................................................

	Table 4-5 Table 4-6 Table 4-7 Table 4-8

Table 4 -

 4 9 A + and A -values ................................................................................................................................ ) ...........................................................................................

	Table 4-10 Euclidean distance matrix (D i	+ and D i	-

Table 4 -

 4 11 Rank alternatives by M-TOPSIS coefficient R i ............................................................................... Parameter values of NSGA II for different examples ........................................................................ Comparison between WAP and the proposed approach .................................................................... Values of decision variables for the best configuration of each PV technology. Maximum output energy .................................................................................................................................... Output energy for the best configuration of each PV technology. Maximum output energy. ............

	Table 5-1 Table 5-2 Table 5-3 Table 5-4

Table 5 -

 5 5 PBT and EPBT for each configuration. Maximum output energy ..................................................... Final ranking of alternatives. Maximum output energy ..................................................................... Final ranking of alternatives (environmental impact per kWh produced). Maximum output energy ................................................................................................................................................ Best configuration that minimizes the surface of PV system .............................................................

	Table 5-6 Table 5-7 Table 5-8

Table 5 -

 5 9 PBT and EPBT for each configuration. Minimum field area. ............................................................

Table 5 -

 5 10 Final ranking of alternatives. Minimum field area ...........................................................................

Table 5 -

 5 11Weight proposals...............................................................................................................................

Table 5 -

 5 12 Configuration selected by applying M-TOPSIS. Max Q out -Min PBT ...........................................

Table 5 -

 5 13 Ranking of the resulting configurations after applying M-TOPSIS. Max Q out -Min PBT..............

Table 5 -

 5 14 Configurations selected by applying M-TOPSIS. Max Q out -Min EPBT .......................................

Table 5 -

 5 15 Ranking of resulting configuration after applying M-TOPSIS. Max Q out -Min EPBT...................

Table 5 -

 5 16 Configuration selected by applying M-TOPSIS. Min PBT-Min EPBT .........................................

Table 5 -

 5 17 Ranking of the resulting configurations after applying M-TOPSIS. Min PBT -Min EPBT ...........

Table 5 -

 5 18 Configuration selected by applying M-TOPSIS. Min Area -Min PBT ..........................................

Table 5 -

 5 19 Ranking of resulting configuration after applying M-TOPSIS. Min Area -Min PBT ....................

Table 5 -

 5 20 Configuration selected by applying M-TOPSIS. Min Area -Min EPBT ........................................

Table 5 -

 5 21 Ranking of resulting configuration after applying M-TOPSIS. Min Area -Min EPBT ..................

Table 5 -

 5 22 Correlation matrix for the 15 environmental categories ...................................................................

Table 5 -

 5 23 PCA results. Eigenvalues and eigenvectors for the 15 environmental categories ............................

Table 5 -

 5 24 Comparison of results of two multi-objective optimization .............................................................

Table 5 -

 5 25 Ranking of the configurations ..........................................................................................................

Table 5 -

 5 26 Selected configurations by applying M-TOPSIS. Four objectives ...................................................

Table 5 -

 5 27 Ranking of the resulting configuration after applying M-TOPSIS. Four objectives ........................

Table 6-1 End-of-life PV modules collected and treated in 2012 (European Association for the Recovery of Photovoltaic Modules, 2012) ........................................................................................................ Table 6-2 Results of four scenarios for m-Si based PVGCS configuration........................................................ Table 6-3 LCIA of CdTe PV module recycling per m²

[START_REF] Held | Life cycle assessment of CdTe module recycling[END_REF]

................................................................. Table 6-4 Results of scenarios for CdTe based PVGCS configuration ..............................................................

A.

COMPARATIVE LIFE CYCLE ASSESSMENT OF AUTONOMOUS AND CLASSICAL HELIOSTATS FOR HELIOTHERMODYNAMIC POWER PLANTS FOR CONCENTRATED SOLAR POWER Appendix A B. ANGLE RELATIONSHIP FOR GLOBAL IRRADIANCE ESTIMATION Appendix B C. VALIDATION OF IRRADIANCE SIMULATION TOOL Appendix CTable C -

 C 2 RMSE and MBE for global irradiance onto PV collector plane for different tilt angle. Hay et al. model

Table 1-4 Summary of literature works for environmental assessment of PV systems

Table 2-1 Environmental assessment techniques

Table 2-5 LCI data for manufacturing process of m-Si PV module from (de Wild-Scholten & Alsema, 2005)

Table 2-7 LCI of total emission for manufacturing a m-Si based PV module (extracted from SimaPro)

A kW p specifies the output power achieved by a PV module under Standard Test Conditions (an incident sunlight of 1000 W/m², a cell temperature of 25°C and an Air Mass of 1.5)

Table 3-1 System sizing and simulation programs

Figure 3-11 Results of the environmental impacts normalized to unity

Ecodesign of large-scale photovoltaic (PV) system

Table C-1 RMSE and MBE for global irradiance onto PV collector plane for different tilt angle. Lu et al. model

Table C-3 RMSE and MBE for global irradiance onto PV collector plane for different tilt angle. Reindl et al. model

DSSC EPIA GHG LCA LCI LCIA MEA PV PVGCS ROW a-Si c-Si m-Si p-Si ribbon-Si STE TF Asia-Pacific region Industry Association Greenhouse Gas Life-Cycle Assessment Life-Cycle Inventory Life-Cycle Impact Assessment Middle East and Africa

Multiple-criteria decision making (MCDM)

MCDM approaches are major parts of decision theory and analysis. MCDM are analytic methods to evaluate the advantages and disadvantages of multicriteria alternatives.

The objective is to help decision-makers to learn about the problems they face, and to identify a preferred course of action for a given problem. Huang et al. [START_REF] Huang | Decision analysis in energy and environmental modeling[END_REF] mentioned that decision analysis (DA) was first applied to study problems in oil and gas exploration in the 1960s

Kaiser-Guttman criterion selected for the multi-objective case. The conditions and considerations in this case are the same as those described in Section 5.4 for the five PV technologies considered.

Not surprisingly, the average time for each optimization run increases significantly, around 15.6 hours CPU.

The correlation matrix as indicated in steps 2 and 3 of the PCA guidelines was generated from the obtained results. Table 5-22 indicates the correlation values for the 15 environmental categories that were obtained using the correlation analysis available in Excel software.

A high percentage of correlation between many of the categories is observed by the information provided by the correlation matrix which would indicate the possibility of eliminating some them for future optimization cases. The next step is to obtain the eigenvalues and eigenvectors matrix. The "pca" function integrated in the Statics toolbox of MATLAB was used to generate them. Substitution method. This allocation method is based upon the substitution of primary raw material by the reprocessed (secondary) material at the end-of-life stage: it occurs when a primary raw material is replaced by a recycled material (Figure 6-5). It is also called the avoided burden or avoided impact method. This method is applied to materials which maintain their inherent properties when they are recycled.

The easiest method to apply is the cut-off method, but the substitution approach is widely used in LCA studies where recycling at the end-of-life is involved [START_REF] Frischknecht | LCI modelling approaches applied on recycling of materials in view of environmental sustainability, risk perception and eco-efficiency[END_REF][START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF].

Other allocation methods such as system expansion, economic allocation, input oriented, valuecorrected substitution, multiple recycling method could be used (see [START_REF] Ligthart | Post-Consumer Waste Recycling and Optimal Production[END_REF] for more detail).

The substitution method of allocation of environmental impacts is widely used in LCAs where recycling at the end-of-life is involved [START_REF] Frischknecht | LCI modelling approaches applied on recycling of materials in view of environmental sustainability, risk perception and eco-efficiency[END_REF]. This last method will be used in the two cases of PV modules recycling. First, a brief description of the recycling processes of different PV module technologies will be provided. Some of these processes are still under development.

Recycling process of spent PV modules

Among the components of a PVGCS, PV modules contribute most largely to the environmental impacts as highlighted by the results obtained in Chapter 2. A brief description of the recycling process for the five PV module technologies addressed in this work is given in what follows.

Crystalline silicon modules 6.3.1

The recycling process of spent silicon PV modules (m-Si and p-Si) is based on the processes of a flat glass recycling line (Fraunhofer IBP, 2012;[START_REF] Klugmann-Radziemska | Chemical treatment of crystalline silicon solar cells as a method of recovering pure silicon from photovoltaic modules[END_REF][START_REF] Klugmann-Radziemska | Current Trends in Recycling of Photovoltaic Solar Cells and Modules Waste[END_REF][START_REF] Sander | Study on the development of a take back and recovery system for photovoltaic products[END_REF]. Figure 6-6 shows a flowsheet of the recycling process for the

LISTE OF TABLES

211

To estimate the radiation received onto the PVGCS, it is necessary to know the position of the sun during the day throughout the year. As presented in Figure 3-5, to calculate the irradiance received by each PV module that composes the PVGCS, a three-step computation is used. The relationship between these 3 levels can be established through the concepts that are explained below [START_REF] Duffie | Solar Engenierring of Thermal Process[END_REF][START_REF] Lorenzo | Energy Collected and Delivered by PV Modules[END_REF][START_REF] Perpiñan Lamigueiro | Energía Solar Fotovoltaica[END_REF].

The extraterrestrial radiation in the plane normal to the radiation referred as G on on the n th day of the year is the variation of extraterrestrial radiation flux resulting from the distance between the Sun and the Earth due to the orbit that Earth follows. An expression is proposed by Spencer (1971) -Best DC/AC efficiency.

-Maintenance (no redundancy).

-Energy loss by matching and shadow.

-High voltage wiring.

A2

Each line of PV modules is connected to an inverter (some inverters can connect two or three parallel lines directly).

The inverters are connected in parallel to the network.

-Modular block design.

-Correct DC/AC conversion -Incompatibility if different inverters of different manufacturer are used.

A3

Each PV module is connected to a small inverter (some inverters can connect up to five PV modules in series). The inverters are connected in parallel over the network.

-Modularity.

-Independence of modules (minimum loss by pairing).

-Reliable architecture (high redundancy and parallelism).

-The lowest DC/AC conversion efficiency

The number of DC/AC inverters required is determinate according to the methodologies presented by [START_REF] Hayoun | Les installations photovoltaïques: Conception et dimensionnement des installations raccordées au réseau[END_REF], and Kornelakis and Koutroulis (Aris Kornelakis & Marinakis, 2010). The first step is to set the maximum (N s,max ) and minimum (N s,min ) number of PV modules connected in series. Equations (E.8) and (E.9) obtain these values from voltage compatibility between PV modules and the DC/AC inverter.