Joaquim Moreira Lima

José Barata

Miguel Fernandez

Angel Montiel

The Distributed Generation as an Important Contribution to Energy Development in Angola and Other Developing African Countries

Keywords: Distributed Generation, Intentional Islanding, Energy Efficiency, Demand Side Management, Smart Metering

Distributed generation (DG) is related to the use of small or medium size generating units close to the load centers. DG has great benefits for application in places distant from the major centers of production where the population needs a reliable and cheap supply of electric power. This is the case of Angola and the majority of other African countries. This paper discusses the potential that distributed generation may have in these countries highlighting four crucial aspects: the utilization of a realistic and simple optimal allocation method, the consideration of intentional islanding mode of operation, the utilization of all the existing sources of energy in the region, specially the renewable ones, and the demand side management of energy through the application of smart metering and communication techniques.

Introduction

Although there is a lot of definitions of distributed generation in the technical literature [START_REF] Ackermann | Distributed generation: a definition[END_REF][START_REF] Pepermans | Distributed generation: definition, benefits and issues[END_REF][START_REF] Bessmertnykh | Development of distributed power generation[END_REF][START_REF] Lopes | Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities[END_REF][START_REF] González-Longatt | Review of the Distributed Generation Concept: Attempt of Unification[END_REF], we are going to present our own definition, considering that, on one hand, nowadays there are a large number of technologies to be incorporated into this concept and on the other hand, the increasing incorporation of artificial intelligence and the new technologies of computing and communications. Facing these facts, it is proposed the following definition of distributed generation:

Distributed generation consists of the supply and storage sources of electrical and thermal energy located in the distribution network, close to the points of consumption, together with the possibility of forming intelligent and highly automated networks, able to control and integrate the operation of the different supply and storage sources with the active participation of consumers in both the purchase and sale of energy, as well as in their more economical and environmental use.

The benefits of employing distribution generation in existing distributed networks have economical, technical and environmental implications and they are interrelated. In spite of the fact that in the literature many advantages for the distributed generation are exposed, it is possible to reduce them to four main aspects that comprise in one way or another, the rest of the advantages that can be argued [START_REF] Chiradeja | An approach to quantify the technical benefits of distributed generation[END_REF][START_REF] Poullikkas | Implementation of distributed generation technologies in isolated power systems[END_REF][START_REF] Castro | Distributed Generation and Renewable Energy: The Cuban Experience[END_REF]. These four aspects are: are increased reliability, line loss reduction, voltage profile improvement and environmental impact reduction.

This paper seeks to explain the most important aspects that must be taken into account to justify the introduction in Angola a mix of this concept and the challenges that the application of this generation paradigm might imply.

Relationship to Collective Awareness Systems

The current challenges faced by the Energy sector involve complex interactions between humans and machines with intelligent decision making or behavior. More and more intelligent energy based devices are being applied to create intelligent and adaptive systems in the production, transport, and distribution of electrical energy. The grid is becoming more intelligent (SMART GRID) both at device level and at system level. It is important to understand that the SMART GRID is above all a distributed system composed of intelligent nodes (machines and humans) that regulates and control de demand and supply in a sustainable way.

The SMART GRID and distributed energy in particular are completely dependent on cyberphysical systems that are at its kernel. Considering the different actors involved and interacting with the grid what is available is a complex system in which different services are being used and created in a dynamic way to accommodate the different actors. In particular it is important to stress the importance of customers that more and more interact with producers and suppliers. The internet of things is therefore a concept well in line with this new world of energy.

It is clear that the emerging energy systems (SMART GRID) are socio-technical systems capable of harnessing collective intelligence for promoting innovation and taking better, informed and sustainability-aware decisions, Therefore, the development of Collective Awareness Systems will have a strong and beneficial impact on the creation of effective and sustainable energy systems.

Motivation

Angola is a country where almost all energy resources known all over the world can be found. The energy potential from national rivers is enormous and it is one of the major oil producers in Africa. This has resulted on a energy generation planning based on the intensive exploitation of these energy resources and in the construction of big and expensive infrastructures to transport the electric power to very distant places [START_REF]MINEA, Ministry of Energy and Water of Angola -Plano de Desenvolvimento Energético (Energy development Plan)[END_REF].

It was only in the last few years that a strategic plan was developed to include in the country's energy mix the varied and abundant sources of renewable energy. However, it was not carried out large-scale plans to introduce the energy efficiency and power management on the demand side.

Accordingly, our proposal to improve the reliability of the Angolan electric system is based on the hypothesis that it is possible to consider the comprehensive use of distributed generation in Angola taking into account four important factors: the optimal allocation of the units of distributed generation, the possibility of operating on intentional island mode, the use of all available energy resources and the active participation of the demand side utilizing smart grids and smart metering

Optimal Allocation of Distributed Generation

From the very beginning of the introduction of the generation in the distribution networks this has been one of the most important problems faced by specialists and researchers due to the fact that an incorrect planning of these units can lead to an increase in the cost of both investment and exploitation thus losing one of the main advantages of this type of generation.

In the early years of this century there were several papers about the location and sizing of the units taking into account only the decrease of losses in the network [START_REF] Hadjsaid | Dispersed generation impact on distribution networks[END_REF][START_REF] Wang | Analytical approaches for optimal placement of distributed generation sources in power systems[END_REF][START_REF] Griffin | Placement of Dispersed Generations Systems for Reduced Losses[END_REF]. These studies consider also that there is a source of generation in each node and limited load scenarios. Also at this time there were several important researchers that attempted to quantify the benefits from the introduction of distributed generation with the goal of providing tools for the economic feasibility studies of this type of generation. Among these works is particularly important the paper submitted by Chiradeja Ramakumar [START_REF] Chiradeja | An approach to quantify the technical benefits of distributed generation[END_REF].

A large number of methods for the optimal location and size of the GD based on the use of genetic algorithms or fuzzy logic were proposed [START_REF] Celli | A multiobjective evolutionary algorithm for the sizing and siting of distributed generation[END_REF][START_REF] Borges | Optimal distributed generation allocation for reliability, losses, and voltage improvement[END_REF][START_REF] Cano | Utilizing Fuzzy Optimization for Distributed Generation Allocation[END_REF][START_REF] Varikuti | Optimal Placement of DG Units Using Fuzzy and Real Coded Genetic Algortihm[END_REF]. It also introduced new variants of evolutionary algorithms such as the so-called Particle Swarm Optimization (PSO), Honey Bee Mating (HBM), Tabu Search (TS), Simulated Annealing (SA), Differential Evolution (DE) and Cokoo Search (CS). All of them are heuristic or meta-heuristic methods based on iterative procedures and considering different objective functions [START_REF] Abdi | Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing[END_REF][START_REF] Doagou-Mojarrad | Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm[END_REF][START_REF] Nekooei | An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems[END_REF][START_REF] Viral | Optimal planning of distributed generation systems in distribution system: A review[END_REF][START_REF] Porkar | Optimal allocation of distributed generation using a two-stage multiobjective mixed-integer-nonlinear programming[END_REF][START_REF] Moradi | A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems[END_REF][START_REF] Moravej | A novel approach based on cuckoo search for DG allocation in distribution network[END_REF][START_REF] El-Zonkoly | Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation[END_REF]. Among the major aspects to be considered in the proposed objective functions are the losses in the network, the voltage profile, the greenhouse gas emissions, the savings in the costs of expansion of the transmission system and the reliability.

There has been a great contrast between the level of development achieved by the theoretical and academic studies of optimal location of distributed generation units and, in some cases, a lack of will and rejection of the utilities to its introduction due to the fact that these studies have not consider the uncertainties and risk factors present at all times. From the point of view of real-world application, geographical, climatic and social factors could change completely the results obtained by means of sophisticated methods purely mathematical since they have not been taken into account. It is also true that the advantages of one method over another are mainly based on an easier consideration of the objective functions or in the saving of computing time.

Concerning the optimal localization studies, the fact of variability and the forecasting difficult to deliver energy and the influence of these factors both the size and the location of the units of distributed generation have been considered by some authors only [START_REF] Niknam | A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources[END_REF][START_REF] Atwa | Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization[END_REF].

According to these facts, the proposed method optimal location will take some aspects into consideration: A mathematical method that could simplifies the development and implementation of the proposed software, the reliability of the whole system, the transmission losses, the voltage profiles at the more frequent load scenarios, also as a priority, the geographic, climatic and social more important factors as well as the presence of renewable energy sources and their variability as part of the possible scenarios.

This well in line with the research question of this PhD work is which computational methods and tools should be used for the optimal location of distributed generation units in the context of under developing countries and considering environmental factors.

Islanding as a Normal Mode of Operation of Distributed Generation Networks

A set of distributed generation units having different technologies are usually connected to the Utility Network through a common point of connection (CPC) where there is a coupling transformer.

It can happen that, mainly due to faults in the utility or distributed generation network, the CPC is disconnected and form an island or isolated system. The rules of connection of the distributed generation to the network disconnect immediately all the distributed generation units by interrupting the service users. But more recently has been questioned this policy due to the economic losses that brings to the users the energy not supplied caused by these blackouts.

It has been proposed to continue operating the distribution network in isolation mode, diminishing the quality of the energy parameters but guaranteeing the supply to users. In this way, an increased service quality is achieved. This policy has almost always been rejected by the large utilities but has been seen in recent years a greater flexibility in this regards.

The most important challenges in adopting a policy of intentional islanding operation are the sharing of the power load, which could include load shedding, the frequency and voltage control and the subsequent synchronization of the island with the network [START_REF] Gomez | Letter to the Editor: On Islanding Operation in Systems with Distributed Generation[END_REF].

The solution of these problems, even without reducing the quality of service, is nowadays a reality due to the application of the most modern techniques of computing and communications and the introduction of new concepts and techniques of operation of networks such as the smart grids and metering and the virtual power plants [START_REF] Dash | Estimation of power quality indices in distributed generation systems during power islanding conditions[END_REF][START_REF] Best | Synchronous islanded operation of a diesel generator[END_REF][START_REF] Wissner | The Smart Grid -A saucerful of secrets?[END_REF].

We propose that, as a part of the incorporation of distributed generation to the Angolan mix, the operation on intentional islanding mode should be considered.

Sustainability of the Projects of Distributed Generation

Sustainability means an equitable distribution of limited resources and opportunities in the context of the economy, the society, and the environment seeking the wellbeing of everyone, now and in the future [START_REF] Alanne | Distributed energy generation and sustainable development[END_REF][START_REF] Bugaje | Renewable energy for sustainable development in Africa: a review[END_REF]. A sustainable energy solution generates enough energy for all at a reasonable price in a clean, safe and reliable way.

In regard to the technologies used, machines such as internal combustion engines or Diesel, gas turbines, microturbines, fuel cells, biomass and renewable energies extracting the energy of the sun and wind as the photovoltaic solar energy and wind power, are recognized as sources of energy utilized in distribution generation [START_REF] Huang | A review on distributed energy resources and MicroGrid[END_REF].

Within these technologies, are the renewable energy, solar and wind, which have the biggest sustainability and greater benefits from an environmental point of view but have the disadvantage that depend on climatic factors that vary according to the time of the day and the season. On account of that, they are difficult to predict and, therefore, are not "dispatchable". In spite of that, its use and expanding potential are increasing [START_REF] Tan | Optimal distributed renewable generation planning: A review of different approaches[END_REF][START_REF] Loughran | Demand-side management and energy efficiency in the United States[END_REF].

A few years ago, while recognizing the great potential of renewable energy sources as part of distributed generation in the more rational use of electric energy, it was a great concern that precisely this dispersal and not "dispatchable" characteristic of this type of sources, could give rise to a liability diluted among the large utilities, the costumers and local producers [START_REF] Bugaje | Renewable energy for sustainable development in Africa: a review[END_REF].

In the last years this situation has been changing mainly motivated by the development of new organization concepts of the electrical networks such as the microgrid and the virtual power plants, already mentioned, and also by the increase in international pressure for searching energy productions solutions more efficient, clean and reliable [START_REF] Atwa | Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization[END_REF][START_REF] Tan | Optimal distributed renewable generation planning: A review of different approaches[END_REF].

Consequently we propose that clean and renewable energies available in Angola must be effectively used.

Demand Side Management & Smart Metering

The participation of the consumer and the local producer of energy in the implementation of energy efficiency policy is so-called Demand Side Management. Due to the lack of awareness of the Angolan population and some authorities concerning the necessity of saving electrical energy and use it more rationally this DSM concept is necessary to evaluate at the proposal to solve the Angolan electric system develop.

This participation is proposed through several methods such as financial incentives and education. The goal of demand side management is to encourage the consumer to use less energy during peak hours or to translate the energy utilization to off peak hours such as nighttime and weekends. Peak demand management does not necessarily decrease total energy consumption but could be expected to reduce the need for investment in networks components and the size of distributed generation energy sources.

There are three models for implantation the Demand Side Management:

1. Energy efficiency. Using less power to perform the same tasks. 2. Demand Response. Any preventive method to reduce, flatten or shift peak demand that includes all intentional modifications to consumptions patterns of electricity. It is related to a wide range of actions which can be taken at the customer side of the electricity meter in response to particular condition of the electricity meter (peak period, network congestion, high prices) [START_REF] Albadi | A summary of demand response in electricity markets[END_REF]. 3. Dynamic Demand. Advance or delay appliance operating cycle by a few seconds to increase the Diversity Factor of the whole load. The concept is that by monitoring the power factor of the power grid, as well as their own control parameters, individual, intermittent loads would switch on or off at optimal moments to balance the overall system load with generation, reducing critical power mismatches [START_REF] Torriti | Demand response experience in Europe: Policies, programmes and implementation[END_REF].

The successful implementation of the demand side management would be impossible without the application of the so-called Smart Metering based on the smart meter.

A smart meter is usually an electrical meter that records consumption of electric energy in intervals of an hour or less and communicates that information at least daily back to the utility for monitoring and billing purposes. Smart meters enable two-way communication between the meter and the central system. The possibility of this twoway communication of smart metering makes its utilization mandatory in our proposal.

Conclusions

We conclude that, to solve the lack of reliability of the electrical system in Angola it is mandatory to apply a new concept of distributed generation who bears in mind the needs of the different regions of the country, the available resources and the application of the last advances in the information and communication technologies, emphasizing the following aspects:

• The optimal allocation of the units of distributed generation taking into account the decrease of power losses, the improvement of the voltage profile, the characteristics of the demand and the possible variability of the sustainable available sources of energy as well as utilizing an adequate and simple software. This the main focus of this PhD research. • To consider, as a part of the proposal, the possibility and necessity to create intentional islands that increase the availability of the electric power to the consumers • To utilize all the energy resources available in the region in a sustainable way and considering the possible environmental aspects. • To apply the demand side management of the energy consumed and produced utilizing the smart metering and communication possibilities.

Acknowledgments. This work was partially supported by CTS multi-annual funding, through the PIDDAC Program funds.


P Cruz

H M I Pousinho
email: hpousinho@gmail.com

R Melício
email: ruimelicio@uevora.pt

V M F Mendes
email: vfmendes@deea.isel.pt

M Collares-Pereira
email: collarespereira@uevora.pt

Optimal Operation Planning of Wind-Hydro Power Systems Using a MILP Approach

Keywords: Collective awareness, day-ahead market, wind-hydro power system, optimal operation

This paper addresses an approach for a day-ahead operation of a wind-hydro power system in an electricity market. A wind-hydro system is able to mitigate the intermittence and the variations on wind power, mitigating the economic penalty due to unbalance in the satisfaction of the compromises. The approach consists in a model given by a mixed-integer linear programming. This model maximizes the profit in the day-ahead market, taking into consideration the operating constraints of both the wind the farm and the pumping-hydro system. Finally, numerical case studies illustrate the interest and effectiveness of the proposed approach.

Introduction

New operational challenges have come into view in power system due to large-scale integration of renewable energy. In particular, high levels of wind integration have strongly conditioned the power system operating security and stability due to the inherent intermittence and variability on wind power [START_REF] Al-Awami | Coordinated Trading of Wind and Thermal Energy[END_REF], implying that wind power dispatchability is a challenging task, namely when the control and management of the active power output is required [START_REF] Castronuovo | On the Optimization of the Daily Operation of a Wind-Hydro Power Plant[END_REF]. Management of the system requires the use of wind power forecasting methods in order to mitigate the impact of wind power variability. But forecasting methods are not able to provide enough accuracy results for wind power. Hence, in order to accommodate the absence of the enough accuracy, uncertainty on the wind power forecast should be included on the approach for optimal operational planning. Otherwise, for a wind power producer that acts in the day-ahead market the disregarding of uncertainty can lead to monetary penalties due to a significative deviation from offers [START_REF] Angarita | Combined Hydro-Wind Generation Bids in a Pool-Based Electricity Market[END_REF]. An effective way to minimize the deviation losses is to combine a wind farm with conventional generating units. Examples of such combinations can be found in [START_REF] Castronuovo | On the Optimization of the Daily Operation of a Wind-Hydro Power Plant[END_REF], [START_REF] Angarita | Combined Hydro-Wind Generation Bids in a Pool-Based Electricity Market[END_REF] and [START_REF] Chen | Optimal Wind-Thermal Generating Unit Commitment[END_REF]. In [START_REF] Angarita | Combined Hydro-Wind Generation Bids in a Pool-Based Electricity Market[END_REF], a comprehensive study about the economic benefits of a wind farm with pumped-hydro system is analyzed in an electricity market environment. In [START_REF] Maddaloni | Network Constrained Wind Integration on Vancouver Island[END_REF], the effect of wind power with varying degrees of integration is examined in order to determine the costs and carbon emissions associated with a hydro-dominated with gas unit's power system.

In this paper, the main contribution is to provide an effective approach based on mixed-integer linear programming (MILP) to find the optimal planning for the operation of a single entity having to manage a wind farm and a pumped-hydro system, so as to maximize the profit in the day-ahead market.

Relationship to Collective Awareness Systems

The technological evolution on electric power system encouraged by the expansion of distributed generation has been crucial to create collective awareness systems useful to define new energy consumption and production patterns. A collective awareness system can result from the development of powerful optimization approaches for the management of power systems, helping to make decisions. The collective awareness system not only promotes the sustainable use of energy resources in favor of an effective low-carbon economy [START_REF]Collective Awareness Platforms for Sustainability and Social Innovation[END_REF], but also processes the optimal decision. In order to achieve this optimal decision, besides real-time information on data, such as, the electricity prices and wind power, collective tools are essential to allow maximizing the profit. Hence, research on technological innovation for collective tools based on approaches for solving the day-ahead operation planning of a power producer is crucial to achieve guidelines for the best bidding in an electricity market.

State of the Art

The increased wind power integration in power system has drawn attention to largescale energy storage techniques. In the technical literature, multiple storage technologies and optimization approaches have been analyzed from several perspectives. In [START_REF] Maddaloni | Network Constrained Wind Integration on Vancouver Island[END_REF] and [START_REF] Lund | Large Scale Integration of Wind Power into Different Energy Systems[END_REF], the impact of the intermittence and variability of wind power generation from an operation and economic perspective has been addressed respectively by a discrete dynamic quadratic program with linear constraints and by using the EnergyPLAN computer model. In order to accommodate excessive wind power generation, batteries energy storage [START_REF] Zalani | An Improved Control Method of Battery Energy Storage System for Hourly Dispatch of Photovoltaic Power Sources[END_REF] and flywheels [START_REF] Sebastián | Flywheel Energy Storage Systems: Review and Simulation for an Isolated Wind Power System[END_REF] have been proposed. However, the aforementioned propose is unattractive for large-scale power systems [START_REF] Karamanou | Operating Policies for Wind-Pumped Storage Hybrid Power Stations in Island Grids[END_REF]. A viable alternative to overcome the unattractiveness is the wind-hydro power system, combining a wind farm system with a pumped-hydro system, storing the excess wind energy and otherwise providing electric energy in favorable economic conditions. This alternative as shown attractiveness not only to balance the intermittence and variability of wind power in combined optimization settings [START_REF] Castronuovo | On the Optimization of the Daily Operation of a Wind-Hydro Power Plant[END_REF] and [START_REF] Angarita | Combined Hydro-Wind Generation Bids in a Pool-Based Electricity Market[END_REF], but also to enable the provision of firm capacity [START_REF] Angarita | Combining Hydro-Generation and Wind Energy: Biddings and Operation on Electricity Spot Markets[END_REF]. In [START_REF] Bakos | Feasibility Study of a Hybrid Wind/Hydro Power-System for Low-Cost Electricity Production[END_REF], the operation of a wind-hydro power system is analyzed for the Greek islands, showing that the electricity cost is significantly reduced when conventional thermal units are changed by the wind-hydro power system. In [START_REF] Bueno | Wind Powered Pumped Hydro Storage Systems, a means of Increasing the Penetration of Renewable Energy in the Canary Islands[END_REF], the sizing of a wind-hydro power system and the operational performance is examined for the Canary Islands, to optimize exploitation of available hydraulic and wind potential. In [START_REF] Anagnostopoulos | Pumping Station Design for a Pumped-Storage Wind-Hydro Power Plant[END_REF] and [START_REF] Anagnostopoulos | Simulation and Size Optimization of a Pumped-Storage Power Plant for the Recovery of Wind-Farms Rejected Energy[END_REF], a methodology is proposed for defining the best size of the several devices of a windhydro power system. In [START_REF] Castronuovo | On the Optimization of the Daily Operation of a Wind-Hydro Power Plant[END_REF], daily planning for a wind-hydro power system is modeled by a linear programming. In [START_REF] Korpaasa | Operation and Sizing of Energy Storage for Wind Power Plants[END_REF], a dynamic programming for the operation planning of energy storage for wind farms in the electricity market is presented. In [START_REF] García-González | Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market[END_REF], a wind-hydro system is studied in order to find an energy balance analysis and economic viability. In [START_REF] Angarita | Combined Hydro-Wind Generation Bids in a Pool-Based Electricity Market[END_REF], two approaches for minimizing the imbalance costs of the wind farm power output are addressed. The first approach considers only bidding for the wind farm in the day-ahead market, trying to minimize the risk of the bidding; the second one considers a wind-hydro power system to minimize the imbalance costs incurred by the wind farm system.

Unless other technology for storing energy can into play, for instance, the promising liquid storage battery [START_REF] Bradwell | Magnesium-Antimony Liquid Metal Battery for Stationary Energy Storage[END_REF], the state of the art has as a point in case the wind-hydro power system. This system has been shown in the literature as convenient for a wind farm in a day-ahead market, avowing imbalances due to wind power intermittence and variability.

Problem Formulation

The proposed MILP approach for the day-ahead operation planning of a wind-hydro power producer in an electricity market is developed in order to access an economic viability of this type of system. In the proposed approach, the operation planning is considered with hourly discretization. For each hour, the wind-hydro power producer offers in the day-ahead market based on assumption that the electricity prices are derived from a forecast. Also, the wind power is considered with an hourly discretization. The operation planning comprises at each hour the amount of: power output that should be offer; wind power; hydro power; and pumping power.

The wind-hydro power system considered for the optimal operation planning of a single entity managing a wind farm and a pumped-hydro system is shown in Fig. 1.

Wind

Objective Function

The problem formulation for the wind-hydro power system aims to maximize the power producer profit in a day-ahead market. The objective function to be maximized is stated as:

∑ ∈ - T t pump t pump t da t da t p c p ) (λ . (1)
In (1), t stands for the hour index and T stands for the time horizon, da t λ is the forecast electricity price in the day-ahead market, da t p is the power output injected into the grid, pump t c is the cost of the pumping operation; and pump t p is the power consumed by the pump operation. In (1), the objective function is the difference between the revenue of selling the energy and the cost of pumping.

Constraints

The objective function is subject to a set of technical and operational constraints. The constraints are stated as:

pump t hydro t W t da t p p p p - + = (2) hydro W da t hydro W p p p p p + ≤ ≤ + (3) W W t W p p p ≤ ≤ (4) } 1 , 0 { ) 1 ( 0 ∈ ∀ - ≤ ≤ x x p p t hydro hydro t (5) } 1 , 0 { 0 ∈ ∀ ≤ ≤ x x p p t pump pump t (6)                   + - ≤ hydro hydro pump t pump t hydro t p p t E E p , min η η ∆ (7)                   - - ≤ pump hydro hydro hydro t t pump t p p t E E p , 1 min η η ∆ ( 8
) hydro t hydro pump t pump t t p t p t E E η ∆ η ∆ - + = +1 (9) initial E E = 1 (10) final E E = 24 (11) E E E t ≤ ≤ ( 12
) hydro t hydro t p t E E η ∆ - ≤ (13) E p t E pump t pump t ≤ + η ∆ . ( 14
)
In ( 2), the power output injected into the grid in each hour t is composed by three terms: (i) the amount of the available wind power producer, W t p ; (ii) the amount of the hydro power generated that is delivered to the grid, hydro t p ; and (iii) the amount of the power consumed by the pump operation of the plant. In (3), the power output injected into the grid is limited by the lower and upper capacities of hydro and wind power systems. In (4), the wind power of the wind farm must be within operating limits.

In ( 5) and ( 6), the limits on the hydro and pump power are set, which include a 0/1 variable, t x , to avoid enabling both hydro and pumping operation modes. In [START_REF] Lund | Large Scale Integration of Wind Power into Different Energy Systems[END_REF], the maximum value of hydro t p depends on two main physical limits: (i) the amount of available energy in the reservoir, t E , due to wind energy stored by pumping; and (ii) the maximum generation capacity of the hydro turbines, hydro p

. In [START_REF] Zalani | An Improved Control Method of Battery Energy Storage System for Hourly Dispatch of Photovoltaic Power Sources[END_REF], the pumping power is limited by the maximum power of pumping and the power associated with the energy available in the reservoir. In ( 7) and ( 8), pump η and hydro η are the efficiencies of pumping and hydro generation, respectively. In ( 9), the energy balance in the reservoir is computed. At the beginning of the ( t + 1 ) hour, the reservoir energy level depends on the energy level in the previous hour as well as the pumped energy and the energy supplied to the grid by the hydro generation. The reservoir energy level is reduced by hydro generation and is reloaded by pumping operation. In [START_REF] Karamanou | Operating Policies for Wind-Pumped Storage Hybrid Power Stations in Island Grids[END_REF] and [START_REF] Angarita | Combining Hydro-Generation and Wind Energy: Biddings and Operation on Electricity Spot Markets[END_REF], the initial, initial E , and final, final E , energy levels of the reservoir must be satisfied. These energy levels are assumed to be known, in order to obtain a consistent planning scheme for the reservoir. In [START_REF] Bakos | Feasibility Study of a Hybrid Wind/Hydro Power-System for Low-Cost Electricity Production[END_REF], the limits of the reservoir energy levels are set. In [START_REF] Bueno | Wind Powered Pumped Hydro Storage Systems, a means of Increasing the Penetration of Renewable Energy in the Canary Islands[END_REF], the energy level has to satisfy the constraint depending on two terms: (i) the reservoir energy level; and (ii) the energy generated by the hydro plant. In ( 14), the energy level upper limit has to satisfy the constraint depending on two terms: (i) the reservoir energy level; and (ii) the stored energy through by the pump operation of the plant.

Case Study

The proposed MILP approach has been tested on case studies based on a windhydro power system composed of eight wind turbines and two cascading river dams with pumping-storage capability. The time horizon considered is one day, divided into 24 hourly intervals. The input to the proposed approach includes the day-ahead hourly electricity price from the Iberian electricity market [START_REF] Eléctrica De España | Sistema de Información del Operador del Sistema[END_REF] and the forecasted available wind power. The electricity price and available wind power are shown in Fig. 2. The cost of the pumping operation assumes the same value as the electricity price at each hour. The computing time is less than 60 seconds on a 1.9-GHz-based processor with 2 GB of RAM using as a computing language the VBA for Microsoft Excel platform which is reasonable within a day-ahead decision making framework.

The operating data of the wind-hydro power system are shown in Table 1. Two cases are presented in this section, considering different initial energy levels for the upper reservoir. The considered cases are as follows:

Case 1) The initial E is constrained to be equal to 20% of maximum energy level.

Case 2) The initial E is constrained to be equal to 75% of maximum energy level. The two cases are analyzed with respect to the power producer's energy stored in the upper reservoir, optimal hourly power output injected into the grid and profit as follows.

The profiles of the energy stored in the upper reservoir over the 24 hour for both case studies are shown in Fig. 3. In Fig. 3, the results for Case 1 show that the pumping operation occurs when the prices are low enough, i.e., for the hourly intervals between [1 , 7] h and [14 , 18] h. For the intervals [9 , 13] h and [19 , 24] h, the generating operation occurs in order to maximize the profit. In Case 2, the upper reservoir has an initial energy level greater than Case 1 then, the profit maximization is achieved with hydro production during 22 hours. Because the price is low in hours 4 and 5 the pumping operation occurs.

The hourly power output for both case studies is shown in Fig. 4. In Fig. 4, a comparison between Case 1 and Case 2, as expected, shows that the wind-hydro power system tends to operate at a high production level when a high initial energy level in the reservoir is available. For a high initial energy level, the hydro system operates as a generator and sends the stored energy to the market until reaching the final value of energy required in the reservoir.

The power contribution of the wind farm and pumped-hydro system for both case studies in the day-ahead market is show in Fig. 5. In Fig. 5, for the wind power data is shown that when the upper reservoir starts with a higher level of energy stored the pumping is less required for the same electricity prices data. So, should be recommend that the initial energy stored in the starting of the time horizon should be kept at a highest value, i.e., the final energy stored should be aimed at this value.

The profits for each case study are shown in Table 2. In Table 2, a comparison of the profits shows, as expected by the last figures, that the profit is higher when the initial energy level is higher.

Conclusions

A MILP approach is proposed for the optimal operation planning of a wind-hydro power system, combining a wind farm with a pumped-hydro system to improve wind farm profits. The approach developed is illustrated considering two case studies. A comparison between these two case studies allows concluding that a coordination between the forecasted wind power and the level of the energy stored at the reservoir in the initial and final hours of the time horizon have to be decided by an upper level hierarchical management system. The approach is able to mitigate wind energy curtailments and avoids penalty risks related to energy deviations.

Fig. 1 .

 1 Fig. 1. Wind-hydro power system.

Fig. 2 .

 2 Fig. 2. Electricity prices (left) and available wind power (right).

Fig. 3 .

 3 Fig. 3. Energy stored in the upper reservoir: Case 1 (dashed-dot line) and Case 2 (solid line).

Fig. 4 .

 4 Fig. 4. Power output: Case 1 (dashed-dot line) and Case 2 (solid line).

Fig. 5 .

 5 Fig. 5. Power contribution: Case 1 (left) and Case 2 (right).

Table 1 .

 1 Wind-hydro power system data

	p	W	p	W	p	hydro	p	hydro	p	pump	p	pump	E	E	E	final	η	hydro	η	pump
	(MW)	(MW)	(MW)	(MW)	(MW)	(MW)	(MWh)	(MWh)	(MWh)
	0	20		0		10		0		10	10	300		10	0.88	0.85

Table 2 .

 2 Case study profits.

		Profit
		(Eur)
	Case 1	25,164
	Case 2	29,666

Acknowledgments. H.M.I. Pousinho thanks FCT for a post-doctoral grant (SFRH/BPD/52163/2013). This work was partially supported by FCT, through IDMEC under LAETA, Instituto Superior Técnico, Universidade de Lisboa, Portugal.


F M Navas-Matos

S Polo-Gallego

E Romero-Cadaval

M I Milanés-Montero

Distributed Smart Metering by using Power Electronics Systems

Keywords: Smart Metering, Power Quality, UDP Protocol and Open data

The main objective of this paper is to develop a monitor system implemented in existing power electronics equipment. The proposed algorithm is integrated in a previously developed Photovoltaic Array Emulator with the principal aim of monitoring important quality parameters that will be sent to a central repository (by using UDP protocol), where they will be available as Open data.

Introduction

Historically, the electrical grid has been a "broadcast" grid, where generators cover the demand in a country or region. Whereas this model has served its purpose for the last century to some extent, the smartgrid [START_REF]European SmartGrids Technology Platform -Vision and Strategy for Europe's Electricity Networks of the Future[END_REF] concept appears due to the growing need to reform the world's electrical grid and systems to address new societal challenges:

• The world is considering being more economical and environmentally friendly, and therefore the electrical grid is becoming more complex with the introduction of power electronics systems present in renewable energy generators. • In a society where information and data are becoming increasingly important, information and communication technologies carry the conception of open data.

Open data [START_REF] Auer | DBpedia: A Nucleus for a Web of Open Data[END_REF] refers to the notion that certain data should be unreservedly available to everyone for using and republishing as wished, free from restrictions imposed by copyright, patents or other mechanisms of control. The European Platform Technology Smartgrids published 19 June 2013 The Summary of Priorities for Smartgrids Research Topics [START_REF] Smartgrids | Summary of Priorities for Smartgrid research topics[END_REF]. Specifically in D section with a priority 1 the sub-topic cluster Modelling Power System and ICT together is found.

In order to fulfill the research topic, we use the actual power electronic systems in order to be the host of the corresponding algorithms, able to get quality parameters on the base of the IEEE Standard 1459-2010 [START_REF]IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions, IEEE Std 1459-2010[END_REF] and send it over the internet to an open grid of data, where everybody would be able to use the producer and consumer data for their work, as could be researching, madding statistical studies, matching generation and consumption as part of the grid control and so on.

The developed algorithm needs to be adaptable and embedded for every power electronics system, taking advantage of the electrical parameters already measured by these types of systems. Therefore, our paper is built upon the development of the aforementioned algorithm and inclusion in a real system to prove its correct operation with the rapid prototyping tool based on xPC Target. We send over Internet to an open repository quality parameters calculated by an algorithm implemented in a photovoltaic array emulator connected to a commercial inverter.

Relationship to Collective Awareness

In order to contribute to collective awareness, the developed algorithm has the ability to send power quality data to a common repository, where they will be available as open data for other researchers that would be able to use them to develop advance strategies in order to operate the electric grid, for instance.

Besides in a vision of future, this kind of algorithms will avail of the increasing use of power electronics systems (for example, the converters used in distributed generators mainly based in renewable resources). These systems usually have electric sensors which the algorithm could use for monitoring the system, and enough resources for running the algorithm. Taking this into account, the power electronic systems will convert into sources of pre-processed data that can be used in high level intelligent systems that will optimize the operation of the grid.

System's Physical Description

The system is composed by a PV array emulator, an electronic converter working as a synchronous rectifier, which constitutes the interface circuit between the electrical grid by means of an autotransformer and the inverter. The real system is a SEMISTACK SKS 230F B8CI 190 V12 inverter by Semikron. We see the system's scheme in Fig. 1. The inverter, which returns the power, once reconverted to AC, to the grid, is a SMA sunny minicentral 6000A inverter.

Thus, we have a close-loop system consisting of a Photovoltaic array emulator controlled by Simulink (where we can control parameters, such as irradiance curve, DC voltage, number of panels in series and parallel, etc [START_REF] Polo-Gallego | Development of a Photovoltaic Array Emulator in a Real Time Control Environment using xPC Target[END_REF]), taking energy from the grid, and connect to a SMA, which returns it to the same point.

The system has a measurement conditioning board with a total of 10 Hall-effect sensors with the main characteristics shown in Table 1. In each one of these point, as if it is a single-phase system, only one voltage and current are measured thanks to a measurement board.

In single phase systems, the calculation of the root mean square (RMS) fundamental voltage U 1 and current I 1 is based on the instantaneous measured components voltage u(k) and current i(k). By getting the RMS fundamental magnitudes, the harmonic ones, U H and I H , can be obtained, since

ܷ ଶ = ܷ ଵ ଶ + ܷ ு ଶ and ܫ ଶ = ܫ ଵ ଶ + ܫ ு ଶ
, where U and I are the RMS values of u and i, respectively. The apparent power S is expressed as:

ܵ ଶ = )ܫܷ( ଶ = ܵ ଵ ଶ + ܵ ே ଶ
(1) The active power P can be calculated as the mean value of the power product of the instantaneous voltage and current. Afterwards, the non-active power is defined as:

ܰ ଶ = ܵ ଶ -ܲ ଶ (2)
Other important quality rates measured are the total harmonic distortion of voltage THD U and current THD I:

ܦܪܶ = ಹ భ , ܦܪܶ ூ = ூ ಹ ூ భ (3)
Finally, the last parameters obtained are the power factor ܲ/ܵ at points 1 and 3, and the DC power at point 2.

Note that we get in our measurement block the three-phase values of power at the beginning and ending points thanks to three times the equivalent single-phase power. Now, the measurement technique used in this work is based on the single phase Quality Meter proposed in [START_REF] Milanés | Quality Meter of Electric Power Systems based on IEEE Standard 1459-2000[END_REF]. Fig. 2 shows us the quality parameters algorithm.

Creating a Real Time Quality Metering

Implementation Stages

The proposed algorithm will be implemented in two stages. First stage, that is the one described in following sections, is a Proof-of-the-concept. The second stage will be a real application implementation that will be done in future works. The resources needed for each step are:

• Proof-of-the-concept stage: A prototype of proof for the first implementation of the algorithm (mode used in the present paper). We establish a system structure based on three different PC's: o Host PC, used for the compilation of the algorithm to control the PV emulator and to calculate the required parameters. o Target PC, which executes in real time the code generated by the host PC to control the system. o Server PC, which receives the data from the target PC and sends them to an Open data. • Real application stage: In this operation mode the Host PC is no longer needed, and only the control platform or PC, where the smart metering and power electronic controls are running, and the WebServer, where the Open data are available (that is a share PC for multiple services), are operating.

Quality Parameters Algorithm and Communication Implementation

The PV emulator was developed in the host PC with its corresponding control [START_REF] Polo-Gallego | Development of a Photovoltaic Array Emulator in a Real Time Control Environment using xPC Target[END_REF]; now to get the smart metering, the quality parameters algorithm is implemented inside of this model too, taking advantage of the inputs needed in the previous project. Thus, once the host and target are connected via TCP/IP, inputs coming from the data acquisition target go through our measurement block, to get the desired quality parameters.

These parameters now have to end in the server computer for the data logging, being the chosen communication the UDP protocol [START_REF]MATLAB, xPC Target for Use with Real-time Workshop 8[END_REF].

The communication has been added to the host Simulink model, with the UDP send block and the previous pack block. The UDP send block needs the IP address to send to (server IP: 158.49.55.93), the IP port to send to (1992) and the sample time. We use an enabled subsystem, controlled by triggered function, which activates what is inside (UDP send Block, Pack and discrete function) every 5 seconds during the sample time. Before the pack block, data are treated by a discrete transfer function to calculate the average value of the five previous values and send it through the UDP send block. We can see this configuration in the Fig. 3.

The pack block is used to convert the Simulink signal of double type to a single vector of uint8 as required by the send block. Once the data has been sent, we need to receive it in the server PC. A simulink model with a UDP receive block and unpack has been created. The UDP receive block needs IP address to receive from (0.0.0.0 for accepting all) and the IP port to receive from (1992). Another important data is the output port width, known thanks to the width block. The Unpack Block changes again the uint8 data to the original state.

Therefore, we have to treat the data. We have configured an S-function to get the values of these data and write them in an array, which goes to a text format file. The S-function is inside of an enabled subsystem activated each 5 seconds (systems are synchronized). We can see the receive scheme in Fig. 4.

At the end, we obtain in our server PC a text format file with all the read quality parameters, ready to be uploaded to the Open data. The chosen format for this open data is an XML file [8], which is a widely used format for data exchange because it gives good opportunities to keep the structure of the data obtained from our original system (in our case text format file with the data array structure). In this way, new data could be added to the text file at the same time the XML format file is updated. In Fig. 5 we have a scheme of the communication system.

Real Time Guarantee

The real time behavior of the system is guaranteed because we only execute the Sfunction, which contains a clock function to write the current time and date to the corresponding data, comparing the mark of real time received from the target with the previous value, activating the function when they are different. This is because the clock block in charge of getting the real time is inside of an enabled subsystem activated each 5 seconds, so when a new data is received is because the time have changed from 5 to 10, for example.

Experimental Results

In table 2 the experimental test parameters are shown. The data obtained from an experimental test are shown in xPC target environment (Fig. 6a) and they can be accessed by conventional web navigator form in its corresponding repository (http://peandes.unex.es/powermonitor/) as it can be seen in Fig. 6b. With the experimental case we advise how the performance of the system has decreased. Note that after including the proposed algorithm in the initial power electronic system, the sample and switching frequency has been decreased from 8 to 3,120 kHz. Otherwise, the system will be stuck.

Conclusion and Future Works

A smart metering system able to generate Open data accessible in a central repository has been developed and tested using xPC Target. It has been used to get quality parameters from a system, which consists of a photovoltaic array emulator and a commercial inverter, saving them in an Open data in real time.

With this experiment we have proved how the metering algorithm could be implemented in an existing power electronics system. The inclusion of the proposed algorithm in the power electronics system causes the decrease of the sample frequency from 8 to 3,120 kHz. In the future, when we afford the real application stage, the metering algorithm will be included as a different execution tier to prevent affecting the original control algorithm. We consider that is possible to include these smart metering functions in the next generation of power electronics systems, which in some cases will required only the change of systems' firmware.

Besides other interesting work would be to develop a virtual lab [START_REF] Masár | Remote Experimentation in Distance for Control Engineers[END_REF] with the system, what would be meaningful and very popular in distance education. We will have with this proposal to implement the control in the opposite way, through the open data network to the real system. It will allow the students to see how the real system reacts when they change the control parameters.

To conclude, in the future we pretend to integrate the Open data generated by our algorithms in existing projects as Green Button Developer [10]. It will have to be done according to the ESPI scheme (Energy Services Provider Interface), which provides a way for Energy Usage Information (EUI) to be shared, in a controlled manner, between participants in the energy services markets.

Fig. 1 .

 1 Fig. 1. Real Scheme of the System.

Fig. 2 .

 2 Fig. 2. Quality Parameters Algorithm (Points 1 and 2).

Fig. 3 .

 3 Fig. 3. UDP Send Scheme.

Fig. 4 .

 4 Fig. 4. Receive scheme.

Fig. 5 .

 5 Fig. 5. Communication System Scheme.

Fig. 6 .

 6 Fig. 6. (a) xPC Target Environment. (b) Data Repository.

Table 1 .

 1 Sensor characteristics.

	Magnitude	Sensor	Number of Sensors
	Voltage	LV 25-P by LEM	5 (Vsa, Vsb, Vsc, Vdc ,Vga)
	Current	LA 25-NP by LEM	5 (Isa, Isb, Isc, Idc, Iga)

4 Quality Parameters defined on the base IEEE Standard 1459- 2010

Our system takes measurements in three different points:

1. Autotransformer to Emulator. 2. Emulator to Inverter.

3. Inverter to Grid.

Table 2 .

 2 Experimental Test Parameters.

	Parameter	Value
	Kind of Solution	Euler
	Simulation Step	1/3120
	Basis Irradiance	1000 W/m2
	Number of panels in series	8
	Number of panels in parallel	1
	V DC	272V
	V Supply	75V
	(a)	(b)


António Vieira Pombo

Vitor Fernão Pires
email: victor.pires@estsetubal.ips.pt

João Murta Pina

Application of NSGA-II Algorithm to Multiobjective Optimization of Switching Devices Placement in Electric Power Distribution Systems

Keywords: Switch Placement, Reliability, Genetic Algorithm, Electrical Distribution Networks

The Electric Utility Industry all around the world is facing numerous challenges, which include amongst others, the optimal use of expensive assets and resources and the maintenance of electric grid and customer quality service levels. The optimal placement of switches in electrical distribution networks will allow the control over service quality levels and the maximization of investments in equipments. This work proposes a genetic evolutionary algorithm NSGA-II for the optimization between the maximal return of investments on existing assets, while maintaining the quality of service provided. The trade off between total cost of investments and service quality levels SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) is analyzed, to choose the optimal placement of switches in the distribution electrical networks. The proposed method was tested with a Portuguese real distribution network. The obtained results allowed to verify the performance of the adopted approach.

Introduction

Planning and operation in Distribution system consists on satisfying the system load and energy demand, as economically as possible, while assuring supply continuity and contracted quality levels. Normally, the approaches that public owned utilities tend to implement consists on designing and operating electrical distribution infrastructures to minimize the total cost to the society. Generally this approach is referred to as value based planning.

On the other hand, private owned utilities tend to defer in time network investments and maximize their return. Their goal consists on supplying safe and reliable electric power at a reasonable cost. Recent trends however show that the future focus will not be only on the companies' economic performance, but rather on successfully combining economic performance with business impacts on social welfare and environmental factors (Fig. 1). Aging infrastructures and reliability are the most critical issues that preoccupy utility CEOs. And these two are closely related. A decrease in infrastructure investment decreases in time the reliability of the network. On the other hand an increase in network investment increases system reliability. But why focus on reliability? The effects of power outages go beyond the inconvenience experienced from the outage itself. Power outages cost local businesses thousands of euros in lost sales, interrupted manufacturing and lost data. The economic cost of outages reaches beyond lost productivity. A multi-day outage can cost residents hundreds of dollars in lost food and can damage other personal items when sump pumps stop working. Under this context, in electric power distribution system planning, the optimal placement of switches is one of the areas that contribute to the utility goal achievement of operating in the social welfare area. By choosing the optimal place and number of switches in the distribution electrical network, it is possible to maximize the quotient between the benefits to the community and the investments made by the utility on the network. By changing the state of the switches (open/close) the distribution network can be reconfigured, after a fault, thus minimizing the number of customers affected, fault duration and frequency. Although with different objectives, all reconfiguration problems are a combinatorial constrained problem, described by a non-linear and non-differential objective function. To address this optimization problem, where the objectives are generally conflicting, preventing simultaneous optimization of each objective, several algorithms have been developed. Due to the importance of the problem, several research articles related to optimal placement of switches in distribution networks have been written. In these works, different mathematical models and optimization methodologies have been used. Some of them present this issue as a single objective problem. Others use several objectives functions, but they are agglomerated into a new single objective function. Finally, some authors deal with this problem with several objective functions that are not agglomerated, allowing in this way to obtain a Pareto Optimality. Several optimization methods have been used to address the optimal switch placement problem such as Genetic Algorithms (GA) [START_REF] Logrono | Multi-Objective Optimal Placement of Automatic Line Switches[END_REF], [START_REF] Ma | Optimal switch placement in distribution networks under different conditions using improved GA[END_REF], Particle Swarm Optimization (PSO) [START_REF] Barroso | Switch placement algorithm for reducing customers outage impacts on radial distribution networks[END_REF], [START_REF] Moradi | Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm[END_REF], Ant Colony Systems (ACS) [START_REF] Teng | A novel ACS-Based Optimum Switch Relocation Method[END_REF], [START_REF] Tippachon | Multiobjective Optimal Placement of Switches and Protective Devices in Electric Distribution Systems using Ant Colony Optimation[END_REF] and Tabu Search Algorithm (TSA) [START_REF] Silva | Opimized Placement of Control and Protective Devices in Electric Distribution Systems Through Reactive Tabu Search Algorithm[END_REF]. In [START_REF] Goroohi Sardou | Modified shuffled frog leaping algorithm for optimal switch placement in distribution automation system using a multi-objective fuzzy approach[END_REF] is used a modified shuffled frog leaping algorithm and in [START_REF] Alves | A hybrid algorithm for optimal placement of switches devices in electric distribution systems[END_REF] a hybrid algorithm, both using a multi-objective fuzzy logic (FL) approach.

In this work, a new algorithm applied to this problem is proposed. Due to the characteristics of this kind of problem, two important reliability measures generally used by electric utilities were selected for the objective functions [10]. These are SAIFI, which is an indicator of utility network performance and SAIDI, which measures the operating performance of the utility in restoring customer interruptions. The evolutive genetic algorithm NSGA-II was adopted for the optimization of this problem. This kind of genetic Algorithm is a popular meta-heuristic algorithm that have been applied to multiobjective optimization in power systems such as environmental/economic power dispatch [START_REF] Abido | Environmental/economic power dispatch using multiobjective evolutionary algorithms[END_REF] and capacitor placement [START_REF] Pires | NSGA-II with local search for a multi-objective reactive power compensation problem[END_REF], and as can be seen by this work is well suited for this type of problem. In this multi-objective optimization the goal is not to find a single optimal solution, but rather a trade off between different objective functions, called the "Pareto Set", which is the set of all feasible solutions whose vector of the various objectives is not dominated by any other solution. Results of a real distribution feeder are presented in order to verify the effectiveness of the proposed approach.

Contribution to the Technological Innovation for Collective Awareness Systems

This paper considers the methodology for best placing switches in a electrical distribution network, taking into account the information ("internet of things") of existing distribution structures, equipments, number of customers, etc., so that the decision taken has a collective awareness of the systems in witch it is going to have an effect. This awareness is manifested in an innovation of the criteria's behind the methodology for switch placement.

On the other hand, the implications of the placement and operation of switches in the distribution network, will provide information to higher systems, so that they too have an awareness of this system and in that measure also take an innovative perspective on other solutions affecting the planning and operation of electrical distribution networks.

Problem Formulation

As said before, in multi-objective optimization we are not interested in finding a single optimal solution, but rather a trade off between the two different objective functions, called the "Pareto Set", which is the set of all feasible solutions whose vector of the various objectives is not dominated by any other solution. Considering that which preoccupies most the CEOs of electric utilities is aging infrastructures and reliability, we chose system reliability (SAIDI and SAIFI) [10] and investment in equipment and installation costs for the objective functions to be minimized. SAIDI (System Average Interruption Duration Index) is considered one the most widely spread indices used by electrical utilities to measure reliability. The mathematical representation of this index is presented below:

t i i N N r SAIDI ∑ = (1)
where:

i r is the average outage time per interruption of load point i due to outages in section s; i N is the number of clients in load point i; t N is the total number of clients in the network;

Another reliability system index that is used is the SAIFI (System Average Interruption Frequency Index). The difference between this index and the previous one (SAIDI), is that instead of considering the average interruption duration, in this index is considered the average interruption frequency. Equation (2) presents the mathematical representation of the SAIFI where λ i is the average outage time per interruption of load point i due to outages in section s. Comparing equation ( 1) and

(2) it is possible to verify that the difference variables i r and λ i . However i r represents interruption time and λ i represents interruption frequency, which gives a completely different concept.

t i i N N SAIFI ∑ = λ (2)
Beside the minimization of the reliability indexes SAIDI and SAIFI, a third minimization function is considered. This last function is the Total Costs (TC) in which is considered the investment in buying and installation of equipment cost associated with each solution for a given reliability [START_REF] Barroso | Switch placement algorithm for reducing customers outage impacts on radial distribution networks[END_REF].

TC = (Equipment Cost) + (Installation Cost) (3)
where: Equipment Cost is the cost of switches; Installation Cost is the cost of the installations of switches in the network; A radial electrical distribution networks characterized by several feeders coming out of a substation and its branches was considered in this work.

The GA used to generate the "Pareto Set" of feasible solutions uses a binary representation representing the place of the switches in each branch.

The NSGA-II Genetic Algorithm

The chosen Genetic Algorithm NSGA-II (Fast Non-dominated Sorting Genetic Algorithm) is an elitist multiobjective evolutionary algorithm (MOEA). This algorithm it is characterized by a fast nondominated sorting and by an efficient crowding-distance assignment approach. Due to its characteristics, it has been used in many applications and its performance tested in many comparative studies.

It consists on finding a vector "x" that minimizes a given set of "n" objective functions. The solutions are normally restricted by a series of constrains and consists on a set of results that satisfies the objective at an acceptable level without being dominated by any other solution. In this case, a solution is said to be Pareto Optimal solution and each solution cannot be improved with respect to any other objective without worsening at least one other objective.

The algorithm NSGA-II achieves the three conflicting goals expected for a GA, that is, the best known Pareto Front: 1) should be as close as possible to the true Pareto Front; 2) should be uniformly distributed and diverse; 3) should capture the all spectrum of the Pareto Front.

The general solving procedure of a GA is as following:

1. Generation of the initial N population, each of which is randomly initialized. Each solution of the population is called a Chromosome; 2. Generation of new Q solutions from existing ones, using two operators:

Crossover and Mutation. In Crossover two chromosomes, called Parents are combined together to form a new chromosome, called offspring. The selection of the Parents considers the preference towards fitness values. Mutation introduces random changes and is applied at gene level. Typically the probability of this change is very small, less then 1%. Crossover leads to the convergence of the population and mutation reintroduces genetic diversity to the population. 3. Evaluation and assignment of a fitness value to each solution; 4. Selection of N solutions from the Q set of solutions based on their fitness; 5. Continuation of the anterior four steps until the stopping criteria is met. The fitness assignment function of the algorithm uses the Crowding Distance approach. This concept aims to obtain a uniform spread of solutions along the bestknown Pareto Front without using a fitness sharing parameter. This is done by: 1) ranking the population and identify non-dominated fronts; 2) for each objective function sort the solutions in the ascending order; 3) finding the total crowding distance of a solution. The main advantage of this approach is that a measure of population density around a solution is compounded without requiring a user-defined parameter. Considering two random solutions, if they are in the same non-dominated front, the solutions with a higher crowding distance wins, otherwise, the solution with the lowest rank is selected.

The problem of Elitism, (the best solution found so far during the search has immunity against selection and always survives in the next generation), is obtained by the NSGA-II algorithm, using a fixed size N population. In step 2 of the general solving procedure, the generation of the Q solutions, considers the offspring solutions and the parent solutions. The fitness assignment considers the two populations and this makes sure that all the non-dominated solutions are included in the next populations N and the selection based on the crowding distance will promote diversity.

Regarding the genetic encoding, a matrix with N lines and M columns is used. The lines of the matrix represent the population. The branches are numerated in a sequential form. Thus, the index of each column is directly related with the beginning of a branch. Inside on each element of the matrix it is used a binary codification, where 1 represents the allocation of a switch and 0 a branch without a switch in the beginning.

Case Study

In order to verify the effectiveness of the adopted algorithm, tests over a real case were implemented. Fig. 3 shows the 94-Bus Portuguese distribution feeder that was used in this work. This is a 15 kV radial network with 94-bus feeder. In order to evaluate the effectiveness of the proposed approach, two different tests have been made. SAIFI versus Cost was used as objective function in the first one. For the switch cost it was considered 2500 € and for the installation 1000 €. The output of the NSGA II algorithm is presented in Fig. 4. In the figure the total costs is given in thousands of euros. As can be seen by the output of the algorithm, the final set of solutions is represented by the Pareto front. Each solution of the Pareto front represents a specific solution that is optimal in its own way. In fact, from these results it is possible to verify that when the costs improves the SAIFI decreases and vice-versa. The decision maker can select a particular solution from these multiple solutions. Table 1 shows the number and location of the switches for the best solution considering only SAIFI and the best solution considering only costs. As expected, for the SAIFI best solution it is required a huge number of switches, 60. However, for the SAIFI worst solution (and best of the costs) the number of switches is much reduced, 12. For the second test SAIDI versos cost was used as objective function. Fig. 4 shows the output of the algorithm using this new objective function. As expected when the cost improves the SAIDI decreases and vice-versa.

Conclusions

The allocation of switches in an electric distribution network represents an important measure to improve the power quality. Under this context, this paper presents a multi objective optimization technique to determine the optimal number and place of switches in an electrical distribution feeder. Reliability index SAIDI and SAIFI and cost of equipment's are the objective functions that are minimized to obtain the number and location of switches in an electrical distribution feeder. The algorithm used to find the optimal placement of switches is a Genetic Algorithm NSGA-II. The multi objective problem uses the Pareto optimal front to obtain the set of non dominated solutions. The adopted algorithm was applied to a 94-Bus Portuguese network to illustrate its performance. The number and location of switches in the distribution feeder have been determined in order to minimize the equipments cost and the reliability indexes SAIDI and SAIFI. This algorithm also shows the advantage of multiobjective optimization over single objective ones, since provides a superior balance between cost and reliability indexes. The proposed planning approach can be useful tool for a practical distribution system planning.

Fig. 1 .

 1 Fig. 1. Value Based Planning and Social Welfare operation.

Fig. 3 .

 3 Fig. 3. Distribution network example for network representation.

Fig. 4 .

 4 Fig. 4. Nondominated solutions obtained from de NSGA II algorithm considering as objective functions -Total Costs vs. SAIDI and Total Costs vs. SAIFI.

Table 1 .

 1 number and location of the switches for nondominated solutions.

	SAIFI	COSTS	LOCATION OF THE SWITCHES
		(thousand(€))
	0.27	150	2,4,6,7,8,9,10,11,12,13,15,16,19,20,21,23,25,29,30,31,33,34,37,
			38,40,42,45,46,48,50,51,52,53,54,55,56,57,58,60,61,62,63,64,65
			67,68,70,72,73,74,75,76,80,81,85,86,87,88,91,92
	0.44	30	6,11,20,34,35,39,43,46,47,55,77,89


Vítor Monteiro
email: vitor.monteiro@algoritmi.uminho.pt

João C Ferreira
email: joao.ferreira@algoritmi.uminho.pt

João L Afonso
email: joao.l.afonso@algoritmi.uminho.pt

Operation Modes of Battery Chargers for Electric Vehicles in the Future Smart Grids

Keywords:

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Nowadays, the electric mobility is the main alternative to the traditional transportation system in order to reduce the greenhouse gases emission and to help to address environmental and energy issues [START_REF] Boulanger | Vehicle Electrification: Status and Issues[END_REF]. It is also important to reduce the oil consumption in this sector [START_REF] Camus | Electric vehicles as a mean to reduce, energy, emissions and electricity costs[END_REF], where it is predictable that 55% of the total oil consumption in the world will be allotted by this sector in 2030 [START_REF]International Energy Outlook 2009[END_REF]. This new paradigm has been supported by the several alternatives that are already available, mainly Electric Vehicles (EVs), as the Nissan Leaf. Nevertheless, the impact of the electric mobility cannot be neglected [START_REF] Dyke | The Impact of Transport Electrification on Electrical Networks[END_REF], [START_REF] Jian | Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Micro-Grid[END_REF], [START_REF] Clement-Nyns | The impact of charging Plug-In hybrid electric vehicles on a residential distribution grid[END_REF]. The uncontrolled EVs proliferation, which represents extra loads to the power grids, can worsen some power quality problems, as power losses and high values of Total Harmonic Distortion (THD) in the currents and voltages [START_REF] Basu | Harmonic distortion caused by EV battery chargers in the distribution systems network and its remedy[END_REF]. These problems are caused by the EV battery chargers that are implemented with static power converters. Considering this scenario, it is extremely necessary equip the EVs with battery chargers with sinusoidal current consumption and controlled power factor, aiming to preserve the power quality [START_REF] Monteiro | Electric Vehicles On-Board Battery Charger for the Future Smart Grids[END_REF], [START_REF] Vítor Monteiro | Impact of Electric Vehicles on power quality in a Smart Grid context[END_REF].

Currently, the energy required to the battery charging process follows from the power grid to the EVs. This operation mode is identified in the literature as Grid-to-Vehicle (G2V) [START_REF] Haghbin | Grid-Connected Integrated Battery Chargers in Vehicle Applications: Review and New Solution[END_REF]. Nevertheless, considering that the energy can follow in opposite sense, arises the operation mode Vehicle-to-Grid (V2G) [START_REF] Kramer | A review of plug-in vehicles and vehicleto-grid capability[END_REF]. Taking into account the future smart grids, where the EVs will be connected to a collaborative broker [START_REF] João | Cloud Collaborative Broker for Distributed Energy Resources[END_REF], the aforementioned operation modes can be used to help the power grid, especially for load-shedding and compensation of renewable production intermittency (providing both backup and storage). Besides the G2V and V2G operation modes, the energy stored in the batteries can also be used to feed other loads, typical at home. This operation mode is denominated as Vehicle-to-Home (V2H) [START_REF]Nissan to launch the "LEAF to Home" V2H power supply system with Nichicon "EV Power Station[END_REF]. The selection of each one of the G2V, V2G and V2H operation modes, will be optimized taking into account the EV driver profile and benefits, and the power grid capabilities. These operation modes are further described in detail. Fig. 1 illustrates the operation modes of the EVs in the future smart grids. As shown in this figure, the G2V and V2G operation modes can be performed at private (homes) or public places, and the V2H is performed at private (homes) places.

This paper results from a preliminary studies conducted in the first year of a PhD work, and intents contribute to the technological innovation of the electric mobility in smart grids. It is presented an on-board bidirectional battery charger with respective control algorithms, which enables the G2V, V2G and V2H operation modes. In order to evaluate these operation modes are presented some experimental results.

Relationship to Collective Awareness Systems

The future smart grids will bring a set of advantages to the end-user. This is more relevant taking into account that it involves the introduction of several technologies aiming to establish a bidirectional communication between the power grid collaborative broker and the users. Knowing that the electric mobility is a topic of highest importance in smart grids, the end-user will have the opportunity to participate actively in the energy market. Thereby, to the smart grids will be possible predict the energy demand and control the energy production [START_REF] Meliopoulos | Smart Grid Technologies for Autonomous Operation and Control[END_REF], [START_REF] Sahin | Smart Grid and Smart Homes: Key Players and Pilot Projects[END_REF]. This interactivity, including supervision, control and communication applications, will be the final topic that will be addressed in the PhD work. For such purpose it will be developed an Information and Communication Technologies (ICT) application.

The main goal of the G2V, V2G and V2H operation modes that are described in this paper is establish a bidirectional flux of energy and information between the EVs and the power grids. This collaboration will strengthen the collective awareness systems, contributing to solve and organize issues related with the energy resources and power grids.

System Architecture and Operation Modes

The on-board bidirectional battery charger that is presented in this paper is composed by two power converters, one ac-dc and other dc-dc. The ac-dc converter is a full-bridge bidirectional converter that works in three distinct ways according to the operation mode. The dc-dc converter is a buck-boost bidirectional converter. Fig. 2 shows the electric diagram of the on-board bidirectional battery charger.

Grid-to-Vehicle (G2V) Operation Mode

During the G2V operation mode the energy flows from the power grid to the batteries. In this operation mode the ac-dc full-bridge bidirectional converter works as active rectifier with sinusoidal current consumption and unitary power factor. The dc-dc buck-boost bidirectional converter works as buck converter aiming to charge the batteries with different stages of current and voltages. In this operation mode, the current reference (i G *) is obtained through:

݅ ீ * = ವ * ା ಳ ௩ ಳ ಸ మ ݒ ீ , (1)
where, ܸ ீ is the RMS value of the power grid voltage, ݒ ீ the instantaneous value of the power grid voltage, ݅ ் and ݒ ் the current and voltage in the batteries, and ܲ * is the power reference obtained through the dc-link voltage. The dc-link voltage is controlled by a Proportional-Integral (PI) control:

ܲ * = ݇ ሺݒ * -ݒ തതതതതሻ + ݇ ݒሺ * -ݒ തതതതതሻ݀ݐ , (2)
where, ݒ * is the dc-link voltage reference and ݒ തതതതത the average value of the dc-link voltage during one cycle. In this controller is used the ݒ തതതതത instead of ݒ because it avoid introduce the dc-link voltage oscillation into the control. For such purpose, ݒ തതതതത in discrete samples is obtained by:

ݒ [݊] തതതതതതതതത = ଵ ் ∑ ݒ [݊] ் ୀଵ , (3)
where, T corresponds to one cycle of the power grid voltage (50 Hz), and taking into account that the sampling frequency is 40 kHz in digital control corresponds to 800 samples. Analyzing the circuit presented in Fig. 2 it can be established:

ݒ ீ ሺݐሻ = ݒ భ ሺݐሻ + ݒ ሺݐሻ , (4)
where, ݒ ீ ሺݐሻ, ݒ భ ሺݐሻ and ݒ ሺݐሻ are, respectively, the instantaneous values of the power grid voltage, inductance voltage, and voltage produce by the ac-dc converter.

With the instantaneous values of the current ݅ ீ ሺݐሻ, and the current reference (i G *) can be determined the current error by:

݅ ீ ሺݐሻ = ݅ ீ * ሺݐሻ -݅ ீ ሺݐሻ . (5)
Combining ( 4) and ( 5), and substituting the inductance voltage can be established that:

ݒ ሺݐሻ = ݒ ீ ሺݐሻ -L ଵ ಸ * ሺ௧ሻ ௗ௧ + L ଵ ୧ ಸ ሺ௧ሻ ௗ௧ . (6)
Rewriting [START_REF] Clement-Nyns | The impact of charging Plug-In hybrid electric vehicles on a residential distribution grid[END_REF] in terms of discrete samples, where k is the actual sample and k-1 the previous sample, it is obtained:

ݒ [݇] = ݒ ீ [݇] - భ ் ሺ2݅ ீ * [݇] -݅ ீ * [݇ -1] -݅ ீ [݇] -݅ ீ [݇ -1]ሻ . ( 7
)
With this voltage ݒ( ) is implemented a predictive current control. With the help of a unipolar sinusoidal Pulse Width Modulation (PWM) strategy with a 20 kHz are calculated the gate pulse patterns. In order to avoid the effects of the deadtime in the produced current, the voltage reference ݒ( ௪ * [݇]) that is compared with the triangular carrier is given, at each k sample, by:

ݒ ௪ * [݇] = ݒ [݇] + ݒ∆ݑ , (8)
where, ݒ∆ is the voltage that is added to the voltage ݒ [݇], and ݑ assumes the value 1 when the reference current (݅ ீ

Vehicle-to-Grid (V2G) Operation Mode

During the V2G operation mode the energy flows from the batteries to the power grid.

In this operation mode the ac-dc full-bridge bidirectional converter works also as an inverter, however, with sinusoidal current injection. The dc-dc buck-boost bidirectional converter works as boost converter aiming to discharge the batteries with constant power (as presented in this paper, however, can also be constant current). In this operation mode, the ac-dc converter control is similar to the one used in the G2V operation mode. To synthesize the reference current it was also used the predictive current control. The main difference is the dc-dc operation, which, in this case, operates as boost converter. The power to be delivered to the power grid is established as external input parameter. In the context of this paper, the value of the power is received through a serial port, however, it is predictable that it will be received through a wireless communication aiming to enable the collaborative integration of the EV in the future smart grids. With the reference power (ܲ ் * ) and the batteries voltage ݒ( ் ) is determined the current reference (݅ ் * ):

݅ ் * = ಳ * ௩ ಳ , (11)
and the voltage reference ݒ( ௪_ * ) that the converter must produce is obtained by:

ݒ ௪_ * = ݇ ሺ݅ ் * -݅ ் ሻ + ݇ ሺ݅ ் * -݅ ் ሻ݀ݐ . (12)

Vehicle-to-Home (V2H) Operation Mode

During the V2H operation mode the energy flows from the batteries to feed home loads during power outages. It also can be used to feed loads in places without connection to the power grid. In this operation mode the ac-dc full-bridge bidirectional converter works as inverter aiming to produce a true sine wave voltage output to feed the home loads. The dc-dc buck-boost bidirectional converter also works as boost converter aiming to increase the batteries voltage to an appropriated value to the operation of the ac-dc converter. In this operation mode the ac-dc converter works as voltage source inverter. Thus, analyzing the circuit presented in Fig. 2 it can be established:

ݒ ሺݐሻ = ݒ ீ * ሺݐሻ -ݒ భ ሺݐሻ , (13)
where, ݒ ሺݐሻ, ݒ ீ * ሺݐሻ, and ݒ భ ሺݐሻ are, respectively, the instantaneous values of the voltage produce by the ac-dc converter, the reference voltage, and the inductance voltage. Substituting ݒ భ ሺݐሻ it can established:

ݒ ሺݐሻ = ݒ ீ * ሺݐሻ -L ଵ ௗ ಽ భ ሺ௧ሻ ௗ௧ , (14)
Also in this control was used the strategy presented to the G2V operation mode in order to avoid the effects of the deadtime.

In this operation mode the dc-dc converter operates as boost converter in order to maintain the dc-link voltage regulated to the proper functioning of the ac-dc converter. For such purpose, the dc-link voltage is controlled through a PI and the voltage reference ݒ( ௪_ * ) that the converter must produce is given by:

ݒ ௪_ * = ݇ ሺݒ * -ݒ ሻ + ݇ ݒሺ * -ݒ ሻ݀ݐ (15)

Experimental Results

In order to evaluate the on-board bidirectional battery charger working in the G2V, V2G and V2H operation modes it was developed a laboratory prototype. Fig. 3 shows part of the developed prototype. The experimental results were obtained with the on-board bidirectional battery charger and with a set of 24 sealed 12 V 33 Ah Absorbed Glass Mat (AGM) batteries. Fig. 4 shows the obtained results. More specifically: Fig. 4

Conclusions and Further Work

This paper presents the development of an on-board bidirectional battery charger for Electric Vehicles (EVs), which can work in Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Home (V2H) operation modes, targeting the future smart grids scenario. Taking into account that the introduction of EVs in smart grids will be an important subject, the bidirectional communication between EV drivers and the collaborative brokers of smart grids will strengthen the collective awareness systems. Therefore, it will contribute to solve and organize issues related with energy resources and power grids. The developed laboratory prototype, functioning in the three operation modes, was validated through experimental results.

Currently, the selection of the operation modes (G2V, V2G and V2H) is performed manually by the driver, through the user interface of the EV. Nevertheless, until the final of the PhD work where this paper is encompassed, this selection will be wireless controlled. Besides, it will be developed an Information and Communication Technologies (ICT) application aiming to allow the communication with a collaborative broker of the smart grids.

Fig. 1 .

 1 Fig. 1. Representation of Electric Vehicle with G2V, V2G and V2H operation modes.

Fig. 2 .

 2 Fig. 2. On-board bidirectional battery charger composed by two power converters: ac-dc full-bridge bidirectional converter and dc-dc buck-boost bidirectional converter.

Fig. 3 .

 3 Fig. 3. Parts of the developed prototype of the on-board bidirectional battery charger: (a) Power converters; (b) Signal conditioning circuit; (c) Digital control platform.

  (a) shows the power grid voltage (v G ) and the current (i G ) during the G2V operation mode; Fig. 4 (b) presents the power grid voltage (v G ) and the current (i G ) during the V2G operation mode; and Fig. 4 (c) shows the output voltage (v G ) and the load current (i G ), during the V2H operation mode.

Fig. 4 .

 4 Fig. 4. Experimental results of the power grid voltage (v G -100 V/div) and current (i G -10 A/div): (a) G2V operation mode; (b) V2G operation mode; (c) V2H operation mode.

Acknowledgment. This work is financed by FEDER Funds, through the Operational Programme for Competitiveness Factors -COMPETE, and by National Funds through FCT -Foundation for Science and Technology of Portugal, under the project FCOMP-01-0124-FEDER-022674, and QREN project AAC n.º36/SI/2009 -13844. Mr. Vítor Monteiro was supported by the doctoral scholarship SFRH/BD/80155/2011 granted by the FCT agency.