Thiago R P M Rúbio
email: reis.thiago@fe.up.pt

Henrique Lopes

Eugénio Oliveira

Adaptive Multi-Agent System for Smart Grid Regulation with Norms and Incentives

Keywords: Smart Grids, Multi-Agent Systems, Regulation, Coordination, Norms, Incentives, Business Process Modelling

Regulatory policies applied to traditional energy systems are not sufficient when considering smart grids' new requirements such as distributed and decentralised coordination. New management techniques are needed in order to shape consumers conducts by prohibiting, sanctioning or incentivising specific behaviours leading to more efficient utilisation of resources. This paper discusses the possibility of regulating demand in smart grids by the application of processes reflecting the utilisation of norms and incentives in order to better adjust supply to demand. Thus we consider this soft-control policy based on the business layer rather than traditional control for cyber-physical systems. The Business Process Modelling (BPM) approach will make easier the design, management and observation of the norms and incentives as flexible artefacts, highlighting decision and coordination processes.

Introduction

The introduction of renewable power sources, storage appliances, and domestic production developed in the last years introduced complexities that exceed current systems management capabilities. From one hand, the natural evolution to a smart grid paradigm becomes very important since current structures are not efficient to control loads in a dynamic and decentralised fashion.

From the other hand, the possibility of direct and timing interaction (e.g. control/actuation) among the various actors in the system brings changes also in the structure of the electricity market. Currently, operators keep generation at peak demand level, leading to large load wastes of energy and monetary losses. Typical actors in market are generator companies (GenCo) and brokers (retailer companies) representing the energy supply, and consumers, such as big industrial facilities, households and electric vehicles representing the demand. In the smart grid concept consumers can have also production capabilities; this is the case of prosumers. The regulation of the demand means the application of control policies in order to lead the consumers to adopt specific conducts and dynamic adapting strategies will allow efficient supply-demand adjustments.

Spontaneously some research questions are rising about the way we can gain insights about the smart grid's ecosystem properties and processes. In which way is it possible to influence entities' behaviours and create those adaptive mechanisms that lead the system into an efficient coordination? And last, how could these strategies support managers in real-world's decision processes? The current work results from a first effort to tackle the aforementioned issues. During our research we will seek to address all the related issues that will come up.

Given the domain's inherent characteristics, such as distributed control through autonomous entities, decentralised approaches as Multi-Agent Systems (MAS) are being pointed as the most appropriate [START_REF] Wooldridge | An introduction to multiagent systems[END_REF]. We intend to produce a MAS to represent a smart grid and the connected energy market. In this context, acting as smart grid actors, agents are connected by energy contracts and the regulatory policies are expressed through energy tariffs. Each agent in the system can represent either a supply-side organisation (e.g. GenCo, brokers) or demand-side individual entity (e.g. household, electric vehicles, or industrial facilities).

Our MAS benefits from Business Process Modelling (BPM) enabling the design of more flexible regulation mechanisms composed by norms and incentives represented as process artefacts.

This paper is structured as follows: Section 2 identifies the relationship of this work to cyber-physical systems; Section 3 gives an overview of related literature; Section 4 discusses the contributions and novelty aspects of the work; and Section 5 overviews the critical analysis of this project and forward research directions. Finally, Section 6 describes some conclusions and future work.

Relationship to Cyber-Physical Systems

Smart grids are characterised by the combination of physical power system with information processing and communication capabilities, and business layers. This synergy creates a large-scale distributed system that reflects the Cyber-Physical System (CPS) features [START_REF] Aman | Energy management systems: state of the art and emerging trends[END_REF]. Our contributions are projected to CPS concept in the way it comprises control and coordination of human-agents with autonomous artificial artefacts (with different behaviours and intentions), energy and information. Although CPS are mostly oriented in controlling physical entities through the actuation on hardware agents applying traditional control techniques, our attempts here comprise a type of control based on a behavioural-shift fashion. The controller does not try to regulate the distribution load, but instead tries to modulate the demand by shifting the load peaks. This is achieved by coordinating the consumption (of not necessarily cooperative agents) in order to avoid herding phenomena. Such soft-control [START_REF] Han | Soft control on collective behavior of a group of autonomous agents by a shill agent[END_REF] is obtained by using price signals, as they derived from the energy market evolution. Load usage will ensure the feedback for regulation. Many issues in this domain are still open and the full development of the smart grid depends on solving them.

The importance of adjusting demand and supply and the mobility of energy, linking multiple grids are discussed in [START_REF] Ramchurn | Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence[END_REF], [START_REF] Ramchurn | Agent-based control for decentralised demand side management in the grid[END_REF]. Concerns exist also regarding political gridlocks, market inefficiency, and short-sighted planning as some of the existing bottlenecks. In this sense, BPM is presented as a systematic approach to support the understanding of the interaction between business and control layers to achieve compliance. Moreover, regulatory mechanisms and incentives for small consumers are requirements to achieve success with smart grids [START_REF] Caron | Incentive-based energy consumption scheduling algorithms for the smart grid[END_REF].

State of The Art

According to the European Commission, "A smart grid is an electricity network that can cost efficiently integrate behaviours and actions of all users connected to itgenerators, consumers and those that do both -in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety" [START_REF]The European Commission: Definition, expected services, functionalities and benefits of Smart Grids[END_REF].

Regulation in the current grid is almost exclusively lead by governmental regulatory policies that control supply and demand. In a fully operational smart grid, new regulatory policies will address the necessary coordination for efficient management. Some examples of regulation practises are: legal restrictions, contract regulations, self-regulations, or social regulations [START_REF] Levi-Faur | Regulation and regulatory governance[END_REF].

Norms represent means to control interactions between individuals to achieve desired behaviours and responses [START_REF] Cardoso | Social control in a normative framework: An adaptive deterrence approach[END_REF]. Normative MAS can be created for environments where agents can decide whether to follow or not the explicit norms [START_REF] Boella | Norm negotiation in multiagent systems[END_REF]. Incentives, on the other hand are the instrument that can push agents in a desired way [START_REF] Hermoso | From roles to standards: a dynamic maintenance approach using incentives[END_REF], [START_REF] Centeno Sánchez | Mecanismos incentivos para la regulación de sistemas multiagente abiertos basados en organizaciones[END_REF], [START_REF] Logenthiran | Demand side management in smart grid using heuristic optimization[END_REF]. Some characteristics of the MAS paradigm [START_REF] Wooldridge | An introduction to multiagent systems[END_REF], [START_REF] Jennings | On agent-based software engineering[END_REF] make this an appropriate paradigm to model smart grids. Recent works using MAS in smart grids address coalition formation, distributed control, and market-driven competition [START_REF] Ketter | Power tac: A competitive economic simulation of the smart grid[END_REF].

BPM allows to decouple the business logic layer from agent's inner reasoning and behaviours, centring the focus on the functional aspects of the system management [START_REF] Scholz-Reiter | Business process modelling[END_REF], [START_REF] Chinosi | Bpmn: An introduction to the standard[END_REF]. Few research relates BPM and MAS, although some works on translating from one to another can be found in [START_REF] Küster | Integrating process modelling into multi-agent system engineering[END_REF] and [START_REF] Coria | Intelligent business processes composition based on multi-agent systems[END_REF]. Efforts on the combination of normative systems with incentives mechanisms still lack implementation and analysis, as seen in [START_REF] Ossowski | Agent-based aplications for the smart grid-a playground for agreement tecnologies[END_REF]. Besides, new models might be created to help solving challenging problems as the participation of Electric Vehicles (EVs) [START_REF] Marques | The "smart paradox": Stimulate the deployment of smart grids with effective regulatory instruments[END_REF], tariff composition and efficient appliance scheduling [START_REF] Logenthiran | Demand side management in smart grid using heuristic optimization[END_REF].

Contributions and Innovative Aspects

Smart grid managers may benefit from regulation since agents should be able to adapt their strategies given system's responses and their own goals [START_REF] Keshtkar | Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives[END_REF]. Customers can also benefit from agents automatic negotiation by improving their ability to buy and sell energy [START_REF] Ramchurn | Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence[END_REF]. However, selfish behaviours are present and managers will try to minimise their impact for the sake of social welfare [START_REF] Marques | The "smart paradox": Stimulate the deployment of smart grids with effective regulatory instruments[END_REF]. Recent works [START_REF] Cardoso | Social control in a normative framework: An adaptive deterrence approach[END_REF], [START_REF] Centeno Sánchez | Mecanismos incentivos para la regulación de sistemas multiagente abiertos basados en organizaciones[END_REF] show some efforts in decentralised approaches to regulate dynamic environments. Due to the inherent complexity of smart grids, big challenges are still open [START_REF] Ramchurn | Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence[END_REF].

Research on MAS has, in the last years, devoted a strong attention to demand regulation. Although other existing proposals concern the use of norms and incentives to regulate multi-agent systems separately, appropriate models that combine norms and incentives in order to promote cooperative behaviours still lack flexibility and realistic characteristics. The challenge of using regulation as coordination mechanism is among the objectives of our research effort. Figure 1 show the four cornerstones in our smart grid representation. Customers (end-users), market agents (managers), operators and transmission are the main parts of the environment. Regulation is a pervasive element that coordinates all the actions: managers establish contracts that govern customer's consumption and production, constrained by energy availability and prices from both physical (transmission) and operational (economics) parts.

Incentives and Norms as Processes Artefacts

In this work we discuss the use of BPM aspects that could help users (either acting on supply, or in demand side) to define and authorise agent to act on their behalf and trigger the proper processes. BPM systems consist on the systematic characterisation of the actor's actions when executing a process. Figure 2 shows a typical process, represented with BPMN. The process carry all the logic flow that correspond to the control tasks in a specific scenario, without defining the lower-level actions. Our approach intends to merge processes' tasks into agent's actions and benefit from such flexibility.

This approach offer a dynamic way to analyse and design managers' goals and user's actions, coordinating their interactions. Connecting the business logic layer to physical and economical aspects that constrain agent's actions allows to create a modular configuration for the smart grid scenario. Describing the business processes in the setting of current and future grids will help to understand and develop novel regulation strategies. Figure 3 shows that processes will develop a major role on organising the interactions of the agents. We propose that the soft-control should occur in business-logic level activities.

Fig. 2. A typical BPMN Process

Managers can observe and act accordingly to enforce system's states and achieve their goals. To do so, we will study how to design norms and incentives as artefacts, such as sub-processes that can be triggered and respond to system's changes on-line. On the other hand, customers will use processes as the linking element that can respond to the environment. Manipulating preferences and contract restrictions, customers can act changing behaviour or facing consequences. Automatic scheduling from agent's decisions will allow customers to optimise demand, decrease costs and improve budgeting.

Following a BPM approach, our models will comprise the process design cycle, describing regulation in current grids as-is and new processes proposed for coordination in the smart grid context to-be. The problems tackled will focus the creation and adaptation of tariffs in order to ensure management efficiency (e.g. demand peak avoidance). Therefore, the test bed and validation of the current work is being considered in this context. Fig. 3. Acting on the business process layer will help to achieve the desired states

Results and Critical View

The insights of this work are to create a vision and to formalise concepts necessary to future lines of research. Regarding the regulatory tools on smart grids, default control is related to the physical transmission lines and the electrical system. The other way, used on this work, is a system management based on tariff actuation. Representing the contract between the broker (manager) and the customer, tariffs can wrap financial tools enabling regulation.

Our preliminary work on this thread considers the PowerTAC1 framework, a suitable simulation focused on the tariff composition problem. We have created and validated a new model for tariff creation, based on fuzzy logic that uses energy imbalances as a feature for incentivising specific prosumer behaviours [START_REF] Rúbio | Tugatac broker: A fuzzy logic adaptive reasoning agent for energy trading[END_REF]. The model creates new tariffs aiming for controlling broker's portfolio and improving its participation on the market. Results showed great potential in our fuzzy strategy. The broker agent is been refined in order to be able to compete in PowerTAC homonym official international competition.

Changing tariff features, such as the energy price, sign-up or withdraw fees can lead to customers' behaviour changes [START_REF] De Weerdt | Pricing mechanism for real-time balancing in regional electricity markets[END_REF]. The first step in order to characterise relationship between the consumption and tariffs consists on analysing real consumption data-sets. Using smart metering data from the U.S. Department of Energy's Green Button initiative 2 we observe that there are some consumption changes accordingly to energy prices. Figure 4 (left) shows the patterns for a typical day on the data-set. Market prices goes higher as consumption increases. In the same way, Figure 4 (right) shows similar patterns for both consumption and prices on working days. Weekend days are evidenced on days 4th and 5th. It appears that exists a correlation between consumption behaviour and energy prices. This is in line with our intentions to apply control mechanisms for shaping customers' behaviour to desired states.

Finally, our best efforts will consider norms and incentives as process artefacts.

Conclusions and Future Work

The adoption of smart grids brings new challenges in energy management and thus in the society [START_REF] Ramchurn | Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence[END_REF]. The development of an enhanced business layer infrastructure will allow the analysis and design of more complex and distributed strategies for demand coordination. Adopting \textit{soft-control} mechanisms allow some kind of social management, based on incentivising specific behaviours while avoiding others.

Here, a flexible process-oriented approach for regulation in multi-agent systems is proposed, describing how management on the business layer can include norms and incentives as flexible process artefacts. For this purpose the BPM approach is discussed. Following, some preliminary studies are considered. First we have used an existing electricity market simulation framework to address the tariff creation problem in order to incentivise specific customer behaviours. Results indicate the feasibility of applying demand-side soft control based on tariff actuation.

Next, we have analysed the relationship between consumption and energy price from real data-sets. This opens the possibility for exploring the correlation between consumption behaviour and energy prices.

Future work will deploy data-mining techniques for prediction and creation of synthetic load profiles. We will also study how the combination of norms and incentives can suppress or support the emergence of behaviours that are not benevolent to the system.

Fig. 1 .

 1 Fig. 1. Regulation in a Smart Grid architecture

Fig. 4 .

 4 Fig. 4. Consumption vs Price in a typical day (left) and Consumption vs Average daily price during a week (right)

www.powertac.org

Green Button initiative -http://energy.gov/data/green-button

Acknowledgments. This work is funded through an IBRASIL Grant. IBRASIL is a Full Doctorate programme selected under Erasmus Mundus, Action 2 -STRAND 1, Lot 16 and coordinated by University of Lille.


A Setas Lopes

Rui Castro
email: rcastro@tecnico.ulisboa.pt

Carlos Silva
email: carlos.santos.silva@tecnico.ulisboa.pt

Contributions to the Design of a Water Pumped Storage System in an Isolated Power System with High Penetration of Wind Energy

Keywords: Renewables integration, Water pumped storage systems (WPSS), Particle Swarm Optimization (PSO), Computational algorithms, Cyber-Physical Systems

The increasing penetration of renewable energies in the electrical systems, particularly in small and isolated systems, like the case studied in this paper, Terceira Island in Azores, Portugal, creates challenges in the dispatch related to the variability and the difficulty to forecast the renewable resources. One way to deal with such issues is to use Water Pumping Storage Systems (WPSS) to regulate the system electricity production by storing energy surplus in low load periods and returning it back in high load periods, reducing at the same time the need to curtail wind. This paper describes and compares a deterministic and a metaheuristic methodology, using Particle Swarm Optimization (PSO), used to determine the best configuration of the WPSS in terms of number and unit power of pumps and turbines, and upper and lower reservoir capacity that lead to the best economic value, determined by the Net Present Value (NPV) of the investment.

Introduction

The integration of renewable energies in the electrical systems has brought issues related with the variability of the primary sources, such as network instability, with possible negative impacts, namely in small and isolated systems.

Wind curtailment strategies combined with high reserve margin values for the thermal base generating units are the usual measures to cope with this issue. This has the consequence of increasing system costs, and wasting renewable resources. One way to deal with the problem is to use WPSS, designed to absorb excess energy in low load periods and later return it in peak periods, providing a balance to the system. However, it is necessary to verify the feasibility of such systems, as compared to the above mentioned solutions.

The models used to simulate the energy balance of such systems generate the need to develop tools that are able to deal with large amounts of data. There are several tools available as described in [START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF], where an extended list of energy system analysis tools is covered, for a wide range of applications, in terms of time scale and application area; as well as several optimization methods described in [START_REF] Baños | Optimization methods applied to renewable and sustainable energy: A review[END_REF], where a review on several studies approaching the use of optimization methods supporting the integration of renewables is done, concluding that this type of tools is becoming increasingly important.

The study presented in this paper intends to verify if the use of such optimization methods, namely Particle Swarm Optimization (PSO), to the design of a WPSS, leads to the same results for the configuration of the WPSS, with better performance in terms of computational times, than the deterministic model.

PSO was used, because there are several examples in the available literature showing that it presents good approximations of the correct results, the convergence is fast and the involved computational time is reasonable.

The decision to develop a dedicated algorithm for Terceira case, instead of using available tools, was based on the fact that none of the tools seems to cover, in an integrated way, the optimization of a WPSS, considering both the energy balance and investment analysis models.

All the algorithms were implemented using MATLAB language. MATLAB was chosen because it is a well-known language with a wide network of developers, providing a large set of toolboxes that can be used in this type of problems.

Benefits and Contribution from Cyber-Physical Systems

In this study several computational algorithms implemented in MATLAB language, using both deterministic and metaheuristics models were used to solve the problem of determining the configuration of a WPSS, leading to the best Net Present Value (NPV).

Using an integrated approach that includes the electrical system energy balance, the system design, and the economical analysis, leads to a complex problem that involves large amounts of data; the use of computational calculus is therefore mandatory.

Besides that, the use of metaheuristics methods, namely PSO, shows that with the support of computational optimization algorithms is possible to reach optimal results in solving complex problems, using less CPU time than the deterministic methods.

The research presented in this paper intends to be a contribution to the optimization of the electrical energy production in an islanded power system. This can be regarded also as a contribution to the development of the Smart Grid concept, in which renewable energies are expected to be fully integrated under the Smart Environment paradigm.

Given the nature and dimension of the problem, it would not be practical to build a real model, even at laboratory scale, where several WPSS configurations could be tested in order to find the one that leads to the best results. Nevertheless, it is the authors purpose to compare the results obtained using the presented model with the results of the real system, after its construction and commercial operation starts. Another issue that we would like to point out is that economic aspects are much relevant and have to be assessed. As so, a theoretical economic model seemed to be the best way to accomplish this task.

With the increasing complexity of Energy Systems (ES), Cyber-Physical Systems (CPS) will play an important role in the management of ES. As so, the development of simulation tools that are able to recreate the conditions found in the operation of ES is also of importance, as mentioned in [START_REF] Marija | Modeling of Future Cyber-Physical Energy Systems for Distributed Sensing and Control[END_REF].

We believe that a possible contribution of our work is to provide a simple simulation tool of the energy balance between load, generation and storage. Although the generation and storage is dealt in the model in an aggregated form, it would be possible to further refine it including unit commitment modules for optimum scheduling of the thermal units. Furthermore, the data supplied by this model could then be used to feed the models that deal with the dynamic behavior of the ES components.

State of the Art

Several papers on the utilization of computational optimization methods to find the best configurations of WPSS are available, in [START_REF] Papaefthymiou | Optimum sizing of wind-pumped-storage hybrid power stations inisland systems[END_REF] the sizing of a WPSS is done with the support of Genetic Algorithms (GA), on the investor's perspective, by looking at the tariffs and applicable energy effects in the investment, and on the system perspective, by looking at the reduction of the Levelized Cost of Electricity (LCOE) and the increase of renewable energy sources penetration. [START_REF] Stoppato | A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area[END_REF] explores the application of a PSO approach to the design optimization of a pump storage system, combined with a photovoltaic system. In [START_REF] Erdinc | Optimum design of hybrid renewable energy systems: Overview of different approaches[END_REF] the authors compare different approaches using several optimization methods, GA, PSO and Simulated Annealing, concluding that the suitable approach may depend on the type of application and user requirements, nevertheless, promoting the applicability of renewable energy systems (RES).

Research Contribution and Innovation

Energy Balance and Economic Models

Characterization of Terceira Power Generation System. This system, as planned for 2020, comprises the power plants of: Central Térmica de Belo Jardim (CTBJ), a thermal power plant using as prime movers internal combustion engines, with total installed power of 47.6 MW divided by 4 units of 5.9 MW each and 2 units of 12 MW each; Parque Eólico da Serra do Cume (PESC), a wind farm with 10 units, each of 0.900 MW, plus 4 equal additional units to be installed; 3 small-hydro adding to 1,432 MW; A geothermal power plant of 3 MW; A waste to energy power plant of 1,7 MW. The voltage and frequency control are assigned to the CTBJ power plant, by choice of the system operator. The base data set consists of the electricity demand; hydro and wind based electricity production records, as well as wind speed, in periods of 30 minutes, for the year of 2012, geothermal and waste to energy were considered to run continuously at power plant rated power.

The used methodology aims at determining, for each analyzed period of 30 minutes, the excess of electric energy available after the demand is supplied by the existing power plants, using as much as possible the electricity produced by wind energy conversion systems. This surplus energy is stored, subject to operational restrictions and limits of the electricity production and water storage systems. The stored energy in a WPSS is dispatched for each period, in order to minimize the thermal based electricity.

Calculation of Excess Energy Available For Storage. The excess energy Eeei, for each period i is calculated by:

Eee i =(Ectbj i +Eh i +Epesc i +Egth i +Ewte i -Eload i )η pump . ( 1
)
where: 𝐸𝑙𝑜𝑎𝑑 𝑖 , is the total demand for period i; 𝐸𝑐𝑡𝑏𝑗 𝑖 , is the thermal power production for period i; 𝐸ℎ 𝑖 , is the hydro power production for period i,; 𝐸𝑝𝑒𝑠𝑐 𝑖 , is the wind power production for period i; 𝐸𝑔𝑡ℎ 𝑖 , is the total geothermal power production for period i, assumed equal to continuous rating; 𝐸𝑤𝑡𝑒 𝑖 , is the total waste to energy power production for period i, assumed equal to continuous rating; 𝜂 𝑝𝑢𝑚𝑝 , is the pumping efficiency.

Constraints and Limits. Constraints: Load satisfaction; Spinning Reserve; Technical operational minimum and maximum load of the thermal units; Feasible states of operation of the thermal power plant; Upper and lower reservoirs maximum capacity; Minimum number of consecutive operation periods for thermal units.

Limits: Pump efficiency and its minimum load; Turbine efficiency and its minimum load.

Load Satisfaction. As in any electrical power system, the load must be fulfilled independently of existing storage or not, and this is translated by: Ectbj i +Eh i +Epesc i +Egth i +Ewte i -Eee i-1 η turbine =Eload i .

(

Where, 𝐸𝑒𝑒 𝑖-1 , is the total excess energy stored up to the end of period i-1 and 𝜂 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 , is the turbine efficiency.

Spinning Reserve. SR is provided by the thermal units plus the stored energy (turbine units), and is established by the power system operator as a function of the average wind speed at PESC.

Technical Operational Minimum and Maximum Load of Thermal Units.

A minimum of two thermal units operating and at no less than 6 MW.

Feasible States of Operation of the Thermal Units. Determined by simple enumeration, considering that a minimum of two units has to be operating, running on 50% minimum load. Note that for the units with 5.9 MW rated power, the minimum power was set to 3 MW, in order to satisfy the operator's demand of 6 MW minimum.

Upper and Lower Reservoirs Maximum Capacity. The upper and lower reservoir maximum capacity was obtained by calculating the volume of water corresponding to the potential energy equal to the maximum excess energy available, for each 24 hour period, affected by the pump efficiency.

Minimum Number of Consecutive Operation

Periods for Thermal Units. Established as a minimum of 2 hours of continuous operation for the same configuration of units, except where transition to a higher power state is needed in order to satisfy demand.

Pump and Turbine Operating Limits. For units 1-5 MW, based on existing references [START_REF] Katsaprakakis | Introduction of a wind powered pumped storage system in the isolated insular power system of Karpathos-Kasos[END_REF] efficiencies of 80%, for both the pump and turbine, were considered.

The considered minimum operating loads are 70% of nominal rated power for pumps and 15% for the Pelton turbines [START_REF] Ardizzon | A new generation of small hydro and pumpedhydro power plants: Advances and future challenges[END_REF]. Economic Model. The economic assessment is based on an NPV model, for a 30 years period, of the cash flows resulting from the difference between the WPSS investment and operation costs and the savings obtained by not running the thermal units.

The WPSS investment costs are obtained by estimates of the civil works, including reservoir, electromechanical and engineering costs; based on the cost functions determined in [START_REF] Ogayar | Cost determination of the electro-mechanical equipment of a small hydro-power plant[END_REF] and cost breakdown explained in [START_REF] Zhang | Small Hydropower Cost Reference Model[END_REF], as: Civil costs: 60% of total investment; Electromechanical costs: 33%; Engineering costs: 7%.

Electromechanical equipment costs were determined using the cost function for single Pelton units [START_REF] Ogayar | Cost determination of the electro-mechanical equipment of a small hydro-power plant[END_REF]: Em cost1 =aP b-1 H c , a=17.693, b=0.635275,c=-0.281735 .

(

) 3
Adapted to multiple units using the cost formulas determined in [START_REF] Singal | Cost analysis of low-head dam-toe small hydropower plants based on number of generating units[END_REF], resulting in the following cost formulas:

Two units Em cost2 =a 2 P b-1 H c , a 2 =27.070 . ( 3a
)
Three units

Em cost3 =a 3 P b-1 H c , a 3 =35.209 . ( 3b
) Four units Em cost4 =a 4 P b-1 H c ,a 4 =42.109 . ( 3c
)
Where: Emcost, is the cost of the electromechanical equipment, in €/kW; P, is the rated power, in kW; H, is the net head in meters, in this case 300 meters.

The costs for the pump units, including the electrical drive, were estimated as equal to the turbine/generator groups.

Calculation Process: Excess energy, storage, wind curtailment and NPV, A flow chart describing the process is depicted in Figure 1

Optimization Approaches

For both approaches, the same models that simulate the energy balance and that make the investment economic analysis, are used.

Fast Deterministic method. To reduce computational time, the applied method uses a stepped approach: #1 considers combinations of one turbine and one pump, starting with 100 kW for each and increasing in steps of 100 kW, up to the maximum of 5000 kW; the combination leading to the best NPV is then identified. #2 a new range of values is defined, by adding and subtracting 1000 kW to the previous values of the best combination obtained; values are then divided in 1 to 4 units each, for pump and turbine, and run again for NPV calculation with steps of 10 kW; #3 is similar to #2, but with steps of 1 kW and a tighter variation band for the range of power values, the combination with best NPV results is then chosen as the best one. With this approach is possible to reach to the pump and turbine combination values for the WPSS configuration, without the need of looking through all possible combinations, which would take too long time to be considered, for instance, considering that each iteration takes about 0.1 second, all the combinations of 4 pumps plus all the combination of 4 turbines, would take approximately 6.7e+20 days. In the stepped approach it took only 31697 seconds.

Fig. 1. Excess energy, storage, wind curtailment and NPV calculation

Particle Swarm Optimization method. The PSO method is a meta-heuristic optimization model initially developed by James Kennedy and Russell Eberhart [START_REF] Kennedy | Particle swarm optimization[END_REF].

The method used here results from several evolutions of the first method [13]- [START_REF] Clerc | The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization[END_REF] and is based in the work of [START_REF] Pso | An Off-The-Shelf PSO[END_REF]. The method starts by randomly initializing a group of possible configuration of pump and turbines, the so called particle positions, however testing for limits in the number and unit power of each unit. For each particle position the corresponding best NPV is calculated and stored (called Local Best), the best NPV of all particles is also stored (called Global Best). Then the next particle position is calculated based on its previous position and its velocity. The velocity is calculated from the difference between its current position and the Local Best and Global Best. The procedure is: Initialize particle positions Pij (pnij, ppij, tnij, tpij) and velocity vij; For each particle i position and current iteration j, calculate its NPV value and determine its Local Best (LBi); For all particles determine the best NPV, which becomes the Global Best (GB); Update next iteration (j+1) particle position, by adding its updated velocity to its current position, according to following equations: P ij+1 (pn ij+1 , pp ij+1 , tn ij+1 , tp ij+1 ) = P ij (pn ij , pp ij , tn ij , tp ij ) + v ij+1 (4)

v ij+1 =K(v ij+1 +𝜑 1 rand (Pbest i -P ij (pn ij , pp ij , tn ij , tp ij )) +𝜑 2 rand (Gbest-P ij (pn ij , pp ij , tn ij , tp ij )) ) (5) K= 2 |2-φ-√φ 2 -4φ| (6)
With φ= φ1+ φ2. The values of φ1 and φ2 are chosen with basis on experience from several runs of the algorithm, as the ones that lead to convergence and best results. Several literature [START_REF] Tuppadung | Comparing nonlinear inertia weights and constriction factors in particle swarm optimization[END_REF], [START_REF] Pso | An Off-The-Shelf PSO[END_REF], [START_REF] Clerc | The particle swarm -explosion, stability, and convergence in a multidimensional complex space[END_REF] advise the use of φ1=2.8; φ2=1.3; in any case, φ>4.

Pbesti, is the position of the particle that corresponds to the best result of all particles i, up to the current iteration, therefore corresponding to the value LBi, Gbest, is the position of the particle that corresponds to the best result of all the particles, up to the current iteration, therefore corresponding to the value GB. The stopping criteria is determined by a minimum number of consecutive iterations, where the position of a significant number of particle remains unchanged. In any case, the PSO stops at the maximum number of iterations.

Results and Discussion

With the Fast Deterministic method, values reached for the optimal configuration are: 2 pumps of 853 kW each and 1 turbine of 3843 kW; NPV:2,574,857 €; Computation time: 31,697 seconds.

With the PSO method, applied for several different combinations of parameters, each combination run 50 times, the results leading to the best NPV in each combination, are displayed in Table 1. With the PSO method, the obtained results for the combination that leads to the best NPV, are the same as the ones obtained with the fast deterministic method, Furthermore, the best PSO result in terms of CPU time has a total CPU time of 12,519 seconds, which is less than half of the FD result. This shows a clear advantage for the PSO method.

Important to note that, although the frequencies of occurrence of the optimal solution are generally low, the sub-optimal solutions, not shown here, have NPV values close to the optimal NPV.

Conclusions

This study shows that metaheuristics methods can be used with good results to solve complex and large data problems in the field of renewable energy integration, namely in the study of economically feasibility of WPSS to support the integration of renewable energy sources.

The main advantage of using PSO is the reduction in computational time as compared to the fast deterministic approach. With PSO, it is possible to reach very good approximate results for the optimal WPSS configuration, in less than half of the computational time required for the fast deterministic approach. In fact, the results are the same, due to the precision used (1 kW for the pumps and turbines capacity)

Relaxing the number of iterations of the PSO method, as well as the number of particles, leads to higher dispersion of results, however resulting in faster total calculation times, as compared with the FD method.

Future work could be done in comparing other metaheuristic methods, like genetic algorithms, and compare its results with the findings of this work.

Table 1 .

 1 PSO results

	NPV (M€)	pn	pp (kW)	tn	tp (kW)	φ1 φ2 ptn	nmx	Stopping criteria	TCT (s) ANI f (%)
	2574900 2	853	1	3843 2.3 1.8	20	3000	15 of 50	95,577	2564	22
	2574900 2	853	1	3843 2.3 1.8	10	1000	8 of 20	16,105	818	8
	2574900 2	853	1	3843 2.3 1.8	10	2000	8 of 50	24,183	1291	16
	2574900 2	853	1	3843 2.3 1.8	15	2000	11 of 50	57,238	1512	8
	2574900 2	853	1	3843 2.1 2.0	10	2000	8 of 50	23,607	1280	8
	2574900 2	853	1	3843 2.1 2.0	15	2000	11 of 50	38,152	1363	11
	2574900 2	853	1	3843 2.05 2.05 10	2000	8 of 50	12,519	250	10
	2574900 2	853	1	3843 2.05 2.05 15	2000	11 of 50	25,069	501	6
	2574900 2	853	1	3843 2.05 2.05 20	3000	15 of 50	31,973	640	10

Acknowledgements. This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013. Alstom Portugal, S.A. is deeply acknowledged for supporting the execution of this work. The authors are intensely grateful to EDA -Eletricidade dos Açores (Azores System Operator) for providing all the necessary data to perform this work.


Andrea Thais

Luis M Baldissera

Camarinha-Matos

Towards a Collaborative Business Ecosystem for Elderly Care

Keywords: Collaborative Business Services, ICT and Ageing, Collaborative Networks, Context Awareness

In fast changing environments, companies and organizations have to continuously adapt their operating principles in response to new business or collaboration opportunities in order to remain competitive. The growing demand for high-quality services is taking organizations to format their operations to offer more personalized service packages and seek collaboration with other stakeholders. Moreover, provision of personalized services depends on frequent contextual information analysis. In the case of elderly care, the use of assistive technology is expected to have a positive contribution to the diversity of required services that support aging well. Demographic trends show that the percentage of elderly population is increasing, while ageing entails several limitations, calling for assistance services adapted to the specific needs of each person. These needs can evolve along the ageing process, requiring an evolution of the care services. In this paper, we make an overview of related concepts and propose a personalized and evolutionary care services model supported on collaborative networking, context-awareness and Internet of Things.

Introduction

In the last decades, one of the most important demographic changes in the history of humanity has been taking place: the aging of the global population. In fact, current trends [1; 2] clearly suggest that elderly population will surpass young people in many regions of the world. This change is already a consolidated fact in countries such as Portugal, where the population over 60 years is 10% higher than people under 15 years old, a situation that was inversely proportional 30 years ago. By 2050, it is expected that the population over 80 years exceeds the young [START_REF] Helpageinternational | Global AgeWatch Index 2014[END_REF]. This situation seem to concern mainly the developed world, but it no exclusive theirs.

Current estimates indicate that 60% of elderly people live by themselves or in the company of another elderly person, and strive to guarantee their autonomy and perform their own duties. With continuous advances in medicine and wellness areas, people aspire to an active and enjoyable aging [START_REF] Kearney | Understanding the Needs and Consequences of the Ageing Consumer[END_REF], although aging might entail several limitations due to physical and cognitive decay. In extreme cases, regular daily activities such as cooking, personal hygiene, housework, etc., might be affected [1; 2].

Traditional approaches to deal with this problem require intense care from family. Relatives need to actively participate in the aging process and support elderly. As such, many people live with their family, especially when they begin to loose capabilities. Those who remain living alone either care for themselves or require caregivers to assist them on daily activities. Alternatively, the elderly may stay in nursing homes. All these changes can directly affect the senior life style [START_REF] Czaja | Information Technology and Older Adults[END_REF].

The use of technology in assisting elderly is expected to have a positive contribution to overcome identified challenges by supporting a diversity of services that promote independent living [1; 3-5]. However, most elderly people still face several barriers in an increasingly technological society. This fact puts older adults at a disadvantage, requiring that researchers and practitioners deliver more suitable products for this community [3; 4; 6]. For each elderly individual, specific care and assistance services might be necessary, according to his/her life context. A care service may be adequate for one elderly and completely useless for another one. As a single care organization can hardly meet all needs of each individual senior with the best services available in the market, collaboration between companies may be a promising way for delivery of integrated and personalized services.

In this context the main research question addressed by this work is: How to provide personalized and evolutionary collaborative care services for elderly in an effective and reliable way?

The pursued hypothesis is: Effective and reliable evolutionary services for elderly care can be provided if a suitable set of multi-provider business services are composed and integrated in the context of a collaborative network environment and supported by context awareness methods, mechanisms and systems.

In this paper, we propose a Collaborative Business Ecosystem Model for elderly care based on collaborative networking and relevant context-aware technologies that can be applied to personalization and evolution of care and assistance services. The Collaborative Networks paradigm (Section 3) supports provision of integrated services and helps services providers to acquire agility and better survive in market. In particular, we present collaborative business services in the elderly care domain and context-awareness technologies for personalization and evolution of care and assistance services. A Collaborative Business Ecosystem (CBE) Model (Section 4) gives the adequate organizational context for collaboration. Finally, we conclude (Section 5) with a discussion on the main advantages and concerns on emergent technologies for elderly care and assistance.

Benefits from Cyber-Physical Systems

It is expectable that with the increased intelligence in the new systems, interaction with technology becomes part of daily life for seniors, family members, caregivers and integrated service providers, while respecting the individual life context. Some examples of technological solutions with impact in aging well include smart devices, like wearable devices, sensors and associated software services that provide real-time data about an individual and the environment he/she lives in; a bracelet that uses a GPS sensor to set up a "safe zone" for people with cognitive disorders; an at-home patient-tracking device with a panic button for emergencies; cameras and sensors that capture movements, calculate body weight and make the measurement of heart-bit rate; and intelligent home appliances [START_REF] Scoble | Age of Context: mobile, sensors, data and the future of privacy[END_REF].

With advances in sensor technology, a growing number of sensors that communicate with each other with minimum human intervention will be attached to objects around us, generating a massive amount of data. Context-aware computing allows us to extract contextual information linked to sensor data so the interpretation can be done easily and more meaningfully [7; 8]. The adoption of context-aware analysis techniques, combined with smart devices and sensors, are likely to enable a great number of new care and assistance services. These technologies can be a strong ally in helping on the identification and analysis of the user's context in order to provide relevant inputs for the evolution of assistance services.

In the elderly care domain, context aware technologies supported by CPS can: (i) provide inputs to personalize or evolve the service based on multi-source contextual information, (ii) monitor individuals to provide best options and recommendation of services, (iii) enable businesses to redefine key aspects of their customer relationships, and (iv) cater direct interaction between senior and his/her living environment (devices and people) and services provided.

Related Areas

Collaborative Networks. In highly dynamic domains, such as health and personal services, companies are challenged to be able to efficiently interplay with multiple organizations to compose personalized offers without losing competitiveness and quality in their services. In such context, new collaborative strategies can facilitate the engagement and interaction of distinct stakeholders in any effort towards common or compatible goals. Advances in ICT, namely Internet and pervasive computing, have enabled or induced the emergence of new collaboration paradigms. However, the rapid formation of a collaborative network (CN) to respond to a business opportunity faces a number of challenges, whereas two are most relevant: (i) the large heterogeneity of the autonomous participants involved in the process, in terms of their technological infrastructures, business practices, culture, etc., and (ii) the time needed to build trust [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF]. To face these challenges, the concepts of Virtual Organization Breeding Environment and Collaborative Business Ecosystem were proposed [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF].

Participation in collaborative networks has the potential of bringing benefits to the involved entities such as: survivability in a context of turbulence, reaching higher levels of agility, acquisition of a larger apparent dimension, access to new/wider markets and new knowledge, sharing risks and resources, joining of complementary skills, and better achieving common goals [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF][START_REF] Crispim | Partner selection in virtual enterprises[END_REF][START_REF] Bititci | Performance measurement: Challenges for tomorrow[END_REF].

In the elderly care domain, CNs can: (i) provide organizational structures and governance models for groups of care and assistance stakeholders; (ii) support provision of integrated services; (iii) enable offering services tailored to each senior and his/her life style; (iv) facilitate composition of new services in line with new demands (current situation, specific requirements, new technologies); (v) facilitate lobbying & market influence; (vi) provide a framework for organizing collaborative processes among stakeholders. Collaborative Business Services. Research and development in elderly care services traditionally focused on the development of isolated services, often considering a single service provider and showing an excessive techno-centric flavor. However, to satisfy current demanding market challenges, organizations must collaborate to overcome their weaknesses and strengthen their expertise, so that they can offer better integrated and user-centric services, and gain competitive advantage [START_REF] Camarinha-Matos | Care services ecosystem for ambient assisted living[END_REF]. Furthermore, in the elderly care context there is a need for personalized services that respect the individuality of each senior and the evolution of needs as senior's life context changes [START_REF] O'grady | Towards evolutionary ambient assisted living systems[END_REF]. In order to support this perspective of service provision, the term Collaborative Business Ecosystem (CBE) can be adopted [START_REF] Graça | The Need of Performance Indicators for Collaborative Business Ecosystems[END_REF]. A CBE can assist in the integration of different services from distinct providers. For the elderly care domain the term collaborative business service can be named as Collaborative Care and Assistance Service. Such services can be seen as the result of collaboration among various stakeholders, including local communities, governmental institutions, care professionals, family and caregivers, and thus require a supporting collaboration environment. Context Aware Technologies. Context awareness is about capturing a broad range of contextual attributes (such as the user's current position, activities, and surrounding environment) to better understand what the customer is trying to accomplish, and what services might be of interested [START_REF] Lee | Deploying personalized mobile services in an agent-based environment[END_REF]. According to Costa et al. [START_REF] Costa | Services Platforms for Context-Aware Applications[END_REF] contextawareness seeks to exploit human-computer interactions by providing computing devices with knowledge of the users' environment, i.e., with context. Context awareness computing is a recent and development [START_REF] Scoble | Age of Context: mobile, sensors, data and the future of privacy[END_REF] which appears quite promising for the elderly care domain. Many recent technologies contribute to the development of context-aware solutions. Scoble and Israel [START_REF] Scoble | Age of Context: mobile, sensors, data and the future of privacy[END_REF] highlight five forces that have shaped and cooperated with the advances on context-aware technologies: Mobile, Social Media, Data, Sensors, and Location-based services. These forces can directly influence decisions through a context analysis and supported by a wide range of sensors, smart devices, and new evolutionary services. These devices in a communicating-actuating collaborative network are part of the IoT, where sensors and actuators blend seamlessly with the environment around us.

Collaborative Business Ecosystem Model

The proposed CBE model for elderly care includes: Service Composition: A care service can be either a single (atomic) or integrated business service. Therefore, service provision can be done by a single entity or by several entities. Some entities participating in a composite service may already be a composite service provider. This leads to a hierarchical categorization of service providers. For instance, in Fig. 1 two providers deliver one collaborative service CS1, and three other service providers offer atomic services SS2, SS3, and SS4. CS1 is seen by the CBE like a simple service, which combined with other services, compose the personalized service CS2. This collaborative service (CS2) is provided to the customer as one composite service, delivered by a collective provider (i.e. a collaborative network). Service Personalization and Evolution for Elderly Care: In the elderly care domain, personalization involves the analysis of the senior´s life context. For instance, let us consider a senior, 68 years old that lives alone and still works in a company. The organization responsible for delivering care and assistance services identifies specific requirements for this context and builds a service package for this customer, including, for instance, adjusted working space and home security monitoring services. At a later stage in life additional needs are identified as he got (in this scenario) some chronic diseases, diabetes, and less vision. As such, new services need to be delivered, e.g. personal management notices for application of insulin, control of medication, and continuous health monitoring. One year later, he recovered from some disease and is now retired: his life context changed again. So the care and assistance provider organization identifies the new situation and adjusts the provided service. The customer does not need an adjusted working space in the company anymore and this service will be removed from the package, but a social program for meeting with friends and services allowing the participation in virtual professional senior communities is added to his package. In other words, the service package evolves to cope with the new life stage.

For each new context change, the care and assistance service provider organization shall analyze the situation (in collaboration will all relevant stakeholders), and evolve the service to fit that context. This perspective is illustrated in Fig. 2.

Under this perspective, the notion of evolutionary service [7; 13; 17; 18] means that the provided service adapts to the senior's needs, to the environment and to any changes that affect the senior´s life context. In the previous example, the care provider organization adjusts the service due to new senior's context requirements (e.g. when he retired). But this service evolution could also be done due to technological reasons, as sensors and devices are improving and becoming cheaper, or new technologies emerge better meeting the user needs. Additionally, market factors (companies may disappear or merge with others) or new governmental regulations may also lead to service evolution.

CBE Structure:

The proposed CBE is based on the ARCON reference model [19] and the Ambient Assisted Living (AAL) Ecosystem concept [START_REF] Camarinha-Matos | Integrated Care Services in Ambient Assisted Living[END_REF], focusing on a care and assistance community of interacting entities, comprising both organizations and individuals participating in elderly care. It is aimed that the ecosystem community is supported by common operating principles and cooperation agreements, and also includes the basic profiles of all stakeholders [START_REF] Camarinha-Matos | Care services ecosystem for ambient assisted living[END_REF].

Fig. 2. Example of Personalized and Evolutionary Service Stages

In such ecosystem, as illustrated in Fig. 3, the main intervenient actors are divided into two groups:

Elderly Living Environment (ELE), representing the elderly costumers and their ambient and context, including informal caregivers such as family and friends, and all applications and devices with which they interact such as context aware devices;

Services Providers Environment (SPE), representing the care provision stakeholders with the goal to provide care and assistance services, facilitating support, companionship, all based on community participation with established trust. The SPE may include private companies and/or governmental institutions;

To operate the CBE, three main modules (Tab. 1) are considered:

Tab. 1. CBE Modules

Module 1 -Service Design Module 2 -Service Evolution Module 3 -Service Runtime

Responsible for receiving service requests from seniors and family and searching the providers and best services available for the senior and his/her living environment (ELE costumer).

Responsible for analyzing sensorial information and proposing a personalized and evolutionary care and assistance service for ELE costumer;

Responsible for notifying the SPE and delivering to ELE the care and assistance services according to the corresponding agreed terms.

Besides atomic business services, the CBE, with the characteristics of a Virtual Organizations Breeding Environment, also facilitates the rapid composition of business services into integrated business services, from one or several stakeholders, being thus one of the requirements that services are prepared to collaborate with each other [START_REF] Camarinha-Matos | The Role of Collaborative Networks in Sustainability[END_REF]. Additionally, the planned CBE Structure considers that service descriptions are kept in a Service Catalogue, to be consulted whenever needed.

The main goal of these functionalities (Fig. 3) is to support a middle layer between the Service Provider Environment and the Elderly Living Environment, supporting both parts in service provision. At a higher level, as the focus is on environments, the model is also intended to support service provision considering several customers' requirements under a context awareness perspective. That is, the generation of a multi-stakeholders consortium to provide services to a certain customer also takes into consideration historical data and requirements from ELE customers and their personal routines, the requirements of other customers that are in the same context (although each customer is treated respecting his/her individuality), the CBE can recommend some changes or additional services (evolutionary service). Whenever a customer wants to make a request or subscribe a certain service, Module 1, through a Collaborative Service Provision Support activity, collects information about ELE costumer´s care and assistance service requirements (through questionnaires, similarities with other customers, etc.) and verifies if a suitable service description is already defined in the service catalogue. If it is an undefined service, this module is responsible for conducting a new care and assistance service design involving the relevant stakeholders [START_REF] Camarinha-Matos | Care services ecosystem for ambient assisted living[END_REF].

In the first interaction, after performing the data analysis, the CBE suggests a service to the senior and offers an experimental period. During this period, the Multisource Information activity collects sensorial data from multiple devices, such as: home sensorial information (motion detectors, pressure sensors, etc.); mobile sensorial information (mobile phones, wearable/ubiquitous devices/ computing, etc.); and medical devices (mobile electrocardiogram, blood glucose monitoring systems, etc.) and helps the Collaborative Service Provision Support activity to store the new description in the service catalogue (iterative co-design). This service represents the first evolution of the service based on context awareness analysis. Furthermore, Module 1 initiates a negotiation round with suitable service providers in order to create a consortium capable of fulfilling the request in the best optimized manner. Due to the characteristic of the ELE customers, the mentioned optimization can also include sensorial and contextual information of other ELE customers, so that SPE can fulfill the request considering other activities that are already being carried out in the similar contexts. Module 2, Service Evolution, is responsible for proposing a personalized and evolutionary care and assistance service for the ELE costumer through data analysis (current needs, IoT-based sensing and, context awareness). The Sensorial Devices Information Management System gets information from the Multi-Source Information activity and analyzes these data. The result is sent to Intelligent Service Evolution activity which, by using relevant information, is able to recommend and/or suggest changes in evolutionary services for each ELE customer. Types of input information include: (i) sensorial information, essentially for context-awareness; (ii) relevant information from the ELE customer, to verify if additional services are necessary; (iii) analysis of the ELE customer's daily habits to recommend or suggest a care and assistance services adapted to the corresponding routines; and (iv) analysis of ELE to discover new patterns in ELE customer´s behavior. Fig. 5 shows a preliminary adapted i* Rationale Strategic Model for Service Evolution Module, with the main dependencies of this module and the other actors and systems.

The suggested evolutionary care and assistance service implies a SPE provider (or several) for service provision for the ELE customer. Module 3 delivers the care and assistance service. Also, this module is responsible for service report to SPE.

Fig. 6 shows an adapted i* Rationale Strategic Model for Service Runtime Module, with the main dependencies of this layer and the other actors and systems.

Conclusions and Future Work

In the era of IoT, many services will promote the extension of capabilities of elderly people for active aging. In this paper we highlight the design of a CBE model for elderly care based on collaborative networking and context-awareness to support active and productive aging. The adoption of context-aware analysis techniques, combined with smart devices and sensors, will enable a great number of seamless and, evolutionary and personalized services. As the intelligence in new systems grows, the technology becomes more interactive and gradually will be part of daily life of seniors, family members, caregivers and integrated service providers. While envisioned as a revolution on services, it is important to recognize that the economic growth alone will not improve older people's well-being and specific policies are required to address the implications of aging. Many computer tasks are still characterized by having high cognitive demands and, for this reason, it is important to carefully handle age-related changes on cognitive skills, when designing systems that target older adults. Because of the importance of improving the management and delivery of health care and social services, ICT plays a key role on developing quality of life of older people. Nevertheless, such initiatives are expected to assist on the mitigation of situations of isolation and loneliness, and improve family and community relationships. This paper represents the first phase of PhD and future work will rely on the progress of the CBE development and definition of mechanisms for personalization and evolution of services, as well as system's validation through tool prototype, publications, simulation and formulation of case studies (scenarios), comparison with other proposals and discussion with/assessment by stakeholders in elderly care.

Fig. 1 .

 1 Fig. 1. Example of Creation of Composite Services from Providers of Distinct Types

Fig. 3 .

 3 Fig. 3. Collaborative Business Ecosystem (CBE Structure)

Fig. 4

 4 Fig. 4 illustrates an adapted i* Rationale Strategic Model for the Service Design Module (illustrative example), including the main dependencies of this layer with other actors and systems.

Fig. 4 .

 4 Fig. 4. Adapted i* Rationale Strategic Model for the Service Design Module

Fig. 5 .

 5 Fig. 5. Adapted i* Rationale Strategic Model for the Service Evolution Module

Fig. 6 .

 6 Fig. 6. Adapted i* Rationale Strategic Model for the Service Runtime Module

T. A. Baldissera and L. M. Camarinha-Matos

Acknowledgments. This work has been funded in part by the Center of Technology and Systems and the Portuguese FCT-PEST program UID/EEA/00066/2013 (Impactor project), and by the Ciência Sem Fronteiras and Erasmus Mundi project (Brazil and European Commission).


Catarina Nobre
email: cp.nobre@campus.fct.unl.pt

Margarida Gonçalves

Dieimes Resende
email: dieimes.rr@posgrad.ufla.br

Cândida Vilarinho

Benilde Mendes

Removal of Chromium and Aluminum from Aqueous Solutions Using Refuse Derived Char

Keywords: RDF, char, adsorption, chromium, aluminum

Refuse derived fuel (RDF) was subject to torrefaction in order to produce a char with higher homogeneity and lower moisture content than the RDF raw materials. The resulting product, RDF char, showed increased fixed carbon and ash contents, decreased moisture and volatile matter contents, and a very significant increase in density. The torrefaction of RDF may therefore contribute to reduce the landfill volume needed to accommodate these materials to one third of the presently used. This new char material was also tested for its adsorption capacities and the results show that it could be used for the removal of chromium and aluminum from aqueous solutions.

Introduction

Refuse derived fuel (RDF) is an heterogeneous material comprehending polymeric and lignocellulosic waste that cannot be recycled and must be disposed of in landfills or used for energy purposes by co-combustion in cement kilns or power plants [START_REF] Krüger | Separation of harmful impurities from refuse derived fuels (RDF) by fluidized bed[END_REF]. Landfill deposition is a source of environmental problems and it is not advised by current legislation that foresees a reduction of 59% of the presently eliminated wastes to be achieved by 2020 [START_REF]Portuguese Diary of the Republic: Ministry Council Resolution nº11[END_REF].

On the other hand, the energetic valorization of waste derived fuels faces several constrains namely their high degree of heterogeneity, low density and high moisture, chlorine and ash contents. Moreover, the fuel applications of RDFs are often limited by their high content of polymeric wastes that causes agglomeration problems in the feeding systems of boilers or gasifiers and increases solid and gaseous emissions associated with incomplete combustion or gasification.

The research question addressed in this PhD program is the development sustainable solutions for the upgrading and valorization of RDFs either as better quality fuels or as refuse derived materials with industrial applications.

These technological solutions aim to modify the RDFs in order to increase density, homogeneity, hydrophobicity and calorific value by reduction of moisture, volatile matter, microbiological contamination and by rearrangement of the chemical bonds from the non-volatile fraction.

The techniques selected to achieve this goal are thermochemical processes, namely torrefaction, pyrolysis and gasification. These processes apply high temperatures, in the presence of oxidizing agents or inert gases, leading to an extensive reorganization of the molecular structure and contributing to a composition upgrading by eliminating oxygen, chlorine and other heteroatoms.

Torrefaction occurs at 200-300 ºC under an inert atmosphere. It is the first step to obtain a stabilized RDF and has proven to be a useful tool to upgrade solid fuels such as raw biomass [START_REF] Chen | A study on torrefaction of various biomass materials and its impact on lignocellulosic structure simulated by thermogravimetry[END_REF]. The torrefied RDF can then be directed to various final applications such as land reclamation, production of solid, liquid or gaseous fuels, incorporation into building materials or production of refuse derived materials such as refuse derived chars. The program of activities of this PhD program comprises the following tasks: a) Evaluate the influence of torrefaction conditions and initial RDF composition in the final physico-chemical properties of torrefied RDFs; b) Determine gaseous emissions associated with RDF torrefaction; c) Evaluate the combustion behavior of torrefied RDFs in what concerns efficiency and associated emissions; d) Study the conversion of torrefied RDFs in liquid fuels using pyrolysis or hydrothermal liquefaction; e) Study the conversion of torrefied RDFs in gaseous fuels using thermal gasification; f) Evaluate the adsorbent characteristics of torrefied RDFs in different industrial applications (dyes, heavy metals and pesticides). Modern industries use a significant amount of resources in the remediation of contaminated effluents that contain relevant concentrations of organic and inorganic hazardous compounds which are considered environmental priority pollutants [START_REF] Fu | Removal of heavy metal ions from wastewaters: A review[END_REF].

Activated carbons are the most used adsorbents but they have high prices and are difficult to regenerate so they represent a constant investment as raw materials and a constant source of contaminated solid wastes.

In this context there is a growing interest in the production of low-cost adsorbents, derived from less expensive or residual materials such as agricultural wastes, industrial by-products, waste materials or natural substances [START_REF] Salam | A study of the removal characteristics of heavy metals from wastewater by low cost-adsorbents[END_REF].

In this work, a refused derived char (RDF char) was produced by torrefaction of RDF at 300 ºC during 30 minutes. The RDF char was characterized for its proximate composition, heating value and density and its adsorption capacity towards chromium and aluminum was tested using different metal concentrations, adsorption times, initial pH and adsorbent-to-metal mass ratios.

Relationship to Cyber-Physical Systems

The torrefaction of RDF is a thermochemical process that requires the heating of the raw RDF at temperatures in the range of 250ºC to 350 ºC during variable periods of time. The efficiency of the process depends strongly on the characteristics of the raw RDF namely its moisture content that influences the real temperature in the torrefaction unit. For the same amount of energy supplied, the maximum temperature measured in a given period of time is inversely proportional to the moisture content of the raw material. Thus, torrefaction is effective when the moisture and a substantial fraction of the volatile components are eliminated, therefore the automated monitoring of the torrefaction process requires the use of temperature sensors that enable the establishment of real temperature profiles during the process.

On the other hand, the thermal capacity of the raw RDF also influences the kinetics and extension of the torrefaction process, so the flow of gaseous products is also a key parameter to control this process.

The combination of those two variables (temperature and flow of the gaseous emissions) reflects the completion of the torrefaction process and may be used as a boundary condition to regulate the feed, for different RDF compositions.

The remediation of industrial effluents using adsorption processes is a technology that requires the implementation of automatic control and monitoring systems that comprehend sensors for the continuous evaluation of effluent composition and concentrations of critical components (for instance, electrochemical sensors), in association with intelligent systems for the control of mechanical operations such as pressure and flow control as well as diversion of the effluent flow to alternative adsorption columns or stopping the process when the adsorbent becomes saturated.

Methods and Materials

RDF Char Production and Characterization

Industrial refuse derived fuel was supplied by CITRI, S.A. This raw material was mainly composed of plastics, cigarette buds, rubber, fabric and lignocellulosic material. The torrefaction of RDF was performed in closed lid crucibles placed in a muffle furnace (Nabertherm) for 30 minutes at 300 ºC.

Both the RDF and the RDF char were grinded and the char was sieved to a particle diameter of less than 500 µm and were characterized for proximate analysis (moisture, volatile matter, ash and fixed carbon contents) and density. All proximate analysis parameters were determined according to ASTM standards for RDF. The high heating values (HHV) were evaluated using an equation that relates this property to the proximate analysis of the materials [START_REF] Sheng | Estimating the higher heating value of biomass fuels from basic analysis data[END_REF].

Adsorption Experiments

The adsorption of Cr and Al was studied using solutions of AlCl3 and K2Cr2O7 prepared with ultra-pure water at a concentration of 100 mg/L. These solutions (100 mL) contacted with the RDF char (0.2g), in stopped Schott glass flasks, at room temperature, for 2h, at a pH of 8.0, under agitation (15 rpm) in an overhead shaker (Heidolph). The RDF char and metal solutions were separated by filtration and the concentrations of Cr and Al in the filtrate were determined by atomic absorption spectroscopy (Thermo Elemental Solaar). All the measurements were performed in triplicate and the reported values correspond to the average values.

The effect of pH on the adsorption of chromium and aluminum was studied in a range from 2.0 to 10.0 and the initial pH value was adjusted to the target by using NaOH 1M or HCl 1M (Panreac). The influence of the contact time on the metal adsorption by the RDF char was evaluated for contact times from 0 to 600 minutes. The effect of initial metal concentration was determined varying the initial concentrations from 30 to 150 mg/L. The effect of adsorbent mass was tested in a range of 0.1 to 0.8 g, for metal solutions with an initial concentration of 100 mg/L. The adsorption capacity, q (mg/g), of the RDF char was determined according to Equation 1:

q (mg/g) = ((C0 -Cf)*V)/m . (1)
The metal removal efficiency, R (%), was determined according to Equation 2: R (%) = ((C0 -Cf)/ C0).100 .

where C0 and Cf are the initial and final metal concentrations (mg/L), respectively, V is the volume of the solution (L), and m is the char mass (g).

Results and Discussion

RDF Char Characterization

The results for the characterization of standard RDF and its corresponding RDF char are presented in Table 1. When compared to the standard RDF, the RDF char shows a very significant increase in the fixed carbon content and density, which has very important implications in handling and storage. The ash content of the RDF char is very high (19.83%), affecting negatively the estimated high heating value (HHV) and limiting the applications of these chars in energy conversion systems such as boilers, mainly due to their potential for slagging and fouling phenomena. Nevertheless the torrefaction process may be regarded as an advanced treatment to increase the density and reduce the moisture of RDFs, enabling important reductions in landfill land use and reducing the effluent emissions in storage sites. Another approach regarding the valorization of this material is its use as a low cost adsorbent for industrial applications. Our goal is to eventually use this char to remediate the liquid effluents produced by the same companies that produce the RDF in an integrated perspective of waste management. Following this approach, the heavy metals chromium and aluminum (that are frequently present in these effluents) were chosen as adsorbates for a series of tests concerning the adsorption capacity of the RDF char.

Adsorption Experiments

Effect of pH. It is well documented that pH affects significantly the adsorption capacity of an adsorbent used for heavy metal removal from aqueous solutions, mostly because it influences the ionic forms of the metals in solution [START_REF] Pehlivan | Sorption equilibrium of Cr (VI) ions on oak wood charcoal (Carbo Ligni) and charcoal ash as low-cost adsorbents[END_REF]. The effect of the initial pH of the metal solutions in the removal efficiency by the RDF char is shown in Fig. 1. Aluminum presents its higher removal efficiency value for pH 6 (95.5%), which is in accordance with the determinations of other authors using different adsorbents [START_REF] Choksi | Adsorption kinetic study for the removal of nickel (II) and aluminum (III) from an aqueous solution by natural adsorbents[END_REF].

On the other hand, chromium is better adsorbed at more acidic pH (pH 2), reaching a removal efficiency of 66.0%. This high removal efficiency of chromium at low pH can be due to the increase in the protonation of functional groups from the char surface, producing a strong electrostatic interaction between the char and negatively charged oxyanion forms of Cr (VI) as HCrO4 -, Cr2O7 2-, and CrO4 2-which are prevalent in the potassium dichromate solutions and may be significant in real effluents [START_REF] Deveci | Adsorption of hexavalent chromium from aqueous solutions by biochars obtained during biomass pyrolysis[END_REF]. Regardless of the obtained removal efficiencies it was decided to perform the remaining tests at the natural pH of real landfill leachates samples (pH 8.0).

Effect of adsorbent mass. The adsorbent mass or adsorbent dose is a significant parameter considering removal efficiency because it is related to the adsorbentadsorbate equilibrium [START_REF] Kılıç | Adsorption of heavy metal ions from aqueous solutions by bio-char, a by-product of pyrolysis[END_REF]. The results obtained for this parameter are shown in Fig. 2. Fig. 2. Effect of adsorbent dose (g/L) on the adsorption capacity (q) and removal efficiency (R)

of Al and Cr.

The removal efficiencies of Al and Cr didn't change significantly when the adsorbent dose changed from 2 g/L to 16 g/L, indicating that maximum extraction was achieved at a 2 g/L dose. The different ionic forms of the metals will bind to the char surface groups through electrostatic and hydrophobic interactions but as their concentration in solution decreases the concentration gradient favors a certain degree of desorption stabilizing in a concentration ratio that is constant for each metal. The average removal efficiency was of 93% for Al while for Cr was of 61%, what may result from a wider speciation of Cr, yielding a variety of ionic species with different net charges, limiting their removal by adsorption when the electrostatic and hydrophobic interactions are dominant.

On the other hand, the adsorption capacity decreases as the adsorbent dose increases indicating that the equilibrium between char and solution is achieved at 2 g/L. Increasing the adsorbent mass has a dilution effect in the adsorption capacity since the final concentration of metals in the char was inversely proportional to the adsorbent dose.

Effect of metal initial concentration. The removal efficiency of the RDF char in both metal solutions showed a tendency to decrease with the initial metal concentration (Fig. 3).

Aluminum presented its highest removal efficiency at 50 mg/L (95%), and this property decreased slightly to values of 88-89% for initial metal concentrations of 100 and 150 mg/L, suggesting that in this metal concentration range, binding sites present in the char surface were not saturated when the adsorbent dose was 2 g/L.

The removal efficiency for chromium was more sensitive to the initial metal concentration, varying from 79%, at an initial concentration of 50 mg/L to 44% at 150 mg/L. RDF char showed a sharp increase in the adsorption capacity during the first 15 minutes of adsorption (for both metals) and remained almost constant for higher contact times, reinforcing the idea that the adsorption mechanisms are dominated by electrostatic and hydrophobic interactions with the external surface of the char.

Conclusions and Further Work

The torrefaction process can contribute to upgrade RDF materials yielding a RDF char that is more homogeneous, hydrophobic and dense, with a higher stability and less land use requirements if stored in landfills. This char presents high ash content and reduced HHV so it could be more efficiently used in material applications than for energy recovery purposes. The RDF char presented good adsorption characteristics for Al and Cr in aqueous solutions, reaching removal efficiencies above 90% for aluminum and above 70% for chromium.

These results highlight the potential use of RDF char in industrial effluent remediation and future work includes tests with other RDF chars to evaluate the influence of initial RDF composition, tests with other heavy metals and other organic and inorganic contaminants, both in model systems and in real industrial effluents.

Industrial implementation of these technologies will require the adoption of cyber physical approaches to monitoring and control in view of the dimension of the material flows to be converted and because the heterogeneity of the materials requires automated adjustment of process operational conditions.

Fig. 1 .

 1 Fig. 1. Effect of solution pH on the removal efficiency (R) of Al and Cr.

Fig. 3 .

 3 Fig.3. Effect of initial metal concentration, C0, (mg/L) on the removal efficiency R (%) of Al and Cr. Effect of contact time. The results for the adsorption capacities as a function of contact time are depicted in Fig.4.

Fig. 4 .

 4 Fig. 4. Effect of contact time on adsorption capacities of Al and Cr.

Table 1 .

 1 Fuel characteristics of standard RDF and corresponding RDF char.

		Standard RDF RDF char
	Proximate analysis
	Moisture (% w/w, wb)	6.02±0.34	2.32±0.18
	Volatile matter (% w/w, db)	82.58±1.03	59.69±1.18
	(% w/w, db)	8.20±1.21	19.83±0.47
	Fixed carbon (% w/w, db)	9.22±0.26	20.48±0.74
	Estimated HHV (MJ/Kg) 1	17.68	15.70
	Density (g/cm 3 )	0.12±0.01	0.46±0.04