Coordination of the Charging of Electric Vehicles Using ... - IEEE Xplore

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IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 4, DECEMBER 2013

Coordination of the Charging of Electric Vehicles Using a Multi-Agent System Panagiotis Papadopoulos, Member, IEEE, Nick Jenkins, Fellow, IEEE, Liana M. Cipcigan, Member, IEEE, Inaki Grau, and Eduardo Zabala

Abstract—An agent-based control system that coordinates the battery charging of electric vehicles in distribution networks is presented. The objective of the control system is to charge the electric vehicles at times of low electricity prices within distribution network technical constraints. Search techniques and neural networks are used for the decision making of the agents. The ability of the control system to work successfully when the distribution network is operated within its loading limits and when the loading limits are violated is demonstrated through experimental validation. Index Terms—Distribution networks, electric vehicles, multiagent systems, real-time systems, smart grids. Fig. 1. Hierarchy of agents in a power distribution network.

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I. INTRODUCTION

LECTRIC vehicle (EV) battery charging will increase the load on power networks. Studies that used distribution network models from various countries have concluded that the load from EV battery charging can overload transformers and cables and modify voltages of distribution feeders [1]–[4]. The coordination of EV battery charging within distribution network capacity limits was studied in [2], [5], [6]. Centralized optimization algorithms were used to obtain EV charging schedules assuming that forecasts for loads and EVs would be available from the previous day. Multi-agent systems (MASs) have been used in power systems control problems including outage management and service restoration [7], microgrid control [8] and virtual power plant control [9]. Real-time coordination of EV charging using distributed optimization algorithms with MASs was addressed in [10], [11] assuming an amount of energy available for trading and in [12] assuming a transformer loading capacity. The Powermatcher software is reported in [13] to charge EVs within distribution network constraints by means of an electronic market mechanism; however, a clear framework that includes the sequence of communications between the different entities/agents during both normal and abnormal operating conditions for the distribution network is not considered. Comparisons between centralized EV charging scheduling Manuscript received June 08, 2012; revised September 29, 2012; accepted July 18, 2013. Date of publication September 12, 2013; date of current version November 25, 2013. This work was supported in part by the European Union’s 7th Framework Program project Distributed Energy Resources Research Infrastructures under grant agreement 228449 and the Engineering and Physical Sciences Research Council U.K. funded project, Centre of Integrated Renewable Energy Generation and Supply, with a reference number EP/E036503/1. Paper no. TSG-00351-2012. P. Papadopoulos, N. Jenkins, L. M. Cipcigan, and I. Grau are with the Institute of Energy, Cardiff University, CF243AA, U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). E. Zabala is with the Tecnalia, Parque Tecnológico de Bizkaia E-48160 Derio (Vizcaya), Spain (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2013.2274391

and de-centralized MAS-based methods that use three-layer hierarchical architectures, have been presented in [12], [14]. It was proved that MAS methods outperform the centralized one in terms of computational time and scalability. This MAS uses a three-layer architecture similar to [12]–[14]. However, this paper contributes to the literature by proposing a MAS framework for EV charging coordination within distribution network loading capacity that considers: i) Two operating modes; in normal operation, the distribution network is operated within its capacity. In emergency operation the voltage limits or the transformer or cable limits are breached. ii) Two entities; the distribution system operator (DSO) and the electricity supplier. In the U.K., only companies that hold a license to supply electricity sell electrical energy [15]. In the proposed MAS, the hierarchical agents (with orange color in Fig. 1) are assumed to belong to such a company. II. MULTI-AGENT SYSTEM DESCRIPTION A. Design The MAS design methodology of [16] was used to define the EV charging problem, analyze it and design the MAS. Atomic roles [16] were defined and then formed into agents: • The DSO agent is located at the primary substation (HV/MV) and responsible for the technical operation of the distribution network; during normal operation the DSO agent validates the EV demand before power delivery and during emergency operation, initiates corrective actions to restore normal operating conditions. • The Regional Aggregator (RA) agent is located at the primary substation (HV/MV) and is responsible for managing the EV demand satisfying the EV owners’ charging preferences and complying to the distribution network capacity limits. • Local Aggregator (LA) agents located at secondary substations (MV/LV) are used as mediators between EV agents located in the EVs and the RA agent, to minimize the amount of data that have to be transferred and processed by

1949-3053 © 2013 IEEE

PAPADOPOULOS et al.: COORDINATION OF THE CHARGING OF ELECTRIC VEHICLES USING A MULTI-AGENT SYSTEM

Fig. 2. Planning and operation periods of the MAS.

the RA agent, and enable the application of different policies, since each LA agent can incorporate different characteristics of the LV area it manages. The algorithms that solve each layer’s problem and described in Section III and Section IV, were chosen to be the simplest suitable search algorithms. The Request Interaction protocol with the performatives request, agree, refuse, inform, failure, cancel specified in [17] by the Foundation of Intelligent and Physical Agents (FIPA) was used between the agents because it was the simplest suitable FIPA protocol. An agent content language (ACL) and an ontology are not obligatory parameters for a FIPA compliant MAS [18] and were therefore omitted for simplicity. However, an ontology based on a common information model could be developed and included. Assumptions A matrix with the capacity that is available for EV charging for each LV feeder and each hour of a day is produced by the DSO agent. The technical constraints considered are steady state distribution transformer loading, LV cable loading, and voltage of LV feeders. The matrix is made available by the DSO agent to the RA agent. The RA agent sends the available LV feeder capacity to each LA agent, which uses them to choose the schedules for EV battery charging within technical limits. A spot electricity price for each hour of a day is sent to each EV agent. A network monitoring system provides the DSO agent with real-time measurements of the distribution grid. These measurements are used to determine network flows and voltages, to forecast customer loads, and to initiate corrective actions in the case of emergency, curtailing EV charging. B. Operation In each hour there is a planning period and an operational period (Fig. 2). During each planning period, the charging setpoints of the EVs are decided for the next operational period. III. NORMAL OPERATION OF THE MAS A. Electric Vehicle Agent The EV agent represents the EV owner who can choose for each charging period: • The EV connection period. • The desired state of charge (SoC) of the EV battery at the time of disconnection. • Whether power injections are allowed from the EV battery to the grid, if the EV is vehicle to grid (V2G) capable. At the beginning of each planning period, the EV agent receives from the LA agent the hourly electricity prices for a day ahead and an integer 1 that stands for the number of charging 1The

number is adjustable and depends on the bandwidth of the commuand the nication system. In the case studies presented in Section V-E, message transferred from each EV agent to the LA agent is 1 kbyte.

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schedules that the EV agent should form into a priority list. The reply message of the EV agent contains a risk factor and the EV charging schedules with their costs. At the beginning of each operational period, the EV agent receives a charging set-point from the LA agent and sends it to the EV. (1) is defined by the EV agent and is used The risk factor in the decision making of the DSO agent for EV curtailment in the case of network emergency. It is unique for each EV agent in each planning period and expresses the urgency of the EV agent to charge the battery during the next operational period. (1) where: SoCd is the desired SoC at the end of the connection is the estimated SoC at the beginning period in kWh, is the average battery of the next operational period in kWh, is the average efficiency of the EV charging point, efficiency, is the power rating of the charging point in kW, and is the number of hours that the EV will be connected. The feasible EV battery charging schedules are calculated by the EV agent using a Breadth-First-Search (BFS) algorithm with a pruning step. A plain BFS [19] follows a simple strategy to calculate all charging schedules: the root is expanded first then all the root successors are expanded next, then their successors. Using a pruning step, the schedules that do not satisfy the following constraints in each planning period are discarded: a) 20% b) c) d) where: is the estimated SoC at every hour of the conis the nominal battery capacity in nection period in kWh, is a tolerance factor in kWh, is the SoC of each kWh, schedule produced by the algorithm at the end of the connection period in kWh. An example of the BFS algorithm is provided in the Appendix. is used to allow schedules to be developed that The factor do not result in the desired state of charge. If additional schedules are required, the tolerance is increased from 0 to , where is the estimated SoC at the beginning of the following operational period, and the algorithm re-runs. The constraint are schedules that do not satisfy the desired in a descending order and put sorted according to the in the EV agent’s priority list after the schedules that satisfy the constraint. desired If the EV owner allows power injections from the EV to the grid, the constraints are the same with constraint (c) removed. The battery can be used to arbitrage on price. The rule shown in (2) performs consistency checking 2 to discard the schedules where profit is not foreseen. (2) A list of EV battery charging schedules, in ascending order of total hourly cost, is sent to the LA agent. The remaining feasible schedules are kept in the memory of the EV agent to be used in the case that the LA agent requires more schedules in the priority list. 2“A heuristic h(n) is consistent if, for every node and every successor of n generated by any action , the estimated cost of reaching the goal from is plus the estimated cost of reaching no greater than the step cost of getting to the goal from ” [19].

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B. Local Aggregator Agent The LA agent manages the EV agents of a LV area. At the beginning of each planning period, the LA agent receives from the RA agent the capacity of each feeder that is available for EV charging. The LA agent multiplies the LV feeder available capacity by a node factor to calculate the charging capacity is the ratio of the number of EVs connected of each node. to an LV network node, to the number of EVs of the feeder. The LA agent requests from the EV agents their schedule priority lists and their risk factor. The LA agent uses an exhaustive search routine to find the combination of schedules for each node of each LV feeder that satisfies the charging capacity of each node and minimizes the total cost. In a first step, the LA agent adds the cheapest schedules of all EV agents’ priority lists that are located in EVs connected to an LV network node. If no combinations that satisfy the charging capacity for each node are found, the combinations of the cheapest and the next cheapest are evaluated. This process is repeated until a solution is found is reached. If the limit is reached and no soluor the limit tion is found, the LA agent requests more schedules from the EV agents and the search routine re-runs with an increased search space. The exhaustive search is solved per node starting with the node with the highest average of risk factors and moves on to the remaining nodes prioritizing them in a descending order according to the average of risk factors. When the solution for each node is found, each LA agent sends to the RA agent the risk factor of each EV agent and the aggregated EV demand per node for the following operational period. At the beginning of each operational period, the LA agent sends the decided charging set-points to the EV agents.

Fig. 3. Algorithm for the calculation of the matrix with capacity available for EV charging.

C. Regional Aggregator Agent The RA agent manages the LA agents. At the beginning of every planning period the RA agent receives from the LA agents the list of the EV agents’ risk factors and the proposed EV load demands for the following operational period. The RA agent sends this information to the DSO agent and requests technical validation for the subsequent operational period. If the DSO agent accepts the proposed EV demand, the RA agent informs the LA agents that their proposal has been accepted. If the RA agent’s proposal is not accepted, the DSO agent updates the matrix with the capacity available for EV charging and sends it to the RA agent which in turn sends it to the LA agents to re-evaluate the schedules of the EV agents. At the beginning of every operational period the RA agent requests the LA agents to send to the EV agents the decided charging set-points. D. DSO Agent

Fig. 4. Interaction diagram of agents during normal operation.

The DSO agent is located at the HV/MV substation and is responsible for the technical operation of the downstream distribution network, knows its topology and the historical and actual load demand. The DSO agent calculates the available capacity for EV battery charging in a LV feeder by increasing gradually the load in each LV network node, starting from the most remote node, until a technical limit is reached (Fig. 3). At the end of each planning period, the DSO agent receives from the RA agent the proposed EV demand and the risk factors of the EV agents for the next operational period. The DSO agent forecasts the residential load demand of each network node for the next period using artificial neural networks

(ANNs) and adds the forecasted residential demand to the proposed EV demand. The DSO agent executes a load flow and checks if the proposed EV demand will affect normal operating conditions in the next operational period. If the technical limits are exceeded, the DSO agent updates the matrix with the capacity available for EV charging and sends it to the RA agent. ANNs have been used by utilities in the United States [20]–[22] and Greece [23] for load forecasting. The DSO agent uses historical load data from two previous weekdays and the current weekday to train the ANNs with Encog’s improved algorithm [24] until the Resilient


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TABLE I CHARACTERISTICS OF ANN USED FOR LOAD FORECASTING

Fig. 7. Forecasted and desired load for the case with the worst daily MAPE.

Fig. 8. Forecasted and desired load for the case with the best daily MAPE.

Where: is the daily and is the hourly perturbation factor randomly generated from a uniform distribution with a maximum of 15% and 20% respectively [4]. The mean absolute percentage error (MAPE) defined in (4)–(6) was used to evaluate the accuracy of the ANN.

Fig. 5. Interaction diagram of agents during emergency operation.

(4) (5) Fig. 6. Domestic load and electricity price profiles used in the MAS.

(6) mean square error (MSE) falls below 0.1%. The ANN structure was decided after tests. Table I shows the ANN characteristics and Fig. 4 summarizes the normal operation of the MAS. IV. EMERGENCY OPERATION OF THE MAS If voltage limits are exceeded or transformers/cables are overloaded (i.e., technical limits are breached), the DSO agent curtails the charging of the EVs by sending a charging set-point of zero current until normal operating conditions are restored. If there are no EVs connected to a node where the technical limit breach was found, the DSO agent curtails EVs from neighboring nodes. The neighboring node is chosen based on the lowest risk factor of the EV agents. When normal operating conditions are restored, the RA agent initiates an emergency planning period to consider the EV curtailment and re-schedule EV battery charging. The emergency operation algorithm is shown in Fig. 5. V. EVALUATION OF THE MULTI-AGENT SYSTEM A. ANN Evaluation The accuracy of the ANN was tested using data for 500 weekdays. Three profiles for each day were created from a 24-customer load profile (Fig. 6) [4]. Two of the profiles were used to train the ANN and the third for the testing procedure. Each profile was generated using a perturbation factor (3). (3)

where: is the day index, is the hour index, is the forecasted output, and the actual output. In [20]–[23], MAPEs vary befor all simulated cases was tween 0.66% and 6%. The 2.17%. Figs. 7 and 8 show the worst and best daily forecasts. B. Test Network Configuration A U.K. distribution network model was simulated in software (left side of Fig. 9) using a load flow algorithm described in [4] and data from [25]. One residential feeder of 96 residences, 16 battery electric vehicles (BEVs) and 16 plug-in hybrid electric vehicles (PHEVs) was modeled in hardware in the laboratory of Tecnalia [26] (right side of Fig. 9). The load of the feeder was represented using two Avtron load banks [27] and a DMMS300 measurement acquisition device [28]. The EV-ON platform [29] represented an electric vehicle. The Communication Software for Distributed Energy Resources (CSDER) [30] was used as an interface between the MAS and the load banks and measurement device. Within the MAS, a DSO agent, an RA agent, an LA agent, and 31 EV agents were simulated. One EV agent was installed on the EV-ON platform. The Java Agent Development Framework (JADE) [31] was used for the MAS development. C. EV Battery Model A model of a Lithium battery was used in each EV agent. The battery charging characteristic was taken from [32]. The

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Fig. 9. U.K. generic LV distribution network and test network that was used in Tecnalia for the MAS validation. TABLE III CASE STUDIES DESCRIPTION

TABLE II BATTERY CHARACTERISTICS

charging current remains constant until 90% of SoC is reached and then it reduces exponentially according to (7). (7) where: is the time (hours) after 90% SoC, during the expo, is the nominal charging nential range with current of 13 A, is a constant calculated from (7) to 1.026 ashours from suming that the charging current is 1 A when [32]. Lithium battery discharge characteristics for EVs are not currently available in the literature. It was assumed that the battery is discharged according to current set-points with the highest efficiency that Xantrex XW4024 hybrid inverter/charger [33] ac, ii) for cepts: i) for 20–30% SoC the current set-point is , iii) for 50–100% 30–50% SoC the current set-point is . SoC the current set-point is A battery utilization cost factor is used that stands for the cost of the battery to provide energy back to the grid. It is expressed in £/kWh and is calculated by (8) according to [34]. The lifetime of the battery in years is calculated by (9). D. Input Data and Assumptions for the Case Studies (8) (9) is the battery utilization cost in £/kWh, is the where: is an annual interest rate of capital cost of the battery in £, is the battery cost, is the lifetime of the battery in years, is the battery capacity in the lifetime of the battery in cycles, is the average battery efficiency, is the average kWh, and charge/discharge cycles of the EV battery per day.

The hourly electricity prices from the winter of 2010 were drawn from [35] and averaged to create a single profile which is shown in Fig. 6. The LV feeder loading limit of the U.K. generic LV distribution network is 175 kVA that is 25% of the distribution transformer winter loading limit of 700 kVA, based on [4]. The cable impedances of the laboratory connections were neglected and the equivalent load of the EVs was assumed purely resistive. The line losses of the LV feeder were 4.1% in [4]. The LV feeder loading limit was assumed to be 167.825 kVA to consider the losses.


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Fig. 10. Feeder loading without MAS control and results for the five case studies described in Table III.

The battery capacities of BEVs were assumed to be 35 kWh and the battery capacities of PHEVs 9 kWh, both with a maximum allowable depth of discharge (DoD) of 80% to prolong battery life [4]. The daily energy requirement for each EV was assumed to be 6.5 kWh based on [36]. The initial SoC of the EVs at the time of connection was assumed to be 30%. The BEVs were assumed to connect to domestic charging points of 13 A at 18:00, half of the PHEVs at 17:00, and the rest at 19:00. The

time of disconnection for all EVs was the end of the simulated day (i.e., 06:00 the following morning). Table II shows the battery factors from [36], [37]. E. Description of Case Studies Five case studies were conducted (Table III). In all cases, the goal was to minimize the cost of charging. Three cases were

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used to examine the operation of the MAS during normal operation. A fourth case was used to examine the operation of the MAS during a load increase foreseen by the DSO agent in the short term (i.e., the hour before energy delivery). A fifth case was used to examine the operation of the MAS during an emergency event. F. Results of the Case Studies Fig. 10(a) shows the loading of the feeder with no control (black line). The grey area shows the residential load of the feeder. Figs. 10(b)– 10(f) show the loading of the feeder for Cases 1–5 described in Table III (solid lines corresponding to primary axes). The secondary axes show the measurements acquired from the hardware EV-ON platform when it was representing a BEV (dashed lines) and a PHEV (dotted lines). Fig. 10(b) shows the results of Case 1. The EV load demand was satisfied during the cheap hours of the day during normal operation. Fig. 10(c) shows the results of Case 2. The EV load demand was zero during the hour 03:00–04:00. The EV load demand was satisfied during the remaining cheap hours of the day during normal operation. Fig. 10(d) shows the results of Case 3. The BEVs (which had the available time to recharge and satisfy the EV owner charging preferences) provided power back to the grid during the hour 19:00–20:00, the most profitable hour for discharging during their connection period. The EV load demand was satisfied during the cheap hours of the day during normal operation. Fig. 10(e) shows the results of Case 4. An artificial load increase, that would violate the feeder limit, was created for the period 03:00–04:00 by modifying the DSO agent’s residential load forecast. The EV battery charging of EVs was re-scheduled to keep the feeder loading within limits. The EV load demand was satisfied during the cheap hours of the day. Fig. 10(f) shows the results of Case 5. The feeder loading was manually increased after the beginning of the operational period of the hour 04:00–05:00 to a value above the feeder loading limit. The DSO agent curtailed the charging of three EVs to restore the feeder loading within limits. The EV load demand was satisfied during the cheap hours of the day. VI. CONCLUSIONS A multi-agent system that coordinates EV charging in distribution networks using a distributed control method, with search techniques and neural networks was described. A series of laboratory experiments that modified EV charging and returned power to the network were undertaken. The ability of the proposed MAS to satisfy EV owners’ preferences, within distribution network loading limits, minimizing the cost of charging, was demonstrated. The paper contributes to the literature by proving through laboratory results the MAS ability to satisfy EV owners’ charging preferences autonomously under both normal and emergency conditions for the distribution network described in Table III. There are five aspects that have not been considered in this paper and require further investigation for the particular MAS to be used in anger: i) EV load forecasting to facilitate the participation of the aggregator to electricity markets, ii) pricing framework that includes the encapsulation of distribution network capacity limits into the electricity price signals that are sent to EV agents, iii) additional communication between Local Aggregator agents and Regional Aggregator agents for fine-tuning the contractual position of the aggregator, iv) billing framework

Fig. 11. Example of BFS algorithm with pruning step.Fig. 10. Feeder loading without MAS control and results for the five case studies described in Table III.

to ensure fairness among EV owners, and (v) fault tolerance investigation of the proposed MAS. APPENDIX Example of BFS Algorithm With Pruning Step: The EV and the desired battery SoC in the hour T is SoCend at the time of disconnection is . The possible energy exchange between the EV battery and the . The EV agent’s possible grid at each time-step is actions are charge, idle, or discharge; therefore, after the first , the possible SoC of the EV battery would time-step , SoCT and SoCT-Ex. After the generation of be each node, the pruning step checks the feasibility of each action. In this example the only constraint is the minimum battery . The node whose action is discapacity is discarded/pruned because it does charge at time-step not satisfy the constraint (Fig. 11a). The two nodes that satisfied the constraint are stored and the algorithm moves to the , only two next time-step. After the second time-step schedules satisfy the EV owner’s desired SoCd. These are the feasible EV charging schedules (Fig. 11b). ACKNOWLEDGMENT P. Papadopoulos gratefully acknowledges the help of J. Jimeno, M. Fernandez, and J. Anduaga from Tecnalia in setting-up the laboratory test-rig, and the partners of the project Mobile Energy Resources in Grids of Electricity (MERGE). REFERENCES [1] J. A. P. Lopes, F. J. Soares, and P. M. R. Almeida, “Integration of electric vehicles in the electric power system,” Proc. IEEE, vol. 99, no. 1, pp. 168–183, 2011. [2] K. Clement-Nyns, E. Haesen, and J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 371–380, 2010. [3] L. P. Fernández, T. G. S. Román, R. C. Cossent, M. Domingo, and P. Frías, “Assessment of the impact of plug-in electric vehicles on distribution networks,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 206–213, 2010. [4] P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, L. M. Cipcigan, and N. Jenkins, “Electric vehicles’ impact on british distribution networks,” IET Electr. Syst. Transp., vol. 2, no. 3, 2012. [5] E. Sortomme, M. M. Hindi, S. D. J. MacPherson, and S. S. Venkata, “Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 198–205, 2011. [6] P. Richardson, D. Flynn, and A. Keane, “Optimal charging of electric vehicles in low-voltage distribution systems,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 268–279, 2011. [7] D. Staszesky, D. Craig, and C. Befus, “Advanced feeder automation is here,” IEEE Power Energy Mag, vol. 3, no. 5, pp. 56–63, 2005. [8] A. L. Dimeas and N. D. Hatziargyriou, “Operation of a multiagent system for microgrid control,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1447–1455, 2005.


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Panagiotis Papadopoulos (M’08) was born in Patras, Greece on February 22, 1982. He received the Ph.D degree from the Institute of Energy within the School of Engineering at Cardiff University, U.K., in 2012, where his last position was Research Associate. He is at present with EDF Energy R&D UK Centre, U.K. He is a member of the IEEE Working Group, the NIST SGIP, and the IET.

Nick Jenkins (SM’97–F’05) received the Ph.D. degree from Imperial College, London, U.K., in 1986. He is the Director of the Institute of Energy within the School of Engineering at Cardiff University, U.K. Before moving to academia, his career included 14 years industrial experience, of which 5 years were in developing countries. His final position in industry was as Projects Director for Wind Energy Group, a manufacturer of large wind turbines. While at university he has developed teaching and research activities in both electrical power engineering and renewable energy. Dr. Jenkins is a Fellow of the IET and Royal Academy of Engineering. Liana M. Cipcigan (M’08) is a Lecturer at School of Engineering, Centre for Integrated Renewable Energy Generation and Supply, Cardiff University, U.K. Her current research activities are focused on smart grids, distributed generation, and she is leading the research on electric vehicles’ integration in distribution networks. Dr. Cipcigan is a member of IEEE P2030.1 Working Group.

Inaki Grau Unda received the electrical engineering degree from Alfonso X El Sabio University, Madrid, Spain, the M.Sc. in renewable energy and distributed generation from Heriot Watt University, Edinburgh, U.K., and the Ph.D. degree from Cardiff University, U.K., in 2013.

Eduardo Zabala received a Ph.D. in electronics engineering in 1994 and a M.Sc. degree in energy engineering in 1984, both from the School of Engineering of the University of the Basque Country, Bilbao, Spain. He has 10 years experience in electronics design and 5 years in EMC consultation and research. Now he is in charge of the EV Programme in TECNALIA ENERGY. He has been a lecturer in the Engineering School of Bilbao, Spain, since 1988.