A Game Theory Strategy to Integrate Distributed Agent ... - IEEE Xplore

568

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

A Game Theory Strategy to Integrate Distributed Agent-Based Functions in Smart Grids P. H. Nguyen, Member, IEEE, W. L. Kling, Member, IEEE, and P. F. Ribeiro, Fellow, IEEE

Abstract—The increasing incorporation of renewable energy sources and the emergence of new forms and patterns of electricity consumption are contributing to the upsurge in the complexity of power grids. A bottom-up-agent-based approach is able to handle the new environment, such that the system reliability can be maintained and costs reduced. However, this approach leads to possible conflicting interests between maintaining secure grid operation and the market requirements. This paper proposes a strategy to solve the conflicting interests in order to achieve overall optimal performance in the electricity supply system. The method is based on a cooperative game theory to optimally allocate resources from all (local) actors, i.e., network operators, active producers, and consumers. Via this approach, agent-based functions, for facilitating both network services and energy markets, can be integrated and coordinated. Simulations are performed to verify the proposed concept on a medium voltage 30-bus test network. Results show the effectiveness of the approach in optimally harmonizing functions of power routing and matching. Index Terms—Active distribution network, coalitional game, cooperative game theory, multi-agent system, smart grid.

I. INTRODUCTION

M

ODERN electric power systems experience a fundamental change from a vertically to a horizontally controlled and operated structure. This transition has posed significant challenges for its reliable and economic operation, control and management. The concept of smart grids has been developed to deal with various challenging issues such as increasing load demand, the transition to sustainable sources, and higher quality requirements. In smart energy grids, distributed, intelligent and flexible control systems, communication facilities, and management and decision support tools should all be implemented to enable the large-scale integration of both concentrated and massively distributed renewable production. Among the emerging technologies, ICT is playing an essential role in the development of smart grids. As an application of ICT for distributed monitoring and control, multi-agent system (MAS) is regarded as a key technology to adapt with dispersed and intermittent features of distributed generation (DG) mainly based on renewable energy Manuscript received September 07, 2011; revised February 02, 2012, August 18, 2012; accepted December 09, 2012. Date of current version February 27, 2013. This work is part of the project: Electrical Infrastructure of the Future (Elektrische Infrastructuur van de Toekomst, in Dutch), sponsored by the Ministry of Economic Affairs, Agriculture and Innovation of The Netherlands. Paper no. TSG-00528-2011. The authors are with the Department of Electrical Engineering, Eindhoven University of Technology, 5600MB Eindhoven, The Netherlands (e-mail: [email protected]; [email protected]; [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.2012.2236657

sources (RES). By using characteristics of reactivity, pro-activeness, and social ability, the MAS technology can offer numerous benefits in these contexts on both technical network services [1], [2] and energy trading perspective [3], [4]. Though agent-based applications have been studied extensively [11]–[15], most of them are highly specific and developed independently from each other while their mutual influences are insufficiently investigated. It might cause conflicts of interests leading to undesirable (sub-optimal) operation of the system when integrating and coordinating these distributed intelligences in a common framework. The paper tackles the mentioned problem by proposing an optimal strategy for integration and arbitration of conflicting interests between producer/consumer agents and network agents. The interrelated problems of network and market operations are considered in a dual way. Cooperative game theory is used together with agent-based techniques to optimally allocate resources among involved actors. The combination of these techniques reveals several advantages as follows: • Enabling an advanced control layer at distribution level; • Integrating decentralized network functions and real-time market service to empower local actors with appropriate incentives; • Solving the cooperation of functions by multi-objective optimization. The organization of this article is as follows: Section II describes the development of smart grids; Section III presents a trend for using agent-based technique to manage smart grids; Section IV proposes a solution based on the cooperative game theory for integrating and cooperating agent-based functions; Section V is about simulation, results, and discussions; Section VI draws out the main conclusions of the research. II. SMART GRID DEVELOPMENT A. Power Grid Complexity Due to the large-scale integration of uncertain—and to a large extent decentralized—RESs, as well as new devices with intensive energy consumption, the management and control of power systems becomes substantially more complex unless they are properly designed and equipped. This requires new ICT techniques to be implemented, in order to match demand with the uncertain supply reliably and efficiently, and to prevent network congestion. Among complex interrelation of involved entities, this paper considers a combination and integration of market optimization and network relief as the ultimate goal which is a real challenge from the scientific point of view. The complexity of system operation and control can be adapted from [30], as follows:

1949-3053/$31.00 © 2013 IEEE

NGUYEN et al.: A GAME THEORY STRATEGY TO INTEGRATE DISTRIBUTED AGENT-BASED FUNCTIONS IN SMART GRIDS

569

1) Objective Function: (1) , and represent respectively the where total generation (supply) cost, the total transmission cost, the cost function for ancillary services, and the total demand benefits. Vectors , and represent set points (amount of power) for generation, transmission, ancillary services, and demand. The objective function (1) is the minimization of total cost for operating system and provision of necessary ancillary services. 2) Supply-Demand Balancing Constraint: The supply-demand balancing constraint must be hold at all time as follows: (2) taking into account the transmission loss vector derived from Kirchhoff laws as follows:

that can be (3)

3) Provision of Reserve Capacity: There must be also a requirement to reserve enough capacity for ancillary services followed by: (4) as s set of committed (production/consumption) with units for providing ancillary services. For the bottom-up approach described in this paper, it is assumed that there are appropriate incentives for local engagement. Thus, the index of is not necessary included as local actors are willing to be involved. 4) Steady-State Constraints: The steady-state constraints of the system must be satisfied as follows: (5) (6) (7) (8)

Fig. 1. Requirements for advanced control layer.

erators (TSOs) acting for the overall system, as shown in the area bounded by a dash line in Fig. 1. A need to enhance this structure has emerged due to the appearance of more and more electronic-based devices within the power system over the past few decades [6]. Due to the expansion of the grid size and the significant participation of small-scale producers and consumers in the control, present-day control systems are facing another challenge: to become decentralized, integrated, flexible, and open [7]. This trend occurs mainly in the secondary control layer, which is seen as too slow and not flexible enough to cope with unpredictability of RES. It is a challenge for power balancing to meet a stochastic demand with a supply that is also stochastic. Since their uncertainties are taken into account, the balancing (2) is rewritten as follows:

5) Dynamic Constraints: The system is required to have a redundancy for dynamic states with following constraints: (9) (10) (11) The above mathematical model presents the problem of a multi-objective optimization for both maintaining network security and economic operation. B. Requirements for Advanced Decentralized Controls The conventional control strategies to maintain grid frequency and voltage are divided into three main layers: primary control, secondary control, and tertiary control. Depending on control interval, the above dynamic constraints are incorporated with additional adjustment limits (ramping rate) as follows: (12) (13) In general, existing control systems are designed with a top-down approach coordinated by transmission system op-

(14) Market mechanisms, with different time intervals, e.g., dayahead, hour-ahead, and real-time, aim to mitigate the imbalance that keeps the system stable. An important contribution from local producers and consumers towards facilitating power balance and network services is therefore needed. In this changing environment, the secondary control must work in either normal interconnected or local islanded (microgrid) modes [8]. Appropriate incentives for those aggregators can stimulate distributed producers and consumers to participate in a new layer of distributed control functions at distribution system level. This advanced control layer can relieve centralized control efforts at transmission system level, as shown in the area bounded by solid lines in Fig. 1. Timescale dimension of the new control layer can range from seconds to minutes for fast actions, and from minutes to hours for slower actions. III. AGENT-BASED SOLUTIONS A. Agent-Based Aggregators for Smart Grids In [9], the key role of aggregation was addressed in revealing the different values of distributed energy resources (DER) when

570

Fig. 2. MAS-based platforms for smart distribution grids.

entering energy markets and providing services to network operators. The MAS technology has been identified as one of the most suitable approaches due to its proper data structure and control actions for individual DERs. This bottom-up approach can simplify the way in which the DER units interact with the energy system at large [10]. Several research works are investigating or have used MAS as an approach for managing distributed power system operation, trading, and control center support. A recent study addressed some applications of agentbased techniques to facilitate services such as distributed monitoring [11], coordinated voltage regulation [12], power flow management [13], and power quality issues [28]. In addition, MAS has also proved to be an effective way to assist end-users participating in energy trading in different scales, from microgrids [3] or local area networks [14], to large-scale integration of RES units in virtual power plant (VPP) [15]. To integrate distributed agent-based functions for ancillary network services and energy trading, a platform is proposed, as shown in Fig. 2. Each network agent represents a local area network, a part of it, or a node. It handles three functional aspects: management, coordination, and execution of actions of the active parties within their areas. This network agent acts as a third party to offer different ancillary services to the network operator. In the market segment, the so-called prosumer agents will be active on the energy and balancing market, and will be expected to react to time-varying price signals. The agent-based structure enables above advanced decentralized controls by introducing agent-based functions of power routing and power matching. Actually, these functions aim to tackle different parts in the optimal objective function (1)–(14) with decentralized approaches. B. Power Matching In principle, power matching is a continuous system-wide problem that must be solved at all times. It aims to keep the balance between power supply and demand, which is mentioned

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

in (2). Until now this matching process has been centrally organized, based on generators which follow the passive loads in a coordinated way. With a massive amount of intermittent power sources and more stochastic patterns of new forms of load, the uncertainties increases significantly in (14). This problem can no longer only be treated in the centralized way, but must be supported by decentralized actions triggered through incentives and price signals [16]. Local producers and consumers however, cannot participate directly in the power matching process, but can act via prosumer agents. The PowerMatcher concept, for instance, is an application of MAS technology for power matching via a bottom-up market approach [14]. It has been demonstrated in a number of field tests in real smart grid applications [14], [15]. The principle of this application is that software agents connected to electricity generating or consuming devices are involved in an electronic market that also contains an auctioneer, who determines the market equilibrium between demand and supply via pooling or an event based mechanism. The function of power matching can be reformulated from (2) as follows: (15) and represent respectively the power supply where and demand that are matched by the equilibrium price at the time ; superscript denotes the matching process. For each time interval , participants committed in matching process send bid vectors to their aggregator (prosumer agent) indicating how many Watts (P) available at price . Depending on the structure of the agent platform, the prosumer agent will aggregate these supply/demand functions and sends the aggregated bid vector to its upper level. This real-time and scalable market mechanism for supply-demand matching relieves the burden of information gathering and communication using a centralized market approach. The matching mechanism can be further enhanced by adding additional sub-markets to serve local or more grid-related optimization objectives. These objectives can be related to reaction to events in the grid within very short time response. Therefore, prosumer agents can trade simultaneously in energy, capacity and network capability markets, each with a different timescale. Especially, it focuses on stochastic behavior of both renewable generation and new types of loads with real-time responses. C. Power Routing One of the main features of smart grids is the capability to handle variable and bi-directional power flows. This can be done through online, (near) real-time and distributed control mechanisms, adapting set points of network components (tap changers, power electronic interfaces), or customer devices (distributed generators, flexible loads). The use of the Optimal Power Flow (OPF) framework is common practice for managing power flows within the power grid, where the algorithm is centralized and deployed at the operational planning stage [17]. Although some distributed OPF techniques have been proposed, they need complex input information and take relatively long processing times [18]. Along with OPF, stability constraints and congestion management problems can implicitly be

NGUYEN et al.: A GAME THEORY STRATEGY TO INTEGRATE DISTRIBUTED AGENT-BASED FUNCTIONS IN SMART GRIDS

investigated [19]. However, those procedures are most suitable for transmission systems with a limited number of components, e.g., generators, transmission lines and substations. Since the distribution networks of the future are expected to include numerous DGs dispersed over wide areas, solutions for power flow management must be adapted. In [20], the power flow management is represented as constraint satisfaction. Algorithms in that research determine the level of DG curtailment necessary for handling thermal network constraints in a smallscale test network. Based on a local information network, a distributed two-level control scheme was proposed to adjust power output from clusters of photovoltaic (PV) generators when disturbances occur [21]. The MAS technology is expected to enable active management in the distribution network [22]. By using an advanced ICT-based approach for the operation of large numbers of small-scale producers and consumers, it has been shown that the embedded functions will be able to facilitate ancillary services by fully exploring local resources. Agent-based functions can deal with possible network problems prompted by market decisions of consumers and producers. Power routing in particular can be used to minimize the total costs from generation and transmission. By using graph theory within the context of MAS, the power routing function can assist DSOs in dealing with bi-directional and unpredictable power flow problems in online, real-time and dynamic conditions [23]. The function exhibits self-healing properties under the presence of changes in network configuration, thermal capacity, or the demand/supply situation. Basically, the function of power routing is the same as the optimization of the power flow which can be formulated in a mathematical model as follows: (16) subject to supply-demand balancing constraint: (17) while physical constraints (5)–(8), dynamic constraints (9)–(11), and adjustment limits (12)–(13) are satisfied. As the power routing function might change the power generation, the power flow, and the demand compared to the pre-conditions, the objective function takes the representative costs into account. The objective function (16) is the total cost for re-routing power when a disturbance occurs in the system or an action has to be taken because of changing circumstances. It presents a particular situation of the system operation stated in (1)–(14). Power routing algorithms can consider the optimal power flow as a minimum cost flow in the graph theory. Different techniques for finding the minimum cost flow, e.g., Successive Shortest Path and Scaling Push-Relable, can be deployed to update the routing table of agents [23]. IV. APPLYING COOPERATIVE GAME THEORY With direct control actions, the function of power routing can respond to any significant change in the power grid within a very short time after the action of protection equipment, for example, taking out circuit. Though power matching is event-based, it

571

Fig. 3. Time response of power routing and matching actions.

is a rather slow process due to the propagation of price-based signals. In the scheme shown in Fig. 3, the prosumer agents can update market clearing conditions and use these as input information for different network services provided by the network agents. The network agents have authority over the prosumer agents in their areas in the way of monitoring and controlling. These primary (grid) agents act as coordinators of secondary (prosumer) agents to provide set points, give constraints, or gather information. They can also communicate with their same-level neighbors in the single layer structure. In addition, it is scalable when they are considered as inferiors of the agents at higher layers. A. Issues for Integrating and Cooperating of Functions 1) Conflicting Interests: There is always an interaction between network services and supply-demand markets. The needs and interests of network operators on the one hand and market participants on the other can lead to conflicting operational situations. In particular, conflicting interests of players with respect to active demand services were identified in [24]. The consequence is that inconsistent signals might be given by the aggregators to producers and consumers. Another example is the conflicting objectives of the commercial aggregators and DSOs mentioned in [4]. The DSO was requested to reduce the power exchange due to local congestion while the VPP was asked for more power. However, each solution to conflicting interests between actors is treated unique and has not been investigated in most demonstration projects for smart grids or discussed in depth in related research. A centralized solution for treating technical and commercial aspects of VPPs was mentioned in [25]. In [34], the operation of distributed storage has been studied from a different perspective. An agent-based model is suggested to support grid stability with incentive a momentary compensation. The authors in [35] proposed a smart pricing system that can be used as an incentive for electrical vehicles to participate in the power balance service. 2) Provision of Capacity for Ancillary Services: It is a challenge to yield appropriate incentive for DER to facilitate ancillary services. Especially, RES-based generation tends to operate with its maximum capacity. As mentioned in (4), the provision of reserve capacity aims for ancillary services, which is organized in the centralized way. On the contrary, agent-based functions supports network services in the decentralized approach by means of power routing. If there is an available capacity from a local customer, it will be

572

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

used either for power matching in normal situation or for power routing in emergency situation. When both power matching and power routing functions are integrated and cooperated, it is necessary to resolve total cost optimization of the whole system mentioned in (1)–(14). 3) Momentary Marginal Cost for Power Routing: Note that DER units normally have very low marginal costs, or even zero. They are highly dependent on local context and, hence, change over time [14]. For example, the marginal cost for a combined heat and power generating ( CHP) is related to the amount of heat demanded, i.e., the higher heat demand is, the lower marginal cost is and vice versa. Therefore, it is difficult to get a suitable value of to enable the function of power routing in (2). In the following section, a method is proposed not only to solve the internal conflicting interests of the aggregators but also yield properly values for . B. Coalitional Game Formulation One possibility of achieving optimization of multi-objectives is the utilization of game theory. A combination of a multi-agent model and game theory to resolve coalition formation in multilateral trade was mentioned in [26], [27]. This paper considers a coalitional game to combine the power matching and power routing functions by fully considering the minimization problem in (1). The game is created with a pair when is the set of players (agents), and is a function that assigns for every coalition a real number representing the total benefit achieved by . The game starts from the results of the matching progress and requests of ancillary services from network operator. As a kind of ancillary services, active power management can be supported by power routing via engagement with local producers and consumers. In the power matching process, most of local resources are used naturally. Without an appropriate allocation of resources, the power routing function is not able to handle locally network disturbances. Consequently, the required ancillary service will be taken of charge by other entities. To fully exploit local resources for both energy trading and network service, the values function must be defined for any coalition as follows:

(18) . This payoff function presents the benefits of the coalition to take over responsibility for reserve service, i.e., power routing while deducts the cost efforts and it reserved part that is not involved in matching process . Fig. 4 presents the proposal method as a block diagram. After receiving input data from power matching and requirements for ancillary services, the game proposes possible coalitions regarding supply-demand balancing, reserve capacity and physical network constraints. This process is finished since the grand coalition, i.e., including all players, is formed. Then an arbitration outcome of the coalitional game can be found by Shapley

Fig. 4. Cooperation of agent-based functions via the coalitional game.

Value [31], [36]. Shapley allocation for each player culated by following equation:

is cal(19)

This value can be considered as a measure of the benefit of a player in the game. In the following simulation, the toolbox TUGlab [33] is used to compute the Shapley value of the game. It gives an incentive for local producers and consumers in participation of power routing with the following equation:

(20) C. Example With Microgrid Case Taken is an example of a microgrid with three device agents (players). In this case, network agent and prosumer agent have the same function and are combined as one. Player 1 represents a diesel generator, generating 11 kW with a possibility of 1 kW up and down regulation, and marginal costs of 7 and 5 pu respectively. Player 2 represents a group of three CHP units, operating at 3 kW and can be adjusted up and down in a 1 kW range with regulating costs of 3 pu and 2 pu respectively. Player 3 represents ten households with 11 kW load demand and 3 kW electrical vehicles (EV) charging. The EV charging pattern can be adjusted that allows the total load demand shaving up to 1 kW. Fig. 5 shows the single-line diagram of the microgrid test network together with its parameters. In the microgrid test network, the aggregator aims to manage these players by entering both the energy market as a market agency and the network service market as a service provider. The function of power matching and power routing can be integrated together by the aggregator. It is assumed that at the operating time , the matching process determines the market

NGUYEN et al.: A GAME THEORY STRATEGY TO INTEGRATE DISTRIBUTED AGENT-BASED FUNCTIONS IN SMART GRIDS

573

Fig. 6. Single-line diagram of the MV test network. TABLE III INPUT DATA OF 30-BUS TEST NETWORK

Fig. 5. Microgrid test network.

TABLE I COALITIONAL CHARACTERISTIC FUNCTION OF 3-PLAYER TEST NETWORK

TABLE II GAME RESULTS OF 3-PLAYER TEST NETWORK

clearing price pu. In this microgrid case, the matching process takes one step and the function (14) is hold locally. For autonomous operation and enabling power routing capability, the microgrid is requested to provide kW with price pu. The cooperative game must determine the coalition with the most optimal benefit and allocate the coalitional benefit fairly among players. Table I presents possible coalitions formulated to achieve the optimal strategy with their respective profit. In this case, physical constraints and power losses were neglected due to the grid size. The first three coalitions do not meet the supply-demand balancing constraint, thus their profits are zero. Other coalitions can provide positive benefits for all players. The Shapley Values for three players are shown in Table II that represents their benefits. It can be seen, the third player representing the household cluster gains the major part for supporting ancillary services locally. It will play a main role for the power routing function by shifting their EV charging load. Consequently, it will have the smallest momentary marginal cost for the routing process. V. SIMULATION RESULTS This section evaluates the performance of the power routing and power matching functions together with support of

coalitional game theory. A power network will be simulated in MATLAB/Simulink while the Java Agent Development Framework (JADE) [32] is used for creating the MAS platform. The communication between the two platforms is based on TCP/IP client/server socket communication. The server socket proxy is created in JADE Matlab/Simulink while the client socket proxy is based in Matlab. The communication period is set at 0.5 s. The protocol for communication between the two environments is based on client/server socket communication. A simulation is employed to investigate the performance of the proposed algorithm on a medium voltage (MV) network. This network includes three feeders with ten buses on each feeder, connected to the same substation. The three ends of these feeders are connected through a normally open point (NOP). Parameters of the test network are given as follows: • Line section: -equivalent circuit: S; MW. • Base load: Each bus has a base load of MVAr. • Distributed generation: DG units are available at bus 1, 3, and 5 of feeder 1 and 16, 18, and 20 of feeder 2. Fig. 6 shows a single-line diagram of the radial test network. In the diagram, the square symbols show nodes having DG units. The remaining nodes have only load demand. Table III presents the case parameters and results from the 6-player game. Two situations of having one network agent for the whole area and having one network agent for each feeder are considered as follows: A. One Network Agent Versus One Prosumer Agent (Microgrid Case) This case is similar to the microgrid example as it includes a single network agent for managing the local area while a single prosumer agent aggregates six DG units. It is assumed that MW for ancillary service with price pu. Table IV shows the results of this situation.

574

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

TABLE IV GAME RESULTS OF THE MICROGRID CASE IN 30-BUS TEST NETWORK

Fig. 8. A snapshot of exchanged agent messages in JADE.

TABLE V GAME RESULTS OF THE VPP CASE IN 30-BUS TEST NETWORK

negotiate with two network agents over the local engagement for power routing. This case is similar to operation of a VPP. Table V shows the results of each game according to combinations of two network agent. As can be seen from the table, the bold rows present combinations that all participants get benefits from. Fig. 7. Change of DG power generation and cost.

The power routing function follows the matching process. In this case, there is no significant disturbance occurring but an update of the generation cost initiate also the routing function. A new generation dispatch, [10.94 MW; 10 MW; 10 MW; 5 MW; 5 MW; 5 MW], is established following the order of DG at bus number 1, 3, 5, 16, 18, 20. Fig. 7(a) presents the variation of DG power generation. In Fig. 7(b), the total transmission cost and operation cost change according to the routing function is shown. The total transmission cost decreases from 113.68 pu to 85.61 while the total operating cost decreases from 426.08 to 374.38 pu. This 12.13% reduction shows the effectiveness the proposed method. Fig. 8 shows a snapshot exchanged agent messages in JADE. With a relative numbers of agents in the computational platform, the system starts giving back results at the message 1139. The convergence and robustness of the algorithm are verified. B. Two Network Agents Versus One Prosumer Agent (Virtual Power Plant Case) In the second case, the test network is asked to provide 10 MW for ancillary services with pu. Since each feeder of the test network is represented by a network agent each agent is requested for providing a certain amount of power. Therefore, the prosumer agent that aggregates six DG units needs to

VI. CONCLUSION This paper presents an agent-based conceptual strategy for managing distributed energy resources entering both energy market and ancillary service. On the one hand, power matching is mentioned as market mechanism that gives possibility for balancing power locally. On the other, power routing is introduced as an active network management that can handle network issues, e.g., network congestions, active power management. However, the self-interest nature of these agent-based functions cause internal conflicts inside the aggregator and involved actors, i.e., network operators, producers, and consumers. An approach based on a coalitional game is proposed to integrate and resolve the conflicting interests. An example of a microgrid with three players is used to explain the approach. Simulation is then used to verify the proposed conceptual strategy on a medium voltage 30-bus test network with two case studies. The first case is similar to the example of a microgrid in which a single network agent representing the whole test network versus the prosumer agent is used. In a more complex situation as the VPP case, the prosumer agent must coordinate with two separated network agents to achieve optimally both commercial and technical values. The simulation results show the effectiveness the method to deal with the complexities introduced by market requirements on the grid operation. In this research study, the system states are assumed to be monitored and predicted well in advance. Massive integration

NGUYEN et al.: A GAME THEORY STRATEGY TO INTEGRATE DISTRIBUTED AGENT-BASED FUNCTIONS IN SMART GRIDS

of DERs in future grids is emerging new decentralized monitoring scheme, for instance in [37]. Combination of this kind of environment with proposed control strategies is a crucial step toward the development and deployment of future smart grids.

REFERENCES [1] P. H. Nguyen, W. L. Kling, and P. Ribeiro, “Smart power router: A flexible agent-based converter interface in active distribution networks,” IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 487–495, 2011. [2] A. A. Aquino-Lugo, R. Klump, and T. J. Overbye, “A control framework for the smart grid for voltage support using agent-based technologies,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 173–180, 2011. [3] 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. [4] F. Bliek et al., “Powermatching city, a living lab smart grid demonstration,” in Proc. Innov. Smart Grid Technol. Conf. Eur. (ISGT Eur.), 2010, pp. 1–8. [5] V. Giordano, F. Gangale, G. Fulli, and M. S. Jiménez, “Smart grid projects in Europe: Lessons learned and current developments,” [Online]. Available: http://ses.jrc.ec.europa.eu/sites/ses/files/documents/smart_grid_projects_in_europe_lessons_learned_and_current_developments.pdf 2011 [6] B. Fardanesh, “Future trends in power system control,” IEEE Comput. Appl., vol. 15, no. 3, pp. 24–31, 2002. [7] F. F. Wu, K. Moslehi, and A. Bose, “Power system control centers: Past, present, and future,” Proc. IEEE, vol. 93, no. 11, pp. 1890–1908, 2005. [8] J. A. P. Lopes, C. L. Moreira, and A. G. Madureira, “Defining control strategies for MicroGrids islanded operation,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 916–924, 2006. [9] EU-DEEP The EU-DEEP project home page [Online]. Available: http://www.eu-deep.com/index.php?id= 397 [10] J. Li, G. Poulton, and G. James, “Coordination of distributed energy resource agents,” Appl. Artif. Intell., vol. 24, no. 5, pp. 351–380, 2010. [11] P. H. Nguyen and W. L. Kling, “Distributed state estimation for multiagent based active distribution networks,” in Proc. 2010 IEEE Power Energy Soc. Gen. Meet., pp. 1–7. [12] P. H. Nguyen, J. M. A. Myrzik, and W. L. Kling, “Coordination of voltage regulation in active networks,” in Proc. IEEE Transm. Distrib. Conf. Expo., 2008, pp. 1–6. [13] P. H. Nguyen, W. L. Kling, and J. M. A. Myrzik, “An application of the successive shortest path algorithm in multi-agent system based active networks,” Eur. Trans. Electr. Power, vol. 20, no. 8, pp. 1138–1152, 2010. [14] ECN PowerMatcher: Smartgrid technology [Online]. Available: http:// www.powermatcher.net/ [15] M. P. F. Hommelberg, C. J. Warmer, I. G. Kamphuis, J. Kok, and G. J. Schaeffer, “Distributed control concepts using multi-agent technology and automatic markets: An indispensable feature of smart power grids,” in Proc. IEEE Power Eng. Soc. Gen. Meet., 2007. [16] P. P. J. van den Bosch et al., “Price-based control of ancillary services for power balancing,” Eur. Trans. Electr. Power, vol. 21, no. 6, pp. 1889–1901, 2010. [17] M. Huneault and F. D. Galiana, “A survey of the optimal power flow literature,” IEEE Trans. Power Syst., vol. 6, no. 2, pp. 762–770, 1991. [18] B. H. Kim and R. Baldick, “A comparison of distributed optimal power flow algorithms,” IEEE Trans. Power Syst., vol. 15, no. 2, pp. 599–604, 2000. [19] E. Bompard, P. Correia, G. Gross, and M. Amelin, “Congestion-management schemes: A comparative analysis under a unified framework,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 346–352, 2003. [20] M. Dolan, E. Davidson, G. Ault, and J. McDonald, “Techniques for managing power flows in active distribution networks within thermal constraints,” in Proc. 20th Int. Conf. Exhib. Electr. Distrib. Part 2, 2009, p. 1. [21] H. Xin, Z. Qu, J. Seuss, and A. Maknouninejad, “A self-organizing strategy for power flow control of photovoltaic generators in a distribution network,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1462–1473, 2011.

575

[22] A. Korbik, S. D. J. Mcarthur, G. W. Ault, G. M. Burt, and J. R. Mcdonald, “Enabling active distribution networks through decentralised autonomous network management,” in Proc. 18th Int. Conf. Exhib. Electr. Distrib. (CIRED), 2005, pp. 1–5. [23] P. H. Nguyen, W. Kling, G. Georgiadis, M. Papatriantafilou, A.-T. Le, and L. Bertling, “Distributed routing algorithms to manage power flow in agent-based active distribution network,” in Proc. 2010 IEEE PES Conf. Innov. Smart Grid Technol. Conf. Eur. (ISGT Eur.). [24] “ADDRESS—Active distribution networks with full integration of demand and distributed energy resources,” [Online]. Available: http://cordis.europa.eu/search/index.cfm?fuseaction=proj.document&PJ_RCN=10072530 [25] E. Mashhour and S. M. Moghaddas-Tafreshi, “Bidding strategy of virtual power plant for participating in energy and spinning reserve markets—Part I: Problem formulation,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 949–956, 2011. [26] C. S. K. Yeung, A. S. Y. Poon, and F. F. Wu, “Game theoretical multiagent modelling of coalition formation for multilateral trades,” IEEE Trans. Power Syst., vol. 14, no. 3, pp. 929–934, 1999. [27] W. Saad, Z. Han, and H. V. Poor, “Coalitional game theory for cooperative micro-grid distribution networks,” in Proc. 2011 IEEE Int. Conf. Commun. Workshops (ICC), pp. 1–5. [28] S. Srivastava, S. Suryanarayanan, D. Cartes, M. Steurer, and P. F. Ribeiro, “A conceptual multi-agent system architecture for power quality monitoring,” in Proc. 37th North Amer. Power Symp., Ames, IA, USA, Oct. 2005, pp. 358–363. [29] S. Suryanarayanan, W. Ren, M. Steurer, P. Ribeiro, and G. T. Heydt, “A real-time controller concept demonstration for distributed generation interconnection,” in Proc. 2006 IEEE Power Eng. Soc. Gen. Meet., Montreal, QC, Canada, Jun. 2006. [30] F. D. Galiana, F. Bouffard, J. M. Arroyo, and J. F. Restrepo, “Scheduling and pricing of coupled energy and primary, secondary, and tertiary reserves,” Proc. IEEE, vol. 93, no. 11, pp. 1970–1983, 2005. [31] T. S. Ferguson, Game theory [Online]. Available: http://www.math. ucla.edu/~tom/Game_Theory/Contents.html [32] Telecom Italia S.p.A., Java Agent Development Framework Jun. 2011 [Online]. Available: http://jade.tilab.com/ [33] M. Á. M. Calvo and E. S. Rodríguez, TUGlab—TUGlabExtended [Online]. Available: http://webs.uvigo.es/mmiras/TUGlab/ accessed: 02 Aug. 2012. [34] P. Vytelingum, T. D. Voice, S. D. Ramchurn, A. Rogers, and N. R. Jennings, “Agent-based micro-storage management for the smart grid,” in Proc. 9th Int. Conf. Auton. Agents Multiagent Syst. (AAMAS 2010), pp. 39–46. [35] C. Wu, H. Mohsenian-Rad, and J. Huang, “Vehicle-to-aggregator interaction game,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 434–442, 2012. [36] L. S. Shapley, , H. W. Kuhn and A. W. Tucker, Eds., “A value for n-person games,” in Contributions to the Theory of Games, ser. Annals of Mathematics Studies. Princeton, NJ, USA: Princeton Univ. Press, 1953, vol. 2, pp. 307–317 [Online]. Available: http://www.rand.org/ pubs/research_memoranda/2008/RM670.pdf [37] B. Luitel and G. K. Venayagamoorthy, “Decentralized asynchronous learning in cellular neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1755–1766, 2012.

Phuong H. Nguyen (M’06) was born in Hanoi, Vietnam, in 1980. He received the B.E. degree in electrical engineering at Hanoi University of Technology, Vietnam, in 2002 and the M.Eng. degree in electrical engineering at the Asian Institute of Technology, Thailand, in 2004. In 2006 he started the Ph.D. program at Eindhoven University of Technology, The Netherlands, with the “Electrical Infrastructure of the Future” project. At the end of 2010 he received his Ph.D. and was employed at the same group as a postdoctoral researcher. His research interests include distributed state estimation, control and operation of the power system, multi-agent system, and their applications in the future power delivery system.

576

Wil L. Kling (M’95) was born in Heesch, The Netherlands, in 1950. He received the M.Sc. degree in electrical engineering from the Eindhoven University of Technology, The Netherlands, in 1978. From 1978 to 1983 he worked with Kema and from 1983 to 1998 with Sept. Since then he is with TenneT, the Dutch Transmission System Operator, Arnhem, The Netherlands, as Senior Engineer for Network Planning and Network Strategy. Since 1993 he is a part-time Professor at the Delft University of Technology, The Netherlands, and since 2000 he is also a part-time Professor in the Electric Power Systems Group at the Eindhoven University of Technology, The Netherlands. From December 2008 he is appointed as a full-time Professor and a chair of Electrical Energy Systems group at the Eindhoven University of Technology. He is leading research programs on distributed generation, integration of wind power, network concepts, and reliability.

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

Mr. Kling is involved in scientific organizations such as Cigre and IEEE. He is the Dutch Representative in the Cigre Study Committee C6 Distribution Systems and Dispersed Generation.

Paulo F. Ribeiro (M’78–SM’88–F’03) received the Ph.D. degree from the University of Manchester, Manchester, U.K., in 1985. Presently, he is at Eindhoven University of Technology, the Netherlands. He is a Registered Professional Engineer in the State of Iowa, USA, and an European Engineer (EurIng).