A Game Theory Strategy to Integrate Distributed Agent-Based Functions in Smart Grids

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.

[1]  Graham Ault,et al.  Enabling active distribution networks through decentralised, autonomous network management , 2005 .

[2]  Khosrow Moslehi,et al.  Power System Control Centers: Past, Present, and Future , 2005, Proceedings of the IEEE.

[3]  P.H. Nguyen,et al.  Coordination of voltage regulation in Active Networks , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[4]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[5]  Fulli Gianluca,et al.  Smart Grid Projects in Europe - Lessons Learned and Current Developments , 2011 .

[6]  Paulo F. Ribeiro,et al.  Smart Power Router: A Flexible Agent-Based Converter Interface in Active Distribution Networks , 2011, IEEE Transactions on Smart Grid.

[7]  Marina Papatriantafilou,et al.  Distributed routing algorithms to manage power flow in agent-based active distribution network , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[8]  L. S. Shapley,et al.  17. A Value for n-Person Games , 1953 .

[9]  Thomas J. Overbye,et al.  A Control Framework for the Smart Grid for Voltage Support Using Agent-Based Technologies , 2011, IEEE Transactions on Smart Grid.

[10]  Walid Saad,et al.  Coalitional Game Theory for Cooperative Micro-Grid Distribution Networks , 2011, 2011 IEEE International Conference on Communications Workshops (ICC).

[11]  Sarvapali D. Ramchurn,et al.  Agent-based micro-storage management for the Smart Grid , 2010, AAMAS.

[12]  Graham Ault,et al.  TECHNIQUES FOR MANAGING POWER FLOWS IN ACTIVE DISTRIBUTION NETWORKS WITHIN THERMAL CONSTRAINTS , 2009 .

[13]  van den Ppj Paul Bosch,et al.  Price-based control of ancillary services for power balancing , 2011 .

[14]  B. H. Kim,et al.  A comparison of distributed optimal power flow algorithms , 2000 .

[15]  A.L. Dimeas,et al.  Operation of a multiagent system for microgrid control , 2005, IEEE Transactions on Power Systems.

[16]  Frits Bliek,et al.  PowerMatching City, a living lab smart grid demonstration , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[17]  Hamed Mohsenian Rad,et al.  Vehicle-to-Aggregator Interaction Game , 2012, IEEE Transactions on Smart Grid.

[18]  S. Suryanarayanan,et al.  A conceptual power quality monitoring technique based on multi-agent systems , 2005, Proceedings of the 37th Annual North American Power Symposium, 2005..

[19]  François Bouffard,et al.  Scheduling and Pricing of Coupled Energy and Primary, Secondary, and Tertiary Reserves , 2005, Proceedings of the IEEE.

[20]  Bruce Fardanesh,et al.  Future trends in power system control , 2002 .

[21]  E. Bompard,et al.  Congestion-management schemes: a comparative analysis under a unified framework , 2003 .

[22]  Felix F. Wu,et al.  Game Theoretical Multi-agent Modelling of Coalition Formation for Multilateral Trades , 1999 .

[23]  J.A.P. Lopes,et al.  Defining control strategies for MicroGrids islanded operation , 2006, IEEE Transactions on Power Systems.

[24]  Philip Wolfe,et al.  Contributions to the theory of games , 1953 .

[25]  Zhihua Qu,et al.  A Self-Organizing Strategy for Power Flow Control of Photovoltaic Generators in a Distribution Network , 2011, IEEE Transactions on Power Systems.

[26]  Paola Petroni.  Active distribution network with full integration of demand and distributed energy resources , 2009 .

[27]  Jiaming Li,et al.  COORDINATION OF DISTRIBUTED ENERGY RESOURCE AGENTS , 2010, Appl. Artif. Intell..

[28]  M.P.F. Hommelberg,et al.  Distributed Control Concepts using Multi-Agent technology and Automatic Markets: An indispensable feature of smart power grids , 2007, 2007 IEEE Power Engineering Society General Meeting.

[29]  G.T. Heydt,et al.  A real-time controller concept demonstration for distributed generation interconnection , 2006, 2006 IEEE Power Engineering Society General Meeting.

[30]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .

[31]  Ganesh K. Venayagamoorthy,et al.  Decentralized Asynchronous Learning in Cellular Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[32]  P. H. Nguyen,et al.  Distributed state estimation for multi-agent based active distribution networks , 2010, IEEE PES General Meeting.

[33]  Wil L. Kling,et al.  An application of the successive shortest path algorithm to manage power in multi-agent system based active networks , 2010 .