Dynamic coalition formation in a smart grid: A game theoretic approach

In this paper, the problem of optimal energy distribution by dynamically changing the size of coalition, which consists of one micro-grid and several customers, is studied using the theory of Markov Decision Process (MDP) - a discrete optimization method. In this paper, the micro-grid, which acts as one of the players, needs to decide the size of the coalition for utilizing the generated energy optimally. On the other hand, the customer, which acts as another player, needs to decide its strategies, so as to optimize a trade-off between the associated cost, i.e., communication cost and energy distribution cost, and effective power supply. Using MDP, it is shown how dynamically coalition can be formed and the customer can be assured of an efficient power distribution.

[1]  Walid Saad,et al.  Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications , 2012, IEEE Signal Processing Magazine.

[2]  Gerard J. M. Smit,et al.  Management and Control of Domestic Smart Grid Technology , 2010, IEEE Transactions on Smart Grid.

[3]  Frank C. Lambert,et al.  A survey on communication networks for electric system automation , 2006, Comput. Networks.

[4]  Sudip Misra,et al.  Policy controlled self-configuration in unattended wireless sensor networks , 2011, J. Netw. Comput. Appl..

[5]  A. Molderink,et al.  Demand Side Load Management Using a Three Step Optimization Methodology , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

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

[7]  Christian Ibars,et al.  Distributed Demand Management in Smart Grid with a Congestion Game , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[8]  Sudip Misra,et al.  Localized policy-based target tracking using wireless sensor networks , 2012, TOSN.

[9]  Sudip Misra,et al.  A probabilistic approach to minimize the conjunctive costs of node replacement and performance loss in the management of wireless sensor networks , 2010, IEEE Transactions on Network and Service Management.

[10]  P.T. Krein,et al.  Game-Theoretic Control of Small-Scale Power Systems , 2009, IEEE Transactions on Power Delivery.

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

[12]  P. Venkata Krishna,et al.  Learning automata as a utility for power management in smart grids , 2013, IEEE Communications Magazine.

[13]  Sarvapali D. Ramchurn,et al.  Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.