Cooperatives of distributed energy resources for efficient virtual power plants

The creation of Virtual Power Plants (VPPs) has been suggested in recent years as the means for achieving the cost-efficient integration of the many distributed energy resources (DERs) that are starting to emerge in the electricity network. In this work, we contribute to the development of VPPs by offering a game-theoretic perspective to the problem. Specifically, we design cooperatives (or "cooperative VPPs"---CVPPs) of rational autonomous DER-agents representing small-to-medium size renewable electricity producers, which coalesce to profitably sell their energy to the electricity grid. By so doing, we help to counter the fact that individual DERs are often excluded from the wholesale energy market due to their perceived inefficiency and unreliability. We discuss the issues surrounding the emergence of such cooperatives, and propose a pricing mechanism with certain desirable properties. Specifically, our mechanism guarantees that CVPPs have the incentive to truthfully report to the grid accurate estimates of their electricity production, and that larger rather than smaller CVPPs form; this promotes CVPP efficiency and reliability. In addition, we propose a scheme to allocate payments within the cooperative, and show that, given this scheme and the pricing mechanism, the allocation is in the core and, as such, no subset of members has a financial incentive to break away from the CVPP. Moreover, we develop an analytical tool for quantifying the uncertainty about DER production estimates, and distinguishing among different types of errors regarding such estimates. We then utilize this tool to devise protocols to manage CVPP membership. Finally, we demonstrate these ideas through a simulation that uses real-world data.

[1]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

[2]  E. M. Davidson,et al.  The Use of Constraint Programming for the Autonomous Management of Power Flows , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

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

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

[5]  L. J. Savage Elicitation of Personal Probabilities and Expectations , 1971 .

[6]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[7]  A.L. Dimeas,et al.  Agent based control of Virtual Power Plants , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[8]  J. K. Kok,et al.  Intelligence in Electricity Networks for Embedding Renewables and Distributed Generation , 2010 .

[9]  Xiaotie Deng,et al.  On the Complexity of Cooperative Solution Concepts , 1994, Math. Oper. Res..

[10]  Matthias Klusch,et al.  Coalition Formation in a Power Transmission Planning Enviornment , 1997 .

[11]  Walter L. Smith Probability and Statistics , 1959, Nature.

[12]  Matthias Klusch,et al.  Multi-agent coalition formation in power transmission planning , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[13]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[14]  Sarvapali D. Ramchurn,et al.  Trading agents for the smart electricity grid , 2010, AAMAS.