Cooperative Virtual Power Plant Formation Using Scoring Rules

Virtual Power Plants (VPPs) are fast emerging as a suitable means of integrating small and distributed energy resources (DERs), like wind and solar, into the electricity supply network (Grid). VPPs are formed via the aggregation of a large number of such DERs, so that they exhibit the characteristics of a traditional generator in terms of predictability and robustness. In this work, we promote the formation of such "cooperative" VPPs (CVPPs) using multi-agent technology. In particular, we design a payment mechanism that encourages DERs to join CVPPs with large overall production. Our method is based on strictly proper scoring rules and incentivises the provision of accurate predictions from the CVPPs-and in turn, the member DERs-which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK.We show that our mechanism incentivises real DERs to form CVPPs, and outperforms the current state of the art payment mechanism developed for this problem.

[1]  Fabian Mueller,et al.  Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing , 2012 .

[2]  R. L. Winkler,et al.  Scoring Rules for Continuous Probability Distributions , 1976 .

[3]  Ramachandra Kota,et al.  Cooperatives for Demand Side Management , 2012, ECAI.

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

[5]  E. Peterson,et al.  On the Use of Power Laws for Estimates of Wind Power Potential , 1978 .

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

[7]  M. P. Moghaddam,et al.  Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment , 2012 .

[8]  J. Sijm,et al.  The Performance of Feed-in Tariffs to Promote Renewable Electricity in European Countries , 2002 .

[9]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[10]  H. Hersbach.  Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .

[11]  Ramachandra Kota,et al.  An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles , 2013, IEEE Transactions on Smart Grid.

[12]  P. Asmus.  Microgrids, Virtual Power Plants and Our Distributed Energy Future , 2010 .

[13]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[14]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[15]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.

[16]  Nicholas R. Jennings,et al.  Online mechanism design for electric vehicle charging , 2011, AAMAS.

[17]  Nicholas R. Jennings,et al.  Mechanism design for the truthful elicitation of costly probabilistic estimates in distributed information systems , 2011, Artif. Intell..

[18]  Georgios Chalkiadakis,et al.  Agent Cooperatives for Effective Power Consumption Shifting , 2013, AAAI.

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

[20]  Nicholas R. Jennings,et al.  An Online Mechanism for Multi-Unit Demand and its Application to Plug-in Hybrid Electric Vehicle Charging , 2013, J. Artif. Intell. Res..

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

[22]  Ramachandra Kota,et al.  Cooperatives of distributed energy resources for efficient virtual power plants , 2011, AAMAS.