A Faithful Distributed Mechanism for Demand Response Aggregation

A faithful distributed mechanism is proposed in this paper for sharing the cost of electricity among a large number of strategic and distributed household agents that have private information, and discrete and continuous energy levels. In contrast to mechanisms in prior works, which charge the agents based on their day-ahead allocation, the proposed mechanism charges the agents based on their day-ahead allocation and their actual consumption. The mechanism is proven to be asymptotically dominant strategy incentive compatible, weakly budget balanced, and fair in charging the agents. However, the proposed mechanism's payment function, which requires computing a marginal allocation problem for each agent, renders the mechanism intractable for a large number of household agents if it is computed centrally. Thus, a distributed implementation of the mechanism is proposed, in which the agents share the computational burden with the aggregator. The distributed implementation is based on a penalty/reward scheme inspired by the prisoner's dilemma that brings faithful computation to a dominant strategy equilibrium.

[1]  Vincent W. S. Wong,et al.  Optimal energy consumption scheduling using mechanism design for the future smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2]  D.C. Parkes,et al.  Distributed implementations of Vickrey-Clarke-Groves mechanisms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[3]  Lazaros Gkatzikis,et al.  Electricity markets meet the home through demand response , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[4]  Xinping Guan,et al.  Optimal demand response using mechanism design in the smart grid , 2012, Proceedings of the 31st Chinese Control Conference.

[5]  Alex Rogers,et al.  A scoring rule-based mechanism for aggregate demand prediction in the smart grid , 2012, AAMAS.

[6]  Gregor Verbic,et al.  A healthy dose of reality for game-theoretic approaches to residential demand response , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

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

[8]  Giovanni Brusco,et al.  Energy Management System for an Energy District With Demand Response Availability , 2014, IEEE Transactions on Smart Grid.

[9]  Gregor Verbic,et al.  Towards a realistic implementation of mechanism design in demand response aggregation , 2014, 2014 Power Systems Computation Conference.

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

[11]  C. Gentile,et al.  Tighter Approximated MILP Formulations for Unit Commitment Problems , 2009, IEEE Transactions on Power Systems.

[12]  Deepak Rajan,et al.  IBM Research Report Minimum Up/Down Polytopes of the Unit Commitment Problem with Start-Up Costs , 2005 .

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

[14]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[15]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[16]  Kate Larson,et al.  A truth serum for sharing rewards , 2011, AAMAS.

[17]  Fernando L. Alvarado,et al.  Using utility information to calibrate customer demand management behavior models , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[18]  Nicholas R. Jennings,et al.  Prediction-of-use games: a cooperative game theoryapproach to sustainable energy tariffs , 2014, AAMAS.

[19]  Georgios B. Giannakis,et al.  Scalable and Robust Demand Response With Mixed-Integer Constraints , 2013, IEEE Transactions on Smart Grid.