Real-Time Pricing with uncertain and heterogeneous consumer preferences

Consumer demand profiles and fluctuating renewable power generation are two main sources of uncertainty in matching demand and supply. This paper proposes a model of the electricity market that captures the uncertainties on both, the operator and the user side. The system operator (SO) implements a temporal linear pricing strategy that depends on real-time demand and renewable generation in the considered period combining Real-Time Pricing with Time-of-Use Pricing. The announced pricing strategy sets up a Bayesian game among the users with unknown, heterogeneous and correlated consumption preferences. The explicit characterization of the optimal selfish user behavior using the Bayesian Nash equilibrium solution concept allows the SO to derive pricing policies that influence demand to serve practical objectives such as minimizing peak-to-average ratio or attaining a desired rate of return while at the same time hedging renewable generation uncertainty.

[1]  X. Vives Strategic Supply Function Competition with Private Information , 2008, SSRN Electronic Journal.

[2]  Daniel Pérez Palomar,et al.  Demand-Side Management via Distributed Energy Generation and Storage Optimization , 2013, IEEE Transactions on Smart Grid.

[3]  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.

[4]  Alejandro Ribeiro,et al.  Bayesian Quadratic Network Game Filters , 2013, IEEE Transactions on Signal Processing.

[5]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[6]  Zhu Han,et al.  Efficient and Secure Wireless Communications for Advanced Metering Infrastructure in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[7]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[8]  Alejandro Ribeiro,et al.  Demand Response Management in Smart Grids With Heterogeneous Consumer Preferences , 2015, IEEE Transactions on Smart Grid.

[9]  Hamed Mohsenian Rad,et al.  Demand side management for Wind Power Integration in microgrid using dynamic potential game theory , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[10]  Lingfeng Wang,et al.  Smart meters for power grid — Challenges, issues, advantages and status , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[11]  Gongguo Tang,et al.  A game-theoretic approach for optimal time-of-use electricity pricing , 2013, IEEE Transactions on Power Systems.

[12]  Adam Wierman,et al.  Real-time deferrable load control: handling the uncertainties of renewable generation , 2013, e-Energy '13.

[13]  Quanyan Zhu,et al.  A differential game approach to distributed demand side management in smart grid , 2012, 2012 IEEE International Conference on Communications (ICC).

[14]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[15]  Steven H. Low,et al.  Multi-period optimal energy procurement and demand response in smart grid with uncertain supply , 2011, IEEE Conference on Decision and Control and European Control Conference.

[16]  Alejandro Ribeiro,et al.  Distributed demand side management of heterogeneous rational consumers in smart grids with renewable sources , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[18]  Antoni Calvó-Armengol,et al.  Information Gathering in Organizations: Equilibrium, Welfare, and Optimal Network Structure , 2009 .

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