Demand Side Management in Smart Grids Using a Repeated Game Framework

Demand-side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

[1]  Lang Tong,et al.  Optimal pricing for residential demand response: A stochastic optimization approach , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[2]  Lang Tong,et al.  Modeling and Stochastic Control for Home Energy Management , 2013, IEEE Transactions on Smart Grid.

[3]  John N. Tsitsiklis,et al.  Efficiency loss in a network resource allocation game: the case of elastic supply , 2004, IEEE Transactions on Automatic Control.

[4]  G. Mailath,et al.  Repeated Games and Reputations , 2006 .

[5]  J. Oyarzabal,et al.  A Direct Load Control Model for Virtual Power Plant Management , 2009, IEEE Transactions on Power Systems.

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

[7]  J. Limb,et al.  Editorial on the IEEE/OSA Journal of Lightwave Technology and the IEEE Journal on Selected Areas in Communications , 1986 .

[8]  Sangtae Ha,et al.  Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility , 2012, IEEE Journal on Selected Areas in Communications.

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

[10]  G. Mailath,et al.  Repeated Games and Reputations: Long-Run Relationships , 2006 .

[11]  Han-I Su,et al.  Modeling and analysis of the role of fast-response energy storage in the smart grid , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

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

[13]  Karen Herter,et al.  Residential response to critical-peak pricing of electricity: California evidence , 2010 .

[14]  B.F. Wollenberg,et al.  Toward a smart grid: power delivery for the 21st century , 2005, IEEE Power and Energy Magazine.

[15]  V. Vittal,et al.  A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption , 2008, IEEE Transactions on Power Systems.

[16]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[17]  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.

[18]  Shaolei Ren,et al.  Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid , 2013, IEEE Journal on Selected Areas in Communications.

[19]  Anno Accademico,et al.  Smart Grid Communications: Overview of research challenges, solutions and standardization activities , 2013 .

[20]  Anna Scaglione,et al.  From Packet to Power Switching: Digital Direct Load Scheduling , 2012, IEEE Journal on Selected Areas in Communications.

[21]  Na Li,et al.  Optimal demand response: Problem formulation and deterministic case , 2012 .

[22]  Chen Wang,et al.  Managing end-user preferences in the smart grid , 2010, e-Energy.

[23]  Jhi-Young Joo,et al.  Option Valuation Applied to Implementing Demand Response via Critical Peak Pricing , 2007, 2007 IEEE Power Engineering Society General Meeting.

[24]  Zhu Han,et al.  Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

[25]  Jean C. Walrand,et al.  Optimal demand response with energy storage management , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).