Value of demand response in the smart grid

In this paper, we raise the question: What is the value that demand response management (DRM) can bring to generation companies and consumers in the smart grid? The question is fundamental for understanding the efficiency and impact of DRM on the future power grid. To answer this question, we first establish a Stackelberg game framework that captures the hierarchical communication architecture of the energy system, and the rational behaviors of the consumers and the market operator. We define the value of demand response based on the Stackelberg equilibrium (SE) solution to the hierarchical two-person game problem, and the standard optimal solution to economic dispatch problem. In order to compute the equilibrium solution, we show that a consistency principle can be used to characterize the SE of the game in which the follower responds to the dual variable of the leader's problem. We use logarithmic utility functions to illustrate the solution concept and show that in some cases, DRM provides conflicting values to the gencos and consumers.

[1]  Marija D. Ilic,et al.  From Hierarchical to Open Access Electric Power Systems , 2007, Proceedings of the IEEE.

[2]  Clark W Gellings,et al.  The Smart Grid: Enabling Energy Efficiency and Demand Response , 2020 .

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

[4]  T. Başar,et al.  A Stackelberg Network Game with a Large Number of Followers , 2002 .

[5]  Daniel K. Molzahn,et al.  Examining the limits of the application of semidefinite programming to power flow problems , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[6]  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.

[7]  Quanyan Zhu,et al.  Deceptive routing games , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[8]  D. Kirschen Demand-side view of electricity markets , 2003 .

[9]  T. Başar,et al.  Incentive-Based Pricing for Network Games with Complete and Incomplete Information , 2007 .

[10]  S. Cvijic,et al.  Optimal clustering for efficient computations of contingency effects in large regional power systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[11]  Goran Strbac,et al.  Fundamentals of Power System Economics: Kirschen/Power System Economics , 2005 .

[12]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[13]  Quanyan Zhu,et al.  Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach , 2013, IEEE Transactions on Smart Grid.

[14]  Quanyan Zhu,et al.  End‐to‐end DWDM optical link power‐control via a Stackelberg revenue‐maximizing model , 2008, Int. J. Netw. Manag..

[15]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .

[16]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

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

[18]  Quanyan Zhu,et al.  A multi-resolution large population game framework for smart grid demand response management , 2011, International Conference on NETwork Games, Control and Optimization (NetGCooP 2011).

[19]  Marija D Ilić,et al.  Dynamic Monitoring and Decision Systems for Enabling Sustainable Energy Services , 2011, Proceedings of the IEEE.

[20]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.