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Real-Time Pricing Decision Based on Leader-Follower Game in Smart Grid
Yeming DAI, Yan GAO
Journal of Systems Science and Information ›› 2015, Vol. 3 ›› Issue (4) : 348-356.
PDF(120 KB)
PDF(120 KB)
Real-Time Pricing Decision Based on Leader-Follower Game in Smart Grid
The real-time pricing plays an important role in demand-side management for smart grid. In this paper, we study real-time pricing strategy of electricity retailers by means of game theory in smart grid. The retailers are in the game situation where there is one leader with multi-followers. We propose a real-time electricity demand function and analyze the interactions between the retailers, then obtain its equilibrium solution. The analysis and simulation results of the equilibrium solution show the e ectiveness of the proposed method.
smart grid / demand-side management / real-time pricing / game theory {{custom_keyword}} /
[1] Bu S R, Yu F R, Liu P X. A game-theoretical decision-making scheme for electricity retailers in the smart grid with demand-side management. IEEE International Conference on Smart Grid Communications, 2011: 387-391.
[2] Mei S W, Zhu J Q. Mathematical and control scienti c issues of smart grid and its prospects. ACTA Automatica Sinica, 2013, 39(2): 119-131.
[3] Logenthiran T, Srinivasan D, Shun T Z. Demand side management in smart grid using heuristic optimiza- tion. IEEE Transaction on Smart Grid, 2012, 3: 1244-1252.
[4] Samadi P, Mohsenian-Rad A H, Schober R, et al. Advanced demand side management for the future smart grid using mechanism design. IEEE Transaction on Smart Grid, 2012, 3: 1170-1180.
[5] O'Neill D, Levorato M, Goldsmith A, et al. Residential demand response using reinforcement learning. IEEE Smart Grid Communications, 2010: 409-414.
[6] Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transaction on Smart Grid, 2010, 2: 120-133.
[7] Maharjan S, Zhu Q, Zhang Y, et al. Dependable demand response management in the smart grid: A Stackelberg game approach. IEEE Transaction on Smart Grid, 2013, 1: 120-132.
[8] Mohsenian-Rad A H, Wong V W S, Jatkevich J, et al. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 2010, 1: 320-331.
[9] Asadi G, Gitizadeh M, Roosta A. Welfare maximization under real-time pricing in smart grid using PSO algorithm. 21st Iranian Conference on IEEE Electrical Engineering (ICEE), 2013.
[10] Xu W Q, Feng Z L, Huang J, et al. Fast real-time pricing method based on improved dual decomposition for smart grid. Power System Protection and Control, 2012, 40(21): 42-47.
[11] Holland S P, Mansur E. Is real-time pricing green? The environmental impacts of electricity demand variance. Review Economic, 2008, 90: 550-561.
[12] Dai Y M, Gao Y. Real-time pricing decision making for retailer-wholesaler in smart grid based on game theory. Abstract and Applied Analysis, 2014, Article ID 708584.
[13] Dai Y M, Gao Y. Real-time pricing strategy with multi-retailers based on demand-side management for the smart grid. Proceedings of the Chinese Society for Electrical Engineering, 2014, 34(25): 4244-4249 (in Chinese).
[14] Bu S R, Yu F R, Liu P X. Dynamic pricing for demand-side management in the smart grid. IEEE Online Conference on Green Communications, 2011.
[15] You P S, Wu M T. Optimal ordering and pricing policy for an inventory system with order cancellations. OR Spectrum, 2007, 29: 661-679.
[16] Ouardighi F E, Jørgensen S, Pasin F. A dynamic game with monopolist manufacturer and price-competing duopolist retailers. OR Spectrum, 2013, 35: 1059-1084.
[17] Stackelberg H. The theory of the market economy. Oxford University Press, Oxford, 1952.
[18] Van Damme E, Hurkens S. Endogenous Stackelberg leadership. Games and Economic Behavior, 1999, 28(1): 105-129.
[19] Hou W H, Li Y, Wang S Y. Endogenous Stackelberg leadership with uncertain information. Journal of Systems Engineering and Electronics, 2002, 13(1): 74-79.
Supported by the National Natural Science Foundation of China (11171221), Shanghai Leading Academic Disci- pline (XTKX2012), Program of Natural Science of Shanghai (14ZR1429200), and SUR (Optimization Methods on Smart Grid)
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