Review of Real-time Pricing based on Demand Response
Huang Haixin1, 2, Deng Li1, Zhang Lu1
1. Shenyang Ligong University, College of Information Science and Engineering, Shenyang 110159; 2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
Abstract：With the development of economy and society, the power industry runs through all areas and the demand for electricity is increasing. Smart grid has become a main trend with the development of the grid. As the component of the electricity market, electric price plays a lever effect invisibly. The real-time pricing strategy based on demand response is one of the essential technologies, which can achieve validity and reliability of the smart grid. Demand response and the real-time pricing were introduced. Research models and algorithms in domestic and overseas were summarized and analyzed systematically from the level of the demand and supply in both the users and the providers. The research challenges and possible real-time pricing strategy in the future smart grid were to be expected.
[1] US Department of Energy, BENEFITS OF DEMAND RESPONSE IN ELECTRICITY MARKETS AND RECOMMENDATIONS FOR ACHIEVING THEM. A REPORT TO THE UNITED STATES CONGRESS PURSUANT TO SECTION 1252 OF THE ENERGY POLICY ACT OF 2005 (FEBRUARY 2006) [2014-01-05] [EB/OL]. [2] 陶小马, 周雯. 电力需求响应的研究进展及文献述评[J]. 北京理工大学学报(社会科学版), 2014, 16(1): 32-40. [3] 曲家余. 面向智能电网的基于梯度投影法的实时电价算法研究[D]. 沈阳: 东北大学, 2013. [4] 雷霞, 刘俊勇, 党晓强. 配电市场购售电优化模型研究综述[J]. 电力系统保护与控制, 2010, 38(2): 136-145. [5] 毕超. 基于功率分解理论的用户实时电价研究[D]. 北京: 华北电力大学, 2008. [6] U.S. Department of Energy, 2008 Buildings Energy Data Book[R]. Energy Efficiency and Renewable Energy, Mar. 2009. [7] Mohsenian-Rad A H, Wong V, Jatskevich J, et al. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid[C]// Innovative Smart Grid Technologies (ISGT), 2010, 2010: 1-6. [8] Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in Real-Time electricity pricing environments[J]. Smart Grid, IEEE Transactions on, 2010, 1(2): 120-133. [9] Lujano-Rojas J M, Monteiro C, Dufo-Lopez R A. Optimum residential load management strategy for real time pricing (RTP) demand response programs[J]. Energy Policy, 2012, 45(2): 671-679. [10] 张延宇, 曾鹏, 臧传治. 智能电网环境下家庭能源管理系统研究综述[J]. 电力系统保护与控制, 2014, 42(18): 144-154. [11] Yu Rongshan, Yang Wenxian, Rahardja S. Optimal real-time price based on a statistical demand elasticity model of electricity[C]//Smart Grid Modeling and Simulation (SGMS), 2011 IEEE First International Workshop on, 2011: 90-95. [12] Kirschen D S, Strbac G, Cumperayot P, et al. Factoring the elasticity of demand in electricity prices[J]. Power Systems, IEEE Transactions on, 2000, 15(2): 612-617. [13] Evens S K C, Kärkkäinen S. Pricing models and mechanisms for the promotion of demand side integration[J]. VTT Technical Research Centre of Finland, Tech. Rep. VTT. [14] Samadi P, Mohsenian-Rad A H, Schober R, et al. Optimal Real-Time pricing algorithm based on utility maximization for smart grid[C]//Smart Grid Commu- nications (SmartGridComm), 2010 First IEEE International Conference on, 2010: 415-420. [15] Tarasak P. Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on, 2011: 321-326. [16] Yang Jie, Zhang Guoshan, Ma Kai. Matching supply with demand: A power control and real time pricing approach[J]. International Journal of Electrical Power & Energy Systems, 2014, 61: 111-117. [17] Asadi G, Gitizadeh M, Roosta A. Welfare maximization under real-time pricing in smart grid using PSO algorithm[C]//Electrical Engineering (ICEE), 2013 21st Iranian Conference on, 2013: 1-7. [18] Weckx S, Driesen J, D'hulst R. Optimal real-time pricing for unbalanced distribution grids with network constraints[C]//Power and Energy Society General Meeting (PES), 2013 IEEE, 2013: 1-5. [19] Ganesh A, Laevens K, Steinberg R. Congestion pricing and user adaptation[C]//INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2, 2001: 959-965 vol.2. [20] 韩水, 汪定伟, 张近朱. 利用边际成本法计算输电节点电价[J]. 东北电力技术, 2002, 23(3): 8-11. [21] Chen L, Suzuki H, Wachi T, et al. Components of nodal prices for electric power systems[J]. Power Engineering Review, IEEE, 2001, 21(11): 54-54. [22] 陈之栩. 节点边际电价与阻塞管理算法的研究[D]. 北京: 华北电力大学:北京, 2007. [23] Herter K, Mcauliffe P, Rosenfeld A. An exploratory analysis of California residential customer response to critical peak pricing of electricity[J]. Energy, 2007, 32(1): 25-34. [24] Taylor T N, Schwarz P M, Cochell J E. 24/7 hourly response to electricity Real-Time pricing with up to eight summers of experience[J]. Journal of Regulatory Economics, 2005, 27(3): 235-262. [25] Wolak F A. Residential customer response to real-time pricing: The anaheim critical peak pricing experiment[J]. Center for the Study of Energy Markets, 2007. [26] Meng Fanlin, Zeng Xiaojun, Qian Ma. Learning customer behaviour under Real-Time pricing in the smart grid[C]//Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013: 3186-3191. [27] Gottwalt S, Ketter W, Block C, et al. Demand side management—A simulation of household behavior under variable prices[J]. Energy Policy, 2011, 39(12): 8163-8174. [28] 张钦. 职能电网下需求响应热点问题探讨[J]. 中国电力, 2013, 46(6): 85-90. [29] 熊雄, 杨仁刚, 叶林, 等. 电力需求侧大规模储能系统经济性评估[J]. 电工技术学报, 2013, 28(9): 224-230. [30] 孙近文, 万云飞, 郑培文, 等. 基于需求侧管理的电动汽车有序充放电策略[J]. 电工技术学报, 2014, 29(8): 64-69. [31] 张犁, 孙凯, 吴田进, 等. 基于光伏发电的直流微电网能量变换与管理[J]. 电工技术学报, 2013, 28(2): 248-254.
2015, Vol. 16 
