考虑风险的智能电网实时电价定价策略

doi:10.16182/j.issn1004731x.joss.201804020

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (4): 1376-1383.doi: 10.16182/j.issn1004731x.joss.201804020

考虑风险的智能电网实时电价定价策略

朱红波^1,2, 高岩¹, 代业明³

1.上海理工大学管理学院,上海 200093;
2.淮阴工学院数理学院,江苏淮安 223003;
3.青岛大学数学与统计学院,山东青岛 266071

收稿日期:2016-04-29 修回日期:2016-05-28 出版日期:2018-04-08 发布日期:2019-01-04
作者简介:朱红波(1981-),女,吉林蛟河,博士生,研究方向为智能电网实时定价机制;高岩（1962-）,黑龙江五常,博士,教授,研究方向为系统分析与优化,智能电网实时定价机制。
基金资助:
国家自然科学基金(11171221)

Real-Time Pricing Strategy Considering the Risk of Smart Grid

Zhu Hongbo^1,2, Gao Yan¹, Dai Yeming³

1. School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
2.Faculty of Mathematics and Physics, Huaiyin Institute of Technology, Huai’an 223003, China;
3.School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China

Received:2016-04-29 Revised:2016-05-28 Online:2018-04-08 Published:2019-01-04

摘要/Abstract

摘要： 基于需求响应的智能电网实时电价机制是调节电力供需平衡的理想手段。用户用电需求行为则对设计高效可行的实时电价定价策略起到调控作用。考虑用户电力总需求的动态变化,以社会福利最大化作为研究目标,改进已有在线实时电价风险模型中的用电风险项,将个体用户的用电量波动调整为全体用户电力总需求的波动,建立优化模型。通过对偶方法等价转换原优化问题,克服原有的在线算法不能求解用户总体用电量的困难,设计一种算法对其求解,得到实时电价。数值仿真结果验证了所建模型的合理性以及算法的有效性和可行性。

关键词: 智能电网, 实时电价, 风险, 优化

Abstract: The real-time electricity price mechanism is an ideal method to adjust the power balance between supply and demand in smart grid. Its implementation has profound impacts on the users' behavior and the operation and management of electricity power grid’s safety. The users’ demand behavior plays a regulatory role in designing real-time electricity pricing strategy. Aiming at maximizing social welfare, the dynamic change of users’ aggregate demand is analyzed, which corrects the electricity risk items in online real-time risk model in the way of changing the individual user’s power fluctuations to all the users’ demand power fluctuations, and the optimization model is rebuilt. An algorithm is presented to overcome the existing online one without computing the overall power consumption by equivalently conversing the optimization problem through dual method. The algorithm will help to get real-time electricity price. The model rationality and the proposed algorithm validity and feasibility are verified by numerical simulation results.

Key words: smart grid, real-time pricing, risk, optimization

中图分类号:

朱红波, 高岩, 代业明. 考虑风险的智能电网实时电价定价策略[J]. 系统仿真学报, 2018, 30(4): 1376-1383.

Zhu Hongbo, Gao Yan, Dai Yeming. Real-Time Pricing Strategy Considering the Risk of Smart Grid[J]. Journal of System Simulation, 2018, 30(4): 1376-1383.

参考文献

[1] Caramanis M C, Bohn R, Schweppe F C.Optimal spot pricing: practice and theory[J]. IEEE Transaction on Power Apparatus and Systems(S0272-1724), 1982, 10(9): 3234-3245.
[2] Dai Y, Gao Y, Gao H W, et al.Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers[J]. Neurocomputing
(S0925-2312), 2017,260: 149-156.
[3] 代业明, 高岩. 分布式发电系统动态定价决策[J]. 系统工程, 2016, 33(2): 70-75.
Dai Yeming, Gao Yan.Dynamic pricing decision based on distributed generation system[J]. Systems Engineering, 2016, 33(2): 70-75.
[4] Ma K, Hu G, Spanos C J.Distributed energy consumption control via real-time pricing feedback in smart grid[J]. IEEE Transactions on Control Systems Technology(S1063-6536), 2014, 22(5): 1907-1914.
[5] Dai Y M, Gao Y.Real-time pricing decision based on leader-follower game in smart grid[J]. Journal of Systems Science and Information(S1478-9906), 2015, 3(4): 348-356.
[6] Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments[J]. IEEE Transactions on Smart Grid(S1949-3053), 2010, 1(2): 120-133.
[7] 代业明, 高岩. 具有多类资源多类用户智能电网实时定价决策[J]. 系统工程理论与实践, 2015, 35(9): 2315-2323.
Dai Yeming, Gao Yan.Real-time pricing decision-making in smart grid with multi-type users and multi-type power sources[J]. Syetems Engineering Theory & Practice, 2015, 35(9): 2315-2323.
[8] Chen C, Kishore S, Snyder L V.An innovative RTP-based residential power scheduling scheme for smart grids[C]//2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2011: 5956-5959.
[9] 代业明, 高岩. 基于智能电网需求侧管理的多零售商实时定价策略[J]. 中国电机工程学报, 2014, 34(25): 4244-4249.
Dai Yeming, Gao Yan.Real-time pricing strategy with multi-retailers based on demand-side management for the smart grid[J]. Proceedings of the CSEE, 2014, 34(25): 4244-4249.
[10] Pagani G A, Aiello M.Generating realistic dynamic prices and services for the smart grid[J]. IEEE Systems Journal(S1932-8184), 2015, 9(1): 191-198.
[11] 金颖丰, 高岩. 智能电网分段实时定价优化策略研究[J]. 计算机仿真, 2016, 33(4): 171-175.
Jin Yingfeng, Gao Yan.Optimal piecewise real-time pricing strategy for smart grid[J]. Computer Simulation, 2016, 33(4): 171-175.
[12] Deng R L, Yang Z Y, Chow M Y, et al.A survey on demand response in smart grids: mathematical models and approaches[J]. IEEE Transactions on Industrial Informatics(S1551-3203), 2015, 11(3): 570-582.
[13] 李江波, 王波, 高岩, 等. 马尔可夫决策过程下的智能电网实时电价模型[J]. 系统仿真学报, 2016, 28(11): 2756-2763.
Li Jiangbo, Wang Bo, Gao Yan, et al.Optimal real-time pricing model of smart grid based on markov decision process[J]. Journal of System Simulation, 2016, 28(11): 2756-2763.
[14] Samadi P, Mohsenian-Rad A H, Schober R, et al. Optimal real-time pricing algorithm based on utility maximization for smart grid[C]//First IEEE International Conference on Smart Grid, 2010: 415-420.
[15] Song X, Qu J Y.An improved real-time pricing algorithm based on utility maximization for smart grid[C]//11th World Congress on Intelligent Control and Automation (WCICA), 2014: 2509-2513.
[16] Asadi G, Gitizadeh M, Roosta A.Welfare maximization under real-time pricing in smart grid using PSO algorithm[C]//21st Iranian Conference on Electrical Engineering(ICEE), 2013: 1-7.
[17] Zhang W, Chen G, Dong Z Y.An efficient algorithm for optimal real-time pricing strategy in smart grid[C]//PES General Meeting Conference & Exposition, IEEE. 2014: 1-5.
[18] 张钦, 王锡凡, 王建学. 需求侧实时电价下供电商购售电风险决策[J]. 电力系统自动化, 2010, 34(3): 22-27.
Zhang Qin, Wang Xifan, Wang Jianxue.Electricity purchasing and selling risk decision for power supplier under real-time pricing[J]. Automation of Electric Power Systems, 2010, 34(3): 22-27.
[19] 李鹏, 李存斌, 祁之强. 智能电网下考虑风险的供电企业购电优化模型[J]. 运筹与管理, 2014, 23(1): 108-115.
Li Peng, Li Cunbin, Qi Zhiqiang.Power supply enterprises purchase’ optimization model considering risk in smart grid[J]. Operations Research and Management Science, 2014, 23(1): 108-115.
[20] 李鹏, 李存斌, 牛利霞. 考虑信息风险元不确定性的智能电网调度决策风险传递模型[J]. 华北电力大学学报, 2015, 42(2): 43-49.
Li Peng, Li Cunbin, Niu Lixia.Smart grid dispatching decision-making risk transmission model considering information risk element uncertainty[J]. Journal of North China Electric Power University, 2015, 42(2): 43-49.
[21] Poramate T.Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//2011 IEEE International Conference on Smart Grid Communications, 2011: 321-326.
[22] Wang Y, Mao S W, Nelms R M.Distributed online algorithm for optimal real-time energy distribution in the smart grid[J]. IEEE Internet of Things Journal (S2327-4662), 2014, 1(1): 70-80.

考虑风险的智能电网实时电价定价策略

Real-Time Pricing Strategy Considering the Risk of Smart Grid

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