基于需求侧管理的智能电网两种时段集成定价策略

doi:10.16381/j.cnki.issn1003-207x.2023.1540

摘要/Abstract

摘要：

智能电网环境下实时定价机制在供需平衡基础上促进了能源的高效利用。现有的实时定价按时段划分为单阶段定价和多阶段定价，其分别具有单时段实时信息交互和多时段全局信息同时交互的优点。然而，单阶段定价容易导致峰谷转移，多阶段定价下无法实时调整用电计划。为弥补单独使用一种定价机制的缺点，以社会福利最大化为目标，建立同时刻画多阶段和单阶段的社会福利集成定价模型，其中多阶段定价期间考虑用户与化石能源发电交易问题，单阶段定价期间考虑可再生能源发电和用户行为不确定性。在具体定价机制执行过程中，首先，求解多阶段定价问题，在每个周期前确定多阶段各时段的电力价格。其次，考虑用户和供电侧之间电力的买入和卖出，求解单阶段定价问题，在每个时段内确定单阶段的电力价格。其中，多阶段与单阶段定价下的社会福利最大化模型的求解均采用对偶方法。最后，进行仿真实验，与单一的多阶段定价和单阶段定价机制相比，本文所提出的集成定价机制对应的社会福利更高。在此基础上，对电力定价实际运作提出了若干建议。

关键词: 智能电网, 需求侧管理, 实时定价, 社会福利最大化, 时段集成, 对偶优化

Abstract:

The real-time pricing in the smart grid can adjust the price dynamically according to the electricity demand， which guides the users to adjust the pattern of electricity consumption via price signals. It is one of the most direct methods， which balances electricity supply and demand and promotes the efficient use of energy. Real-time pricing method is based on single-time slot and multi-time slots by periods. Multi-time slots pricing determines the price of each time slot in a cycle in advance， and has the advantage of simultaneous interaction of global information in multiple time slots， which helps users to determine their electricity consumption plans in advance. Single-time slot pricing determines the price during each time slot and has the advantage of real-time information interaction， which enables users to adjust their energy utilization instantaneously in response to demand. However， single-time slot pricing tends to lead to peak and valley shifting， and multi-time slots pricing cannot adjust electricity consumption in real time. In order to avoid the drawbacks of using one pricing mechanism alone， an integrated pricing based on social welfare maximization model that characterizes both multi-time slots and single-time slot is established. The fossil energy generation is considered during multi-time slots pricing， and renewable energy generation and users’ behavioral uncertainty are considered during single-time slot pricing. In the implementation of the proposed pricing mechanism， the multi-time slots pricing problem is solved to determine the electricity price for each time slot before the cycle， then the single-time slot pricing problem considering the buying and selling of electricity between the users and the supply side is solved to determine the electricity price during each time slot. Wherein， the dual approach is utilized to solve the proposed multi-time slots pricing model and single-time slot pricing model， respectively. The simulation experiments validate the effectiveness of the proposed integrated pricing mechanism. It is concluded that：（i） Compared with the social welfare maximization model using single-time slot pricing or multi-time slots pricing， it improves social welfare significantly. （ii） Under integrated pricing mechanism， users can obtain the global information of electricity price and make the electricity plan in advance. Then， users adjust electricity consumption in real time. Thus， users can increase their electricity consumption utility， reduce the waste of electricity resources， and obtain greater welfare.

Key words: smart grid, demand side management, real-time pricing, social welfare maximization, time slot integrated, dual optimization

中图分类号:

F206

朱泽怡,高岩,丁晓东. 基于需求侧管理的智能电网两种时段集成定价策略[J]. 中国管理科学, 2026, 34(10): 409-419.

Zeyi Zhu,Yan Gao,Xiaodong Ding. Integrated Pricing Strategy Based on Demand Side Management via Two Kinds of Time Slots[J]. Chinese Journal of Management Science, 2026, 34(10): 409-419.

图/表 10

表1

风电参数"

参数	值	参数	值	参数	值	参数	值
$c w 0$	$0.1$	$P w m i n$	$70$	$σ w$	$3$	$ζ$	$1$
$c w 1$	$0.05$	$P w m a x$	$150$	$θ$	$0.5$	$ϵ$	$0.001$
$c w 2$	$0.05$	$μ w k$	$6$	$β$	$0.5$

表1

图1

图2

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图5

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图9

参考文献 35

[1]	李军祥，陈鸣，邵馨平. 基于信息间隙决策理论的虚拟电厂低碳需求响应调度模型［J］. 中国管理科学， 2025， 33（4）： 345-356.
	Li J X， Chen M， Shao X P. A low-carbon demand response dispatch model for virtual power plants based on information gap decision theory［J］. Chinese Journal of Management Science， 2025， 33（4）： 345-356.
[2]	Fan W， Tan Z F， Li F Q， et al. A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response［J］. Energy， 2023， 263： 125783.
[3]	高岩. 智能电网实时电价社会福利最大化模型的研究［J］. 中国管理科学， 2020， 28（10）： 201-209.
	Gao Y. The social welfare maximization model of real-time pricing for smart grid［J］. Chinese Journal of Management Science， 2020， 28（10）： 201-209.
[4]	Li Y Z， Huang J J， Liu Y， et al. Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation： A Singapore electricity market study［J］. Energy， 2022， 254： 123923.
[5]	Fatemi S， Ketabi A， Mansouri S A. A four-stage stochastic framework for managing electricity market by participating smart buildings and electric vehicles： Towards smart cities with active end-users［J］. Sustainable Cities and Society， 2023， 93： 104535.
[6]	Ullah Z， Mirjat N H， Baseer M. Optimisation and management of virtual power plants energy mix trading model［J］. International Journal of Renewable Energy Development， 2022， 11（1）： 83-94.
[7]	Yuan Z P， Li P， Li Z L， et al. Data-driven risk-adjusted robust energy management for microgrids integrating demand response aggregator and renewable energies［J］. IEEE Transactions on Smart Grid， 2023， 14（1）： 365-377.
[8]	Lei Y Z， Jasin S. Real-time dynamic pricing for revenue management with reusable resources， advance reservation， and deterministic service time requirements［J］. Operations Research， 2020， 68（3）： 676-685.
[9]	Wu C Y， Gu W， Luo E B， et al. An economic cybernetic model for electricity market operation coupled with physical system dynamics［J］. Applied Energy， 2023， 335： 120764.
[10]	Ma J， Venkatesh B. New real-time demand response market co-optimized with conventional energy market［J］. IEEE Systems Journal， 2022， 16（4）： 6381-6392.
[11]	Dai Y M， Yang Y Q， Leng M M. A novel alternative energy trading mechanism for different users considering value-added service and price competition［J］. Computers & Industrial Engineering， 2022， 172： 108531.
[12]	Zhao W H， Zhang S C， Xue L， et al. Research on model of micro-grid green power transaction based on blockchain technology and double auction mechanism［J］. Journal of Electrical Engineering & Technology， 2024， 19（1）： 133-145.
[13]	Zhang L， Gao Y， Zhu H B， et al. Bi-level stochastic real-time pricing model in multi-energy generation system： A reinforcement learning approach［J］. Energy， 2022， 239： 121926.
[14]	高岩. 基于需求侧管理实时电价优化方法综述［J］. 上海理工大学学报， 2022， 44（2）： 103-111+121.
	Gao Y. A review on real-time electricity price optimization methods based on demand side management［J］. Journal of University of Shanghai for Science and Technology， 2022， 44（2）： 103-111+121.
[15]	Williamson O E. Peak-load pricing and optimal capacity under indivisibility constraints［J］. The American Economic Review， 1966， 56（4）： 810-827.
[16]	Li N N， Gao Y. Real-time pricing based on convex hull method for smart grid with multiple generating units［J］. Energy， 2023， 285： 129543.
[17]	Zhu H B， Yin H， Feng X， et al. Demand response management via real-time pricing for microgrid with electric vehicles under cyber-attack［J］. Electronics， 2023， 12（6）： 1321.
[18]	Li J X， Dong P， Liu M B， et al. Two-stage robust optimal scheduling with price-based demand response［J］. Electric Power Systems Research， 2024， 232： 110326.
[19]	Yang Y X， Du S Q， Chen YY. Real-time pricing method for smart grid based on social welfare maximization model［J］. Journal of Industrial and Management Optimization， 2023， 19（3）： 2206-2225.
[20]	Lyu C， Jia Y W， Xu Z. Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles［J］. Applied Energy， 2021， 299： 117243.
[21]	Lin J， Xiao B， Zhang H L， et al. A novel multitype-users welfare equilibrium based real-time pricing in smart grid［J］. Future Generation Computer Systems， 2020， 108： 145-160.
[22]	Aggarwal S， Kumar N. A consortium blockchain-based energy trading for demand response management in vehicle-to-grid［J］. IEEE Transactions on Vehicular Technology， 2021， 70（9）： 9480-9494.
[23]	Li YY， Li J X， Yu Z S， et al. A cosh-based smoothing Newton algorithm for the real-time pricing problem in smart grid［J］. International Journal of Electrical Power & Energy Systems， 2022， 135： 107296.
[24]	吴志强，王菁祺，高岩. 实时电价的社会福利解析［J］. 中国管理科学， 2024， 32（3）： 218-227.
	Wu Z Q， Wang J Q， Gao Y. Social welfare analysis of real-time pricing［J］. Chinese Journal of Management Science， 2024， 32（3）： 218-227.
[25]	Chen N， Gallego G. Welfare analysis of dynamic pricing［J］. Management Science， 2019， 65（1）： 139-151.
[26]	Zakeri G， Pritchard G， Bjorndal M， et al. Pricing wind： A revenue adequate， cost recovering uniform price auction for electricity markets with intermittent generation［J］. INFORMS Journal on Optimization， 2019， 1（1）： 35-48.
[27]	Kajanova M， Bracinik P. Social welfare-based charging of electric vehicles in the microgrids fed by renewables［J］. International Journal of Electrical Power & Energy Systems， 2022， 138： 107974.
[28]	Niaei H， Masoumi A， Jafari A R， et al. Smart peer-to-peer and transactive energy sharing architecture considering incentive-based demand response programming under joint uncertainty and line outage contingency［J］.Journal of Cleaner Production， 2022， 363： 132403.
[29]	Ye B， Gao Y， Yuan G X. Renewable energy investment study for electric power enterprise based on a time period with expected supply［J］. Enterprise Information Systems， 2023， 17： 1923064.
[30]	Chiu T C， Shih Y Y， Pang A C， et al. Optimized day-ahead pricing with renewable energy demand-side management for smart grids［J］. IEEE Internet of Things Journal， 2017， 4（2）： 374-383.
[31]	王宇倩，李军祥，徐敏. 区块链框架下基于前景理论的微网分布式能源协同优化［J］. 系统工程理论与实践， 2022， 42（9）： 2551-2564.
	Wang Y Q， Li J X， Xu M. Collaborative optimization of distributed energy resources in microgrid based on prospect theory under the framework of blockchain［J］. Systems Engineering-Theory & Practice， 2022， 42（9）： 2551-2564.
[32]	Chen G， Li J Y. A fully distributed ADMM-based dispatch approach for virtual power plant problems［J］. Applied Mathematical Modelling， 2018， 58： 300-312.
[33]	Lu Z G， Lu S F， Xu M R， et al. A robust stochastic stability analysis approach for power system considering wind speed prediction error based on Markov model［J］. Computer Standards & Interfaces， 2021， 75： 103503.
[34]	Yuan G X， Gao Y， Ye B， et al. Real-time pricing for smart grid with multi-energy microgrids and uncertain loads： A bilevel programming method［J］. International Journal of Electrical Power & Energy Systems， 2020， 123： 106206.
[35]	Guo Z W， Pinson P， Chen S B， et al. Chance-constrained peer-to-peer joint energy and reserve market considering renewable generation uncertainty［J］. IEEE Transactions on Smart Grid， 2021， 12（1）： 798-809.