考虑双重不确定性和用户负效用的多目标双层售电商决策

doi:10.16381/j.cnki.issn1003-207x.2021.1504

Abstract

Abstract:

With the liberalization of market access for electricity retail， different kinds of power companies have entered the electricity market. The trading mechanism of electricity retail market has been a focus. A multi-objective bi-level stochastic optimization modelwith nonlinear constraints is developed to study the decision of electricity retailer considering the influence of disutility of users under demand response. The uncertainty of day-ahead pricing and renewable energy generation are also taken into account.The retailer'sprofit maximization and the users' cost minimization are defined as the upper-layerand lower-layer problem of developed bi-level model， respectively.Then an improved GA algorithmis used to solve this model.The electricity sales of electricity retailers in each market are defined as individuals in the GA population， and the expected revenues of electricity retailers are used as the fitness functions.The Monte Carlo method is used to generate different scenarios， and simulation analysis is carried out under these different scenarios.Finally， the developed model is verified with real data from the US PJM market.It can be seen from the simulation results that after the implementation of DR， the decision model constructed in our paper can effectively balance the power load of users at various times of one day， and the use of the negative utility mechanism can effectively avoid peak-valley transfers and reduce the electricity cost of users.Energy storage equipment and renewable energy power generation equipment have significantly improved the revenue of electricity retailer， while the impact of uncertain factors such as price fluctuations in the spot marketand the randomness of renewable energy output can be alleviated.Specially， the users' negative utility cost function in the renewable energy power generation scenario is considered， which provides a new idea for studying users' electricity consumption behavior.

Key words: smart grid, renewable energy, demand response, disutility, uncertainty

CLC Number:

O224

Yeming Dai,Xilian Sun,Yao Qi,Jinpeng Duan. Multi-objective Bi-level Research on Decision-making of Power Retailers Considering Dual Uncertainty and User Disutility[J]. Chinese Journal of Management Science, 2024, 32(7): 163-171.

Figures/Tables 15

二层随机优化模型求解过程
Step1: $t = 1$ ，初始化电价、场景、储能装置数据， $n G A = 1$ ;
Step2:利用MATLAB优化工具包求解(26)-(31)式，求解负荷 $∑ k P k, t$ 代入(18)式;
Step3:利用MATLAB中GA工具包求解(18)-(25)式，更新数据;
Step4: $n G A = N G A M A X$ ，转Step 5，否则 $n G A = n G A + 1$ ，转Step2;
Step5:结束

References 26

1	Jin L S， Chen C， Wang X Y， et al. Research on information disclosure strategies of electricity retailers under new electricity reform in China［J］. The Science of the Total Environment， 2020， 710（25）： 1-6.
2	杨甲甲，赵俊华，文福拴，等. 电力零售核心业务架构与购售电决策［J］. 电力系统自动化， 2017， 41（14）： 10-23.
	Yang J J， Zhao J H， Wen F S， et al. Key business framework and purchase sale decision making for electricity retailers［J］. Automation of Electric Power Systems， 2017， 41（14）： 10-23.
3	Ghazvini M A， Soares J， Horta N， et al. A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers［J］. Applied Energy， 2015， 151： 102-118.
4	Nojavan S， Mohammadii B， Zare K.Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties［J］. Generation， Transmission & Distribution， 2015， 9（4）： 328-338.
5	Pilerood A E， Heydari M， Mazdeh M M. A two-stage greedy heuristic for a flowshop scheduling problem under time-of-use electricity tariffs［J］. South African Journal of Industrial Engineering， 2018， 29（1）： 143-154.
6	Zhang C， Xu Y， Dong Z Y， et al. Robust coordination of distributed generation and price-based demand response in microgrids［J］. IEEE Transactions on Smart Grid， 2018， 9（5）： 4236-4247.
7	Marzband M， Javadi M， Pourmousavi S A， et al. An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory［J］. Electric Power Systems Research， 2018， 157： 187-199.
8	陶莉，高岩，朱红波，等. 有可再生能源和电力存储设施并网的智能电网优化用电策略［J］. 中国管理科学， 2019， 27（2）： 150-157.
	Tao L， Gao Y， Zhu H B， et al. Optimal scheduling for smart grids with the integration of renewable resourcesand storage devices［J］. Chinese Journal of Management Science， 2019， 27（2）： 150-157.
9	任艺，周明，李庚银. 考虑用户需求响应的售电公司购售电决策双层模型［J］. 电力系统自动化， 2017， 41（14）： 30-36.
	Ren Y， Zhou M， Li G Y. Bi-level model of electricity procurement and sale strategies for electricity retailers considering users’ demand response［J］. Automation of Electric Power Systems， 2017， 41（14）： 30-36.
10	Chai Y X， Xiang Y， Liu J Y， et al. Incentive-based demand response model for maximizing benefits of electricity retailers［J］. Journal of Modern Power Systems and Clean Energy， 2019， 7（6）： 1644-1650.
11	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.
12	Nojavan S， Nourollahi R， Pashaei D H， et al. Uncertainty：Based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange［J］. International Journal of Electrical Power & Energy Systems， 2019， 105（2）： 237-248.
13	Tan Z， Yang S， Lin H， et al. Multi-scenario operation optimization model for park integrated energy system based on multi-energy demand response［J］. Sustainable Cities and Society， 2020， 53： 101973.
14	Sekizaki S， Nishizaki I， Hayashida T， et al. Electricity retail market model with flexible price settings and elastic price： Based demand responses by consumers in distribution network［J］. International Journal of Electrical Power & Energy Systems， 2016， 81： 371-386.
15	Dadashi M， Haghifam S， Zare K， et al. Short-term scheduling of electricity retailers in the presence of demand response aggregators： A two-stage stochastic bi-level programming approach［J］. Energy， 2020， 205： 17926-17944.
16	Josue C P， Qiao W. A stochastic decision-making model for an electricity retailer with intermittent renewable energy and short-term demand response［J］. IEEE Transactions on Smart Grid， 2018，3（10）： 2581-2592.
17	Wang Y W， Wang J M， Sun W， et al. Optimal day-ahead bidding strategy for electricity retailer with inner-outer 2-layer model system based on stochastic mixed-integer optimization［J］. Mathematical Problems in Engineering， 2019， 19： 1-14.
18	Nojavan S， Zare K， Mohammadii B. Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory［J］. International Journal of Electrical Power & Energy Systems， 2017， 93： 156-170.
19	Nojavan S， Aalami H A. Stochastic energy procurement of large electricity consumer considering photovoltaic， wind-turbine， micro-turbines， energy storage system in the presence of demand response program［J］. Energy Conversion & Management， 2015， 103： 1008-1018.
20	Xu P， Tao X M. Cooperative game of electricity retailers in China's spot electricity market［J］. Energy， 2018， 145（15）： 152-170.
21	Ju L W， Wu J， Lin H Y， et al. Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response［J］. Applied Energy， 2020， 271： 115155.
22	Golmohamadi H， Keypour R. Stochastic optimization for retailers with distributed wind generation considering demand response［J］. Modern Power Systems， 2018， 6（4）： 733-748.
23	Nojavan S， Mohammadi-ivatloo B， Zare K. Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties［J］. Generation， Transmission & Distribution， IET， 2015， 9（4）： 328-338.
24	Sekizaki S， Nishizaki I， Hayashida T， et al. Electricity retail market model with flexible price settings and elastic price： Based demand responses by consumers in distribution network［J］. International Journal of Electrical Power & Energy Systems， 2016， 81： 371-386.
25	Viktor J， Lisa G.Impacts of variation management on cost-optimal investments in wind power and solar photovoltaics［J］.Refocus，2020，32：10-22.
26	李军祥，张文财，高岩. 基于用户电器分类的智能电网实时定价研究［J］. 中国管理科学， 2019， 27（4）： 210-216.
	Li J X， Zhang W C， Gao Y. Research on real-time pricing based on the classification of the user's electrical appliances in smart grid［J］. Chinese Journal of Management Science， 2019， 27（4）： 210-216.

场景	储能装置	风力发电设备	利润
1	√	√	60586.63
2	√	×	46803.23
3	×	√	57330.95
4	×	×	43547.56

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Multi-objective Bi-level Research on Decision-making of Power Retailers Considering Dual Uncertainty and User Disutility

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