基于VaR理论的综合能源零售市场最优策略

doi:10.12204/j.issn.1000-7229.2021.06.002

电力建设 ›› 2021, Vol. 42 ›› Issue (6): 9-16.doi: 10.12204/j.issn.1000-7229.2021.06.002

• 去中心化能源系统规划、运行与互动交易·栏目主持高红均副教授、徐宪东副教授、胡俊杰副教授· • 上一篇下一篇

基于VaR理论的综合能源零售市场最优策略

郭祚刚¹, 徐敏¹, 李睿智², 袁智勇¹, 谈赢杰¹, 陈柏沅², 雷金勇¹, 刘念²

1.南方电网科学研究院,广州市 510663
2.新能源电力系统国家重点实验室(华北电力大学),北京市 102206

收稿日期:2020-10-27 出版日期:2021-06-01 发布日期:2021-05-28
作者简介:郭祚刚(1985),男,博士,高级工程师,研究方向为新能源及综合能源;|徐敏(1988),男,博士,高级工程师,研究方向为配电网规划及综合能源技术;|李睿智(1997),男,硕士研究生,研究方向为综合能源系统;|袁智勇(1978),男,博士,教授级高级工程师,从事直流输电和智能配电相关科研、工程和管理工作;|谈赢杰(1989),男,博士,工程师,研究方向为分布式电源接入与微电网控制技术;|陈柏沅(1995),男,硕士研究生,研究方向为综合能源市场运行;|雷金勇(1982),男,博士,教授级高级工程师,研究方向为智能配电、分布式能源并网以及微电网技术;|刘念(1981),男,博士,教授,博士生导师,研究方向为智能配用电、综合能源系统、信息物理系统。
基金资助:
南方电网公司科技项目(ZBKJXM20180209)

Optimal Strategy of Integrated Energy Retail Market Based on VaR Theory

GUO Zuogang¹, XU Min¹, LI Ruizhi², YUAN Zhiyong¹, TAN Yingjie¹, CHEN Baiyuan², LEI Jinyong¹, LIU Nian²

1. Electric Power Research Institute,CSG,Guangzhou 510663,China
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University), Beijing 102206, China

Received:2020-10-27 Online:2021-06-01 Published:2021-05-28
Supported by:
Science and Technology Project of China Southern Power Grid(ZBKJXM20180209)

摘要/Abstract

摘要：

随着综合能源系统推广,多能源供给-传输-使用耦合程度加深,用户用能形式逐渐多样化,能源市场改革形成的零售市场内涵不断丰富。传统单一能源独立供给-定价的零售市场不能有效引导用户用能、提高系统经济性,综合能源系统零售市场的定价策略亟待研究。计及用户多能负荷、现货价格的随机性和用户需求响应提出基于风险价值(value at risk, VaR)理论的综合能源园区零售最优定价方法,并建立了静态零售定价-日前优化调度两阶段模型,研究了综合能源服务商的静态零售定价最优策略。第一阶段为计及日前阶段用户负荷、现货价格随机性的零售定价优化模型。第二阶段为第一阶段最优零售价格下的日前优化调度模型。最后采用粒子群-线性规划算法对所提两阶段模型进行迭代求解。算例结果表明,基于VaR的零售定价方法可以有效降低综合能源服务商参与零售市场的风险。

关键词: 综合能源零售市场, 综合能源服务商, 需求响应, 风险价值

Abstract:

With the promotion of integrated energy system, the coupling of multiple energies in terms of supply, transmission and use increases, the forms of energy use are gradually diversified, and the connotation of retail market is constantly enriched under the reform of energy market. Under the traditional retail market of single energy, independent supply and pricing method cannot effectively guide users to use energy and improve the economic efficiency. Therefore, it is urgent to study the pricing strategy for retail market of integrated energy system. In this paper, optimal retail pricing method for the comprehensive energy park on the basis of value at risk (VaR) theory is proposed considering multi-energy load, randomness of spot price and demand response. A two-stage model of static retail pricing and day-ahead optimal scheduling is established to study the optimal static retail pricing strategy of integrated energy service providers. The first stage is a retail pricing optimization model that takes into account day-ahead energy load and spot price randomness. The second stage is the day-ahead optimal scheduling model under the optimal retail price in the first stage. The particle swarm optimization (PSO) linear programming algorithm is used to solve the two-stage model iteratively. The results show that VaR-based retail pricing can effectively reduce the risk of integrated energy service providers participating in the retail market.

Key words: integrated energy spot market, integrated energy service provider, demand response, value at risk

中图分类号:

TM-9

郭祚刚, 徐敏, 李睿智, 袁智勇, 谈赢杰, 陈柏沅, 雷金勇, 刘念. 基于VaR理论的综合能源零售市场最优策略[J]. 电力建设, 2021, 42(6): 9-16.

GUO Zuogang, XU Min, LI Ruizhi, YUAN Zhiyong, TAN Yingjie, CHEN Baiyuan, LEI Jinyong, LIU Nian. Optimal Strategy of Integrated Energy Retail Market Based on VaR Theory[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(6): 9-16.

图/表 10

图1 静态零售定价算法流程图

Fig.1 Flowchart of static retail pricing algorithm

图2 调用子程序的流程图

Fig.2 Flow chart of calling a subroutine

表1 零售电负荷弹性参数

Table 1 Elastic parameters of retail electric load

表2 零售热负荷弹性参数

Table 2 Elastic parameters of retail heat load

表3 零售负荷概率分布参数

Table 3 Probability distribution parameter of retail load

图3 最优粒子迭代过程

Fig.3 Optimal number of particle iterations

图4 Mday概率分布

Fig.4 Probability distribution of Mday

图5 某典型场景日前调度情况

Fig.5 Day-ahead scheduling of a typical scenario

图6 场景数为20时Mday的概率分布

Fig.6 Probability distribution of Mday under 20 scenarios

图7 实际定价情况下Mday的概率分布

Fig.7 The probability distribution of Mday under actual pricing

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基于VaR理论的综合能源零售市场最优策略

Optimal Strategy of Integrated Energy Retail Market Based on VaR Theory

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