基于改进CVaR的售电公司电力现货日前申报优化策略

叶海; 杨苹; 王雨

doi:10.12204/j.issn.1000-7229.2024.05.013

PDF(7439 KB)

电力建设 ›› 2024, Vol. 45 ›› Issue (5) : 131-140. DOI: 10.12204/j.issn.1000-7229.2024.05.013

电力经济研究

基于改进CVaR的售电公司电力现货日前申报优化策略

作者信息 +

Optimization Strategy for Electricity Retailer’s Day-Ahead Bidding in the Electricity Spot Market Based on Improved CVaR

Author information +

文章历史 +

摘要

在电力现货市场环境下,售电公司需要面向市场电价及用户负荷的双重不确定性,在日前申报的环节易造成额外购电成本。然而现有基于条件风险价值(conditional value at risk, CVaR)等随机优化方法的购电方案与风险管理策略中存在等概率缩减关键场景与主观进行置信度选值的问题,为此基于传统的中性风险模型及CVaR优化模型,引入基于K-means的场景聚类缩减方法,提出基于外推内插法的置信度选值优化方法,综合形成改进CVaR的售电公司日前申报优化模型及其求解策略。仿真算例结果验证了改进CVaR优化模型能有效降低售电公司的综合购电成本及潜在风险损失,并探究了在不同的风险厌恶程度与市场波动程度的情况下对日前申报优化策略的影响,体现了改进优化申报策略的适用性与鲁棒性。

Abstract

In the electricity spot market, electricity retailers face dual uncertainties that arise from market electricity prices and user loads. The day-ahead bidding process can incur additional purchasing costs owing to these uncertainties. However, existing stochastic optimization methods for electricity purchasing strategies and risk management, such as the conditional value at risk (CVaR), suffer from problems related to equiprobable reduction in key scenarios and subjective confidence level selection. To address these challenges, this study introduces a scenario reduction method based on k-means and a confidence level optimization method based on extrapolation-interpolation, proposing an improved CVaR day-ahead bidding optimization model and its solution strategy based on the traditional neutral risk model and CVaR optimization model. The simulation results validate that the improved CVaR optimization model effectively reduces the overall purchasing costs and potential risk losses for the electricity retailer. This study explores the impact of the day-ahead bidding optimization strategy under different levels of risk aversion and market volatility, demonstrating the applicability and robustness of the improved optimization strategy.

导出引用

叶海, 杨苹, 王雨. 基于改进CVaR的售电公司电力现货日前申报优化策略[J]. 电力建设. 2024, 45(5): 131-140 https://doi.org/10.12204/j.issn.1000-7229.2024.05.013

Hai YE, Ping YANG, Yu WANG. Optimization Strategy for Electricity Retailer’s Day-Ahead Bidding in the Electricity Spot Market Based on Improved CVaR[J]. Electric Power Construction. 2024, 45(5): 131-140 https://doi.org/10.12204/j.issn.1000-7229.2024.05.013

中图分类号： TM73

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]

陈洪禹, 张冶, 关艳, 等. 考虑源网荷储效益提升的电力现货市场出清优化模型研究[J]. 电测与仪表, 2022, 59(5): 50-57.

CHEN

Hongyu

, ZHANG

, GUAN

Yan

, et al. Research on clearing optimization model of power spot market incorporating generation, network, load and storage benefit improvement[J]. Electrical Measurement & Instrumentation, 2022, 59(5): 50-57.

本文引用 [1]

[2]	陈艺华, 张炜, 张成刚, 等. 新型电力系统中促进新能源消纳的电力现货市场交易机制研究[J]. 智慧电力, 2022, 50(2): 97-104. CHEN Yihua, ZHANG Wei, ZHANG Chenggang, et al. Electricity spot market trading mechanism for promoting renewable energy integration in new power system[J]. Smart Power, 2022, 50(2): 97-104. 本文引用 [1]

[3]

MOUSAVI

, ALVAREZ

. A contract-based trading of power flexibility between a variable renewable energy producer and an electricity retailer[J]. Sustainable Energy, Grids and Networks, 2023, 34: 101067.

https://doi.org/10.1016/j.segan.2023.101067

https://linkinghub.elsevier.com/retrieve/pii/S2352467723000759

本文引用 [1]

[4]	任艺, 李庚银, 周明. 售电公司购售电决策及风险管理评述[J]. 现代电力, 2017, 34(4): 79-85. REN Yi, LI Gengyin, ZHOU Ming. A survey on electricity procurement decision-making and risk management for electricity sale company[J]. Modern Electric Power, 2017, 34(4): 79-85. 本文引用 [1]

[5]

汤旸, 刘翊枫, 王静, 等. 电力市场售电公司最优购售电量决策模型及其应用[J]. 电力科学与技术学报, 2022, 37(4): 3-12.

TANG

Yang

, LIU

Yifeng

, WANG

Jing

, et al. Optimal decision model and application of electricity purchasing and selling ofelectricity retailer in electricity market[J]. Journal of Electric Power Science and Technology, 2022, 37(4): 3-12.

本文引用 [1]

[6]	吴华华, 张思, 甘雯, 等. 面向多时间尺度市场交易的电网企业代理购电决策模型[J]. 浙江电力, 2023, 42(9): 42-51. WU Huahua, ZHANG Si, GAN Wen, et al. Decision-making model of electricity purchasing agent service of power grid enterprises for multi-time-scale market transactions[J]. Zhejiang Electric Power, 2023, 42(9): 42-51. 本文引用 [1]

[7]	裴力耕, 张欣, 赵明, 等. 售电公司分时电价盈利策略研究[J]. 电网与清洁能源, 2020, 36(11): 45-52. PEI Ligeng, ZHANG Xin, ZHAO Ming, et al. A study on profit strategy of time-of-use electricity price of electricity sale companies[J]. Power System and Clean Energy, 2020, 36(11): 45-52. 本文引用 [1]

[8]

叶圣永, 魏俊, 阮贺彬, 等. 多样化柔性负荷深度互动下的虚拟电厂分布鲁棒购售电策略[J]. 电力建设, 2021, 42(1): 59-66.

https://doi.org/10.12204/j.issn.1000-7229.2021.01.007

摘要

随着当前电力改革以及电力市场的不断推进,虚拟电厂的发展已成为其重点组成部分,也是市场交易复杂因素的主要集中部分,其中柔性负荷和清洁能源给虚拟电厂带来了巨大的挑战。考虑不同类别用户柔性负荷的运行特性差异,分别对工业负荷、商业负荷和家用负荷进行了特性分析并建模,充分调动虚拟电厂内部的一些可控资源进行互动,并结合风电、光伏、燃气轮机(gas turbine,GT)运行特性,建立了考虑多样化柔性负荷深度互动的虚拟电厂购售电优化模型。此外,随着清洁能源的发展,针对风电、光伏所带来的不确定性挑战,提出了基于场景数据驱动的两阶段变量的分布鲁棒虚拟电厂购售电优化计算模型,再利用列与约束生成算法求解,算例分析验证了该优化模型的有效性。

Shengyong

, WEI

Jun

, RUAN

Hebin

, et al. Distributional robust electricity trading strategy of virtual power plant with deep interaction of diversified flexible loads[J]. Electric Power Construction, 2021, 42(1): 59-66.

https://doi.org/10.12204/j.issn.1000-7229.2021.01.007

本文引用 [1] 摘要

With the current electric power reform and the continuous promotion of the electric power market, the development of virtual power plant has become a key part of them. It is also the main concentration part of the complex factors of market transactions, and the flexible load and clean energy also bring great challenges to the virtual power plant. Considering the operation characteristics of different types of users’ flexible load, this paper analyzes and models the characteristics of industrial, commercial and household loads, fully mobilizes some controllable resources within the virtual power plant to interact, and combines the operation characteristics of wind power, photovoltaic power and gas turbines to establish a purchase and sale optimization model for virtual power plant considering the deep interaction of diversified flexible loads. In addition, with the development of clean energy and the challenge of uncertainty brought by photovoltaic and wind power, a scenario data-driven distributional robust model with two-stage variable for power purchase and sale optimization of virtual power plant is proposed, which is solved by column and constraint generation algorithm. The effectiveness of the optimization model is verified by the analysis of examples.

[9]	李欢欢. 电改环境下我国售电公司风险识别与控制优化模型研究[D]. 北京: 华北电力大学, 2019. LI Huanhuan. Study on electricity retailers’ risk identification and control optimization model in light of electric market reform environment in China[D]. Beijing: North China Electric Power University, 2019. 本文引用 [1]

[10]	李博. 考虑现货电价不确定性和用户需求响应的售电公司购售电决策模型及应用[D]. 重庆: 重庆大学, 2019. LI Bo. Model and application of electricity procurement and sale strategies for electricity retailers considering demand response and price uncertainties[D]. Chongqing: Chongqing University, 2019. 本文引用 [1]

[11]	KETTUNEN J, SALO A, BUNN D W. Optimization of electricity retailer’s contract portfolio subject to risk preferences[J]. IEEE Transactions on Power Systems, 2010, 25(1): 117-128. https://doi.org/10.1109/TPWRS.2009.2032233 http://ieeexplore.ieee.org/document/5337980/ 本文引用 [1]

[12]	柯东鹤. 电力现货市场售电侧日前申报策略分析与研究[J]. 机电信息, 2019(23): 140-141. KE Donghe. Analysis and research on the strategy of power sales side declaration in the spot market of electricity[J]. Mechanical and Electrical Information, 2019(23): 140-141. 本文引用 [1]

[13]	孔祥玉, 张禹森, 杨世海, 等. 市场机制下考虑风险的售电公司日前电价决策方法[J]. 电网技术, 2019, 43(3): 935-943. KONG Xiangyu, ZHANG Yusen, YANG Shihai, et al. Day-ahead pricing decision method considering risk of electricity selling company under market mechanism[J]. Power System Technology, 2019, 43(3): 935-943. 本文引用 [1]

[14]	郭红霞, 高瑞, 杨苹. 基于条件风险价值的微电网现货市场两阶段调度[J]. 电网技术, 2019, 43(8): 2665-2674. GUO Hongxia, GAO Rui, YANG Ping. Two-stage dispatch of microgrid based on CVaR theory under electricity spot market[J]. Power System Technology, 2019, 43(8): 2665-2674. 本文引用 [1]

[15]	胡惊涛, 吕飞春, 张文涛, 等. 基于条件风险价值的综合能源系统日前-日内两阶段调度模型[J]. 电力需求侧管理, 2023, 25(2): 43-49. HU Jingtao, LYU Feichun, ZHANG Wentao, et al. A conditional value-at-risk based two stage day-ahead dispatch model for integrated energy systems[J]. Power Demand Side Management, 2023, 25(2): 43-49. 本文引用 [1]

[16]

林细君, 林强, 赵创业, 等. 基于条件风险价值理论的综合能源系统优化调度[J]. 沈阳工业大学学报, 2023, 45(2): 187-192.

https://doi.org/10.7688/j.issn.1000-1646.2023.02.12

摘要

针对综合能源系统调度过程难以兼顾不同能源的调度条件,导致能源调度准确性较差的问题,提出基于条件风险价值理论的综合能源系统优化调度.分析以电、热、气为主的综合能源系统结构和响应过程,探究系统的不确定性供能因素.根据条件风险价值理论建立最优调度目标函数,将能量平衡、功率限制和储电设备容量等作为约束条件,构建优化调度模型.利用粒子群迭代方法求解模型,获取粒子的速度与位置更新方程,得出最佳调度结果.结果表明,所提方法能够准确预测出能源需求量,将输出能源控制在合理范围内,降低系统运行风险,提高经济效益.

LIN

Xijun

, LIN

Qiang

, ZHAO

Chuangye

, et al. Optimal scheduling of integrated energy system based on conditional value-at-risk theory[J]. Journal of Shenyang University of Technology, 2023, 45(2): 187-192.

https://doi.org/10.7688/j.issn.1000-1646.2023.02.12

本文引用 [1] 摘要

In order to solve the problem that the dispatching process of integrated energy system is unable to take the different energy dispatching conditions into account, which leads to poor dispatching accuracy, the optimal scheduling of integrated energy system based on the conditional value-at-risk theory was proposed. The structure and response process of integrated energy system with electricity, heat and gas as main components were analyzed to investigate the uncertainty of energy supply. According to the conditional value-at-risk theory, the objective function of optimal scheduling was established. The energy balance, power limit and capacity of storage equipment were selected as constraints. The particle swarm iterative method was used to solve the model, and the velocity and position updating equations of particles were obtained. The results show that the as-proposed method can accurately predict the energy demand, control the output energy within a reasonable range, reduce system operation risk and improve economic efficiency.

[17]

陈付雷, 方毛林, 汪辰晨, 等. 考虑风光出力相关性与条件风险价值的孤岛型微电网经济调度[J]. 智慧电力, 2023, 51(12): 30-37.

CHEN

Fulei

, FANG

Maolin

, WANG

Chenchen

, et al. Economic dispatching of islanded microgrid considering correlation between wind power and PV output and conditional value at risk[J]. Smart Power, 2023, 51(12): 30-37.

本文引用 [1]

[18]

杨佳澄, 翟晓鹤, 谭忠富, 等. 基于相对鲁棒CVaR的高不确定性机组日前竞价申报优化[J]. 电网技术, 2021, 45(11): 4366-4374.

YANG

Jiacheng

, ZHAI

Xiaohe

, TAN

Zhongfu

, et al. Day-ahead bidding optimization for high-uncertainty units based on relatively robust conditional value at risk[J]. Power System Technology, 2021, 45(11): 4366-4374.

本文引用 [1]

[19]	胡志林, 陈淳, 朱金吉. 基于CVaR的电网企业代理购电交易模型的研究[J]. 自动化应用, 2023, 64(6): 195-198, 201. HU Zhilin, CHEN Chun, ZHU Jinji. Research on the model of power purchase agency transactions of power grid enterprises based on CVaR[J]. Automation Application, 2023, 64(6): 195-198, 201. 本文引用 [1]

[20]	张晶晶, 蒋峰, 吴红斌, 等. 计及CVaR的负荷聚合商双重市场投标策略[J]. 电力自动化设备, 2020, 40(12): 153-158. ZHANG Jingjing, JIANG Feng, WU Hongbin, et al. Dual market bidding strategy of load aggregator based on CVaR[J]. Electric Power Automation Equipment, 2020, 40(12): 153-158. 本文引用 [1]

[21]	喻小宝. 电力市场环境下售电公司购售电交易优化模型研究[D]. 北京: 华北电力大学, 2019. YU Xiaobao. Optimal models of electricity purchase and sale for retailers under electricity market environment[D]. Beijing: North China Electric Power University, 2019. 本文引用 [1]

[22]

吴玲, 刘浩, 刘秋华, 等. 可再生能源配额制下售电公司多市场交易决策[J]. 电力建设, 2023, 44(7): 1-10.

https://doi.org/10.12204/j.issn.1000-7229.2023.07.001

摘要

可再生能源配额制实施后，售电公司面临着电力交易市场和绿证市场的多市场组合交易决策问题。基于配额制下售电公司市场交易框架及模式，考虑售电公司多种购电渠道的购电成本、柔性合约成本、绿证交易成本及售电收入，以条件风险价值法度量相关不确定因素带来的风险值，构建多市场组合交易决策模型。运用多场景法对风电出力、集中竞价市场出清价格、绿证价格等不确定因素生成典型场景集，以售电公司收益最大、市场交易风险最低为决策目标，采用GAMS软件对决策模型进行编程求解。算例仿真分析了风险规避系数、风电出力不确定性、可再生能源配额比例等因素变化对售电公司收益的影响，对模型的有效性进行了验证，为配额制下售电公司参与多市场交易决策提供理论指导。

Ling

, LIU

Hao

, LIU

Qiuhua

, et al. Research on the multi-market decisions of electricity retailers under renewable portfolio standards[J]. Electric Power Construction, 2023, 44(7): 1-10.

https://doi.org/10.12204/j.issn.1000-7229.2023.07.001

本文引用 [1] 摘要

With the implementation of renewable energy quota systems， electricity-selling companies are obligated to consume renewable energy. To study the impact of renewable energy quota systems on the revenue of electricity companies， the market transaction framework and model of electricity companies under such quota systems were analyzed， and a multi-market portfolio transaction model was established. The electricity-selling company considered in the model had different market power purchase costs， flexible contract costs， and electricity-selling incomes. Moreover， it used the method of multiple scenarios for renewable energy power-generation unit output， centralized price bidding in uncertain factors such as market clearing price per month， and the method of conditional value at risk， the relevant risk measurement uncertainty. The model was designed to maximize the profit of the electricity sales company and minimize the market transaction risk. GAMS software was used to solve the model. A simulation analysis of the risk avoidance coefficient， renewable energy unit output uncertainty， renewable energy quota ratio， and other factors affecting the revenue of electricity companies verified the effectiveness of the model， providing theoretical guidance for electricity companies to better participate in market competition under quota systems.

[23]	陈英豪. 我国现货市场环境下售电公司的购售电策略研究[D]. 广州: 华南理工大学, 2019. CHEN Yinghao. Study of power wholesaling and retailing in competitive market of China[D]. Guangzhou: South China University of Technology, 2019. 本文引用 [1]

[24]	罗舒瀚, 蒋传文, 王旭, 等. 新电改背景下售电公司的购售电策略及风险评估[J]. 电网技术, 2019, 43(3): 944-953. LUO Shuhan, JIANG Chuanwen, WANG Xu, et al. Power trading strategy and risk assessment of electricity retailing company under power system reform[J]. Power System Technology, 2019, 43(3): 944-953. 本文引用 [1]

[25]	赵阳, 蒋传文, 赵岩, 等. 基于偏度-加权条件风险价值的售电公司动态购电策略[J]. 电网与清洁能源, 2017, 33(1): 130-136. ZHAO Yang, JIANG Chuanwen, ZHAO Yan, et al. Skewness-WCVaR based dynamic procurement strategy for electricity retailers[J]. Power System and Clean Energy, 2017, 33(1): 130-136. 本文引用 [1]

[26]	贾晨. 电力市场环境下售电公司购电行为分析与决策[D]. 太原: 太原理工大学, 2019. JIA Chen. Anasis and decision for electricity retail companies purchasing behavior under electricity market environment[D]. Taiyuan: Taiyuan University of Technology, 2019. 本文引用 [1]

[27]	杨萌. 电力市场下计及风险规避的售电公司购售电策略研究[D]. 北京: 华北电力大学, 2018. YANG Meng. Research on energy procurement and marketing decision for electricity retailer considering risk aversion in electricity market[D]. Beijing: North China Electric Power University, 2018. 本文引用 [1]

[28]	陈寒, 唐忠, 鲁家阳, 等. 基于CVaR量化不确定性的微电网优化调度研究[J]. 电力系统保护与控制, 2021, 49(5): 105-115. CHEN Han, TANG Zhong, LU Jiayang, et al. Research on optimal dispatch of a microgrid based on CVaR quantitative uncertainty[J]. Power System Protection and Control, 2021, 49(5): 105-115. 本文引用 [1]

[29]

张彬桥, 张松甲, 冉远航, 等. 考虑风电条件风险的水火风联合调度模型及求解[J/OL]. 太阳能学报, 2023: 1-9[2024-01-08]. https://doi.org/10.19912/j.0254-0096.tynxb.2022-1875.

https://doi.org/10.19912/j.0254-0096.tynxb.2022-1875

ZHANG

Binqiao

, ZHANG

Songjia

, RAN

Yuanhang

, et al. Hydro-thermal-wind co-scheduling model and solution method considering conditional risk of wind power[J/OL]. Acta Energiae Solaris Sinica, 2023: 1-9[2024-01-08]. https://doi.org/10.19912/j.0254-0096.tynxb.2022-1875.

https://doi.org/10.19912/j.0254-0096.tynxb.2022-1875

本文引用 [1]

[30]	郭秀英. 预测决策的理论与方法[M]. 北京: 化学工业出版社, 2010. 本文引用 [1]

[31]

谭俊丰, 杨苹, 曾宪锴. 基于场景缩减优化引导置信度选值的园区微能源网改进CVa R日前调度模型[J]. 高电压技术, 2023, 49(4): 1412-1421.

TAN

Junfeng

, YANG

Ping

, ZENG

Xiankai

. Improved CVaR day-ahead dispatch model of micro-energy-grid based on scenario-reduction-optimization guiding confidence value selection[J]. High Voltage Engineering, 2023, 49(4): 1412-1421.

本文引用 [1]

[32]	蒋明亮, 陈文. 我国电力市场电价管理机制关键问题分析与建议[J]. 广西电业, 2022(5): 38-41. JIANG Mingliang, CHEN Wen. Analysis and suggestions on key issues of electricity price management mechanism in China’s electricity market[J]. Guangxi Electric Power, 2022(5): 38-41. 本文引用 [1]

[33]	LIN J T. Improved Markowitz portfolio investment model based on ARIMA model and BP neural network[C]// 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2022: 504-507. 本文引用 [1]

[34]	周明, 聂艳丽, 李庚银, 等. 电力市场下长期购电方案及风险评估[J]. 中国电机工程学报, 2006, 26(6): 116-122. ZHOU Ming, NIE Yanli, LI Gengyin, et al. Long-term electricity purchasing scheme and risk assessment in power markets[J]. Proceedings of the CSEE, 2006, 26(6): 116-122. 本文引用 [1]

[35]	HUANG H Y, ZHANG W C. A two-stage volt/VAR control optimization model considering legacy equipment and smart PV inverters[C]// 2023 International Conference on Power System Technology (PowerCon). IEEE, 2023: 1-5. 本文引用 [1]

[36]	ZHAO R L, ZHANG S H, WANG X. CVaR-based offering strategy for price-maker wind-storage unit in day-ahead electricity market[C]// 2023 3rd Power System and Green Energy Conference (PSGEC). IEEE, 2023: 87-92. 本文引用 [1]

[37]	SONG F, WU F C, ZHANG X Q, et al. CVaR-constrained robust unit commitment for power system with concentrating solar power[C]// 2023 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2023: 1322-1327. 本文引用 [1]

[38]

ZHANG

, ZHONG

, CUI

, et al. CVAR: distributed and extensible cross-region vehicle authentication with reputation for VANETs[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(1): 74-89.

https://doi.org/10.1109/TITS.2023.3306547

https://ieeexplore.ieee.org/document/10234677/

本文引用 [1]

[39]	ROCKAFELLAR R T, URYASEV S. Optimization of conditional value-at-risk[J]. The Journal of Risk, 2000, 2(3): 21-41. https://doi.org/10.21314/JOR.2000.038 http://www.risk.net/journal-of-risk/technical-paper/2161159/optimization-conditional-value-risk 本文引用 [1]

[40]	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, 43. 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, 43. 本文引用 [1]