基于改进CVaR的售电公司电力现货日前申报优化策略  

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

作　　者：叶海 杨苹[1] 王雨 YE Hai;YANG Ping;WANG Yu(School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]华南理工大学电力学院,广州市510640

出　　处：《电力建设》2024年第5期131-140,共10页Electric Power Construction

基　　金：国家自然科学基金项目(51937005);广东省重点领域研发计划项目(2021B0101230003)。

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。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.

关 键 词：改进CVaR模型 售电公司 日前申报 风险管理 

分 类 号：TM73[电气工程—电力系统及自动化]