基于改进CVaR的售电公司电力现货日前申报优化策略

doi:10.12204/j.issn.1000-7229.2024.05.013

摘要/Abstract

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。

关键词: 改进CVaR模型, 售电公司, 日前申报, 风险管理

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.

Key words: improved CVaR model, electricity retailer, day-ahead bidding, risk management

中图分类号:

TM73

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.

YE Hai, YANG Ping, WANG Yu. 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.

图/表 16

图1 售电公司日前购电量申报框架

Fig.1 The day-ahead purchase power bidding framework of electricity retailers

图2 售电公司日前购电量申报决策流程

Fig.2 The decision-making process of day-ahead electricity purchase bidding of electricity retailers

图3 风险评估值随置信度取值变化趋势

Fig.3 The change trend of risk assessment value with confidence value

图4 置信度选值优化流程

Fig.4 Confidence value selection optimization process

图5 改进CVaR的日前申报优化模型求解方法

Fig.5 Improved CVaR day-ahead bidding optimization model solution method

图6 日前申报预测数据取值

Fig.6 Forecast data values of day-ahead bidding

表1 优化模型的参数取值

Table 1 The parameters of the optimization model

图7 不同置信度取值下的风险损失值

Fig.7 Risk loss values under different confidence values

图8 不同置信度取值下的购电成本值

Fig.8 The cost of electricity purchase under different confidence values

图9 多轮模拟场景下的尾部损失值分布

Fig.9 Tail loss values’ distribution in multi-round simulation scenarios

图10 多种申报方案对比

Fig.10 Comparison of various bidding schemes

表2 多种申报方案测算结果对比万元

Table 2 Comparison of the calculation results of various bidding schemes

图11 不同风险厌恶系数下成本及风险损失情况

Fig.11 Cost and risk loss under different risk aversion coefficients

图12 不同风险厌恶系数下申报偏差及收益情况

Fig.12 Bidding deviation and income under different risk aversion coefficients

图13 不同电价波动程度下成本及风险损失情况

Fig.13 Cost and risk loss under different electricity price fluctuations

图14 不同电价波动程度下申报偏差及收益情况

Fig.14 Bidding deviation and income under different electricity price fluctuations

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