清洁能源参与月度合约的多元购电优化研究  

Multi-Purchasing Optimization Model Considering Clean Energy Participating in Monthly Contracts

作　　者：林芬 吕隽 江岳文[2] LIN Fen;LV Jun;JIANG Yuewen(Fujian Electric Power Trading Company Limited,Fuzhou 350003,China;School of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福建电力交易中心有限公司,福建福州350003 [2]福州大学电气工程与自动化学院,福建福州350108

出　　处：《电器与能效管理技术》2021年第5期9-16,共8页Electrical & Energy Management Technology

摘　　要：随着清洁能源的不断发展,其消纳问题越来越受重视。考虑火电、风电、可调水电、不可调水电及核电参与月度合约,并引入惩罚成本量化风电及不可调水电预测偏差造成的影响,同时计及月度合约市场与日前市场关系,将月度购电优化细化至每日;考虑风电、不可调水电、负荷及日前市场电价的不确定性,采用CVaR度量损失的大小;以月度购电费用与日前购电费用之和作为评估购电经济性的指标,建立期望购电费用-损失风险模型。基于某省具体算例分析,得到月度购电优化结果,并分析风电功率预测误差、惩罚成本系数、风险偏好因素对优化结果的影响。With the development of clean energy,the issue of clean energy consumption has received increasing attention.This paper considers thermal power,wind power,hydropower(adjustable and non-adjustable)and nuclear power to participate in monthly contract market,and introduces a penalty cost coefficient to quantify the additional costs caused by wind power and non-adjustable hydropower forecast deviations.In addition,this paper also takes into account the relationship between the monthly contract market and day-ahead market,and refines the monthly power purchase optimization to daily.In view of the uncertainty of wind power,non-adjustable hydropower,load and market price,the CVaR is used to measure the loss.The sum of the monthly electricity purchase cost and the day-ahead purchase cost is used as an indicator to evaluate the economy of purchasing electricity,and a model of expected electricity purchase cost-loss risk is established.Based on the analysis of specific examples,the monthly power purchase optimization results are obtained,and the influences of wind power prediction error,penalty cost coefficient and risk preference on the optimization results are analyzed.

关 键 词：清洁能源 月度合约市场 多元购电优化 惩罚成本系数 

分 类 号：TM911[电气工程—电力电子与电力传动]