电力系统自动化2025，Vol.49Issue(12)：194-207,14.DOI:10.7500/AEPS20240529009
面向流域水-风-光一体化的梯级水电站关键运行风险识别方法
Identification Method for Key Operational Risks of Cascade Hydropower Stations for Integration of Basin Hydro-Wind-Solar Power
摘要
Abstract
Risk identification is the basis for accurate multi-dimensional risk control.Under the multi-dimensional uncertainty conditions of runoff and renewable energy,the cascade hydropower stations for the integration of basin hydro-wind-solar power will face gradually deteriorated operational risks.It is urgent to identify the key risks.Therefore,a method for determining the key operational risks of cascade hydropower stations for the integration of basin hydro-wind-solar power is proposed.Firstly,the principle for quantifying the operational risks of cascade hydropower stations with multiple direct current(DC)transmission is proposed with the annual 8 760 h simulation of refined operation embedded in,and the operation results obtained through simulation are used as samples for risk expectation statistics.Secondly,an improved subjective and objective weighting method is proposed to aggregate the risks of multiple hydropower stations into the overall risk of the cascade hydropower station,and the technique for order preference by similarity to an ideal solution(TOPSIS)is introduced to rank the relative importance of the multi-dimensional risks of hydropower stations.Finally,the rationality and validity of the proposed method are verified by taking the cascade hydropower station in the lower reaches of the Jinsha River for the integration of basin hydro-wind-solar power as an application case.At the same time,it is pointed out that the output power fluctuation and peak-regulation pressure of cascade hydropower stations are the two key risks that need to be preferentially managed and controlled.关键词
风险识别/不确定性/梯级水电站/直流输电/新能源Key words
risk identification/uncertainty/cascade hydropower station/direct current(DC)transmission/renewable energy引用本文复制引用
王明博,张俊涛,曹辉,程春田,徐杨..面向流域水-风-光一体化的梯级水电站关键运行风险识别方法[J].电力系统自动化,2025,49(12):194-207,14.基金项目
国家自然科学基金资助项目(52409010) （52409010）
国家博士后创新人才支持计划资助项目(BX20230055) （BX20230055）
国家自然科学基金重点资助项目(52239001). 本文研究得到中国长江电力股份有限公司项目(2422020008)的资助,特此感谢! This work is supported by National Natural Science Foundation of China(No.52409010),China National Postdoctoral Program for Innovative Talents(No.BX20230055),and Key Program of National Natural Science Foundation of China(No.52239001). （52239001）
