控制理论与应用2024，Vol.41Issue(3)：533-542,10.DOI:10.7641/CTA.2023.20337
计及多元不确定性的综合能源系统优化配置
Optimal configuration for integrated energy system considering multiple uncertainties
摘要
Abstract
The key of achieving the optimal configuration of integrated energy system(IES)is the selection of equipment types and determination of their number,however,the forecasting errors of energy demand load and renewable energy output,and the failure of equipment,will directly affect the rationality and economy of the configuration scheme.Therefore,this paper proposes a multi-objective chance constraint programming method for the IES considering source-network-load multiple uncertainty.This one considers the uncertainty caused by the forecast error of renewable energy output and load demand,and constructs an energy supply and demand balance constraint that satisfies the confidence probability.Aiming at the uncertainty caused by the N-l failure of equipment,we propose an adjustment margin model.On this basis,the chance constraint of adjusting margin and energy deficit of N-1 equipment is constructed.For the obtained Pareto solution set,a multi-criteria evaluation model is carried out by using the information entropy and technique for order preference by similarity to ideal solution(TOPSIS)methods to determine the optimal system energy supply structure.Finally,the proposed method is applied to the optimal configuration of a regional IES,and the effectiveness and reliability are illustrated via experimental results.关键词
综合能源系统/不确定性/N-1故障/机会约束/多准则评价Key words
integrated energy system/uncertainty/N-1 failure/chance constraint programming/multi-criteria evalua-tion引用本文复制引用
周帆,陈龙,赵珺,王伟..计及多元不确定性的综合能源系统优化配置[J].控制理论与应用,2024,41(3):533-542,10.基金项目
国家重点研发计划项目(2017YFA0700300),国家自然科学基金项目(61833003,U1908218,62003072),大连市优秀青年科技人才计划项目(2018RJ01)资助.Supported by the National Key Research and Development Program of China(2017YFA0700300),the National Natural Science Foundation of China(61833003,U1908218,62003072)and the Outstanding Youth Sci-Tech Talent Program of Dalian(2018RJ01). （2017YFA0700300）
