摘要
针对微能源网中可再生能源出力的不确定性,基于微能源网中综合需求响应的"多元互动"特征,提出了综合需求响应平抑可再生能源波动的策略,建立了微能源网综合需求响应日前和日内协同调度的两阶段鲁棒优化模型。模型中引入了风、光的不确定预算参数调节系统的经济性和鲁棒性,日前阶段考虑了最劣场景,确定系统日前调度方案,日内调控阶段基于日前阶段的优化结果,优化系统最劣场景下的调控方案。利用强对偶理论和列约束生成算法对min-max-min结构的鲁棒优化问题进行转化和求解。算例结果表明,鲁棒优化方法可提高系统抵抗不确定风险的能力,综合需求响应可提高微能源网的经济性和自给能力。
Aiming at the uncertainty of renewable energy output in the micro energy grid,based on the"multiple interaction"characteristics of integrated demand response(IDR),a strategy for IDR to smooth the fluctuation of renewable energy is proposed,and a two-stage robust optimization model for IDR day-ahead and intra-day collaborative scheduling in the micro energy grid is built.In the model,the uncertain budget parameters of wind and photovoltaic are introduced for regulating the economy and robustness of the system.In the day-ahead stage,the worst-case scenario is considered to determine the day-ahead scheduling scheme of the system.In the intra-day regulation stage,the regulation scheme in the worst-case scenario is optimized based on the optimization results of the day-ahead stage.The strong duality theory and column and constraint generation algorithm are used to transform and solve the robust optimization problem of min-max-min structure.The results show that the robust optimization method can improve the system ability to resist uncertain risks,and the IDR can improve the economy and self-sufficiency of the micro energy grid.
作者
陈灵敏
吴杰康
张文杰
唐惠玲
茅云寿
CHEN Lingmin;WU Jiekang;ZHANG Wenjie;TANG Huiling;MAO Yunshou(School of Automation,Guangdong University of Technology,Guangzhou 510006,China;Department of Experiment Teaching,Guangdong University of Technology,Guangzhou 510006,China;Huizhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Huizhou 516000,China;School of Physics&Optoelectronic Engineering,Guangdong University of Technology,Guangzhou 510006,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2021年第21期159-169,共11页
Automation of Electric Power Systems
基金
广东省基础与应用基础研究基金区域联合基金项目——粤港澳研究团队项目(2020B1515130001)。
关键词
综合需求响应
微能源网
两阶段鲁棒优化
列约束生成算法
优化调度
integrated demand response
micro energy grid
two-stage robust optimization
column and constraint generation algorithm
optimal scheduling
