电力工程技术2026，Vol.45Issue(3)：105-115,11.DOI:10.12158/j.2096-3203.2026.03.012
考虑新能源的暂态稳定约束多目标最优潮流建模及求解
Modeling and solution of transient stability constrained multi-objective optimal power flow considering renewable energy
摘要
Abstract
In order to cope with the impact of wind power and photovoltaic uncertainty on the safe and stable operation of the power grid and to make up for the shortcomings of the traditional single-objective optimal power flow model,a transient stability constrained multi-objective optimal power flow(TSCMOOPF)model and a solution method are proposed to take into account the wind and solar uncertainty.Firstly,an ensemble learning method based on artificial neural network(ANN),deep neural network(DNN)and surprisal-driven zoneout long short-term memory(SZLSTM)are adopted to construct a wind and photovoltaic output prediction model to improve the prediction accuracy and robustness.Secondly,considering the economy and stability of the system,a multi-objective function including the minimization of active network loss,the minimization of fuel cost,and the optimization of the voltage stability index is established to construct a TSCMOOPF model.Then,an improved reference vector guided evolutionary algorithm(RVEA)is designed for the solution.Finally,simulation experiments are carried out on the improved IEEE 39-bus system.The results show that the proposed ensemble learning method performs well in wind and photovoltaic output prediction,the multi-objective optimization model ensures transient stability while active network loss and fuel cost are reduced significantly,and the improved RVEA algorithm is better than the traditional multi-objective algorithm in terms of convergence and diversity.关键词
不确定性/暂态稳定约束/多目标最优潮流/集成学习/人工神经网络/长短期记忆网络Key words
uncertainty/transient stability constraints/multi-objective optimal power flow/ensemble learning/artificial neural network/long short-term memory network分类
信息技术与安全科学引用本文复制引用
刘颂凯,时良志,胡畔,高坤,杨超,万明..考虑新能源的暂态稳定约束多目标最优潮流建模及求解[J].电力工程技术,2026,45(3):105-115,11.基金项目
国家自然科学基金资助项目(52407118) 本文得到梯级水电站运行与控制湖北省重点实验室(三峡大学)开放基金课题(2023KJX06)资助,谨此致谢! （52407118）
