电力系统保护与控制2025，Vol.53Issue(19)：127-138,12.DOI:10.19783/j.cnki.pspc.241592
计及风光不确定性的综合能源系统容量配置双层优化
Bi-level optimization for capacity allocation of integrated energy systems considering wind-solar uncertainty
摘要
Abstract
Integrated energy systems play a crucial role in the energy transition,but the stochastic fluctuations of wind speed and solar irradiance significantly affect capacity planning and investment decisions.Existing methods struggle to balance adaptability and lifecycle cost optimization.To address this,a bi-level optimization model for capacity allocation is proposed,aiming to minimize the total lifecycle cost while coordinating optimization of capacity allocation and operation scheduling.In the upper-level optimization,a genetic algorithm is used for equipment capacity allocation.Typical wind and solar scenarios are generated using adaptive kernel density estimation combined with an autoregressive model,while Wasserstein distance ensures representativeness and computational efficiency through scenario reduction.The lower-level optimization employs mixed-integer linear programming for operation scheduling,balancing economic efficiency and robustness.The scheduling results are then fed back to the upper level,guiding iterative updates of capacity allocation and forming a bi-level interactive optimization loop.Simulation results show that,compared to traditional optimization methods,the proposed model reduces total lifecycle costs while improving wind and solar utilization and system reliability.This provides both theoretical support and practical reference for the optimal planning of integrated energy systems.关键词
综合能源系统/风光不确定性/双层优化/随机优化/Wasserstein距离Key words
integrated energy system/wind-solar uncertainty/bi-level optimization/stochastic optimization/Wasserstein distance引用本文复制引用
陆瑜,何兆磊,李琰,崔琳,徐天奇..计及风光不确定性的综合能源系统容量配置双层优化[J].电力系统保护与控制,2025,53(19):127-138,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.62062068). 国家自然科学基金项目资助(62062068) （No.62062068）
云南省中青年学术和技术带头人后备人才项目资助(202305AC160077) （202305AC160077）
云南省教育厅科学研究基金项目资助(2023J0587) （2023J0587）
