重庆邮电大学学报（自然科学版）2026，Vol.38Issue(2)：331-341,11.DOI:10.3979/j.issn.1673-825X.202503050045
基于改进粒子群算法的综合能源优化调度策略
Optimal scheduling strategy for integrated energy systems based on improved particle swarm optimization algorithm
摘要
Abstract
To promote coordinated optimization of regional integrated energy systems and maximize the economic benefits of participants in energy trading markets,this paper proposes a multi-objective optimal scheduling strategy that incorporates integrated energy demand response.By analyzing the structure of regional integrated energy systems,a bi-level Stackelberg game model is constructed,in which the operator acts as the leader,while suppliers and clustered users respond as followers.To address the nonlinear challenges in energy pricing and trading within the game model,an improved particle swarm optimization(PSO)algorithm is developed.Specifically,a chaotic initialization strategy and a second-order oscillation mechanism are introduced to enhance global search capability.Additionally,a segmented nonlinear inertia weight is adopted to reduce the risk of falling into local optima.The algorithm iteratively determines the optimal sequence of energy prices and trading quantities across different time periods.Simulation results demonstrate that the proposed optimization strategy effectively balances peak and valley load differences for users and reduces energy purchasing costs.关键词
区域综合能源系统/优化调度/主从博弈/需求响应Key words
regional integrated energy system/optimal scheduling/Stackelberg game/demand response分类
信息技术与安全科学引用本文复制引用
龚子祺,刘迪迪,刘以团..基于改进粒子群算法的综合能源优化调度策略[J].重庆邮电大学学报（自然科学版）,2026,38(2):331-341,11.基金项目
国家自然科学基金项目(62061006) （62061006）
广西类人脑重点实验室基金项目(BCIC-23-Z7) National Natural Science Foundation of China(62061006) （BCIC-23-Z7）
Project of Guangxi Key Laboratory of Brain-like Computing(BCIC-23-Z7) （BCIC-23-Z7）
