电力系统保护与控制2026，Vol.54Issue(7)：13-23,11.DOI:10.19783/j.cnki.pspc.250990
融合先验知识和多阶段QMIX强化学习的综合能源系统优化调度
Optimal scheduling of integrated energy systems based on prior knowledge and multi-stage QMIX reinforcement learning
摘要
Abstract
The multi-energy coupling characteristics and increasingly complex topology of integrated energy systems(IES)make optimal scheduling a pivotal challenge in balancing economy efficiency and operational security.To address the issues of convergence difficulty caused by the curse of dimensionality in traditional multi-agent reinforcement learning,as well as local optima resulting from insufficient exploration mechanisms,a real-time optimal scheduling method based on a prior knowledge-guided multi-stage QMIX architecture is proposed.First,the IES real-time optimal scheduling is formulated as a distributed partially observable Markov decision process,and a QMIX framework based on joint action value function updates is constructed.Then,according to the coupling relationships among energy units,a clustering-based multi-stage QMIX training strategy is designed to alleviate the curse of dimensionality.Finally,an enhanced action exploration mechanism incorporating prior knowledge is developed to guide the convergence trajectory.Scheduling simulations are conducted under multiple load scenarios(40 sample days).The results show that the proposed method exhibits significant advantages in convergence performance and effectively reduces the overall system operation costs.关键词
综合能源系统/实时优化调度/多智能体强化学习/多阶段QMIX/先验知识引导Key words
integrated energy system/real-time optimal scheduling/multi-agent reinforcement learning/multi-stage QMIX/prior knowledge guidance引用本文复制引用
楼劲,汪梦雨,郑凌蔚..融合先验知识和多阶段QMIX强化学习的综合能源系统优化调度[J].电力系统保护与控制,2026,54(7):13-23,11.基金项目
This work is supported by the Natural Science Foundation of Zhejiang Province(No.LY24F030010). 浙江省自然科学基金项目资助(LY24F030010) （No.LY24F030010）
