中国电机工程学报2026，Vol.46Issue(15)：6214-6226,中插6,14.DOI:10.13334/j.0258-8013.pcsee.251605
弥合配电系统恢复调度仿真-现实间隙的两阶段数据-机理融合优化架构
Two-stage Data-mechanism Integrated Optimization Framework to Bridge the Simulation-to-reality Gap of Distribution System Restoration Dispatch
摘要
Abstract
The post-disaster restoration dispatch of distribution systems is a crucial element in enhancing its resilience and ensuring economic development and societal welfare.Existing studies have preliminarily explored the application of data-driven methods,represented by deep reinforcement learning,in distribution system restoration dispatch.However,when the simulation scenario used to train the dispatch agent differs significantly from the real scenario,the decision-making performance of the agent will produce a serious degradation,which is known as the simulation-to-reality gap.To address this issue,this paper proposes a distribution system post-disaster restoration dispatch method with dynamic generalization capabilities.On the one hand,a two-stage(before and during the disaster)hybrid-constrained Markov decision process formulation is developed,where the dispatch policy is further fine-tuned based on known fault information during the disaster to bridge the simulation-to-reality gap caused by uncertain fault locations.On the other hand,this paper designs a mechanism-guided restoration dispatch agent training approach to improve training efficiency,thereby enabling the second-stage fine-tuning to cover an expanded scenario set and mitigate the simulation-to-reality gap induced by uncertain repair durations and prosumer-side power.Test results in case studies based on a dual-substation 66-bus test system validate that the proposed method outperforms traditional deep learning methods in terms of training efficiency,convergence,and generalization capability.关键词
配电系统恢复调度/分布式资源/多阶段随机规划/仿真-现实间隙/数据机理融合Key words
distribution system restoration dispatch/distributed energy resources/multi-stage stochastic programming/simulation-to-reality gap/data-mechanism integration分类
信息技术与安全科学引用本文复制引用
吴奕之,刘浏,康重庆,叶宇剑,胡健雄,周东华,朱瑨,张新松,杨春祥,马彦宏,赵沛霖..弥合配电系统恢复调度仿真-现实间隙的两阶段数据-机理融合优化架构[J].中国电机工程学报,2026,46(15):6214-6226,中插6,14.基金项目
国家自然科学基金项目(52477083,52207082,52507139) （52477083,52207082,52507139）
腾讯高校合作项目(Tencent JR2025TEG001).Project Supported by National Natural Science Foundation of China(52477083,52207082,52507139) （Tencent JR2025TEG001）
Tencent-University Collaboration Program(Tencent JR2025TEG001). （Tencent JR2025TEG001）
