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OA

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.