基于安全深度强化学习的电力-交通耦合网络韧性提升策略OA

[Objective]To address the problems that when large-scale electrification-transportation coupling network(ETCN)encounters sudden resilience faults,traditional schemes have slow generation speed,are difficult to respond to dynamic information interaction in real time,and artificial intelligence algorithms are prone to cause safety accidents such as voltage over-limit due to the lack of security mechanisms in application,this paper proposes a resilience improvement strategy for ETCN based on safe deep reinforcement learning(SDRL).[Methods]First,the paper establishes a two-stage electrification-transportation optimization framework:the first stage prioritizes the protection of high-value loads with minimum reconfiguration cost,while the second stage optimizes electric vehicle(EV)routing with minimum traffic dispatch cost.Second,a hierarchical decision-making model based on a modified Rainbow algorithm is designed.The upper layer outputs the action plan of the power grid interconnection switch and inputs the reconstructed power grid state to the lower layer.The lower layer integrates grid reconfiguration state with real-time traffic information to optimize EV routing selection,with the objective to ensure that EV routing optimization can real-time adapt to the power grid's recovery needs.In addition,the Lagrange multiplier safety mechanism is embedded,and an objective function with risk penalties is constructed to achieve dynamic penalties for risk behaviors such as voltage over-limit and current over-limit.[Results]Finally,the simulation based on the actual road network in Shanghai and the IEEE123-node distribution network shows that the proposed strategy can significantly enhance the resilience and operational safety of the system in fault scenarios.Compared with the mixed integer programming and particle swarm optimization methods,the method proposed in this paper demonstrates superior comprehensive performance in terms of load recovery rate,recovery speed,voltage stability and strategy security.[Conclusions]This paper verifies the effectiveness of hierarchical safe deep reinforcement learning in improving the resilience of ETCN.This method solves the problem of the separation of electrification-transportation targets through a two-stage architecture,achieving a balanced synergy among computing efficiency,load recovery rate and operational safety.