Optimal Pre-disaster and Post-disaster Scheduling of Mobile Energy Storage Considering the Influence of Transportation Network

[Objective] With advancements in energy storage technology and increasing demand for resilient distribution networks， mobile energy storage （MES） has gained traction in enhancing the resilience of distribution networks. However， existing research primarily focuses on the post-disaster scheduling of MES， neglecting the critical impact of traffic conditions， thereby failing to maximize the emergency response capabilities of MES. [Methods] Therefore， this study proposes an MES scheduling strategy that integrates both pre- and post-disaster dynamic scheduling， accounting for the influence of transportation networks. A dynamic transportation network model was developed employing the road weight matrix derived from the Dijkstra algorithm in conjunction with a speed-flow model， to fully capture the impact of traffic flow status on the emergency response capabilities of the MES. Subsequently， a pre-disaster scheduling model of the MES was developed with the objective of minimizing expected power outage losses under random scenarios， thereby aiming to pre-schedule the MES to candidate connection points. Following the disaster， a dynamic optimization scheduling model of the MES was developed for determine the optimal dynamic scheduling scheme of the MES. [Results] Simulation results demonstrate that the proposed MES scheduling strategy significantly outperforms alternative strategies that does not incorporate pre-scheduling or transportation network impacts， particularly in terms of load loss cost and scheduling time. These results highlight the effectiveness and superiority of the proposed strategy. The proposed MES scheduling strategy， which integrates pre-disaster scheduling and dynamic post-disaster scheduling， significantly reduces the time required for the MES to assist in power restoration and minimizes power outage losses. Furthermore， this strategy fully accounts for traffic flow changes in the transportation network， optimizes the selection of MES scheduling paths， reduces the negative impact of traffic congestion， and further improves the scheduling efficiency of the MES. [Conclusions] In conclusion， the proposed MES scheduling strategy comprehensively integrates the influence of prescheduling and traffic flow， optimally exploits the emergency response potential of the MES， and enhances the resilience level of distribution networks.