Upon the arrival of mobile energy storage units, these resources collectively provide power support to critical loads in the distribution system. This scenario demonstrates superior resilience recovery capability in the initial stages of power resilience compared to Scenario II.
Existing methods for emergency mobile energy storage (EMES) allocation often struggle to balance resilience enhancement and economic feasibility under large-scale disasters effectively. To address these challenges, this paper presents an advanced optimization framework for EMES deployment based on multi-agent Deep Reinforcement Learning (DRL).
The action a i, t represents the emergency energy storage optimization allocation strategy of agent i. Subsequently, the actions of all agents A t = {a 1, t, a 2, t,, a n, t} are jointly applied to the environment.
In Scenario I, without mobile energy storage dispatch, the islanded microgrid solely supplies its own loads, resulting in no resilience benefits for load nodes and NEB and AR. Scenario II shows positive AR, however, it still results in negative NEB for some distribution network load nodes. Additionally, the scenario is marked by high costs for C E.
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