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).
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.
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.
To assess the resilience and economic benefits of the proposed allocation strategy, this study analyzes the power support provided by different combinations of EMES and microgrids for distribution networks under extreme events. Four scenarios are investigated.
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