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).
Energy storage congestion constraints: When multiple energy storage devices are scheduled simultaneously, the limited network transmission capacity or overly concentrated deployment of energy storage devices may cause the power flow on certain lines to exceed their rated transmission capacity, resulting in local network congestion .
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.
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