Exchange on mobile energy storage containers for Nordic emergency command

Can deep reinforcement learning improve emergency mobile energy storage allocation?

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).

Can a mobile energy storage dispatch model reduce load curtailment?

However, it is inevitable to consider the complicated coupling relations of mobile energy storage, transportation network, and power grid, which can cause issues of complex modeling and low efficiency. To address that, this paper proposes a mobile energy storage dispatch model to minimize the load curtailment.

What is mobile energy storage?

Mobile energy storage (MES) is a typical flexible resource, which can be used to provide an emergency power supply for the distribution system. However, it is inevitable to consider the complicated coupling relations of mobile energy storage, transportation network, and power grid, which can cause issues of complex modeling and low efficiency.

Do mobile energy storage units provide power resilience?

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.