Pre-event Resilience Enhancement Strategy for Distribution Systems Based on Dueling DDQN

Global warming has led to an increasing number of extreme weather events, sequential attacks of such events cause significant damage to distribution grids, and power utilities have to spend huge amounts of resources to strengthen grid structures. To improve the pre-event resilience of distribution systems in the face of extreme events while saving costs, this paper proposes a deep reinforcement learning-based distribution grid hardening strategy that combines the failure rate of hardened components with the hardening cost to form a bi-objective optimization model. Simulations are performed in the IEEE 37-bus system to verify that the hardening strategy found by the proposed approach can significantly improve system resilience under different hardening conditions and disaster situations.