Dual Agent Framework for Scheduling Networked Microgrids Using DRL to Improve Resilience

The widely reported increase in the frequency of high impact, low probability extreme weather events pose significant challenges to the resilient operation of electric power systems. This paper explores strategies to enhance operational resilience that addresses the distribution network's ability to adapt to changing operating conditions. We introduce a novel Dual Agent-Based framework for optimizing the scheduling of distributed energy resources (DERs) within a networked microgrid (N-MG) using the deep reinforcement learning (DRL) paradigm. This framework focuses on minimizing operational and environmental costs during normal operations while enhancing critical load supply indices (CSI) under emergency conditions. Additionally, we introduce a multi-temporal dynamic reward shaping structure along with the incorporation of an error coefficient to enhance the learning process of the agents. To appropriately manage loads during emergencies, we propose a load flexibility classification system that categorizes loads based on its criticality index. The scalability of the proposed approach is demonstrated through running multiple case-studies on a modified IEEE 123-node benchmark distribution network. Furthermore, validation of the method is provided by means of comparisons with two metaheuristic algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA).