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Aggregating Learning Agents for Microgrid Energy Scheduling During Extreme Weather Events

Efficient utilization of the microgrid generation resources (MGRs) is important especially during weather-related events. Existing single-agent and cooperative multi-agent based reinforcement learning (RL) approaches may be infeasible and computationally expensive for scheduling MGRs effectively when there are some probabilistic weather-related emergency events. In this paper, we propose a Q-learning approach with multiple local agents for a grid-connected microgrid application and show that the proposed integration is capable of scheduling MGRs efficiently for both normal and weather-related emergency events. Specifically, we utilize different local Q-learning agents to learn different microgrid events, and aggregate the learned value functions to the global agent who handles the overall microgrid energy scheduling in a probabilistic way. Numerical simulations are performed to validate the effectiveness of the proposed method. The influences of the effective utilization of the MGRs and power outage duration are discussed. Two case studies with different power outage probabilities are presented to evaluate the performance of our proposed method.