Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model (2005.06161v3)

Abstract: The uncertainty of distributed renewable energy brings significant challenges to economic operation of microgrids. Conventional online optimization approaches require a forecast model. However, accurately forecasting the renewable power generations is still a tough task. To achieve online scheduling of a residential microgrid (RM) that does not need a forecast model to predict the future PV/wind and load power sequences, this paper investigates the usage of reinforcement learning (RL) approach to tackle this challenge. Specifically, based on the recent development of Model-Based Reinforcement Learning, MuZero, we investigate its application to the RM scheduling problem. To accommodate the characteristics of the RM scheduling application, a optimization framework that combines the modelbased RL agent with the mathematical optimization technique is designed, and long short-term memory (LSTM) units are adopted to extract features from the past renewable generation and load sequences. At each time step, the optimal decision is obtained by conducting Monte-Carlo tree search (MCTS) with a learned model and solving an optimal power flow sub-problem. In this way, this approach can sequentially make operational decisions online without relying on a forecast model. The numerical simulation results demonstrate the effectiveness of the proposed algorithm.