Deep Reinforcement Learning for Efficient Scheduling of Renewable Energy Microgrids

This study studies the optimization method of microgrid scheduling based on deep reinforcement learning. By constructing a microgrid model, combining replay mechanism and freezing network parameters, efficient energy scheduling is achieved. A comparative analysis was conducted between the genetic algorithm and the deep Q-network (DQN) through simulation, revealing that DQN excelled in minimizing both operational costs and computation time. This technique introduces a novel strategy for energy management within microgrids, with the potential for future scalability to accommodate various sizes and configurations of microgrids.