Peak shaving of a heterogeneous cluster of residential flexibility carriers using reinforcement learning

Demand response is often defined as an optimal control problem. However, the practical application is challenged by computational complexity and lack of accurate models and data. In this work we extend upon previous work and combine batch reinforcement learning, using function approximators, with a market-based multi-agent system. The resulting adaptive control strategy is model-free and needs no prior knowledge of the cluster configuration. The strategy is evaluated for two distinct heterogeneous clusters of residential flexibility carriers. The evaluation shows that our self-learning strategy supports effective peak shaving and valley filling within a limited convergence time.