Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Reinforcement Learning Framework

Smart grids are contributing to the demand-side management by integrating electronic equipment, distributed energy generation and storage, and advanced meters and controllers. With the increasing adoption of distributed energy generation and storage systems, residential energy management is drawing more and more attention, which is regarded as being critical to demand-supply balancing and peak load reduction. In this paper, we focus on a microgrid in which a large-scale modern homes interact together to optimize their electricity cost. We present an Entropy-Based Collective Multiagent Deep Reinforcement Learning (EB-C-MADRL) framework to address it. Experiments demonstrate that EB-C-MADRL can reduce both the long-term group power consumption cost and daily peak demand effectively compared with existing approaches.