A multi-agent deep reinforcement learning based energy management for behind-the-meter resources

The future communities are becoming more and more electrically connected via increased penetrations of behind-the-meter (BTM) resources, specifically, electric vehicles (EVs), smart buildings (SBs), and distributed renewables. The electricity infrastructure is thus seeing increased challenges in its reliable, secure, and economic operation and control with increased and hard to predict demands (due to EV charging and demand management of SBs), fluctuating generation from renewables, as well as their plug-N-play dynamics. Reinforcement learning has been extensively used to enable network entities to obtain optimal policies. The recent development of deep learning has enabled deep reinforcement learning (DRL) to drive optimal policies for sophisticated and capable agents, which can outperform conventional rule-based operation policies in applications such as games, natural language processing, and biology. Furthermore, DRL has shown promising results in many resource management tasks. Numerous studies have been conducted on the application of single-agent DRL to energy management. In this paper, a fully distributed energy management framework based on multi-agent deep reinforcement learning (MADRL) is proposed to optimize the BTM resource operations and improve essential service delivery to community residents.