Energy Management for Building-Integrated Microgrids Using Reinforcement Learning

As more energy-intensive electrical appliances and active assets appear in the residential sector, electricity end-users are able to provide various demand response services, aiming to balance demand and supply profiles. This paper proposes a state-of-the-art energy management system for a microgrid. The study focuses on a four-apartment building incorporating heat pumps, an energy storage system, and photovoltaic generation. A smart control mechanism based on reinforcement learning is introduced aiming to improve thermal comfort, minimize energy consumption and ensure minimum degradation of the storage system. The reported results offer insights into the optimal residential management practices and evaluate the performance of the proposed control strategy in comparison to other alternative demand response solutions.