Reinforcement Learning-Based Energy Management in Community Microgrids: A Comparative Study

Energy communities represent an important step towards clean energy, however their management is a complex task due to various factors such as fluctuating demand and energy prices, variable renewable generation, and external factors such as power outages. This paper investigates the effectiveness of a Reinforcement Learning agent, based on the Proximal Policy Optimisation (PPO) algorithm, for energy management across three different energy community configurations. The performance of the PPO agent is compared against a Rule-Based Controller (RBC) and a baseline scenario using solar generation but with no active management. Simulations were run in the CityLearn framework to simulate real world data, and results show that the PPO agent was up to 9.2% more effective in reducing annual costs and carbon emissions than the RBC, its effectiveness increasing in scenarios which allowed control over resources such as photovoltaic generation and battery storage. The main contribution of this work is demonstrating the viability of Reinforcement Learning agents in energy optimization problems, providing an alternative to traditional RBC controllers for energy communities.