Data Driven Energy Management of Residential PV-Battery System Using Q-Learning

Data-driven energy management of residential PV-battery systems using Q-learning offers several benefits, including optimal energy consumption, integration of renewable energy, improved grid stability, cost savings, and flexibility. These advantages contribute to the efficient and sustainable operation of residential energy systems and support the transition towards a cleaner and more resilient energy future. This research focuses on making a violation free, automated energy management system for residential loads using a model free reinforcement learning (RL) algorithm. The objective is to minimize the energy consumption of the system by leveraging the capabilities of the Photovoltaic (PV) system, battery storage, and home load. The energy management problem formulates and describes the state space, action space, and reward structure for Q-learning. This approach learns an optimal policy for energy management based on historical data and feedback from the system. A comprehensive reward function is proposed to ensure a proper battery energy utilization policy. The Australian household PV profile and load curve over a 24-hour horizon with an interval of half an hour are used to examine the performance of the proposed method.