Battery Energy Storage Control Using A Reinforcement Learning Approach With Cyclic Time-Dependent Markov Process

Scheduling efficient energy management system operations to respond to the unstable customer demand, electricity prices, and weather increases the complexity of the control systems and requires a flexible and costeffective control policy. This study develops an intelligent and real-time battery energy storage control based on a reinforcement learning model focused on residential houses connected to the grid and equipped with solar photovoltaic panels and a battery energy storage system. Because the reinforcement learning's performance is very dependent on the design of the underlying Markov decision process, a cyclic time-dependent Markov Process is uniquely designed to capture existing daily cyclic patterns in demand, electricity price, and solar energy. The Markov Process is successfully used in the Q-learning algorithm, resulting in more efficient battery energy control and saving electricity costs. The proposed Q-learning algorithm is compared with benchmark models of a deterministic equivalent solution and a One-step Roll-out algorithm. Numerical experiments show the gap between the deterministic equivalent solution and Q-learning approaches for one-month electricity cost decreased from 7.99% to 3.63% for house 27 and 6.91% to 3.26% for house 387 when the discrete size of demand, solar energy, price, and battery energy level adjusted to 20. Accordingly, the better performance of the proposed Q-learning is demonstrated compared to the One-step Roll-out algorithm. Moreover, the effect of discrete size of state-space parameters on the adaptive Q-learning performance and computational time are investigated. Variations in the electricity price significantly affect the Q-learning algorithm's performance more than other parameters.