Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems
Published in Intelligent and Converged Networks, 2024
The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.
Recommended citation: Y. Li, J. Wu and Y. Pan, "Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems," in Intelligent and Converged Networks, vol. 5, no. 1, pp. 28-41, March 2024, doi: 10.23919/ICN.2024.0003 https://ieeexplore.ieee.org/abstract/document/10484537