Control of battery charging based on reinforcement learning and long short-term memory networks. (July 2020)

• Record Type: Journal Article
• Authors: Chang, Fangyuan; Chen, Tao; Su, Wencong; Alsafasfeh, Qais
• Is Part Of: Computers & electrical engineering. Volume 85(2020)
• Publication Date: 2020-07
• Subjects: Energy storage battery/system -- Smart charging control -- Long short-term memory (LSTM) -- Reinforcement learning (RL) -- Electric vehicle (EV); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106670
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In an electricity market with time-varying pricing, uncontrolled charging of energy storage systems (ESSs) may increase charging costs. A novel battery charging control methodology based on reinforcement-learning (RL) is proposed in this paper to minimize the charging costs. A significant characteristic of this method is that it is model-free, with no need for a high-accuracy battery/ESS model. Therefore, it overcomes the challenges brought by limited types of battery models and non-ignorable parametric uncertainties in reality. Additionally, since an accurate prediction of fluctuating electricity prices can promote the control performance, a long short-term memory (LSTM) network is leveraged to improve the prediction precision. The final control objective is to seek an optimal charging portfolio to minimize charging costs. Moreover, the presented control algorithm provides a basic framework for a more complicated electricity market where various types of ESSs, generators, and loads exist.