Deep Reinforcement Learning Based Optimal Schedule for a Battery Swapping Station Considering Uncertainties
Yuan Gao, Jiajun Yang, Ming Yang, Zhengshuo Li
Abstract
For a battery swapping station (BSS), the stochastic operation of electric buses (EBs) and the uncertainty of electricity prices cause unnecessary economic losses. To minimize the operating costs of the BSS, this article applies the deep reinforcement learning (DRL) and proposes a BSS model to determine the optimal real-time charge/discharge power of the charging piles. The predictability of bus operation and the uncertainty of price can be directly captured from historical data without any assumption in the model. Moreover, deep deterministic policy gradient (DDPG), as the DRL algorithm, is implemented in the model to simultaneously control multiple charging piles. Numerical results illustrate that the proposed approach can bring less operating cost than the existing benchmark control methods for a BSS while providing adequate batteries ready for swapping.