Optimal Scheduling for Dual-ESS Considering Life-Cycle Degradation

In this research, we develop an optimal operation strategy for dual Energy Storage Systems (ESS) using reuse batteries to minimize both electricity cost and battery degradation. Our method employs a two-stage optimization: first, leveraging Reinforcement Learning (RL) to determine optimal charging and discharging amounts, then followed by Quadratic Programming (QP) to allocate these amounts across dual batteries efficiently. This approach demonstrates the potential for substantial economic savings using reuse batteries for ESS. The results indicate the ESS with reuse batteries can enhance operational efficiency and achieve 4.3% - 10% cost savings compared to using ESS using new batteries.