Optimize the operating range for improving the cycle life of battery energy storage systems under uncertainty by managing the depth of discharge

Globally, renewable energy penetration is being actively promoted by renewable energy 100% (RE100) policies. BESS operators using time-of-use pricing in the electrical grid need to operate the BESS effectively to maximize revenue while responding to demand fluctuations. Battery energy storage (BESS) is needed to overcome supply and demand uncertainties in the electrical grid due to increased renewable energy resources. BESS operators using time-of-use pricing in the electrical grid need to operate the BESS effectively to maximize revenue while responding to demand fluctuations. However, excessive discharge depth and frequent changes in operating conditions can accelerate battery aging. Deep discharge depth increases BESS energy consumption, which can ensure immediate revenue, but accelerates battery aging and increases battery aging costs. The proposed BESS management system considers time-of-use tariffs, supply deviations, and demand variability to minimize the total cost while preventing battery aging. In this study, we investigated a BESS management strategy based on deep reinforcement learning that considers depth of discharge and state of charge range while reducing the total operating cost. In the proposed BESS management system, the agent takes actions to minimize the total operating cost while avoiding excessive discharge depth and low state of charge. A series of experiments using a real BESS demonstrated that the proposed BESS management system has improved performance compared to the existing methods.

Automated summary

Globally, the promotion of renewable energy penetration is important, and battery energy storage systems (BESS) are needed to overcome supply and demand uncertainties. This study proposes a BESS management strategy based on deep reinforcement learning, which considers discharge depth and state of charge range to reduce total operating costs. Experimental results show improved performance compared to existing methods.