Deep Reinforcement Learning-based SOH-aware Battery Management for DER Aggregation
In smart energy systems, batteries, which assume an important role in filling the temporal gap between generation and consumption, are expected to be a potential distributed energy resource (DER). A resource aggregator (RA) has emerged to collect various DERs to extract demand-side flexibility, and various methods have been proposed based on reinforcement learning. Since battery degradation is unavoidable during utilization, battery management is required to minimize it. This paper proposes state-of-health (SOH)-aware battery management based on deep reinforcement learning. Our experimental results demonstrate an average battery lifetime improvement of 11.2%.