RLEM Workshop 2022

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Deep Reinforcement Learning-based SOH-aware Battery Management for DER Aggregation

Shotaro Nonaka, Daichi Watari, Ittetsu Taniguchi, Takao Onoye

at  9:20 ! Livein  Roomfor  15min
Abstract

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%.

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