Battery State of Health Estimation via Reinforcement Learning

Natella, Domenico; Vasca, Francesco

doi:10.23919/ecc54610.2021.9655199

Cited by 4 publications

(1 citation statement)

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“…The reward does not need to be derived from physics, so the reward formula may include a term that is a simplification of the aging phenomena that ignores the non-linear dynamics of the battery. Such simplifications are commonly used by RL practitioners, and various formulations have been proposed by different authors, such as [20]. The shortcoming of such an approach is that any benefits for reducing battery aging are not demonstrated either with a physical battery or with an accurate battery model.…”

Section: Introductionmentioning

confidence: 99%

Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach

et al. 2022

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Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics-based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.

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Section: Introductionmentioning

confidence: 99%