Battery Storage System Optimization for Multiple Revenue Streams and Savings

Kadri, Abdeslem; Mohammadi, Fazel

doi:10.1109/ccece47787.2020.9255733

Cited by 1 publication

(1 citation statement)

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“…Other authors report increased financial benefits from bidding on more than one market. Such market combinations include frequency regulation and energy arbitrage [33], frequency regulation and real-time markets [34], energy arbitrage and reserve markets [35], energy arbitrage and spinning reserves [36], energy arbitrage and demand response [37] and demand response and nodal markets [38]. It is notable that some authors specify their targeted market in general terms, such as energy arbitrage, while others identify a more specific market, such as real-time electricity markets.…”

Section: Literature Review 21 Financial Exploitation Of Battery Storagementioning

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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