Participation of Battery Energy Storage Technologies in Co-Optimized Energy and Reserve Markets

Gupta, Ambuj; Vaishya, Shri Ram; Gupta, Megha; Abhyankar, A. R.

doi:10.1109/npsc49263.2020.9331865

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Cycle aging cost is one of the key components of BESS's operation cost in agreement to [23]; thus, the battery degradation cost must be accounted for in the electricity cost reduction model, particularly when estimating future total cost. The research report [24] presents a simplified mathematical model for each BESS's degradation cost function based on discharge depth for co-optimized energy and reserve markets. A modified IEEE 30-bus system case study analyses battery technologies' operating and degradation costs and energy arbitrage and reserve market participation.…”

Section: Second Component: Battery Cell Degradation Over Time F (B)mentioning

confidence: 99%

Optimizing Electric Bill Savings: Integrating Peak Shaving, Frequency Regulation, and Battery Performance

Hayat,

Nawaz,

Khan

et al. 2024

Preprint

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The study offers a method for reducing electric bills by combining peak shaving and frequency management with lithium-ion batteries. The integration of lithium-ion battery losses resulting from a dynamic model, cell deterioration, and power conditioning system losses along with the addition of the yearly cost model of uninterruptible power supply greatly improves the consumer’s comprehension of the electricity cost model. Utilizing the load profile of a business user, linear optimization is used on solely peak shaving model to explain how to improve the commercial customer’s generic electricity cost model. The CH75-6 lithium-ion battery losses combined with analytical considerations on Power Conditioning System losses are integrated into the already optimized model of the power bill. The mathematical results of the model revealed that the co-application of peak shaving, frequency controls, and other parameters such as the dynamic loss model, cell degradation constant, and power conditioning losses can assist commercial users in lowering their electricity bills.

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Section: Second Component: Battery Cell Degradation Over Time F (B)mentioning

confidence: 99%

Optimizing Electric Bill Savings: Integrating Peak Shaving, Frequency Regulation, and Battery Performance

Hayat,

Nawaz,

Khan

et al. 2024

Preprint

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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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The role of WES and BESS in the provision of operating electricity reserves-a review

Mndiya,

Ratshitanga,

Krishnamurthy

2024

2024 32nd Southern African Universities Power Engineering Conference (SAUPEC)

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Participation of Battery Energy Storage Technologies in Co-Optimized Energy and Reserve Markets

Cited by 3 publications

References 13 publications

Optimizing Electric Bill Savings: Integrating Peak Shaving, Frequency Regulation, and Battery Performance

Optimizing Electric Bill Savings: Integrating Peak Shaving, Frequency Regulation, and Battery Performance

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

The role of WES and BESS in the provision of operating electricity reserves-a review

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