Optimizing Energy Storage Systems. A Dynamic Framework For Capacity Allocation And profit Maximization In Electricity Markets

Adela Bara () and Simona-Vasilica Oprea ()
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Adela Bara: Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, Romania
Simona-Vasilica Oprea: Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, Romania

Ovidius University Annals, Economic Sciences Series, 2024, vol. XXIV, issue 2, 130-139

Abstract: This paper presents an algorithmic approach for optimizing energy storage system (ESS) capacity allocation across multiple electricity markets to maximize profits. The methodology involves collecting real-time and historical data on market prices, renewable energy forecasts and grid demand. Predictive analytics, including machine learning models, forecast future market conditions, while optimization techniques such as mixed-integer linear programming (MILP) determine the optimal schedule for charging and discharging. Reinforcement learning (RL) is integrated into the framework to enable dynamic, adaptive decision-making, allowing the ESS to continuously refine its market strategies. Key constraints such as storage capacity, charge/discharge rates and market regulations are incorporated into the model. A feedback loop ensures real-time adjustments based on market fluctuations, improving profitability over time. Revenue stacking across day-ahead, intraday, ancillary and balancing markets further enhances the financial viability of ESS investments.

Keywords: energy storage systems; energy transition; operation strategy; algorithmic approach; optimization (search for similar items in EconPapers)
JEL-codes: G14 O13 P18 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xxiv:y:2024:i:2:p:130-139

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