Optimizing Energy Storage Systems. A Dynamic Framework For Capacity Allocation And profit Maximization In Electricity Markets
Year: 2025
Type: article
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.
Authors: Adela Bara, Simona-Vasilica Oprea
Institution: Bucharest University of Economic Studies
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408370784