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

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.