Optimizing the operation of energy storage systems using artificial intelligence for economic performance and energy transition

The utilization of energy storage systems (ESS) and the expansion of their installed capacity are crucial steps towards achieving energy neutrality goals. These systems, often integrated with photovoltaic installations, represent an intermediate stage that introduces new challenges for distribution management and financial burdens for consumers and society. To maximize the benefits of ESS, it is necessary to design an optimization system that considers the battery lifecycle, installed capacity, a nd economic benefits. A key objective is the determination and reduction of the per-unit usage cost of these systems, which represents a significant innovation of this work. This paper proposes an optimization model that incorporates factors such as energy pricing, charging/discharging efficiency, battery lifespan, and renewable energy forecasts. The model leverages artificial intelligence (AI), including machine learning and deep learning, for data analysis and decision-making based on historical data and patterns. This solution is designed to address the needs of both residential and industrial clients, further expanding its applicability. This approach optimizes the economic performance of ESS, minimizes energy transition costs, and fosters favorable conditions for a successful transition.