Optimizing Energy Storage Systems with AI-Based Control Strategies

Abstract

Energy storage systems (ESS) are very important for making power systems more efficient, reliable, and long-lasting. They do this by making green energy sources less unpredictable and by offering grid support services. However, it is still hard to get ESS to work and be controlled in the best way possible because energy markets are always changing, grid conditions are always changing, and the different parts of the system interact in complicated ways. As a result, artificial intelligence (AI) has become an interesting way to improve ESS control methods, providing smart and flexible answers to these complicated issues. This paper gives an in-depth look at various AI-based control methods for making energy storage systems work better. It talks about the latest progress in machine learning, deep learning, reinforcement learning, and evolutionary algorithms used for ESS control. It shows how they can capture nonlinear system dynamics, learn complex patterns from past data, and change control strategies in real time. The study also talks about how to improve the speed and reliability of ESS operation by combining AI techniques with standard optimization and control algorithms. The article looks at several uses of AI-based ESS control, such as lowering high loads, even out loads, controlling frequency, and incorporating green energy. There are case studies and modeling results that show how AI-driven methods can improve ESS performance, lower running costs, and make the most of economic gains. It shows how AI could change the way energy storage systems are built and how they work, making energy grids more efficient, reliable, and long-lasting.