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This paper demonstrates AI's impact on battery energy storage systems, highlighting improvements in system stability and predictive maintenance.
Battery Energy Storage Systems (BESS) are the backbone of modern power grids. They allow for the increase of energy storage, peak shaving, or backup power. Due to their complexity and dynamics, BESS require high-advanced management methods to optimise its performance. This paper focuses on the integration of Artificial Intelligence (AI) into BESS, discussing three main pillars: system stability, battery usage optimisation, and predictive maintenance. The emergence of Artificial Intelligence and in particular deep learning, reinforcement learning, and neural networks, brings significant improvements in the modelling of complex reaction mechanisms, the adaptation to real-time data, and predictive maintenance. By analysing large datasets from various sources, AI can increase the precision of State of Charge (SOC) estimation, reduce maintenance costs, and improve the reliability of the system. The comparison with different case studies underlines the potential implementation of AI in real-life applications, which brings cost savings and increased system efficiency. This paper concludes that the power of AI enables new techniques for BESS management, and it would bring major benefits in the construction of more powerful and resilient energy systems as a whole.
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Cai et al. (2024) studied this question.
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