Application of Artificial Intelligence and Machine Learning Methods to Optimize Energy System Stability

This article presents a systematic analysis of artificial intelligence (AI) and machine learning (ML) methods applied to the optimization of energy system stability. Deep learning architectures for forecasting electricity demand and renewable energy generation, reinforcement learning algorithms for microgrid control, the concept of digital twins, and explainable artificial intelligence (XAI) methods are examined. The application of hybrid models reduces forecasting errors by 20–35% over ultra-short-term forecasting horizons ranging from several minutes to several hours. The transition to probabilistic forecasting reduces balancing costs by 8–15%, while the use of digital twins decreases operating costs by 15–25%. Current challenges limiting the implementation of intelligent technologies in the energy sector are identified, and priority areas for further research are outlined. The results are systematized in comparative tables suitable for practical use when selecting appropriate methodological tools.

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