Using AI to Create Digital Twins of Energy Systems and Conduct Virtual Stability Tests

This paper examines the application of artificial intelligence (AI) in creating digital twins of energy systems and conducting virtual stability tests. Through comparative and inductive analysis of existing literature and industry reports, we explore how AI-driven modeling can address limitations of traditional simulation methods in predicting complex energy system behavior. Key findings include the potential for machine learning algorithms to develop high-fidelity models, integration of big data to enhance simulation realism, and use of generative adversarial networks to simulate rare events. We propose strategies for optimizing energy systems based on virtual test results, including reinforcement learning for developing optimal control strategies. While AI-powered digital twins show promise for improving reliability and efficiency, challenges in model validation and standardization remain. This research highlights AI as a critical tool for advancing digital twin technology in the energy sector and enhancing grid resilience through virtual stability testing.

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