Survey and insights on digital twins design and smart grid's applications

Digital twins are a promising technology for simulating complex systems, especially in the smart grid domain. This paper offers a comprehensive literature review on digital twins, focusing on data gathering, data management, and human-in-the-loop control design aspects. Emphasizing the integration of AI and machine learning in big data, it enhances analytics and decision-making capabilities. We introduce a collaborative framework involving multiple stakeholders to maximize the potential of digital twins. The paper examines digital twin applications in smart grids, covering areas like asset management, predictive maintenance, energy optimization, and demand response. By synthesizing research and implementation findings, we identify trends, challenges, and opportunities in the field.