Keywords
microgrid
artificial neural networks
renewable energy sources
nodal loads
How to Cite
Abstract
Digital twins are gradually becoming an important technology for the digital transformation of the energy sector. They provide integration of physical microgrid objects with their virtual models, which allows for real-time monitoring, analysis, and control. The article presents an overview of modern approaches to the development of digital twins for microgrids, covering issues of architecture, modeling, data acquisition, optimization, and virtual testing. The main technological solutions are considered, including the Internet of Things, cloud computing, machine learning, predictive control methods, and edge computing integration. Three main architectural approaches are analyzed – the three-layer model, the Smart Grid Architectural Model, and cyber-physical systems – and examples of their practical implementation in modern energy platforms are considered. The challenges of implementing digital twins in microgrids are identified, including issues of model accuracy, interoperability, cybersecurity, and high computational costs. It is shown that the development prospects lie in the creation of distributed digital twins integrated with artificial intelligence systems and edge computing, which will ensure the transition from static models to intelligent self-learning energy systems. Bibl. 26, table.
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