Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation
The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174524001934 |
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| author | Opy Das Muhammad Hamza Zafar Filippo Sanfilippo Souman Rudra Mohan Lal Kolhe |
| author_facet | Opy Das Muhammad Hamza Zafar Filippo Sanfilippo Souman Rudra Mohan Lal Kolhe |
| author_sort | Opy Das |
| collection | DOAJ |
| description | The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry. |
| format | Article |
| id | doaj-art-2d34497e4aa840e6ade2c3f5a854ee34 |
| institution | OA Journals |
| issn | 2590-1745 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-2d34497e4aa840e6ade2c3f5a854ee342025-08-20T02:37:05ZengElsevierEnergy Conversion and Management: X2590-17452024-10-012410071510.1016/j.ecmx.2024.100715Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisationOpy Das0Muhammad Hamza Zafar1Filippo Sanfilippo2Souman Rudra3Mohan Lal Kolhe4Department of Engineering Sciences, University of Agder, Grimstad 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad 4879, Norway; Department of Software Engineering, Kaunas University of Technology, Kaunas 51368, Lithuania; Corresponding author.Department of Engineering Sciences, University of Agder, Grimstad 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad 4879, NorwayThe growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.http://www.sciencedirect.com/science/article/pii/S2590174524001934Digital twinMachine learningSmart GridReal-time data communicationPower system digital twinRenewable energy |
| spellingShingle | Opy Das Muhammad Hamza Zafar Filippo Sanfilippo Souman Rudra Mohan Lal Kolhe Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation Energy Conversion and Management: X Digital twin Machine learning Smart Grid Real-time data communication Power system digital twin Renewable energy |
| title | Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation |
| title_full | Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation |
| title_fullStr | Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation |
| title_full_unstemmed | Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation |
| title_short | Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation |
| title_sort | advancements in digital twin technology and machine learning for energy systems a comprehensive review of applications in smart grids renewable energy and electric vehicle optimisation |
| topic | Digital twin Machine learning Smart Grid Real-time data communication Power system digital twin Renewable energy |
| url | http://www.sciencedirect.com/science/article/pii/S2590174524001934 |
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