Digital Twins and Smart Grid Infrastructure: Improving Reliability and Efficiency Through Virtual Models
The integration of Digital Twins within smart grid infrastructure offers transformative solutions for enhancing both reliability and efficiency in modern energy systems. This article explores the concept of Digital Twins, defining their core components and real-time functionality, with an emphasis on their applications across industries, particularly in smart grids. By mapping physical assets to virtual models, Digital Twins provide advanced tools for monitoring, predictive maintenance, and real-time data-driven decision-making in power generation, transmission, and distribution. Their role in improving grid stability, detecting faults, and reducing downtime is discussed alongside case studies demonstrating tangible reliability benefits. Furthermore, this work highlights how Digital Twins contribute to optimizing power distribution, energy efficiency, and asset management, thus reducing energy losses. Finally, the integration of emerging technologies such as IoT, AI, machine learning, big data analytics, blockchain, and Industry 4.0 principles is examined, illustrating how these technologies enhance the operational scope and effectiveness of Digital Twins. Through these innovations, Digital Twins are poised to become indispensable tools in achieving more sustainable, resilient, and efficient smart grids.
Authors
- Almoataz Y. Abdelaziz (ORCID: https://orcid.org/0000-0001-5903-5257)
- Gevork B. Gharehpetian (ORCID: https://orcid.org/0000-0003-1521-790X)
- Hossein Shahinzadeh (ORCID: https://orcid.org/0000-0002-4994-3726)
- Francisco Jurado (ORCID: https://orcid.org/0000-0001-8122-7415)
- Mehdi Abapour (ORCID: https://orcid.org/0000-0003-0241-7021)
- Mohammad Mohsen Hayati (ORCID: https://orcid.org/0000-0002-6104-1928)
Institutions
- Amirkabir University of Technology (IR)
- Future University in Egypt (EG)
- University of Tabriz (IR)
Publication Details
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1109/sgc64640.2024.10982831
- Citations
- 2
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 0.70