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Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review

Author

Listed:
  • Ama Ranawaka

    (Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia)

  • Damminda Alahakoon

    (Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia)

  • Yuan Sun

    (Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia)

  • Kushan Hewapathirana

    (Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia)

Abstract

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.

Suggested Citation

  • Ama Ranawaka & Damminda Alahakoon & Yuan Sun & Kushan Hewapathirana, 2024. "Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review," Energies, MDPI, vol. 17(21), pages 1-52, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5342-:d:1507745
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    1. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    2. Alexandra I. Khalyasmaa & Alina I. Stepanova & Stanislav A. Eroshenko & Pavel V. Matrenin, 2023. "Review of the Digital Twin Technology Applications for Electrical Equipment Lifecycle Management," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    3. Namita Kumari & Ankush Sharma & Binh Tran & Naveen Chilamkurti & Damminda Alahakoon, 2023. "A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced," Energies, MDPI, vol. 16(14), pages 1-19, July.
    4. Amjad Almusaed & Ibrahim Yitmen, 2023. "Architectural Reply for Smart Building Design Concepts Based on Artificial Intelligence Simulation Models and Digital Twins," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    5. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    6. Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
    7. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    8. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    9. Carlotta Tubeuf & Felix Birkelbach & Anton Maly & René Hofmann, 2023. "Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform," Energies, MDPI, vol. 16(4), pages 1-10, February.
    10. Eslami, Ahmadreza & Negnevitsky, Michael & Franklin, Evan & Lyden, Sarah, 2022. "Review of AI applications in harmonic analysis in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    11. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    12. Gian Marco Paldino & Fabrizio De Caro & Jacopo De Stefani & Alfredo Vaccaro & Domenico Villacci & Gianluca Bontempi, 2022. "A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines," Energies, MDPI, vol. 15(6), pages 1-17, March.
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