IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i21p5342-d1507745.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/21/5342/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/21/5342/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sri Nikhil Gupta Gourisetti & Sraddhanjoli Bhadra & David Jonathan Sebastian-Cardenas & Md Touhiduzzaman & Osman Ahmed, 2023. "A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications," Energies, MDPI, vol. 16(13), pages 1-58, June.
    3. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Zeli Ye & Wentao Huang & Jinfeng Huang & Jun He & Chengxi Li & Yan Feng, 2023. "Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models," Energies, MDPI, vol. 16(3), pages 1-22, January.
    5. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    6. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    7. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
    8. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    9. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad & Jonn Are Myhren, 2024. "Construction 5.0 and Sustainable Neuro-Responsive Habitats: Integrating the Brain–Computer Interface and Building Information Modeling in Smart Residential Spaces," Sustainability, MDPI, vol. 16(21), pages 1-30, October.
    10. Liu, Shuwei & Tian, Jianyan & Ji, Zhengxiong & Dai, Yuanyuan & Guo, Hengkuan & Yang, Shengqiang, 2024. "Research on multi-digital twin and its application in wind power forecasting," Energy, Elsevier, vol. 292(C).
    11. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    12. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    13. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    14. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Fernández-Lobato, Lázuli & Jurado, Francisco, 2023. "Robust energy management in isolated microgrids with hydrogen storage and demand response," Applied Energy, Elsevier, vol. 345(C).
    15. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    16. Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    17. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    18. Eduardo Gómez-Luna & John E. Candelo-Becerra & Juan C. Vasquez, 2023. "A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks," Energies, MDPI, vol. 16(14), pages 1-23, July.
    19. Suryakiran, B.V. & Nizami, Sohrab & Verma, Ashu & Saha, Tapan Kumar & Mishra, Sukumar, 2023. "A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network," Energy, Elsevier, vol. 282(C).
    20. Li, Ding & Zhang, Yufei & Yang, Zheng & Jin, Yaohui & Xu, Yanyan, 2024. "Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder," Applied Energy, Elsevier, vol. 353(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5342-:d:1507745. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.