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Energy digital twin technology for industrial energy management: Classification, challenges and future

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  • Yu, Wei
  • Patros, Panos
  • Young, Brent
  • Klinac, Elsa
  • Walmsley, Timothy Gordon

Abstract

Digitalisation of the process and energy industries through energy digital twin technology promises step-improvements in energy management and optimisation, better servicing and maintenance, energy-efficient design and evolution of existing sites, and integration with locally and regionally generated renewable energy. This systematic and critical review aims to accelerate the understanding, classification, and application of energy digital twin technology. It adds to the literature by developing an original multi-dimensional digital twin classification framework, summarising the applications of energy digital twins throughout a site's lifecycle, and constructing a proposal of how to apply the technology to industrial sites and local areas to enable a reduction in carbon and other environmental footprints. The review concludes by identifying key challenges that face uptake of energy digital twins and a framework to apply the energy digital twins.

Suggested Citation

  • Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  • Handle: RePEc:eee:rensus:v:161:y:2022:i:c:s136403212200315x
    DOI: 10.1016/j.rser.2022.112407
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    References listed on IDEAS

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    Cited by:

    1. Vytautas Bocullo & Linas Martišauskas & Ramūnas Gatautis & Otilija Vonžudaitė & Rimantas Bakas & Darius Milčius & Rytis Venčaitis & Darius Pupeikis, 2023. "A Digital Twin Approach to City Block Renovation Using RES Technologies," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    2. 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).
    3. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
    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. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    6. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    7. Vito Introna & Annalisa Santolamazza & Vittorio Cesarotti, 2024. "Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems," Energies, MDPI, vol. 17(5), pages 1-16, March.

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