IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p775-d1585987.html
   My bibliography  Save this article

Applications of Electrical Load Modelling in Digital Twins of Power Systems

Author

Listed:
  • Hasith Jayasinghe

    (School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
    Blue Economy Cooperative Research Centre, Launceston, TAS 7248, Australia)

  • Kosala Gunawardane

    (School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Robert Nicholson

    (Pitt&Sherry, North Sydney, NSW 2060, Australia)

Abstract

Accurate electrical load modeling is crucial for both transient and steady-state power system studies. Although various load modeling techniques are documented in the literature, a comprehensive review of the latest advancements in these techniques is lacking. This manuscript addresses this gap by presenting a detailed review of load modeling techniques, emphasizing their applications, recent advancements, and key distinguishing characteristics. Additionally, it explores the role of Digital Twin Models (DTM) in power systems, which offers a virtual representation of the system to simulate diverse operational scenarios and inform future investment and operational decisions. The integration of load models into DTMs poses challenges, such as computational demands and microcontroller limitations, which can be alleviated by adopting advanced load modeling techniques. This work further examines the application of load modeling techniques in the design and development of DTMs for power systems, as well as strategies to enhance the performance of load models in DTM applications. Finally, the manuscript outlines future research opportunities for integrating load modeling within DTM-based power system applications.

Suggested Citation

  • Hasith Jayasinghe & Kosala Gunawardane & Robert Nicholson, 2025. "Applications of Electrical Load Modelling in Digital Twins of Power Systems," Energies, MDPI, vol. 18(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:775-:d:1585987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/775/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/775/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    3. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    4. Amir Shahcheraghian & Hatef Madani & Adrian Ilinca, 2024. "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices," Energies, MDPI, vol. 17(2), pages 1-45, January.
    5. Zhao Song & Christoph M. Hackl & Abhinav Anand & Andre Thommessen & Jonas Petzschmann & Omar Kamel & Robert Braunbehrens & Anton Kaifel & Christian Roos & Stefan Hauptmann, 2023. "Digital Twins for the Future Power System: An Overview and a Future Perspective," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    6. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    7. Wang, L.X. & Zheng, J.H. & Li, M.S. & Lin, X. & Jing, Z.X. & Wu, P.Z. & Wu, Q.H. & Zhou, X.X., 2019. "Multi-time scale dynamic analysis of integrated energy systems: An individual-based model," Applied Energy, Elsevier, vol. 237(C), pages 848-861.
    8. Sunday Adeleke Salimon & Isaiah Gbadegesin Adebayo & Gafari Abiola Adepoju & Oludamilare Bode Adewuyi, 2023. "Optimal Allocation of Distribution Static Synchronous Compensators in Distribution Networks Considering Various Load Models Using the Black Widow Optimization Algorithm," Sustainability, MDPI, vol. 15(21), pages 1-23, November.
    9. 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).
    10. Hua, Weiqi & Stephen, Bruce & Wallom, David C.H., 2023. "Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems," Applied Energy, Elsevier, vol. 342(C).
    11. Mohammad AlMuhaini & Abass Yahaya & Ahmed AlAhmed, 2023. "Distributed Generation and Load Modeling in Microgrids," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    12. Rouhani, Seyed Hossein & Su, Chun-Lien & Mobayen, Saleh & Razmjooy, Navid & Elsisi, Mahmoud, 2024. "Cyber resilience in renewable microgrids: A review of standards, challenges, and solutions," Energy, Elsevier, vol. 309(C).
    13. Yong Shi & Dong Xu & Jianhui Su & Ning Liu & Hongru Yu & Huadian Xu, 2019. "Black-Box Behavioral Modeling of Voltage and Frequency Response Characteristic for Islanded Microgrid," Energies, MDPI, vol. 12(11), pages 1-16, May.
    14. Chen, Lin & Wang, Jianxiao & Wu, Zhaoyuan & Li, Gengyin & Zhou, Ming & Li, Peng & Zhang, Yihan, 2021. "Communication reliability-restricted energy sharing strategy in active distribution networks," Applied Energy, Elsevier, vol. 282(PB).
    15. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    16. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    17. Sk. A. Shezan & Innocent Kamwa & Md. Fatin Ishraque & S. M. Muyeen & Kazi Nazmul Hasan & R. Saidur & Syed Muhammad Rizvi & Md Shafiullah & Fahad A. Al-Sulaiman, 2023. "Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    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. 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).
    2. Chisom E. Ogbogu & Jesse Thornburg & Samuel O. Okozi, 2025. "Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review," Energies, MDPI, vol. 18(4), pages 1-26, February.
    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. Asadzadeh, Seyed Mohammad & Andersen, Nils Axel, 2024. "Optimal operational planning of a bio-fuelled cogeneration plant: Integration of sparse nonlinear dynamics identification and deep reinforcement learning," Applied Energy, Elsevier, vol. 376(PA).
    5. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org, revised Feb 2025.
    6. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
    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. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    10. 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).
    11. Qin, Xin & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "A generalized quasi-dynamic model for electric-heat coupling integrated energy system with distributed energy resources," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. 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).
    13. Ergun Yukseltan & Esra Agca Aktunc & Ayse H. Bilge & Ahmet Yucekaya, 2024. "An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 96-111, March.
    14. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    15. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    16. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
    17. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    18. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    19. Shi, Qingxin & Li, Fangxing & Dong, Jin & Olama, Mohammed & Wang, Xiaofei & Winstead, Chris & Kuruganti, Teja, 2022. "Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience," Applied Energy, Elsevier, vol. 318(C).
    20. Hanaa Feleafel & Jovana Radulovic & Michel Leseure, 2024. "Should We Have Selfish Microgrids?," Energies, MDPI, vol. 17(16), pages 1-23, August.

    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:18:y:2025:i:4:p:775-:d:1585987. 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.