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Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

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  • Hyang-A Park

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
    The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Gilsung Byeon

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Wanbin Son

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Hyung-Chul Jo

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Jongyul Kim

    (Digital Energy System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Sungshin Kim

    (The School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

Abstract

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5504-:d:431995
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    References listed on IDEAS

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    1. 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.
    2. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
    9. Juan R. Lopez & Jose de Jesus Camacho & Pedro Ponce & Brian MacCleery & Arturo Molina, 2022. "A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network," Energies, MDPI, vol. 15(19), pages 1-25, October.
    10. 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.
    11. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    12. 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.
    13. Zhen Huang & Xuechun Xiao & Yuan Gao & Yonghong Xia & Tomislav Dragičević & Pat Wheeler, 2023. "Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications," Energies, MDPI, vol. 16(17), pages 1-26, August.
    14. 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).
    15. Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.

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