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Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties

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  • You, Minglei
  • Wang, Qian
  • Sun, Hongjian
  • Castro, Iván
  • Jiang, Jing

Abstract

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012125
    DOI: 10.1016/j.apenergy.2021.117899
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    6. 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).
    7. Machado, Diogo Ortiz & Chicaiza, William D. & Escaño, Juan M. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Digital twin of an absorption chiller for solar cooling," Renewable Energy, Elsevier, vol. 208(C), pages 36-51.
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    9. 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.
    10. Fadhil Khadoum Alhousni & Firas Basim Ismail Alnaimi & Paul C. Okonkwo & Ikram Ben Belgacem & Hassan Mohamed & El Manaa Barhoumi, 2023. "Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability," Sustainability, MDPI, vol. 15(11), pages 1-13, May.
    11. 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).
    12. 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).
    13. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2022. "Multi-timescale hierarchical scheduling of an integrated energy system considering system inertia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    14. Bâra, Adela & Oprea, Simona-Vasilica, 2024. "Enabling coordination in energy communities: A Digital Twin model," Energy Policy, Elsevier, vol. 184(C).
    15. 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.
    16. 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).
    17. Zhiyuan Zhang & Zhanshan Wang, 2023. "Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network," Mathematics, MDPI, vol. 11(20), pages 1-18, October.
    18. 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.
    19. 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).
    20. 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).

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