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Event-triggered security-constrained energy management scheme on shared transmission systems for renewable fuels and refined oil: Implementation and field tests in South China

Author

Listed:
  • Wang, Shengshi
  • Fang, Jiakun
  • Wu, Jianzhong
  • Liang, Yongtu
  • Ai, Xiaomeng
  • Cui, Shichang
  • Liu, Jingguan
  • Zhou, Yue
  • Gan, Wei
  • Li, Miao
  • Zhao, Songli
  • Wen, Jinyu

Abstract

This paper proposes an event-triggered security-constrained energy management scheme to accomplish digitalization and secure energy conservation in the emerging shared transmission systems for renewable fuels and refined oil (STS-RRs) during the energy transition. Specifically, a practical energy management model for STS-RRs, considering batch migration processes and multiple practical factors, is firstly proposed. Then, based on this model, the event-triggered optimal coordinated operation is introduced, leveraging on-site data measurements to achieve real-time energy management. In addition, a tailored coordination method is explored for optimal distributed dispatch of STS-RRs. To support secure operation, the nonlinear autoregressive exogenous network-based parameter estimator is also proposed, which adapts to the model and event-triggered operational methods with ultra-high accuracy. Synthetically, a digital twin-fusion smart energy supervision platform is implemented to simulate the actual system, to collect and store field data stably, to validate the proposed methodologies, and to evaluate the system efficiency. Simulations and field tests on real-world STS-RRs in South China are carried out, where secure operation is guaranteed. The results highlight a high fidelity of the digital twin, with practical modeling and extra-small mean absolute error for the proposed estimator, less than 0.045 MPa. Notably, the proposed scheme achieves a 3.37 % energy-saving rate in practice. This can lead to ten million kWh of electrical energy consumption reduction annually, equivalent to 6449.2 tons of carbon dioxide reduction for the studied STS-RRs.

Suggested Citation

  • Wang, Shengshi & Fang, Jiakun & Wu, Jianzhong & Liang, Yongtu & Ai, Xiaomeng & Cui, Shichang & Liu, Jingguan & Zhou, Yue & Gan, Wei & Li, Miao & Zhao, Songli & Wen, Jinyu, 2025. "Event-triggered security-constrained energy management scheme on shared transmission systems for renewable fuels and refined oil: Implementation and field tests in South China," Applied Energy, Elsevier, vol. 389(C).
  • Handle: RePEc:eee:appene:v:389:y:2025:i:c:s030626192500457x
    DOI: 10.1016/j.apenergy.2025.125727
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    1. Xie, Jiahang & Yang, Rufan & Hui, Shu-Yuen Ron & Nguyen, Hung D., 2024. "Dual Digital Twin: Cloud–edge collaboration with Lyapunov-based incremental learning in EV batteries," Applied Energy, Elsevier, vol. 355(C).
    2. Saikia, Pranaynil & Bastida, Héctor & Ugalde-Loo, Carlos E., 2024. "An effective predictor of the dynamic operation of latent heat thermal energy storage units based on a non-linear autoregressive network with exogenous inputs," Applied Energy, Elsevier, vol. 360(C).
    3. Tu, Renfu & Liu, Chunying & Shao, Qi & Liao, Qi & Qiu, Rui & Liang, Yongtu, 2024. "Pipeline sharing: Optimal design of downstream green ammonia supply systems integrating with multi-product pipelines," Renewable Energy, Elsevier, vol. 223(C).
    4. Li, Zhuochao & Guo, Yi & Wang, Bohong & Yan, Yamin & Liang, Yongtu & Mikulčić, Hrvoje, 2024. "Two-stage optimization model for scheduling multiproduct pipeline network with multi-source and multi-terminal," Energy, Elsevier, vol. 306(C).
    5. Zhang, Bo & Xu, Ning & Zhang, Haoran & Qiu, Rui & Wei, Xuemei & Wang, Zhuo & Liang, Yongtu, 2024. "Influence of hydrogen blending on the operation of natural gas pipeline network considering the compressor power optimization," Applied Energy, Elsevier, vol. 358(C).
    6. Diz, Sergio de López & López, Roberto Martín & Sánchez, Francisco Javier Rodríguez & Llerena, Edel Díaz & Peña, Emilio José Bueno, 2023. "A real-time digital twin approach on three-phase power converters applied to condition monitoring," Applied Energy, Elsevier, vol. 334(C).
    7. Shengshi Wang & Lianyong Zuo & Miao Li & Qiao Wang & Xizhen Xue & Qicong Liu & Shuai Jiang & Jian Wang & Xitong Duan, 2021. "The Data-Driven Modeling of Pressure Loss in Multi-Batch Refined Oil Pipelines with Drag Reducer Using Long Short-Term Memory (LSTM) Network," Energies, MDPI, vol. 14(18), pages 1-25, September.
    8. 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).
    9. Chunyu Zhang & Wenge Zeng, 2024. "RETRACTED ARTICLE: Evaluating the Construction of a Digital Supervision Platform for Digital Trade Systems: a Multilateral Perspective," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 12503-12534, September.
    10. Lei, Xiao & Shen, Z.Y. & Štreimikienė, Dalia & Baležentis, Tomas & Wang, Guang & Mu, Yunguo, 2024. "Digitalization and sustainable development: Evidence from OECD countries," Applied Energy, Elsevier, vol. 357(C).
    11. Kasper, Lukas & Schwarzmayr, Paul & Birkelbach, Felix & Javernik, Florian & Schwaiger, Michael & Hofmann, René, 2024. "A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation," Applied Energy, Elsevier, vol. 353(PB).
    12. Wang, Shengshi & Fang, Jiakun & Wu, Jianzhong & Ai, Xiaomeng & Cui, Shichang & Zhou, Yue & Gan, Wei & Xue, Xizhen & Huang, Danji & Zhang, Hongyu & Wen, Jinyu, 2025. "Learning-based spatially-cascaded distributed coordination of shared transmission systems for renewable fuels and refined oil with quasi-optimality preservation under uncertainty," Applied Energy, Elsevier, vol. 381(C).
    13. Granacher, Julia & Nguyen, Tuong-Van & Castro-Amoedo, Rafael & Maréchal, François, 2022. "Overcoming decision paralysis—A digital twin for decision making in energy system design," Applied Energy, Elsevier, vol. 306(PA).
    14. Li, Zhengbing & Liang, Yongtu & Ni, Weilong & Liao, Qi & Xu, Ning & Li, Lichao & Zheng, Jianqin & Zhang, Haoran, 2022. "Pipesharing: economic-environmental benefits from transporting biofuels through multiproduct pipelines," Applied Energy, Elsevier, vol. 311(C).
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