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Refined multi-time scale optimal scheduling of dynamic integrated energy system based on superposition of energy flow response

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  • Wang, Mengxue
  • Zhao, Haoran
  • Liu, Chunyang
  • Ma, Dazhong
  • Jiang, Yibao
  • Yang, Futao

Abstract

Accurately capturing dynamic energy flow in an integrated energy system (IES) is time-consuming and cannot meet the demands of short-time scale dispatching. It also brings other challenges, such as scheduling decision delays caused by transmission inertia and maintaining state continuity between scheduling intervals. However, the neglect of state variables’ dynamic constraints results in the system being overloaded or out of limit, which seriously affects the secure and stable operation of IES. This paper proposes an efficient and accurate multi-time scale dynamic optimal scheduling method that leverages the superposition characteristics of energy flows, considering the network dynamics, topologies, and variable constraints, which have never been factored in short-time scale dynamic optimization for its minute-level time-consuming. First, a linear dynamic energy flow constraint is derived by transforming the energy flow model from the Laplace domain into a discrete time-domain mapping function, which reduces solve time from minutes to seconds without any loss of detail dynamics. Next, a multi-time scale dispatching framework that incorporates a double-layer intra-day rolling optimization is introduced, accounting for the influence of system inertia on scheduling decisions. Finally, the impact of decisions as the base value for subsequent optimizations is superimposed on the variations of decision variables, to ensure consistency in short-time scale scheduling. Comprehensive tests were conducted to validate the proposed dynamic optimization model, which performs 24-hour scheduling with a 100-second interval in just 2.75 s, yielding an error of 6.69e−03%. Compared to steady-state scheduling, the wind curtailment of the proposed multi-time-scale dynamic dispatch method is reduced by 94.6%, while operating costs decrease by 4.03%. Furthermore, the calculation efficiency increases by a factor of 59.1 compared to traditional methods, all while ensuring the system operates safely throughout the day.

Suggested Citation

  • Wang, Mengxue & Zhao, Haoran & Liu, Chunyang & Ma, Dazhong & Jiang, Yibao & Yang, Futao, 2025. "Refined multi-time scale optimal scheduling of dynamic integrated energy system based on superposition of energy flow response," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024553
    DOI: 10.1016/j.apenergy.2024.125071
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    References listed on IDEAS

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    1. Wang, L.L. & Xian, R.C. & Jiao, P.H. & Chen, J.J. & Chen, Y. & Liu, H.G., 2024. "Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading," Renewable Energy, Elsevier, vol. 228(C).
    2. Fan, Guangyao & Yu, Binbin & Sun, Bo & Li, Fan, 2024. "Multi-time-space scale optimization for a hydrogen-based regional multi-energy system," Applied Energy, Elsevier, vol. 371(C).
    3. Chen, Qun & Meng, Nan & He, Ke-Lun & Ma, Huan & Gou, Xing, 2024. "Multi-time scale operation optimization of integrated power and thermal system considering load disturbance," Energy, Elsevier, vol. 302(C).
    4. Ma, Xin & Peng, Bo & Ma, Xiangxue & Tian, Changbin & Yan, Yi, 2023. "Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting," Energy, Elsevier, vol. 283(C).
    5. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2024. "Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity," Energy, Elsevier, vol. 293(C).
    6. 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).
    7. Jiawei Wang & Aidong Zeng & Yaheng Wan, 2023. "Multi-Time-Scale Optimal Scheduling of Integrated Energy System Considering Transmission Delay and Heat Storage of Heating Network," Sustainability, MDPI, vol. 15(19), pages 1-26, September.
    8. Yang, Jingwei & Zhang, Ning & Botterud, Audun & Kang, Chongqing, 2020. "Situation awareness of electricity-gas coupled systems with a multi-port equivalent gas network model," Applied Energy, Elsevier, vol. 258(C).
    9. Li, Peng & Wang, Zixuan & Wang, Jiahao & Guo, Tianyu & Yin, Yunxing, 2021. "A multi-time-space scale optimal operation strategy for a distributed integrated energy system," Applied Energy, Elsevier, vol. 289(C).
    10. Chen, Binbin & Wu, Wenchuan & Guo, Qinglai & Sun, Hongbin, 2022. "An efficient optimal energy flow model for integrated energy systems based on energy circuit modeling in the frequency domain," Applied Energy, Elsevier, vol. 326(C).
    11. Liu, Zhijian & Fan, Guangyao & Meng, Xiangrui & Hu, Yubin & Wu, Di & Jin, Guangya & Li, Guiqiang, 2024. "Multi-time scale operation optimization for a near-zero energy community energy system combined with electricity-heat-hydrogen storage," Energy, Elsevier, vol. 291(C).
    12. Liu, Xinrui & Hou, Min & Sun, Siluo & Wang, Jiawei & Sun, Qiuye & Dong, Chaoyu, 2022. "Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method," Energy, Elsevier, vol. 254(PB).
    13. Shen, Weijie & Zeng, Bo & Zeng, Ming, 2023. "Multi-timescale rolling optimization dispatch method for integrated energy system with hybrid energy storage system," Energy, Elsevier, vol. 283(C).
    14. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    15. Zhang, Suhan & Gu, Wei & Lu, Hai & Qiu, Haifeng & Lu, Shuai & Wang, Dada & Liang, Junyu & Li, Wenyun, 2021. "Superposition-principle based decoupling method for energy flow calculation in district heating networks," Applied Energy, Elsevier, vol. 295(C).
    16. Chen, Yuwei & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Chen, Binbin, 2021. "Generalized phasor modeling of dynamic gas flow for integrated electricity-gas dispatch," Applied Energy, Elsevier, vol. 283(C).
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