Skip to main content
Log in

Day-ahead optimal scheduling for integrated energy system considering dynamic pipe network delay

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

In the integrated energy system, the transmission delay of the cooling and heating pipeline network is long, which has an essential impact on the optimal scheduling of the integrated energy system. In this paper, a day-ahead optimal scheduling method of integrated energy systems considering the dynamic delay of the pipeline network is proposed. The method takes into account the impact of pipe network delay on day-ahead optimal scheduling of the system, solves the problem of considerable time lag of pipe network in the process of optimal scheduling of the system, and improves the real-time, safety, and economy of the system scheduling. The method uses the nodal method to establish a dynamic model of the pipeline network system. It analyses the impact of dynamic characteristics on the system equipment’s output and system scheduling. A mixed integer linear programming method is used to establish an optimal scheduling model for the integrated energy system, and the system is optimized to maximize the daily revenue in combination with time-of-day tariffs. Finally, the proposed model is validated based on the energy station data in a park in Wuhan. The results show that the optimal scheduling strategy of the integrated energy system that considers the dynamic time-delay characteristics of the pipeline network can effectively improve the system’s economy, the system scheduling’s real-time performance, and the system operation’s safety.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Zhan W (2024) Innovative strategy and development of ecological environment management based on the background of new development concept. Heilongjiang Environ Bulletin 37(02):54–56

    Google Scholar 

  2. Al-Wesabi I, Fang Z, Bosah CP et al (2022) A review of Yemen’s current energy situation, challenges, strategies, and prospects for using renewable energy systems. Environ Sci Pollut Res 29(36):53907–53933

    Article  Google Scholar 

  3. Xingyu D, Xianzhang W, Meijuan G () All-out efforts to promote ecological environmental governance capacity and governance system modernization. Inner Mongolia Legal News (Han), 2024-01-30(003)

  4. Xu Z, Zhang F et al (2020) Energy storage development trends and key issues for future energy system modeling. IOP Conf Series: Earth Environ Sci 526(1):012114

    Google Scholar 

  5. Han Y, Hao Wu, Geng Z, Zhu Q (2020) Review: Energy efficiency evaluation of complex petrochemical industries. Energy 203:117893

    Article  Google Scholar 

  6. Lamnatou C, Cristofari C, Chemisana D et al (2024) Renewable energy sources as a catalyst for energy transition: technological innovations and an example of the energy transition in France. Renew Energy 221:119600

    Article  Google Scholar 

  7. Tang H, Wang S, Li H (2021) Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: state-of-the-art and future perspective. Energy 219:119598

    Article  Google Scholar 

  8. Bing G, Guoyi Li, Xiaohui M et al (2024) Operation optimization of cogeneration system with layered energy storage. Chem Eng 52(01):88–94

    Google Scholar 

  9. Jinxia G, Chenzhou L, Hui K (2023) Optimal scheduling strategy for integrated energy system based on improved depth deterministic policy gradient algorithm. Modn Electr Power. https://doi.org/10.19725/j.cnki.1007-2322.2023.0026

    Article  Google Scholar 

  10. Jian XU, Jia HU, Siyang LIAO et al (2021) Coordinated optimization of integrated energy system considering dynamic characteristics of network and integrated demand response. Autom Electr Power Syst 45(12):40–48

    Google Scholar 

  11. Hejun Y, Jingyin W, Yinghao M et al (2024) Joint optimal allocation of energy storage systems for multi-area grids considering power mutualisation. Electr Power Constr 45(02):79–89

    Google Scholar 

  12. Yu-Wen P, Yong-Wang Z, Can-Cheng X et al (2023) Research on low-carbon economic dispatch method of integrated energy system considering demand-side response. Heilongjiang Power 45(06):498–506. https://doi.org/10.13625/j.cnki.hljep.2023.06.005

    Article  Google Scholar 

  13. Shuaidong L, Song H, Na R et al. (2024) A multi-objective optimal scheduling method for electricity-gas-heat integrated energy system taking into account the efficiency[J/OL]. Grid Technology,110[20240202].http://kns.cnki.net/kcms/detail/11.2410.TM.20240111.0931.002.html.

  14. Liu Z, Zhang S, Wang W, Cheng J, Liu T, Chen Q (2021) Optimal dispatching of CCHP-based Microgrid under island operation mode. In: 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 2021, pp. 202–206

  15. Xiaoting D, Fei F (2020) Optimal design of CCHP microgrid based on multi-objective genetic algorithm. In: 2020 5th International conference on automation, control and robotics engineering (CACRE), pp. 478–482

  16. Chenshan W, Chaoxian L, Peng L, Shuquan L (2019) Multiple time-scale optimal scheduling of community integrated energy system based on model predictive control. Proceed CSEE 39(23):6791–6803+7093

    Google Scholar 

  17. Zhou X, Zheng L, Yang L et al (2020) Day-ahead optimal dispatch of an integrated energy system considering multiple uncertainty. Power Syst Technol 44(07):2466–2473

    Google Scholar 

  18. Chen M, Sun Y, Xie Z (2022) The multi-time-scale management optimization for park integrated energy system based on the bi-layer deep reinforcement learning. Trans China Electrotech Soci. https://doi.org/10.19595/j.cnki.1000-6753.tces.211879

    Article  Google Scholar 

  19. Deng B, Fang J, Hui Q et al (2019) Optimal scheduling for combined district heating and power systems using subsidy strategies. CESS J Power Energy Syst 5(3):399–408

    Google Scholar 

  20. Chen Z, Zhang Y, Tang W et al (2019) Generic modelling and optimal day-ahead dispatch of micro-energy system considering the price-based integrated demand response. Energy. https://doi.org/10.1016/j.energy.2019.04.004

    Article  Google Scholar 

  21. Li Z, Zhang Z (2021) Day-ahead and intra-day optimal scheduling of integrated energy system considering uncertainty of source & load power forecasting. Energies 14(9):2539

    Article  Google Scholar 

  22. Li Z, Wu W, Shahidehpour M, Wang J, Zhang B (2016) Combined heat and power dispatch considering pipeline energy storage of district heating network. IEEE Trans Sustain Energy 7(1):12–22

    Article  Google Scholar 

  23. Wanlu W, Li Y, Lei W et al (2018) Optimal dispatch of integrated electricity-heat energy system considering heat storage characteristics of heating network. Autom Electr Power Syst 42(21):45–52

    Google Scholar 

  24. Dong S, Wang C, Xu S et al (2018) Day-ahead optimal scheduling of electricity-gas-heat integrated energy system considering dynamic characteristics of networks. Autom Electr Power Syst 42(13):12–19

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

ZW responsible for the supervision and coordination of the overall research, ensuring the integrity and accuracy of the research, wrote the paper and designed the experiments; ZL analyzed the data; HY collected the data.

Corresponding author

Correspondence to Zining Wu.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Human and animal rights

The study did not include any experiments with animals or humans.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z., Li, Z. & Yang, H. Day-ahead optimal scheduling for integrated energy system considering dynamic pipe network delay. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02367-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02367-y

Keywords

Navigation