IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i20p3831-d274985.html
   My bibliography  Save this article

Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads

Author

Listed:
  • Cheng Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jianyong Zheng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Sai Liu

    (State Grid Jiangsu Electric Power Co., Ltd., Maintenance Branch Company, Nanjing 211102, Jiangsu, China)

  • Yu Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Fei Mei

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yi Pan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Tian Shi

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jianzhang Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

Abstract

Multi-district integrated energy system (IES) can make full use of the complementary characteristics of district power and thermal system, and loads in different districts. It can improve the flexibility and economy of system operation, which has a good development prospect. Firstly, based on the general energy transfer model of the district heating network (DHN), the DHN system is described by the basic equations of the heating network and nodes considering the characteristics of the transmission time delay and heat loss in pipelines. A coupling model of DHN and multi-district IES is established. Secondly, the flexible demand response (FDR) model of electric and thermal loads is established. The load characteristics of each district in IES are studied. A shiftable load model based on the electric quantity balance is constructed. Considering the flexibility of the heat demand, a thermal load adjustment model based on the comfort constraint is constructed to make the thermal load elastic and controllable in time and space. Finally, a mixed integer linear programming (MILP) model for operation optimization of multi-district IES with the DHN considering the FDR of electric and thermal loads is established based on the supply and demand sides. The result shows that the proposed model makes full use of the complementary characteristics of electric and thermal loads in different districts. It realizes the coordinated distribution of thermal energy among different districts and improves the efficiency of thermal energy utilization through the DHN. FDR effectively reduces the peak-valley difference of loads. It further reduces the total operating cost by the coordinated operation of the DHN and multi-district IES.

Suggested Citation

  • Cheng Zhou & Jianyong Zheng & Sai Liu & Yu Liu & Fei Mei & Yi Pan & Tian Shi & Jianzhang Wu, 2019. "Operation Optimization of Multi-District Integrated Energy System Considering Flexible Demand Response of Electric and Thermal Loads," Energies, MDPI, vol. 12(20), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3831-:d:274985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/20/3831/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/20/3831/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    2. Ren, Hongbo & Gao, Weijun, 2010. "A MILP model for integrated plan and evaluation of distributed energy systems," Applied Energy, Elsevier, vol. 87(3), pages 1001-1014, March.
    3. Ren, Hongbo & Zhou, Weisheng & Gao, Weijun, 2012. "Optimal option of distributed energy systems for building complexes in different climate zones in China," Applied Energy, Elsevier, vol. 91(1), pages 156-165.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    2. Zhao Luo & Jinghui Wang & Ni Xiao & Linyan Yang & Weijie Zhao & Jialu Geng & Tao Lu & Mengshun Luo & Chenming Dong, 2022. "Low Carbon Economic Dispatch Optimization of Regional Integrated Energy Systems Considering Heating Network and P2G," Energies, MDPI, vol. 15(15), pages 1-14, July.
    3. Vahid Arabzadeh & Peter D. Lund, 2020. "Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)," Energies, MDPI, vol. 13(9), pages 1-17, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    2. Han, Jie & Ouyang, Leixin & Xu, Yuzhen & Zeng, Rong & Kang, Shushuo & Zhang, Guoqiang, 2016. "Current status of distributed energy system in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 288-297.
    3. Zhang, Chong & Xue, Xue & Du, Qianzhou & Luo, Yimo & Gang, Wenjie, 2019. "Study on the performance of distributed energy systems based on historical loads considering parameter uncertainties for decision making," Energy, Elsevier, vol. 176(C), pages 778-791.
    4. Fichera, Alberto & Frasca, Mattia & Volpe, Rosaria, 2017. "Complex networks for the integration of distributed energy systems in urban areas," Applied Energy, Elsevier, vol. 193(C), pages 336-345.
    5. Chuat, Arthur & Terrier, Cédric & Schnidrig, Jonas & Maréchal, François, 2024. "Identification of typical district configurations: A two-step global sensitivity analysis framework," Energy, Elsevier, vol. 296(C).
    6. Gao, Jiajia & Kang, Jing & Zhang, Chong & Gang, Wenjie, 2018. "Energy performance and operation characteristics of distributed energy systems with district cooling systems in subtropical areas under different control strategies," Energy, Elsevier, vol. 153(C), pages 849-860.
    7. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    8. Lei, Zijian & Yu, Hao & Li, Peng & Ji, Haoran & Yan, Jinyue & Song, Guanyu & Wang, Chengshan, 2024. "A compact time horizon compression method for planning community integrated energy systems with long-term energy storage," Applied Energy, Elsevier, vol. 361(C).
    9. Li, Longxi & Mu, Hailin & Gao, Weijun & Li, Miao, 2014. "Optimization and analysis of CCHP system based on energy loads coupling of residential and office buildings," Applied Energy, Elsevier, vol. 136(C), pages 206-216.
    10. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.
    11. Zachar, Michael & Daoutidis, Prodromos, 2015. "Understanding and predicting the impact of location and load on microgrid design," Energy, Elsevier, vol. 90(P1), pages 1005-1023.
    12. Li, Miao & Mu, Hailin & Li, Nan & Ma, Baoyu, 2016. "Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system," Energy, Elsevier, vol. 99(C), pages 202-220.
    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    14. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    15. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    16. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    17. Xu, Rong-Hong & Zhao, Tian & Ma, Huan & He, Ke-Lun & Lv, Hong-Kun & Guo, Xu-Tao & Chen, Qun, 2023. "Operation optimization of distributed energy systems considering nonlinear characteristics of multi-energy transport and conversion processes," Energy, Elsevier, vol. 283(C).
    18. Yanfeng Liu & Yaxing Wang & Xi Luo, 2020. "Design and Operation Optimization of Distributed Solar Energy System Based on Dynamic Operation Strategy," Energies, MDPI, vol. 14(1), pages 1-26, December.
    19. Ye, Bin & Yang, Peng & Jiang, Jingjing & Miao, Lixin & Shen, Bo & Li, Ji, 2017. "Feasibility and economic analysis of a renewable energy powered special town in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 40-50.
    20. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3831-:d:274985. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.