IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224037861.html

Exploiting geospatial shifting flexibility of building energy use for urban multi-energy system operation

Author

Listed:
  • Liu, Pengxiang
  • Wu, Zhi
  • Zhang, Zijun
  • Gu, Wei
  • Sun, Qirun
  • Qiu, Haifeng

Abstract

As one of the major energy consumers and greenhouse gas emitters, cities are at the center of global energy transition. For city managers, the energy consumption in the building sector is an important flexibility resource that can be utilized to improve the operation of urban multi-energy systems. Although demand response strategies have been widely studied for the temporal shifting of building energy use, the potential of spatial shifting flexibility is still lack of exploration. To this end, this paper proposes a novel approach to estimate and utilize the geospatial load shifting flexibility at the city scale. At the estimation stage, a methods is developed to calculate urban building energy consumption using publicly available databases and simulation tools, and the spatial distribution of supply side (energy infrastructures) and demand side (buildings) is analyzed through geographical information system. In the utilization part, a novel optimal energy flow model is proposed, where the geospatial shifting of load demand among different energy suppliers is achieved by topology reconstruction and equipment switching. To demonstrate the effectiveness and superiority of the proposed method, a real-world case study is conducted. The results show that the proposed approach can effectively exploit the spatial shifting flexibility of load demand, and the operation indicators of urban multi-energy systems such as total operating cost and maximum available capacity have also been improved.

Suggested Citation

  • Liu, Pengxiang & Wu, Zhi & Zhang, Zijun & Gu, Wei & Sun, Qirun & Qiu, Haifeng, 2024. "Exploiting geospatial shifting flexibility of building energy use for urban multi-energy system operation," Energy, Elsevier, vol. 313(C).
  • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037861
    DOI: 10.1016/j.energy.2024.134008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224037861
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.134008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Benjamin Herfort & Sven Lautenbach & João Porto de Albuquerque & Jennings Anderson & Alexander Zipf, 2023. "A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Perera, A.T.D. & Javanroodi, Kavan & Nik, Vahid M., 2021. "Climate resilient interconnected infrastructure: Co-optimization of energy systems and urban morphology," Applied Energy, Elsevier, vol. 285(C).
    3. A. T. D. Perera & Kavan Javanroodi & Dasaraden Mauree & Vahid M. Nik & Pietro Florio & Tianzhen Hong & Deliang Chen, 2023. "Challenges resulting from urban density and climate change for the EU energy transition," Nature Energy, Nature, vol. 8(4), pages 397-412, April.
    4. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    5. Wang, Han & Hou, Kai & Zhao, Junbo & Yu, Xiaodan & Jia, Hongjie & Mu, Yunfei, 2022. "Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters," Applied Energy, Elsevier, vol. 315(C).
    6. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Yeganefar, Ali & Amin-Naseri, Mohammad Reza & Sheikh-El-Eslami, Mohammad Kazem, 2020. "Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources," Applied Energy, Elsevier, vol. 272(C).
    8. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    9. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    10. Zhou, Xiao & Huang, Zhou & Scheuer, Bronte & Wang, Han & Zhou, Guoqing & Liu, Yu, 2023. "High-resolution estimation of building energy consumption at the city level," Energy, Elsevier, vol. 275(C).
    11. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    Full references (including those not matched with items on IDEAS)

    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. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    2. Jin, Xiaoyu & Xiao, Fu & Zhang, Chong & Chen, Zhijie, 2022. "Semi-supervised learning based framework for urban level building electricity consumption prediction," Applied Energy, Elsevier, vol. 328(C).
    3. Zhou, Yizhou & Li, Xiang & Han, Haiteng & Wei, Zhinong & Zang, Haixiang & Sun, Guoqiang & Chen, Sheng, 2024. "Resilience-oriented planning of integrated electricity and heat systems: A stochastic distributionally robust optimization approach," Applied Energy, Elsevier, vol. 353(PA).
    4. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    5. Korsavi, Sepideh Sadat & Azari, Rahman & Iulo, Lisa D. & Mahdavi, Mehrdad, 2025. "Determinants of U.S. residential energy consumption at national and state levels: Policy implications," Energy Policy, Elsevier, vol. 202(C).
    6. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    7. Moshari, Amirhosein & Javanroodi, Kavan & Nik, Vahid M., 2026. "Real-world deployment of model-free reinforcement learning for energy control in district heating systems: Enhancing flexibility across neighboring buildings," Applied Energy, Elsevier, vol. 402(PB).
    8. Ishwar D Ramnarine & Tarek A Sherif & Abdulrahman H Alorabi & Haya Helmy & Takahiro Yoshida & Akito Murayama & Perry P-J Yang, 2025. "Urban revitalization pathways toward zero carbon emissions through systems architecting of urban digital twins," Environment and Planning B, , vol. 52(8), pages 1920-1948, October.
    9. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    10. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Nutkiewicz, Alex & Mastrucci, Alessio & Rao, Narasimha D. & Jain, Rishee K., 2022. "Cool roofs can mitigate cooling energy demand for informal settlement dwellers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    12. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
    13. Tahir Mahmood & Muhammad Asif, 2024. "Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms," Energies, MDPI, vol. 17(19), pages 1-17, October.
    14. Zhuohong Li & Linxin Li & Ting Hu & Mofan Cheng & Wei He & Tong Qiu & Liangpei Zhang & Hongyan Zhang, 2026. "Satellite mapping of every building’s function in urban China reveals deep built environment disparities," Nature Communications, Nature, vol. 17(1), pages 1-14, December.
    15. Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.
    16. Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(C).
    17. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    18. Helistö, Niina & Kiviluoma, Juha & Morales-España, Germán & O’Dwyer, Ciara, 2021. "Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar," Applied Energy, Elsevier, vol. 290(C).
    19. Shatha Hussein Al Rawashdeh & Shatha Aser Aldala’in & Esra’a Alaeed & Zubeida Aladwan & Teh Sabariah Binti Abd Manan, 2025. "GIS-Driven Approach for Selecting Optimal University Locations," Sustainability, MDPI, vol. 17(13), pages 1-22, June.
    20. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:energy:v:313:y:2024:i:c:s0360544224037861. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.