IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v374y2024ics0306261924013540.html
   My bibliography  Save this article

Flexibility enhancement of urban energy systems through coordinated space heating aggregation of numerous buildings

Author

Listed:
  • Dong, Zihang
  • Zhang, Xi
  • Zhang, Linan
  • Giannelos, Spyros
  • Strbac, Goran

Abstract

Due to the intrinsic flexibility of thermal energy in buildings, collective space heating can potentially offer significant benefits to urban energy systems. In this context, this study introduces a novel thermal energy normalisation approach to capturing the collective indoor temperature dynamics of clustered buildings. The feasible operational region of the normalised equivalent building thermal energy model is derived from individual building parameters and temperature requirements through the constraint-tightening technique. By employing the thermal energy normalisation approach, the aggregate flexibility of building populations is quantified and integrated into the energy management optimisation problem for the urban energy system operator. To associate the response of the normalised equivalent building model and individual building models, a deadbeat controller is designed to synchronise the energy levels and realise robust operation for buildings. Through a series of case studies, the simulation results demonstrate that the proposed preheating coordination strategy can facilitate energy savings for energy system operation, cost-effectively perform congestion management to ensure no local network is overloaded, and ensure the satisfaction of indoor temperature requirements.

Suggested Citation

  • Dong, Zihang & Zhang, Xi & Zhang, Linan & Giannelos, Spyros & Strbac, Goran, 2024. "Flexibility enhancement of urban energy systems through coordinated space heating aggregation of numerous buildings," Applied Energy, Elsevier, vol. 374(C).
  • Handle: RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013540
    DOI: 10.1016/j.apenergy.2024.123971
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924013540
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.123971?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Heinen, Steve & Burke, Daniel & O'Malley, Mark, 2016. "Electricity, gas, heat integration via residential hybrid heating technologies – An investment model assessment," Energy, Elsevier, vol. 109(C), pages 906-919.
    2. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    3. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    4. Qiu, Haifeng & Vinod, Ashwin & Lu, Shuai & Gooi, Hoay Beng & Pan, Guangsheng & Zhang, Suhan & Veerasamy, Veerapandiyan, 2023. "Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling," Applied Energy, Elsevier, vol. 350(C).
    5. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    6. Dong, Zihang & Zhang, Xi & Li, Yijun & Strbac, Goran, 2023. "Values of coordinated residential space heating in demand response provision," Applied Energy, Elsevier, vol. 330(PB).
    7. Jin, Xiaolong & Wu, Jianzhong & Mu, Yunfei & Wang, Mingshen & Xu, Xiandong & Jia, Hongjie, 2017. "Hierarchical microgrid energy management in an office building," Applied Energy, Elsevier, vol. 208(C), pages 480-494.
    8. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    9. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    10. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    11. Emrah Öztürk & Klaus Rheinberger & Timm Faulwasser & Karl Worthmann & Markus Preißinger, 2022. "Aggregation of Demand-Side Flexibilities: A Comparative Study of Approximation Algorithms," Energies, MDPI, vol. 15(7), pages 1-25, March.
    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. Dong, Zihang & Zhang, Xi & Li, Yijun & Strbac, Goran, 2023. "Values of coordinated residential space heating in demand response provision," Applied Energy, Elsevier, vol. 330(PB).
    2. Huang, Sen & Lin, Yashen & Chinde, Venkatesh & Ma, Xu & Lian, Jianming, 2021. "Simulation-based performance evaluation of model predictive control for building energy systems," Applied Energy, Elsevier, vol. 281(C).
    3. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    4. Dong, Zihang & Zhang, Xi & Strbac, Goran, 2021. "Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems," Energy, Elsevier, vol. 237(C).
    5. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    7. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    8. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    9. Maier, Laura & Schönegge, Marius & Henn, Sarah & Hering, Dominik & Müller, Dirk, 2022. "Assessing mixed-integer-based heat pump modeling approaches for model predictive control applications in buildings," Applied Energy, Elsevier, vol. 326(C).
    10. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2021. "Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling," Energies, MDPI, vol. 14(2), pages 1-30, January.
    11. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    12. Hussain, Syed Asad & Huang, Gongsheng & Yuen, Richard Kwok Kit & Wang, Wei, 2020. "Adaptive regression model-based real-time optimal control of central air-conditioning systems," Applied Energy, Elsevier, vol. 276(C).
    13. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    14. Sha, Le & Jiang, Ziwei & Sun, Hejiang, 2023. "A control strategy of heating system based on adaptive model predictive control," Energy, Elsevier, vol. 273(C).
    15. Joe, Jaewan & Im, Piljae & Cui, Borui & Dong, Jin, 2023. "Model-based predictive control of multi-zone commercial building with a lumped building modelling approach," Energy, Elsevier, vol. 263(PA).
    16. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
    17. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    18. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    20. Wang, Jiangjiang & Deng, Hongda & Qi, Xiaoling, 2022. "Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks," Energy, Elsevier, vol. 261(PA).

    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:appene:v:374:y:2024:i:c:s0306261924013540. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.