IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v168y2016icp159-170.html
   My bibliography  Save this article

Demand-response in building heating systems: A Model Predictive Control approach

Author

Listed:
  • Bianchini, Gianni
  • Casini, Marco
  • Vicino, Antonio
  • Zarrilli, Donato

Abstract

In this paper we consider the problem of optimizing the operation of a building heating system under the hypothesis that the building is included as an active consumer in a demand response program. Demand response requests to the building operational system come from an external market player or a grid operator. Requests assume the form of price–volume signals specifying a maximum volume of energy to be consumed during a given time slot and a monetary reward assigned to the participant in case it fulfills the conditions. A receding horizon control approach is adopted for the minimization of the energy bill, by exploiting a simplified model of the building. Since the resulting optimization problem is a mixed integer linear program which turns out to be manageable only for buildings with very few zones, a heuristics is devised to make the algorithm applicable to realistic size problems as well. The derived control law is tested on the realistic simulator EnergyPlus to evaluate pros and cons of the proposed algorithm. The performance of the suboptimal control law is evaluated on small- and large-scale test cases.

Suggested Citation

  • Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    • Handle: RePEc:eee:appene:v:168:y:2016:i:c:p:159-170
    DOI: 10.1016/j.apenergy.2016.01.088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916300757
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.01.088?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. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    2. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    3. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    4. Cardell, J.B. & Anderson, C.L., 2015. "Targeting existing power plants: EPA emission reduction with wind and demand response," Energy Policy, Elsevier, vol. 80(C), pages 11-23.
    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. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    2. Gokula Manikandan Senthil Kumar & Sunliang Cao, 2021. "State-of-the-Art Review of Positive Energy Building and Community Systems," Energies, MDPI, vol. 14(16), pages 1-54, August.
    3. Fu, Yangyang & Han, Xu & Baker, Kyri & Zuo, Wangda, 2020. "Assessments of data centers for provision of frequency regulation," Applied Energy, Elsevier, vol. 277(C).
    4. 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.
    5. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    6. Wei Wei & Yusong Guo & Kai Hou & Kai Yuan & Yi Song & Hongjie Jia & Chongbo Sun, 2021. "Distributed Thermal Energy Storage Configuration of an Urban Electric and Heat Integrated Energy System Considering Medium Temperature Characteristics," Energies, MDPI, vol. 14(10), pages 1-34, May.
    7. Nolan, Sheila & Neu, Olivier & O’Malley, Mark, 2017. "Capacity value estimation of a load-shifting resource using a coupled building and power system model," Applied Energy, Elsevier, vol. 192(C), pages 71-82.
    8. Min, C.G. & Park, J.K. & Hur, D. & Kim, M.K., 2016. "A risk evaluation method for ramping capability shortage in power systems," Energy, Elsevier, vol. 113(C), pages 1316-1324.
    9. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    10. Chen, Yuxin & Sun, Yongjun & Yang, Jinling & Tan, Jiaqi & Liu, Yang & Gao, Dian-ce, 2024. "Demand response with PCM-based pipe-embedded wall in commercial buildings: Combined passive and active energy storage in envelopes," Energy, Elsevier, vol. 308(C).
    11. Topi Rasku & Juha Kiviluoma, 2018. "A Comparison of Widespread Flexible Residential Electric Heating and Energy Efficiency in a Future Nordic Power System," Energies, MDPI, vol. 12(1), pages 1-27, December.
    12. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    13. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    14. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    15. Patteeuw, Dieter & Reynders, Glenn & Bruninx, Kenneth & Protopapadaki, Christina & Delarue, Erik & D’haeseleer, William & Saelens, Dirk & Helsen, Lieve, 2015. "CO2-abatement cost of residential heat pumps with active demand response: demand- and supply-side effects," Applied Energy, Elsevier, vol. 156(C), pages 490-501.
    16. Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    17. Hyun, Minwoo & Kim, Yeong Jae & Eom, Jiyong, 2020. "Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment," Energy Policy, Elsevier, vol. 147(C).
    18. Xinan Zhang & Ruigang Wang & Jie Bao, 2018. "A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids," Mathematics, MDPI, vol. 6(4), pages 1-21, April.
    19. Chiara Magni & Alessia Arteconi & Konstantinos Kavvadias & Sylvain Quoilin, 2020. "Modelling the Integration of Residential Heat Demand and Demand Response in Power Systems with High Shares of Renewables," Energies, MDPI, vol. 13(24), pages 1-19, December.
    20. Finck, Christian & Li, Rongling & Kramer, Rick & Zeiler, Wim, 2018. "Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems," Applied Energy, Elsevier, vol. 209(C), pages 409-425.

    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:168:y:2016:i:c:p:159-170. 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.