IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v333y2023ics0306261922018190.html
   My bibliography  Save this article

Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme

Author

Listed:
  • Khatibi, Mahmood
  • Rahnama, Samira
  • Vogler-Finck, Pierre
  • Dimon Bendtsen, Jan
  • Afshari, Alireza

Abstract

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

Suggested Citation

  • Khatibi, Mahmood & Rahnama, Samira & Vogler-Finck, Pierre & Dimon Bendtsen, Jan & Afshari, Alireza, 2023. "Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018190
    DOI: 10.1016/j.apenergy.2022.120562
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922018190
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120562?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. Okur, Özge & Voulis, Nina & Heijnen, Petra & Lukszo, Zofia, 2019. "Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation," Applied Energy, Elsevier, vol. 247(C), pages 426-437.
    2. 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).
    3. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    4. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    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. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    2. Cui, Xueyuan & Liu, Shu & Ruan, Guangchun & Wang, Yi, 2024. "Data-driven aggregation of thermal dynamics within building virtual power plants," Applied Energy, Elsevier, vol. 353(PB).
    3. Engel, Jens & Schmitt, Thomas & Rodemann, Tobias & Adamy, Jürgen, 2024. "Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry," Applied Energy, Elsevier, vol. 372(C).
    4. Dai, Shuangfeng & Mansouri, Seyed Amir & Huang, Shoujun & Alharthi, Yahya Z. & Wu, Yongfei & Bagherzadeh, Leila, 2024. "A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory," Applied Energy, Elsevier, vol. 373(C).

    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. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    3. Shi, Shanrui & Miyata, Shohei & Akashi, Yasunori, 2025. "Event-driven model-based optimal demand-controlled ventilation for multizone VAV systems: Enhancing energy efficiency and indoor environmental quality," Applied Energy, Elsevier, vol. 377(PD).
    4. 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).
    5. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    6. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    7. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    8. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    9. Montuori, Lina & Alcázar-Ortega, Manuel, 2021. "Demand response strategies for the balancing of natural gas systems: Application to a local network located in The Marches (Italy)," Energy, Elsevier, vol. 225(C).
    10. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    11. Vašak, Mario & Banjac, Anita & Hure, Nikola & Novak, Hrvoje & Kovačević, Marko, 2023. "Predictive control based assessment of building demand flexibility in fixed time windows," Applied Energy, Elsevier, vol. 329(C).
    12. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
    13. Hennig, Roman J. & Ribó-Pérez, David & de Vries, Laurens J. & Tindemans, Simon H., 2022. "What is a good distribution network tariff?—Developing indicators for performance assessment," Applied Energy, Elsevier, vol. 318(C).
    14. 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.
    15. Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    16. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    17. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    18. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
    19. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    20. Schwidtal, Jan Marc & Agostini, Marco & Coppo, Massimiliano & Bignucolo, Fabio & Lorenzoni, Arturo, 2023. "Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV," Applied Energy, Elsevier, vol. 334(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:eee:appene:v:333:y:2023:i:c:s0306261922018190. 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.