IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipas0306261923014654.html
   My bibliography  Save this article

Data-driven predictive control for demand side management: Theoretical and experimental results

Author

Listed:
  • Yin, Mingzhou
  • Cai, Hanmin
  • Gattiglio, Andrea
  • Khayatian, Fazel
  • Smith, Roy S.
  • Heer, Philipp

Abstract

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.

Suggested Citation

  • Yin, Mingzhou & Cai, Hanmin & Gattiglio, Andrea & Khayatian, Fazel & Smith, Roy S. & Heer, Philipp, 2024. "Data-driven predictive control for demand side management: Theoretical and experimental results," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014654
    DOI: 10.1016/j.apenergy.2023.122101
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923014654
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122101?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. Cai, Hanmin & You, Shi & Wu, Jianzhong, 2020. "Agent-based distributed demand response in district heating systems," Applied Energy, Elsevier, vol. 262(C).
    2. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    3. Cai, Hanmin & You, Shi & Wang, Jiawei & Bindner, Henrik W. & Klyapovskiy, Sergey, 2018. "Technical assessment of electric heat boosters in low-temperature district heating based on combined heat and power analysis," Energy, Elsevier, vol. 150(C), pages 938-949.
    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).
    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. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    2. Jing, Mengke & Zhang, Shujie & Fu, Lisong & Cao, Guoquan & Wang, Rui, 2023. "Reducing heat losses from aging district heating pipes by using cured-in-place pipe liners," Energy, Elsevier, vol. 273(C).
    3. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    4. Ma, Huan & Chen, Qun & Hu, Bo & Sun, Qinhan & Li, Tie & Wang, Shunjiang, 2021. "A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    5. Chicherin, Stanislav, 2020. "Methodology for analyzing operation data for optimum district heating (DH) system design: Ten-year data of Omsk, Russia," Energy, Elsevier, vol. 211(C).
    6. Stanislav Chicherin & Vladislav Mašatin & Andres Siirde & Anna Volkova, 2020. "Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy," Energies, MDPI, vol. 13(17), pages 1-15, September.
    7. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    9. Arabzadeh, Vahid & Miettinen, Panu & Kotilainen, Titta & Herranen, Pasi & Karakoc, Alp & Kummu, Matti & Rautkari, Lauri, 2023. "Urban vertical farming with a large wind power share and optimised electricity costs," Applied Energy, Elsevier, vol. 331(C).
    10. Shaoying Li & Zhongquan Qu & Zhiming Song, 2020. "A Multifunctional Combination Incubator," Energies, MDPI, vol. 13(24), pages 1-22, December.
    11. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    12. Qiu, Dawei & Ye, Yujian & Papadaskalopoulos, Dimitrios & Strbac, Goran, 2021. "Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach," Applied Energy, Elsevier, vol. 292(C).
    13. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    14. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    15. Brown, Sarah & Beausoleil-Morrison, Ian, 2023. "Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada," Applied Energy, Elsevier, vol. 349(C).
    16. Perera, A.T.D. & Wang, Z. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2021. "Towards realization of an Energy Internet: Designing distributed energy systems using game-theoretic approach," Applied Energy, Elsevier, vol. 283(C).
    17. Frölke, Linde & Sousa, Tiago & Pinson, Pierre, 2022. "A network-aware market mechanism for decentralized district heating systems," Applied Energy, Elsevier, vol. 306(PA).
    18. Leitner, Benedikt & Widl, Edmund & Gawlik, Wolfgang & Hofmann, René, 2020. "Control assessment in coupled local district heating and electrical distribution grids: Model predictive control of electric booster heaters," Energy, Elsevier, vol. 210(C).
    19. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    20. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.

    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:353:y:2024:i:pa:s0306261923014654. 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.