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

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  • 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
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    References listed on IDEAS

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    1. Cai, Hanmin & You, Shi & Wu, Jianzhong, 2020. "Agent-based distributed demand response in district heating systems," Applied Energy, Elsevier, vol. 262(C).
    2. 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.
    3. 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.
    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).
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