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A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

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  • Bürger, Adrian
  • Bohlayer, Markus
  • Hoffmann, Sarah
  • Altmann-Dieses, Angelika
  • Braun, Marco
  • Diehl, Moritz

Abstract

This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real-time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.

Suggested Citation

  • Bürger, Adrian & Bohlayer, Markus & Hoffmann, Sarah & Altmann-Dieses, Angelika & Braun, Marco & Diehl, Moritz, 2020. "A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components," Applied Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:appene:v:258:y:2020:i:c:s0306261919317519
    DOI: 10.1016/j.apenergy.2019.114064
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    Cited by:

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    2. Jansen, Jelger & Jorissen, Filip & Helsen, Lieve, 2024. "Mixed-integer non-linear model predictive control of district heating networks," Applied Energy, Elsevier, vol. 361(C).
    3. Filip Vrbanc & Mario Vašak & Vinko Lešić, 2023. "Simple and Accurate Model of Thermal Storage with Phase Change Material Tailored for Model Predictive Control," Energies, MDPI, vol. 16(19), pages 1-18, September.
    4. Parantapa Sawant & Oscar Villegas Mier & Michael Schmidt & Jens Pfafferott, 2021. "Demonstration of Optimal Scheduling for a Building Heat Pump System Using Economic-MPC," Energies, MDPI, vol. 14(23), pages 1-15, November.
    5. Sun, Qingkai & Wang, Xiaojun & Liu, Zhao & Mirsaeidi, Sohrab & He, Jinghan & Pei, Wei, 2022. "Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market," Applied Energy, Elsevier, vol. 324(C).
    6. 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).
    7. Yao, Leyi & Liu, Zeyuan & Chang, Weiguang & Yang, Qiang, 2023. "Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty," Renewable Energy, Elsevier, vol. 212(C), pages 523-537.

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