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Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage

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  • Yang, Shiyu
  • Oliver Gao, H.
  • You, Fengqi

Abstract

Active latent heat storage (ALHS) involving phase-change materials constitutes a promising energy-efficient solution for building energy management (BEM) by reshaping building energy demands without occupant comfort degradation. Current BEM systems based on conventional reactive control lack the level of control delicacy required to exploit the full potential of ALHS for BEM under certain conditions, such as highly dynamic electricity prices. This study proposes a smart model predictive control (MPC) approach for BEM to minimize the energy cost while maintaining the indoor climate by fully applying ALHS. More specifically, a reduced-order, high-fidelity state-space model (SSM) of ALHS is proposed for fast building control. An MPC framework considering highly dynamic electricity prices and ALHS dynamics is developed based on the proposed ALHS SSM integrated with a building SSM. A case study entailing a set of simulations is designed based on a single-family house with a space heating system, including an ALHS, ground source heat pump, and radiator. The proposed MPC approach, compared to conventional reactive control, enables substantial reductions in the electricity cost (ranging from 53.2% to 122.7% depending on the MPC settings and ALHS capacity), even financial gains under certain scenarios. Further analysis reveals that coupling ALHS with MPC is critical to ensure that ALHS adoption is economically convincing: while conventional reactive control of an ALHS-equipped building increases the electricity cost, an MPC-enabled building could reduce the electricity cost by 45.1% due to ALHS adoption. The proposed MPC approach also exhibits promising feasibility for real-world BEM applications.

Suggested Citation

  • Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013113
    DOI: 10.1016/j.apenergy.2022.120054
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    as
    1. Tao, Y.B. & He, Ya-Ling, 2018. "A review of phase change material and performance enhancement method for latent heat storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 245-259.
    2. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    3. Wang, Kai & Yan, Ting & Zhao, Y.M. & Li, G.D. & Pan, W.G., 2022. "Preparation and thermal properties of palmitic acid @ZnO/Expanded graphite composite phase change material for heat storage," Energy, Elsevier, vol. 242(C).
    4. Li, Yantong & Huang, Gongsheng & Xu, Tao & Liu, Xiaoping & Wu, Huijun, 2018. "Optimal design of PCM thermal storage tank and its application for winter available open-air swimming pool," Applied Energy, Elsevier, vol. 209(C), pages 224-235.
    5. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    6. Raman, Naren Srivaths & Chen, Bo & Barooah, Prabir, 2022. "On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study," Applied Energy, Elsevier, vol. 324(C).
    7. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    8. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    9. Huang, Sen & Lin, Yashen & Chinde, Venkatesh & Ma, Xu & Lian, Jianming, 2021. "Simulation-based performance evaluation of model predictive control for building energy systems," Applied Energy, Elsevier, vol. 281(C).
    10. Hu, Guoqing & You, Fengqi, 2022. "Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    12. Yang, Tianrun & Liu, Wen & Kramer, Gert Jan & Sun, Qie, 2021. "Seasonal thermal energy storage: A techno-economic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    13. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    14. Heier, Johan & Bales, Chris & Martin, Viktoria, 2015. "Combining thermal energy storage with buildings – a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1305-1325.
    15. Krarti, Moncef & Dubey, Kankana & Howarth, Nicholas, 2019. "Energy productivity analysis framework for buildings: a case study of GCC region," Energy, Elsevier, vol. 167(C), pages 1251-1265.
    16. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    17. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
    18. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    19. Li, Dacheng & Wang, Jihong & Ding, Yulong & Yao, Hua & Huang, Yun, 2019. "Dynamic thermal management for industrial waste heat recovery based on phase change material thermal storage," Applied Energy, Elsevier, vol. 236(C), pages 1168-1182.
    20. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    21. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
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    Cited by:

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