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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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    8. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).

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