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Demand response for residential buildings using hierarchical nonlinear model predictive control for plug-and-play

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  • Wang, Cuiling
  • Wang, Baolong
  • You, Fengqi

Abstract

In the smart grid, residential inverter air conditioners (AC) with significant demand response (DR) potential due to their load flexibility and as the major contributors to peak electricity, need to be grid-responsive to relieve power supply-demand imbalance and ensure thermal comfort. Model predictive control (MPC) has strong capabilities for unlocking the flexibility of residential buildings to realize DR by responding to electricity prices. However, the high computational requirements and complex control system integration processes make the application of MPC a significant challenge. A hierarchical nonlinear MPC (HNLMPC) is developed to realize grid-responsive control for residential inverter ACs by responding to real-time electricity price signals. The controller consists of three parts: the upper-level supervisor MPC, the lower-level optimal PID controller, and the signal converter. The indoor air temperature is selected as the optimized setpoint sequence passed from the upper level to the lower level. A nonlinear prediction model is developed considering the dynamic performances of the inverter AC and the coupled thermal response of an air-conditioned room. A test platform is constructed using Simulink and Simscape to access the DR performance of HNLMPC by comparing it with different rule-based control methods, hierarchical linear MPC, and centralized MPC. The control results show that HNLMPC can achieve peak load shifting and peak shaving without sacrificing thermal comfort by adjusting the room temperature to charge and discharge cooling for the building's thermal mass. Additionally, it enables plug-and-play capability for practical applications, reducing the dependency on local computing power and the need for accurate models. Compared to basic rule-based control, HNLMPC reduces peak-hour energy consumption by 31.6% and total electricity costs by 14.3% over the entire cooling season.

Suggested Citation

  • Wang, Cuiling & Wang, Baolong & You, Fengqi, 2024. "Demand response for residential buildings using hierarchical nonlinear model predictive control for plug-and-play," Applied Energy, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009644
    DOI: 10.1016/j.apenergy.2024.123581
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    References listed on IDEAS

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