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Demand response control for the inverter air conditioners based on hierarchical nonlinear model predictive control for plug-and-play.

Number: 3475

Author(s) : WANG C., WANG B.

Summary

Developing the demand response control for the air conditioners of residential buildings has been proven to be a highly effective strategy in assisting grid supply-demand balance and facilitating the integration of renewable energy to decarbonize the energy system. 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. It could utilize cloud-based infrastructure or the Internet of Things, which means the operation not be limited by the local computing power. To enable the proposed MPC framework to perform precise demand-responsive AC control and indoor environment optimization, a nonlinear prediction model is developed considering the dynamic performances of the inverter air conditioner and the coupled thermal response of an air-conditioned room. As a result, HNLMPC enables plug-and-play capability for practical applications, reducing the dependency on local computing power, maintaining the performances, and improving the robustness. 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. Compared with centralized MPC, the HNLMPC has a lower demand for computing power.

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  • Original title: Demand response control for the inverter air conditioners based on hierarchical nonlinear model predictive control for plug-and-play.
  • Record ID : 30032928
  • Languages: English
  • Source: 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
  • Publication date: 2024/07/15

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