Abstract
Air-conditioning demand response has drawn more attention as a means of reducing the ever-increasing peak load of the utility grid. However, the parametric analysis of the demand response performance is still insufficient, given a large number of design parameters of buildings, air-conditioning systems, climate zones, and demand response scenarios. In this study, four different sensitivity analysis methods, including the standardized regression coefficient method, the partial correlation coefficient method, the Morris method, and the extended Fourier amplitude sensitivity testing method, were adopted to screen the most important design parameters in determining the peak load, demand shedding intensity, and demand response potential of a medium-sized office building equipped with a fan-coil air conditioning system under the demand response control strategies of different global temperature adjustments. The results show that the four sensitivity analysis methods presented similar results of parameter screening, even with different degrees of accuracy and computational costs. For example, all the sensitivity analysis methods identified the occupied area per capita, fresh outdoor air per person, internal thermal mass, the safety factor of chiller capacity, and coefficient performance of chillers as the critical parameters for the demand shedding intensity. In addition, the critical parameters have similar rankings for their importance under different adjustment parameters, such as setpoint changes and duration time, in a demand response event. For critical parameters, their degrees of influence on demand shedding intensity are affected by climate zones, and the variation of coefficient for their sensitivity analysis indicators could be 20% higher than the least influential parameters. The study would benefit the design of air conditioning systems with load flexibility and prediction model simplification, etc.
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