RC parameter identification and load aggregation analysis of air-conditioning systems: A multi-strategy improved black-winged kite algorithm

Abstract

As the proportion of air-conditioning loads in power systems continues to increase, their potential as demand response resources is becoming increasingly significant. However, the heterogeneity and dynamic nonlinear characteristics of air-conditioning loads, driven by variations in building environments and user behaviors, often result in insufficient accuracy in traditional parameter identification and aggregation modeling. To address this issue, this study proposes a multi-strategy modified Black-winged Kite Algorithm (MBKA) combined with a first-order Equivalent Thermal Parameter (ETP) model and measured data to identify air-conditioning R and C parameters accurately. Furthermore, the effects of setpoint temperature and initial indoor temperature diversity on aggregation characteristics are analyzed. The results demonstrate that MBKA significantly enhances model identification accuracy, achieving a mean square error (MSE) as low as 0.005860. When considering both setpoint and initial indoor temperature diversity, the volatility of aggregated power is significantly reduced, with the peak-to-average ratio, standard deviation, and coefficient of variation decreasing by 15.83 %, 78.21 %, and 74.43 %, respectively. When only initial indoor temperature diversity is considered, these metrics decrease by 11.18 %, 66.73 %, and 64.95 %, respectively. Additionally, a setpoint temperature-adjustable capacity fitting model is established, exhibiting a high fitting accuracy with an R² value of 0.999. This study provides theoretical and technical support for integrating air-conditioning loads into the flexible scheduling of modern power systems through algorithmic improvements and comprehensive aggregation characterization.