This article introduces the concept of load aggregation

which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection

processing

and system identification

commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration

aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics

economic efficiency

and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes

the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57

indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall

the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.