Load Resource Regulation Methods for Demand-Responsive Air Conditioning Clusters with Two Types

Air conditioning load, as a crucial demand-responsive load resource, possesses significant regulation potential. During the peak summer period, it can be centrally controlled to effectively reduce the peak load, achieving a peak shaving effect for air conditioning clusters. However, due to variations in users' air conditioning models, adopting a uniform control strategy may not only impact user comfort but also result in the loss of demand-responsive resources and increased costs. To develop a sound control strategy, this paper introduces a method for regulating the load in air conditioning clusters, encompassing two distinct types of air conditioning systems. Initially, by leveraging a second-order equivalent thermal parameter model and integrating the operational characteristics specific to both types of air conditioning systems, we establish corresponding models for air conditioning loads. This enables a more targeted simulation of the temperature load curves for each type of air conditioning system. We construct an optimization scheduling model by imposing constraints on user comfort and the air conditioning adjustment range. Subsequently, we formulate compensation strategies based on variations in comfort levels. Subsequently, based on the aforementioned model, this paper quantifies the regulation capabilities and adjustment costs of the two types of air conditioning systems, generates regulation strategies according to the adjustment potentials of the two types of air conditioning systems. Finally, this study validates the proposed strategies through experiments. The results indicate that the generated strategies exhibit a notable peak-shaving effect and effectively enhance regulation capabilities while reducing costs.