Aggregation Method of Distributed Load Resources Based on Non-intrusive Load Identification

Abstract

Because the power system contains a large number of user-side adjustable load resources, it can effectively enhance the operational flexibility of the power system and realize the safe, economical and efficient operation of the power grid by aggregating and modeling all kinds of resources and participating in the interactive response of the system as a whole. In this paper, a user-side adjustable load resource aggregation method based on non-intrusive load identification is proposed, which aims to obtain the load response potential of various users without intruding into the users, thus providing important support for power grid dispatching. Specifically, starting from the basic attributes of electrical equipment, considering the influence of numerical features such as current, harmonics, power, and V-I trajectory image features on load identification, the deep learning algorithm is used to deeply fuse the numerical features and image features in high-dimensional space, and then the fused advanced features are supervised by the Softmax classification algorithm, so as to effectively identify different types of electrical equipment. Finally, a bottom-up aggregation strategy is adopted to aggregate and model all kinds of load resources under the same station, so as to realize the accurate evaluation of the response ability of station resources. The simulation results of a numerical example verify the correctness and effectiveness of the proposed method.