A data-driven approach for online aggregated load modeling through intelligent terminals

Abstract

Load model has significant impact on power system simulation. Current load modeling approaches are inadequate on revealing the accuracy and time-variation of load compositions. The application of wireless sensors dispersed in power distribution networks provides further opportunities for load modeling. In this article, a data-driven online aggregated load modeling approach is proposed systematically. First, all the electricity consumers are clustered according to big data of power consumption behaviors. In each cluster, typical users are designated to stand for the characteristics of the cluster, and intrusive measurement is adapted to capture these typical users’ time-varying information by employing wireless intelligent terminals, which can identify the composition of static load and induction motor load online. Second, the load models of other users in each cluster are assumed identical to typical users, including static impedance–current–power models and induction motor models. Finally, the composite load model is achieved by hierarchical aggregation and bottom-to-up stepwise equivalence. Simulations demonstrate that the load model built by proposed approach reflects higher accuracy and adaptability in power system.