High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

TL;DR

This paper introduces a high-fidelity order reduction method for complex composite load models, significantly reducing computational load while maintaining dynamic accuracy, crucial for large-scale power system simulations.

Contribution

A novel large-signal order reduction approach based on singular perturbation theory is developed and applied to the WECC composite load model, enhancing efficiency and accuracy.

Findings

Reduced computational burden in simulations

Maintained dynamic response accuracy

Validated effectiveness through simulations

Abstract

With the increasing penetration of electronic loads and distributed energy resources (DERs), conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order nonlinear system with multi-time-scale property, which poses challenges on stability analysis and computational burden in large-scale simulations. In order to reduce the computational burden while preserving the accuracy of the original model, this paper proposes a generic high-fidelity order reduction approach and then apply it to WECC composite load model. First, we develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow dynamics to preserve the transient characteristics of fast dynamics. Then, we propose the necessary conditions for accurate order reduction and embed them into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, we develop the reduced-order WECC composite load model using the proposed algorithm. Simulation results show the reduced-order large signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.