Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems

TL;DR

This paper introduces a low-order, scalable model for frequency and voltage dynamics in inverter-dominated power systems, capturing heterogeneity and validated by EMT simulations for improved planning and control.

Contribution

It presents a novel low-order model that simultaneously captures frequency and voltage responses, accounting for spatial-temporal heterogeneity in large-scale inverter-based systems.

Findings

Model accurately predicts frequency and voltage variations.

Validated by electromagnetic transient simulations.

Offers computational efficiency for large-scale system analysis.

Abstract

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.