Frequency Quality Metrics based on Second-Order Derivative and Autocorrelation

TL;DR

This paper introduces new frequency quality metrics based on second-order dynamics and autocorrelation, revealing insights that challenge traditional metrics in power grid analysis.

Contribution

It proposes novel metrics that better capture the dynamic behavior of grid frequency, supported by real-world data and simulations.

Findings

Proposed metrics provide counterintuitive insights into frequency quality.

Power systems can appear good under standard metrics but poor under new metrics.

New metrics enhance understanding of grid frequency dynamics.

Abstract

This industry-oriented paper originates from the observation that current frequency quality metrics utilized by transmission system operators (TSOs) fail to fully capture the dynamic behavior of the grid frequency. Motivated by this gap, the paper proposes novel frequency quality metrics based on second-order dynamics and stochastic autocorrelation. Using real-world data from the Irish, Great Britain and Nordic systems and running dynamic stochastic simulations, the paper shows that the proposed metrics bring new and counterintuitive insights in terms of how good or poor the frequency quality of power grids is beyond current well-known metrics. In particular, the paper shows that a power system may show good frequency quality using standard metrics and poor frequency quality using the proposed metrics. Overall, the paper contributes to improve the understanding of frequency quality.