Frequency Quality Metrics based on Second-Order Derivative and Autocorrelation
This work addresses a gap in frequency quality assessment for transmission system operators, offering incremental improvements to existing metrics.
The paper tackled the problem that current frequency quality metrics used by transmission system operators inadequately capture grid dynamics, proposing new metrics based on second-order derivatives and autocorrelation; using real-world data from Irish, Great Britain, and Nordic systems, it showed these metrics provide counterintuitive insights, such as systems appearing good with standard metrics but poor with the new ones.
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.