Accelerated kriging interpolation for real-time grid frequency forecasting

TL;DR

This paper introduces a fast, data-driven kriging interpolation method for real-time grid frequency forecasting, enabling accurate predictions with sub-second computation times.

Contribution

It presents a novel nonparametric prediction algorithm that leverages kriging interpolation for efficient, real-time power grid frequency forecasting.

Findings

Achieves sub-second computation times for frequency prediction.

Validates the method on a simulated distribution grid case study.

Provides accurate frequency forecasts directly from measurements.

Abstract

The integration of renewable energy sources and distributed generation in the power system calls for fast and reliable predictions of grid dynamics to achieve efficient control and ensure stability. In this work, we present a novel nonparametric data-driven prediction algorithm based on kriging interpolation, which exploits the problem's numerical structure to achieve the required computational efficiency for fast real-time forecasting. Our results enable accurate frequency prediction directly from measurements, achieving sub-second computation times. We validate our findings on a simulated distribution grid case study.