Accelerated kriging interpolation for real-time grid frequency forecasting
This work addresses the need for real-time forecasting in power systems to ensure stability and efficient control, presenting an incremental improvement in computational efficiency.
The paper tackled the problem of fast and reliable grid frequency forecasting for power systems with renewable energy integration, achieving accurate predictions directly from measurements with sub-second computation times.
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.