Identifying Wind and Solar Ramping Events

Abstract

Wind and solar power are playing an increasing role in the electrical grid, but their inherent power variability can augment uncertainties in the operation of power systems. One solution to help mitigate the impacts and provide more flexibility is enhanced wind and solar power forecasting; however, its relative utility is also uncertain. Within the variability of solar and wind power, repercussions from large ramping events are of primary concern. At the same time, there is no clear definition of what constitutes a ramping event, with various criteria used in different operational areas. Here, the swinging door algorithm, originally used for data compression in trend logging, is applied to identify variable generation ramping events from historic operational data. The identification of ramps in a simple and automated fashion is a critical task that feeds into a larger work of 1) defining novel metrics for wind and solar power forecasting that attempt to capture the true impact of forecast errors on system operations and economics, and 2) informing various power system models in a data-driven manner for superior exploratory simulation research. Both allow inference on sensitivities and meaningful correlations, as well as quantify the value of probabilistic approaches for future use in practice.