Using Simple Statistical Analysis of Historical Data to Understand Wind Ramp Events

Abstract

As renewable resources start providing an increasingly larger percentage of our energy needs, we need to improve our understanding of these intermittent resources so we can manage them better. In the case of wind resources, large unscheduled changes in the energy output, called ramp events, make it challenging to keep the load and the generation balanced. In this report, we show that simple statistical analysis of the historical data on wind energy generation can provide insights into these ramp events. In particular, this analysis can help answer questions such as the time period during the day when these events are likely to occur, the relative severity of positive and negative ramps, and the frequency of their occurrence. As there are several ways in which ramp events can be defined and counted, we also conduct a detailed study comparing different options. Our results indicate that the statistics are relatively insensitive to these choices, but depend on utility-specific factors, such as the magnitude of the ramp and the time interval over which this change occurs. These factors reflect the challenges faced by schedulers and operators in keeping the load and generation balanced and can change over the years. We conduct our analysis using data from wind farms in the Tehachapi Pass region in Southern California and the Columbia Basin region in Northern Oregon; while the results for other regions are likely to be different, the report describes the benefits of conducting simple statistical analysis on wind generation data and the insights that can be gained through such analysis.