The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

Markus Gross,Alfredo Peña,Vanesa Magar

doi:10.3390/su12083431

Abstract

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.

Highlights

The need to replace fossil fuels and the continuously increasing demand for energy drive the harvesting of renewable energy sources
We first illustrate the error of estimating the Wind Power Density (WPD) as the cube of the mean wind speed and highlight how the asymmetry of the wind speed distribution amplifies this error
Due to the generally short time series available from observational data, we demonstrate using long time series from model outputs that seasonal changes strongly impact the WPD estimates

Summary

Introduction

The need to replace fossil fuels and the continuously increasing demand for energy drive the harvesting of renewable energy sources. Before new sites can be used for the development of wind energy projects, a resource assessment has to be carried out to identify potential sites and to predict the available energy. For such assessments, the wind power density is a useful metric because it characterizes the energy content of the wind climatology. The wind power density is a useful metric because it characterizes the energy content of the wind climatology It is a relatively simple metric, and it can be computed for large areas, guiding the exploration of potential sites. It is useful when attempting to predict future changes in wind resources using simulations of future climate scenarios

Methods

Results

Conclusion