Wind power short-term prediction over mountain area using a high-resolution WRF model

Zelin Cai,Lei Wang,Tao Feng,Bo Hu,Jun Guo,S Trukhanov,C.-W Chow

doi:10.1051/e3sconf/202126002012

Abstract

Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.

Highlights

Due to the rapid development of society, the demand for energy consumption is rapidly increasing day by day [1]
If using the polynomial fitting method to fit the wind power data with wind speed data, a sixth power relationship will be found between wind speeds and wind powers, the fitting equation is derived as equation (1)
The main conclusions of this study are summarized as follows: (1) A sixth power relationship is found between wind speeds and wind powers over the Baiguoshan Mountain

Summary

Introduction

Due to the rapid development of society, the demand for energy consumption is rapidly increasing day by day [1]. As alternatives to conventional sources, the use of clean and renewable energy sources is rapidly increasing and is highly concerned by many countries around the world nowadays [2]. Many prediction models have combined the statistical methods and traditional physical methods (physical-statics method), and are more and more widely used. These prediction models derive wind power predictions via a series of empirical formulas based on a large amount of the geographical and meteorological parameters.

Objectives

Methods

Results

Conclusion