Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts

Jianqiu Shi,Gregory Roux,Yuewei Liu,Xiaowei Fan,Lan Shi,Yubao Liu,Yang Li

doi:10.3390/en15030896

Abstract

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.

Highlights

Wind energy, with its vast availability, cleanliness, and renewability, is growing rapidly in the energy share, and plays an increasingly important role in the electric energy sector [1].the intermittent and volatile nature of wind speeds poses a great challenge to the grid-connected transmission of wind power output, threatening the security of the grid system and sometimes leading to massive wind abandonment [2]
This paper evaluates the output of the IMEPC mesoscale ensemble prediction system, focusing on its hub-height wind prediction for the wind farms distributed across the Inner
The wind speed over the whole region (Figure 2) exhibits evident diurnal variations, with errors gradually increasing during daytime

Summary

Introduction

With its vast availability, cleanliness, and renewability, is growing rapidly in the energy share, and plays an increasingly important role in the electric energy sector [1].the intermittent and volatile nature of wind speeds poses a great challenge to the grid-connected transmission of wind power output, threatening the security of the grid system and sometimes leading to massive wind abandonment [2]. Wind speed forecasting methods include statistical approaches, machine learning methods [5,6,7,8,9,10,11], and numerical weather prediction [12]. There have been many works on wind prediction reported in the past two decades, especially over the last few years. Most of these works are on the refinement of statistical and AI approaches [13,14,15,16,17,18]; there have been very few studies examining and analyzing the errors of numerical weather models. Improving the performance and capability of numerical weather prediction models and machine learning post-processing for wind farm weather prediction is critical

Results

Discussion

Conclusion