Evolutionary mechanisms of the strong winds associated with an intense cold wave event and their effects on the wind power production

Abstract

Cold wave events (CWEs) often cause major economic losses and serious casualties in the cold seasons, making CWEs among the most significant types of disastrous weather. Previous studies have mainly focused on disasters due to abrupt drops in surface temperatures, with less discussion of the strong winds associated with CWEs. Based on an intense CWE that occurred in late December 2020, we investigated the evolutionary mechanisms of the associated strong winds in terms of kinetic energy (KE) budget and evaluated the effects of this CWE on wind power production based on quantitative comparisons with the mean state. The results showed that the CWE occurred under favorable background conditions, which were characterized by a southward-moving transversal trough and a southeastward-moving shortwave trough in the middle troposphere. The surface high ridge that formed around Lake Baikal and the cold front around the southern periphery of the ridge were key factors related to the CWE evolution. The positive work carried out on the horizontal wind by the pressure gradient force that linked a lower tropospheric high-pressure ridge inland and a low-pressure trough offshore and the downward momentum transportation due to the descending motions behind the cold front dominated the enhancement and sustainment of the CWE-associated strong winds. The CWE contributed to wind power production by 1) increasing the wind power density (by an average of ∼1.05-fold) and 2) improving the availability of the wind to generate power, as it reduced the percentage of zero wind power generation by ∼6.4%, while maintaining the high-wind-velocity cut-out percentage.

Forecasting and Early Warning of Wind‐Farm‐Tripping Event under Strong‐Wind and Cold‐Wave Conditions Based on Combined Nonparametric Estimation Model and K‐Value Method

“…[5] Ma et al researched the cold-wave event in December 2020, analyzed the strong-wind dynamic mechanism related to the cold wave, and evaluated the impact of the cold wave on wind farm power output. [6] Ouyang et al combined NWP and wind power curve models to achieve long-term wind power forecasting, combined with a multisource data-driven model and an improved swing door algorithm to achieve wind power ramp detection, and verified its effectiveness. [7] Cui et al proposed a data-driven wind power ramping forecasting method, which used machine learning to predict wind power and obtained the forecasting error, and extracted wind power ramping scenarios through an optimized swing door algorithm, achieving wind power ramping probability estimation.…”