A wind power ramp prediction method based on value-at-risk

Wind power generation is one of the most important renewable energy technologies, solidifying its position as one of the fastest-growing sectors within the global energy industry. However, the operation of wind power systems often encounters wind power ramp events (WPREs), which cause rapid and serious fluctuations in wind power, thereby endangering the normal functioning of wind power systems. One effective mitigation strategy involves enhancing the prediction accuracy of WPREs. This paper introduces a novel high-precision WPREs prediction method based on Value-at-Risk (VaR). The approach first sets the ramp threshold based on VaR, providing a basis for measuring extreme values in wind power variation through expected shortfall (ES). Consequently, it facilitates the assessment of economic costs associated with WPREs. Wind power data over a specific timeframe and the trend characteristics generated at each time point during the detection process are utilized as model inputs. The method directly forecasts the category of WPREs for the next time point. Partitioning the dataset, this method is compared with three commonly methods in onshore and offshore wind power scenarios. The proposed method predicts WPREs more accurately, with performance metrics such as precision and recall all exceeding 80%. Except for a few exceptional instances where comparison methods perform unusually well in individual metrics, the overall performance of this method is superior, validating its robustness and effectiveness. Based on the actual performance of the method, additional decisionsupport information from a risk assessment perspective is provided to power system operators to mitigate the detrimental impacts of WPREs.