Extreme wind power forecast error analysis considering its application in day-ahead reserve capacity planning

摘要

Accurate wind power forecasting is crucial for setting reserve dimensions of a power system. From the perspective of power grid dispatcher, one of the most critical issues of dealing with the uncertainty brought by wind power forecast error (WPFE) is to analyse its extreme value to decide the new reserve requirements induced by variable wind power. This study targets at predicting the daily maximum of day-ahead WPFE via extreme value analysis approach considering its application of operating reserve planning. Non-stationary block maxima method (NS-BMM) is applied to model the probabilistic characteristic of daily maxima WPFE via fitting the non-linear relationship between distribution parameters and the covariates. To better shape the non-stationarity, a conditional NS-BMM (CNS-BMM) via partition according to wind conditions is proposed. Moreover, indicators to evaluate the quality of applying NS-BMMs in the day-ahead reserve plan are also discussed. Finally, a case study based on the operational data from a typical wind farm in northern China is carried out. Results show that CNS-BMM offers a better model of daily maximum WPFE compared to not only traditional quantile regression but also non-CNS-BMM, and applying the estimate of daily maximum WPFE on reserve plan demonstrates an advantage over conventional three-sigma method.