Conditional Range Metric for Determining Wind Power Variability of Scarce or Noisy Data

Understanding the nature and characteristics of wind power variability is critical to improving power system operations. Using a relatively large volume of measured wind power time series, the conditional range metric (CRM) has successfully been employed to quantify intra-hour wind power variability. The objective of this paper is to extend the application of the CRM-based method to sparsely sampled wind power time series. To improve the accuracy and adaptability of the CRM, a gamma distribution is proposed to model the power-deviation series without introducing the joint probability density matrix. The shape and inverse scale parameters and of the gamma distribution are calculated by the maximum-likelihood estimator (MLE) at each production level. Bayesian estimators (BEs) for and are then developed as a posterior joint distribution to calculate their expectations by taking the mean value of the paired sample points via a modified rejection sampling strategy. The match between the two estimation results indicates a valid assumption of the likelihood distribution and exact estimated parameters. Applying data from the National Renewable Energy Laboratory and their down-sampled subsets, the efficacy of the proposed CRM with the gamma distribution approach is compared to that of the existing CRM with a quantile model.