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Title: A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

Journal Article · · IEEE Transactions on Smart Grid
 [1];  [2];  [2];  [1]
  1. Univ. of Texas, Richardson, TX (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
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Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1457667
Report Number(s):
NREL/JA-5D00-70233
Journal Information:
IEEE Transactions on Smart Grid, Vol. none, Issue none; ISSN 1949-3053
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 44 works
Citation information provided by
Web of Science

Cited By (1)

Joint Point-Interval Prediction and Optimization of Wind Power Considering the Sequential Uncertainties of Stepwise Procedure journal June 2019

Figures / Tables (20)

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Related Subjects

17 WIND ENERGY
42 ENGINEERING
conditional probabilistic forecast
copula theory
Gaussian mixture model
wind power ramps