A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

Brian Hodge, Venkat Krishnan, Mingjian Cui, Jie Zhang, Qin Wang

Research output: Contribution to journalArticlepeer-review

87 Scopus Citations

Abstract

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

Original languageAmerican English
Article number8068999
Pages (from-to)1326-1338
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume10
Issue number2
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2017 IEEE

NREL Publication Number

  • NREL/JA-5D00-73479

Keywords

  • Gaussian mixture model
  • Probabilistic wind power ramp forecasting
  • Scenario generation
  • Wind power ramps

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