Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method

Brian Hodge, Mingjian Cui, Deping Ke, Yuanzhang Sun, Di Gan, J. Zhang

Research output: Contribution to journalArticlepeer-review

148 Scopus Citations


Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

Original languageAmerican English
Article number7018976
Pages (from-to)422-433
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Issue number2
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-63441


  • gallium (Ga)
  • genetic algorithm
  • neural networks
  • stochastic process model
  • stochastic scenario generation
  • wind power
  • wind power ramp events


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