A Scenario Generation Method for Wind Power Ramp Events Forecasting

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

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations

Abstract

Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on 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 data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2015
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States
Duration: 26 Jul 201530 Jul 2015

Conference

ConferenceIEEE Power and Energy Society General Meeting, PESGM 2015
Country/TerritoryUnited States
CityDenver
Period26/07/1530/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

NREL Publication Number

  • NREL/CP-5D00-63878

Keywords

  • Genetic algorithm (GA)
  • neural networks
  • ramp forecasting
  • stochastic process model
  • stochastic scenario generation
  • wind power ramp events

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