Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

Qin Wang, Anthony Florita, Venkat Krishnan, Brian Hodge, Mingjian Cui, Cong Feng, Zhenke Wang, Jie Zhang

Research output: Contribution to conferencePaper


Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive 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 probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
Original languageAmerican English
Number of pages5
StatePublished - 2018
Event2017 IEEE Power & Energy Society General Meeting - Chicago, Illinois
Duration: 16 Jul 201720 Jul 2017


Conference2017 IEEE Power & Energy Society General Meeting
CityChicago, Illinois

Bibliographical note

See NREL/CP-5D00-67750 for preprint

NREL Publication Number

  • NREL/CP-5D00-71029


  • Gaussian mixture model
  • probabilistic wind power ramp forecasting
  • scenario generation


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