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Title: A Scenario Generation Method for Wind Power Ramp Events Forecasting

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.
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Resource Relation:
Conference: Presented at the 2015 IEEE Power and Energy Society General Meeting, 26-30 July 2015, Denver, Colorado
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Country of Publication:
United States
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION genetic algorithm (GA); neural networks; stochastic process model; stochastic scenario generation; wind power ramp events; ramp forecasting