A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

Journal Article · Sun Jan 01 04:00:00 EST 2017 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2017.2763827· OSTI ID:1502106

Cui, Mingjian; Zhang, Jie; Wang, Qin; Krishnan, Venkat; Hodge, Bri-Mathias

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1502106
• Report Number(s): NREL/JA-5D00-73479
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 2 Vol. 10; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
Gaussian mixture model
probabilistic wind power ramp forecasting
scenario generation
wind power ramps