Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint
The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.
- Publication Date:
- OSTI Identifier:
- Report Number(s):
- DOE Contract Number:
- Resource Type:
- Resource Relation:
- Conference: To be presented at the ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2014), 17-20 August 2014, Buffalo, New York
- Research Org:
- National Renewable Energy Laboratory (NREL), Golden, CO.
- Sponsoring Org:
- USDOE Office of Energy Efficiency and Renewable Energy Wind and Water Power Technologies Office, Wind Program
- Country of Publication:
- United States
- 17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION WIND FORECASTING; GRID INTEGRATION; RAMP FORECASTING; PERFORMANCE DIAGRAM; SWINGING DOOR ALGORITHM; Wind Energy
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