DocumentCode :
2049753
Title :
Wind power ramps: Detection and statistics
Author :
Sevlian, R. ; Rajagopal, R.
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
fYear :
2012
fDate :
22-26 July 2012
Firstpage :
1
Lastpage :
8
Abstract :
Ramps events are a significant source of uncertainty in wind power generation. Developing statistical models from historical data for wind power ramps is important for designing intelligent distribution and market mechanisms for a future electric grid. This requires robust detection schemes for identifying wind ramps in data. In this paper, use an optimal detection technique for identifying wind ramps for large time series. The technique relies on defining a family of scoring functions associated with any rule for defining ramps on an interval of the time series. A dynamic programming recursion is then used to find all such ramp events. Identified wind ramps are used to perform an extensive statistical analysis on the process, characterizing ramping duration and rates as well as other key features needed for developing future models.
Keywords :
dynamic programming; power grids; statistical analysis; wind power plants; dynamic programming recursion; electric grid; historical data; intelligent distribution; market mechanisms; statistical analysis; statistical models; wind power generation; wind power ramps; Data models; Detection algorithms; Joints; Market research; Time series analysis; Wind; Wind power generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PESGM.2012.6344969
Filename :
6344969
Link To Document :
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