DocumentCode :
2343741
Title :
Load elasticity analysis in the deregulated electricity market
Author :
Wang, Yi ; Liu, Yuanxin ; Yu, Songqing
Author_Institution :
Sch. of Bus. Manage., North China Electr. Power Univ., Beijing
fYear :
2008
fDate :
3-5 June 2008
Firstpage :
1019
Lastpage :
1022
Abstract :
In the electricity market, load elasticity analysis has been regarded as an effective tool for quantitative analysis of the load risk analysis, which may lead the researchers a profound understanding on load volatility and help market participants make decisions. But due to the complicated load volatility under the competitive surroundings, few studies have been devoted to this. So in order to solve above problem, a novel model for load elasticity demand is proposed based on statistical learning theory. In this work, load multi-class pattern is performed by least squares support vector machines (LS-SVM). After that, by training the following regression models using the samples labeled with their patterns, the mathematical equations of load elasticity analysis is derived, based on which the elasticity coefficients are found. Finally, numerical experiments are used to test the model.
Keywords :
least mean squares methods; power engineering computing; power markets; risk analysis; support vector machines; deregulated electricity market; least squares support vector machines; load elasticity analysis; load multiclass pattern; load risk analysis; market participants; mathematical equations; quantitative analysis; statistical learning theory; Elasticity; Electricity supply industry; Electricity supply industry deregulation; Equations; Least squares methods; Load modeling; Mathematical model; Risk analysis; Statistical learning; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1717-9
Electronic_ISBN :
978-1-4244-1718-6
Type :
conf
DOI :
10.1109/ICIEA.2008.4582669
Filename :
4582669
Link To Document :
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