Title :
Optimisation of the bidding strategy for wind power trading
Author :
Hamilton, David I. ; McMillan, David ; Catterson, Victoria M.
Author_Institution :
Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow, UK
fDate :
June 29 2015-July 2 2015
Abstract :
The optimal bidding strategy for trading electricity from a wind farm is not always clear. This paper outlines a method for predicting whether the market will be long or short and uses this information to select the best quantile regression for the current market conditions. Results from a simulation with a 2.5MW turbine produced a savings of over £1000 over a three month period compared to using only a P50 forecaster.
Keywords :
power markets; regression analysis; wind power; wind power plants; bidding strategy; power 2.5 MW; quantile regression; trading electricity; wind farm; wind power trading; Artificial neural networks; Electricity supply industry; Generators; Predictive models; Pricing; Wind farms; Wind forecasting; Wind energy; energy markets; quantile regression;
Conference_Titel :
PowerTech, 2015 IEEE Eindhoven
Conference_Location :
Eindhoven
DOI :
10.1109/PTC.2015.7232306
