Title :
Energy price forecasting and bidding strategy in the Ontario power system market
Author :
Anders, George J. ; Rodriguez, Claudia
Author_Institution :
Kinectrics Inc., Toronto
Abstract :
This paper introduces a method for forecasting energy prices using artificial intelligence methods such as neural networks and fuzzy logic and a combination of the two. The forecasted price is then used to design an optimal bidding strategy for a generator according to his/her degree of risk aversion. A typical thermal plant is assumed to be located in the Ontario electricity system to apply this methodology for two types of participants: risk averse and risk seeker. Results for the Ontario electricity market are presented.
Keywords :
artificial intelligence; forecasting theory; fuzzy logic; neural nets; power engineering computing; power markets; power system economics; thermal power stations; Ontario power system market; artificial intelligence methods; electricity system; energy price forecasting; fuzzy logic; neural networks; optimal bidding strategy; thermal plant; Artificial intelligence; Artificial neural networks; Demand forecasting; Economic forecasting; Electricity supply industry; Environmental economics; Fuzzy logic; Load forecasting; Power generation economics; Power systems;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524367
