Energy price forecasting and bidding strategy in the Ontario power system market

Author

Anders, George J. ; Rodriguez, Claudia

Author_Institution

Kinectrics Inc., Toronto

fYear

2005

fDate

27-30 June 2005

Firstpage

1

Lastpage

7

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/PTC.2005.4524367

Filename

4524367