By Topic

Forecasting the Mean and the Variance of Electricity Prices in Deregulated Markets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ruibal, C.M. ; Montevideo Univ., Montevideo ; Mazumdar, M.

A fundamental bid-based stochastic model is presented to predict electricity hourly prices and average price in a given period. The model captures both the economic and physical aspects of the pricing process, considering two sources of uncertainty: availability of the units and demand. This work is based on three oligopoly models-Bertrand, Cournot, and supply function equilibrium (SFE) due to Rudkevich, Duckworth, and Rosen-and obtains closed form expressions for expected value and variance of electricity hourly prices and average price. Sensitivity analysis is performed on the number of firms, anticipated peak demand, and price elasticity of demand. The results show that as the number of firms in the market decreases, the expected values of prices increase by a significant amount. Variances for the Cournot model also increase, but the variances for the SFE model decrease, taking even smaller values than Bertrand's. Thus, if the Rudkevich model is an accurate representation of the electricity market, the results show that an introduction of competition may decrease the expected value of prices but the variances may actually increase. Finally, using a refinement of the model, it has been demonstrated that an accurate temperature forecast can reduce significantly the prediction error of the electricity prices.

Published in:

Power Systems, IEEE Transactions on  (Volume:23 ,  Issue: 1 )