By Topic

Statistical characterization of electricity price in competitive power 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)
Shrestha, G.B. ; Sch. of EEE, Nanyang Technol. Univ. (NTU), Singapore, Singapore ; Songbo Qiao

Forecasting of electricity price in the competitive power market has become very important for the market participants in order to support their planning and operations activities. In addition to the change in prices caused by the demand levels (reflected in normal interaction between supply and demand), numerous other factors affect the electricity price including the sudden surges in prices which are frequently caused by unexpected outages. This paper attempts to represent the price at any one period as a random variable which may be treated statistically. Historical price data from Singapore market (USEP) are used to study the behavior of the market price in order to identify the possible statistical distributions and then hypothesis tests are formulated and carried out to determine the theoretical distributions which are best supported by the historical data. It is shown that the raw price data may not be directly amenable to statistical representation. However, suitable processing of the data such as filtering out the extreme values or suitable regrouping of the data may be carried out to bring out the underlying statistical characteristics of the price behavior. It is shown that the market price behavior may support more than one theoretical distribution although the log normal distribution was found to fit the historical data best.

Published in:

Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on

Date of Conference:

14-17 June 2010