Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets

Qun Zhou, Leigh Tesfatsion () and Chen-Ching Liu

ISU General Staff Papers from Iowa State University, Department of Economics

Abstract: Abstract: In current restructured wholesale power markets, the short length of time series for prices makes it difficult to use empirical price data to test existing price forecasting tools and to develop new price forecasting tools. This study therefore proposes a two-stage approach for generating simulated price scenarios based on the available price data. The first stage consists of an Autoregressive Moving Average (ARMA) model for determining scenarios of cleared demands and scheduled generator outages (D&O), and a moment-matching method for reducing the number of D&O scenarios to a practical scale. In the second stage, polynomials are fitted between D&O and wholesale power prices in order to obtain price scenarios for a specified time frame. Time series data from the Midwest ISO (MISO) are used as a test system to validate the proposed approach. The simulation results indicate that the proposed approach is able to generate price scenarios for distinct seasons with empirically realistic characteristics.

Date: 2009-01-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://dr.lib.iastate.edu/server/api/core/bitstre ... 8de464ccfc2b/content
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Working Paper: Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:isu:genstf:200901010800001032

Access Statistics for this paper

More papers in ISU General Staff Papers from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().