Uncertainty and Investment in Electricity Generation with an Application to the Case of Hydro-Québec

Corinne Chaton and Joseph Doucet

Annals of Operations Research, 2003, vol. 120, issue 1, 59-80

Abstract: The electricity industry is undergoing a substantial process of restructuring, with an emphasis on the introduction of competition in the generation sector. Traditional planning methods are not necessarily appropriate for this new environment. This paper extends a previously developed linear programming model to the problem of optimal expansion planning in the face of uncertainty. The model explicitly accounts for equipment availability and load duration curves in selecting optimal investment. The use of the model is illustrated with a simple 2-region problem calibrated with data from Hydro-Québec and the northeastern United States that suggests how the LP model can help decision makers. Copyright Kluwer Academic Publishers 2003

Keywords: uncertainty; investment; electricity planning (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1023370211456 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Uncertainty and Investment in Electricity Generation with an Application to the Case of Hydro-Québec (2003)
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:spr:annopr:v:120:y:2003:i:1:p:59-80:10.1023/a:1023370211456

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1023/A:1023370211456

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().