Short-term congestion forecasting in wholesale power markets

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

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

Abstract: Short-term congestion forecasting is highly important for market participants in wholesale power markets that use Locational Marginal Prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns -- combinations of status flags for generating units and transmission lines. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm.

Date: 2011-01-17
References: Add references at CitEc
Citations:

Downloads: (external link)
https://dr.lib.iastate.edu/server/api/core/bitstre ... cbdd0c95d8df/content
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Working Paper: Short-term congestion forecasting in wholesale power markets (2011)
Working Paper: Short-Term Congestion Forecasting in Wholesale Power Markets (2010)
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:201101170800001091

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 ().