Short-Term Congestion Forecasting in Wholesale Power Markets
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 patternsâ€”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 of creation:||19 Jul 2010|
|Publication status:||Published in IEEE Transactions on Power Systems, November 2011, vol. 26 no. 4, pp. 2185-2196|
|Contact details of provider:|| Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070|
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:isu:genres:31700. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Curtis Balmer)
If references are entirely missing, you can add them using this form.