A new approximation method for generating day-ahead load scenarios

Author

Yonghan Feng ; Gade, Dinakar ; Ryan, Sarah M. ; Watson, Jean-Paul ; Wets, Roger J.-B ; Woodruff, David L.

Author_Institution

Iowa State Univ., Ames, IA, USA

fYear

2013

fDate

21-25 July 2013

Firstpage

1

Lastpage

5

Abstract

Unit commitment decisions made in the day-ahead market and resource adequacy assessment processes are based on forecasts of load, which depends strongly on weather. Two major sources of uncertainty in the load forecast are the errors in the day-ahead weather forecast and the variability in temporal patterns of electricity demand that is not explained by weather. We develop a stochastic model for hourly load on a given day, within a segment of similar days, based on a weather forecast available on the previous day. Identification of similar days in the past is based on weather forecasts and temporal load patterns. Trends and error distributions for the load forecasts are approximated by optimizing within a new class of functions specified by a finite number of parameters. Preliminary numerical results are presented based on data corresponding to a U.S. independent system operator.

Keywords

load forecasting; power generation dispatch; power generation scheduling; power markets; stochastic processes; approximation method; day ahead load scenario; day ahead market; day ahead weather forecast; electricity demand; hourly load; load forecast; resource adequacy assessment process; stochastic model; temporal pattern; unit commitment; Approximation methods; Load modeling; Predictive models; Stochastic processes; Uncertainty; Weather forecasting; Demand forecasting; Load modeling; Power system planning; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting (PES), 2013 IEEE

Conference_Location

Vancouver, BC

ISSN

1944-9925

Type

conf

DOI

10.1109/PESMG.2013.6672564

Filename

6672564