DocumentCode
1429294
Title
Estimating the variance of production cost using a stochastic load model
Author
Chiang, Jia-Yo ; Breipohl, Arthur M. ; Lee, Fred N. ; Adapa, Rambabu
Author_Institution
Sch. of Electr. & Comput. Eng., Oklahoma Univ., Norman, OK, USA
Volume
15
Issue
4
fYear
2000
fDate
11/1/2000 12:00:00 AM
Firstpage
1212
Lastpage
1217
Abstract
The purpose of this paper is to provide a realistic load variation model to be used in short-term (one to three years) planning studies. A stochastic model is proposed, and this model is used to quantify the variation of the estimated production cost that is directly affected by the load uncertainty. The paper presents a method of estimating the variation of production cost. This is the first paper to use a Gauss-Markov stochastic model of load with a chronological production simulation model. This load model captures the stochastic load variation behavior and the correlation between weekly peak demand and weekly energy. A weekly Gauss-Markov sampling scheme is incorporated in the proposed approach to model load variation. This stochastic load model is used to generate sample chronological load profiles that represent the annual load variation in weekly detail. These load profiles are then used in annual Monte Carlo production simulation. Case studies illustrate the implementation of this stochastic load variation modeling. These case studies illustrate that load uncertainty has a significantly larger effect on cost uncertainty than does uncertainty in unit availability
Keywords
Gaussian processes; Markov processes; Monte Carlo methods; load (electric); power generation economics; power generation planning; Gauss-Markov stochastic load model; Monte Carlo production simulation; annual load variation; chronological production simulation model; load uncertainty; load variation modeling; production cost variance estimation; realistic load variation model; short-term planning; stochastic load model; stochastic load variation behavior; stochastic model; unit availability; weekly Gauss-Markov sampling scheme; weekly energy; weekly peak demand; Costs; Energy capture; Gaussian processes; Load management; Load modeling; Monte Carlo methods; Production; Sampling methods; Stochastic processes; Uncertainty;
fLanguage
English
Journal_Title
Power Systems, IEEE Transactions on
Publisher
ieee
ISSN
0885-8950
Type
jour
DOI
10.1109/59.898092
Filename
898092
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