Characterization and distribution model for fuel costs in electricity market

Fu, Chang; Wang, Caisheng; Miller, Carol J.; Wang, Yang

doi:10.1109/iesc.2017.8167479

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Since the normal distribution in ( 11) has already been clarified and proven to be a good selection for estimating the fuel cost distribution in [3]. This paper keeps using the standard normal distribution on fitting the data with maximum likelihood method to estimate the probability density function.…”

Section: B Normal Distribution Fittingmentioning

confidence: 99%

“…The US Energy Information Administration (EIA)-923 form collects detailed monthly and annual electric power data and can be used in developing fuel cost model. In [3], a FC characterization and distribution method is carried out based on publicly available data. The EIA-923 Form [4] is used in [3] to calculate the FC distributions for different area-fuel type combinations based on different plant IDs, fuel quantities, average heat contents, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], a FC characterization and distribution method is carried out based on publicly available data. The EIA-923 Form [4] is used in [3] to calculate the FC distributions for different area-fuel type combinations based on different plant IDs, fuel quantities, average heat contents, and so on. The FC distributions can be used to generate large and realistic power system network models without compromising the confidentiality of the utilities.…”

Section: Introductionmentioning

confidence: 99%

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Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Zhao

Wang

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.

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Section: B Normal Distribution Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Zhao

Wang

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Electrical energy stealing is a notorious problem in electric power systems and has serious implications for both utility companies and legitimate users. The U.S., it is estimated that utility companies lose billions of dollars in revenue every year [47, 131,132], while in developing countries energy theft can amount to 50% of the total energy delivered [46]. Energy stealing also leads to excessive energy consumption which may cause equipment malfunction or damage [47], and often enables criminal activities, such as the illegal production of substances.…”

Section: Ongoing Training and Study Resultsmentioning

confidence: 99%