Consideration of load forecast uncertainty in calculating the optimal bidding strategy of Generating Companies

Kabiri, Mehdi; Akbari, Shahabeddin; Amjady, Nima; Taher, Seyed Abbas

doi:10.1109/eurcon.2009.5167698

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The empirical CCDF is obtained from the frequency table as it was described in Section II while the theoretical CCDF for power law distribution is described in (3) (after setting =1) in which is estimated using the maximum likelihood principal based on the truncated data. The differences between the theoretical and empirical CCDFs are assessed using Kolmogorov-Smirnov (KS) test statistic as represented in (4). Note that the KS test statistic finds the largest distance between the theoretical and empirical CCDFs.…”

Section: A Estimating Power Law Distribution Parametersmentioning

confidence: 99%

“…Conventionally, LF research is focused on providing point estimation of target load [2]. However, there are some decision making applications such as probabilistic load flow [3], electricity market [4], unit commitment [5], and reliability planning [6] which rely on probabilistic load forecasting (PLF). In addition, the uncertainty of forecasting model inputs (weather and economic indicators) [7,8], and the increasing penetration of DERs which imposes high variability into power distribution system cause more attention to PLF.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic models for daily peak loads at distribution feeders

Sangrody

Zhou

Qiao

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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Load forecasting at distribution networks is more challenging than load forecasting at transmission networks because its load pattern is more stochastic and unpredictable. To plan sufficient resources and estimate DER hosting capacity, it is invaluable for a distribution network planner to get the probabilistic distribution of daily peak-load under a feeder over long term. In this paper, we model the probabilistic distribution functions of daily peak-load under a feeder using power law distributions, which is tested by improved Kolmogorov-Smirnov test enhanced by the Monte Carlo simulation approach. In addition, the uncertainty of the modeling is quantified using the bootstrap method. The methodology of parameter estimation of the probabilistic model and the hypothesis test is elaborated in detail. In the case studies, it is shown using measurement data sets that the daily peak-loads under several feeders follow the power law distribution by applying the proposed testing methods.Index Terms-load forecasting, daily peak load probabilistic model, power law distribution, empirical load modeling, probabilistic forecasting I.

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Section: A Estimating Power Law Distribution Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic models for daily peak loads at distribution feeders

Sangrody

Zhou

Qiao

2017

2017 IEEE Power &Amp; Energy Society General Meeting

View full text Add to dashboard Cite

show abstract

“…Beside bi-level problems, there are references that bi-level problem is converted into one-level optimisation problem. In [9], using SEF for modelling competition, the relation between market clearing price and proposed bid is obtained as parametric equation. In [10], a stochastic EPEC model is presented, and load uncertainty effects are considered.…”

Section: Introductionmentioning

confidence: 99%