A New Stochastic Load Forecasting Model to Predict Load Growth on Radial Feeders

Gönen, Turan; Thompson, John C.

doi:10.1108/eb010008

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scenario, loads are modeled with the ND (Gonen and Thompson, 1985), based on a summer working day at 12:00 a.m. The mean and SD values of the active and reactive loads are detailed in Ruiz-Rodriguez et al (2012b).…”

Section: Case Studymentioning

confidence: 99%

Probabilistic load flow computation using saddle-point approximation

Kenari

Sepasian

Nazar

2017

COMPEL

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present a new cumulant-based method, based on the properties of saddle-point approximation (SPA), to solve the probabilistic load flow (PLF) problem for distribution networks with wind generation. Design/methodology/approach This technique combines cumulant properties with the SPA to improve the analytical approach of PLF calculation. The proposed approach takes into account the load demand and wind generation uncertainties in distribution networks, where a suitable probabilistic model of wind turbine (WT) is used. Findings The proposed procedure is applied to IEEE 33-bus distribution test system, and the results are discussed. The output variables, with and without WT connection, are presented for normal and gamma random variables (RVs). The case studies demonstrate that the proposed method gives accurate results with relatively low computational burden even for non-Gaussian probability density functions. Originality/value The main contribution of this paper is the use of SPA for the reconstruction of probability density function or cumulative distribution function in the PLF problem. To confirm the validity of the method, results are compared with Monte Carlo simulation and Gram–Charlier expansion results. From the viewpoint of accuracy and computational cost, SPA almost surpasses other approximations for obtaining the cumulative distribution function of the output RVs.

show abstract

Section: Case Studymentioning

confidence: 99%