NDLI: Improved probabilistic load flow method based on D-vine copulas and Latin hypercube sampling in distribution network with multiple wind generators

Content Provider	IET Digital Library
Author	Huang, Yueshan Chen, Shuheng Chen, Zhe Hu, Weihao Huang, Qi
Abstract	In this study, a D-vine copulas modelling based probabilistic load flow (PLF) computation method is proposed, which considers the dependence among multiple wind generators. Furthermore, this method is not restricted by the type of wind speed distribution, i.e. allow random variables to comply with any types of distribution model. Copula theory plays an important role on dependency modelling. However, when high-dimensional correlation is taken into account, standard multivariate copula suffers from the problems of inflexible structure. Vine copula is flexible to build high-dimensional dependence and able to construct complicated dependence structure by applying bivariate copulas. For marginal distributions of wind speed, non-parametric model can provide a better estimation than those parametric models. An improved Latin hypercube sampling based Monte Carlo simulation method is utilised to solve PLF problems. A modified IEEE 33-node distribution system is used to conduct the numerical experiments for the accuracy and efficiency verification of the proposed PLF method, under the MatlabR2016a platform. The simulation results verify the outstanding accuracy, efficiency and robustness of the proposed PLF method.
Starting Page	893
Ending Page	899
Page Count	7
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 5, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1126
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-01-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bivariate Copulas Complicated Dependence Structure Copula Theory D-vine Copulas Dependency Modelling Distribution Model Distribution Network High-dimensional Correlation High-dimensional Dependence Improved Latin Hypercube Sampling Improved Probabilistic Load Flow Method Inflexible Structure Load Flow Marginal Distribution Modified IEEE 33-node Distribution System Monte Carlo Method Monte Carlo Simulation Method Multiple Wind Generator Nonparametric Model Optimisation Technique Parametric Model PLF Method PLF Problem Probabilistic Load Flow Computation Method Probability Probability Theory Random Variables Sampling Method Standard Multivariate Copula Suffers Statistical Distribution Statistics Stochastic Linearised SCUC Vine Copula Wind Power Plant Wind Speed Distribution
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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