A Copula-Based Uncertainty Modeling of Wind Power Generation for Probabilistic Power Flow Study

Wolf Peter Jean Philippe, Sara Eftekharnejad, Prasanta K. Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, a probabilistic power flow (PPF) modeling of the uncertainty of wind power generation is proposed. The developed PPF is based on the theories of point estimate method (PEM) and regular vine (R-vine) copula. For a large number of wind power resources, the computational burden related to construction of an R-vine copula increases significantly. Therefore, an algorithm based on Kullback-Leibler (KL) distance is introduced to tackle the computational burden to form an R-vine. The proposed PPF is tested on two different testbeds: IEEE 57-bus and Illinois 200-bus systems. The obtained results show that the proposed method is effective and accurate when compared to PPF results obtained by either a Monte Carlo simulation (MCS) or a multivariate Gaussian copula.

Original languageEnglish (US)
Title of host publicationProceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-222
Number of pages5
ISBN (Electronic)9781728124407
DOIs
StatePublished - Aug 2019
Event7th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2019 - Oshawa, Canada
Duration: Aug 12 2019Aug 14 2019

Publication series

NameProceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019

Conference

Conference7th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2019
CountryCanada
CityOshawa
Period8/12/198/14/19

Keywords

  • Monte Carlo Simulation
  • point estimate method
  • probabilistic power flow
  • vine copula
  • wind power generation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A Copula-Based Uncertainty Modeling of Wind Power Generation for Probabilistic Power Flow Study'. Together they form a unique fingerprint.

  • Cite this

    Philippe, W. P. J., Eftekharnejad, S., & Ghosh, P. K. (2019). A Copula-Based Uncertainty Modeling of Wind Power Generation for Probabilistic Power Flow Study. In Proceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019 (pp. 218-222). [8859746] (Proceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SEGE.2019.8859746