Quasi-monte carlo based probabilistic small signal stability analysis for power systems with plug-in electric vehicle and wind power integration

Huazhang Huang, C. Y. Chung, Ka Wing Chan, Haoyong Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

95 Citations (Scopus)

Abstract

This paper presents a new quasi-Monte Carlo (QMC) based probabilistic small signal stability analysis (PSSSA) method to assess the dynamic effects of plug-in electric vehicles (PEVs) and wind energy conversion systems (WECSs) in power systems. The detailed dynamic model of PEVs is first proposed for stability study. To account for the stochastic behavior of PEVs and WECSs in load flow studies, the randomized model and probability density function (PDF) representing their nodal power injections are first developed, and then their stochastic injections are sampled by Sobol sequences. Finally, the distribution of system eigenvalues can be obtained by the PSSSA. The proposed QMC-based PSSSA is tested on the modified 2-area 4-machine system and New England 10-generator 39-bus system. Results showed the necessity of modeling of PEVs and WECSs, and validated the efficiency of the proposed QMC.
Original languageEnglish
Article number6496179
Pages (from-to)3335-3343
Number of pages9
JournalIEEE Transactions on Power Systems
Volume28
Issue number3
DOIs
Publication statusPublished - 15 Apr 2013

Keywords

  • Monte Carlo simulation
  • plug-in electric vehicle
  • probabilistic small signal stability analysis
  • quasi-Monte Carlo
  • Sobol sequence
  • wind energy conversion system

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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