Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10903
Title: Quasi-monte carlo based probabilistic small signal stability analysis for power systems with plug-in electric vehicle and wind power integration
Authors: Huang, H
Chung, CY
Chan, KW 
Chen, H
Keywords: Monte Carlo simulation
Plug-in electric vehicle
Probabilistic small signal stability analysis
Quasi-Monte Carlo
Sobol sequence
Wind energy conversion system
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2013, v. 28, no. 3, 6496179, p. 3335-3343 How to cite?
Journal: IEEE transactions on power systems 
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.
URI: http://hdl.handle.net/10397/10903
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2013.2254505
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