• DocumentCode
    14522
  • Title

    Quasi-Monte Carlo Based Probabilistic Small Signal Stability Analysis for Power Systems With Plug-In Electric Vehicle and Wind Power Integration

  • Author

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

  • Author_Institution
    Dept. of Electr. Eng., Hong Kong Polytech. Univ., Hong Kong, China
  • Volume
    28
  • Issue
    3
  • fYear
    2013
  • fDate
    Aug. 2013
  • Firstpage
    3335
  • Lastpage
    3343
  • 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.
  • Keywords
    Monte Carlo methods; battery chargers; eigenvalues and eigenfunctions; electric vehicles; power system stability; probability; wind power; 2-area 4-machine system; New England 10-generator 39-bus system; PDF; PEV; PSSSA method; Sobol sequences; WECS; plug-in electric vehicles; power systems; probabilistic small signal stability analysis; probability density function; quasiMonte Carlo method; wind energy conversion systems; wind power integration; Monte Carlo simulation; Sobol sequence; plug-in electric vehicle; probabilistic small signal stability analysis; quasi-Monte Carlo; wind energy conversion system;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2013.2254505
  • Filename
    6496179