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http://dx.doi.org/10.5370/JEET.2017.12.5.1743

Probabilistic Evaluation of Voltage Quality on Distribution System Containing Distributed Generation and Electric Vehicle Charging Load  

CHEN, Wei (Dept of Electrical Engineering and Information Engineering, Lanzhou University of Technology)
YAN, Hongqiang (Dept of Electrical Engineering and Information Engineering, Lanzhou University of Technology)
PEI, Xiping (Dept of Electrical Engineering and Information Engineering, Lanzhou University of Technology)
Publication Information
Journal of Electrical Engineering and Technology / v.12, no.5, 2017 , pp. 1743-1753 More about this Journal
Abstract
Since there are multiple random variables in the probabilistic load flow (PLF) calculation of distribution system containing distributed generation (DG) and electric vehicle charging load (EVCL), a Monte Carlo method based on composite sampling method is put forward according to the existing simple random sampling Monte Carlo simulation method (SRS-MCSM) to perform probabilistic assessment analysis of voltage quality of distribution system containing DG and EVCL. This method considers not only the randomness of wind speed and light intensity as well as the uncertainty of basic load and EVCL, but also other stochastic disturbances, such as the failure rate of the transmission line. According to the different characteristics of random factors, different sampling methods are applied. Simulation results on IEEE9 bus system and IEEE34 bus system demonstrates the validity, accuracy, rapidity and practicability of the proposed method. In contrast to the SRS-MCSM, the proposed method is of higher computational efficiency and better simulation accuracy. The variation of nodal voltages for distribution system before and after connecting DG and EVCL is compared and analyzed, especially the voltage fluctuation of the grid-connected point of DG and EVCL.
Keywords
Composite sampling method; Distributed generator; Electric vehicle charging load; Probabilistic load flow; Voltage quality;
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Times Cited By KSCI : 2  (Citation Analysis)
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