A probabilistic modeling based on monte carlo simulation of wind powered ev charging stations for steady-states security analysis

Sunoh Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


As renewable energy resources such as wind and solar power are developing and the penetration of electric vehicles (EVs) is increasingly integrated into existing systems, uncertainty and variability in power systems have become important issues. The charging demands for EVs and wind power output are recognized as highly variable generation resources (VGRs) with uncertainty, which can cause unexpected disturbances such as short circuits. This can deteriorate the reliability of existing power systems. In response, research is required to identify the uncertainties presented by VGRs and is required to examine the ability of power system models to reflect those uncertainties. The deterministic method, which is the most basic method that is currently in use, does not reflect the uncertainty of system components. Therefore, this paper proposes a probabilistic method to assess the steady-state security of power systems, reflecting the uncertainty of VGRs using Monte Carlo simulation (MCS). In the proposed method, the empirical EVs charging demand and wind power output data are modeled as a probability distribution, and then MCS is performed, integrating the power system operation to represent the steady-state security as a probability index. To verify the method proposed in this paper, a security analysis was performed based on the systems in Jeju Island, South Korea, where the penetration of wind power and EVs is expanding rapidly.

Original languageEnglish
Article number5260
Issue number20
StatePublished - Oct 2020

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (2019371010006B, Development of core stabilizing technology for renewable power management system).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Electric vehicles charging demands
  • Gaussian mixture distribution
  • Monte-Carlo simulation
  • Steady-states security analysis
  • Weibull distribution
  • Wind power output


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