Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling

Songyu Huang; Chengjin Ye; Si Liu; Wei Zhang; Yi Ding; Ruoyun Hu; Jianbai Li

doi:10.17775/CSEEJPES.2020.01030

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Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling

Regular Papers | 更新时间：2026-10-15

- Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 5, Pages: 1845-1853(2023)
- 作者机构：
  
  1. College of Electrical Engineering Zhejiang University,Hangzhou,China
  2. State Grid Zhejiang Electric Power Research Institute,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.01030
  CLC：
- Published：01 September 2023
- 稿件说明：
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Songyu Huang, Chengjin Ye, Si Liu, et al. Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling[J]. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1845-1853.
DOI：

Songyu Huang, Chengjin Ye, Si Liu, et al. Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling[J]. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1845-1853. DOI： 10.17775/CSEEJPES.2020.01030.

摘要

Abstract

Due to the stochasticity of charging behaviors of electric vehicles (EVs)

it is difficult to anticipate when charging load demand will be densely concentrated. If massive charging loads and the system peak profile appear at the same time

it may pose a risk to the reliable operation of power grids. For a system integrated with renewable energies

this risk can be much higher because of their unsteady power output. With load measurements more widely collected

this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs. Specifically

the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads

which possesses both regional adaptivity and good boundary estimation performance. Then

charging load samples are produced through slice sampling. It is capable of sampling from irregularly-shaped distributions with high accuracy. The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.

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references

1. International Energy Agency. ( 2019, May ). Global EV Outlook 2019 . [Online ] . Available: https://www.iea.org/reports/global-ev-outlook-2019.

22. M. Steyvers. ( 2011, May ). Computational Statistics with MATLAB. [Online]. Available: http://entsphere.com/pub/pdf/Papers/Physical%20Simulations/2011%20Steyvers,%20Computational%20Statistics%20with%20Matlab.pdf

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