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CSEE Journal of Power and Energy Systems 2023, 9(5): 1845-1853 https://doi.org/10.17775/CSEEJPES.2020.01030
Regular Paper | Open Access | Issue | Published: 21 December 2020

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

Show Author's Information Songyu Huang1,  Chengjin Ye1( ),  Si Liu2,  Wei Zhang2,  Yi Ding1,  Ruoyun Hu2,  Jianbai Li2
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China
Keywords:
data-driven, electric vehicles, Charging loads, diffusion estimator, slice sampling, system reliability
Cite this article:
Huang S, Ye C, Liu S, et al. Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1845-1853. https://doi.org/10.17775/CSEEJPES.2020.01030
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Abstract Full text About this article

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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About this article

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

Show Author's information Songyu Huang1, Chengjin Ye1( ), Si Liu2, Wei Zhang2, Yi Ding1, Ruoyun Hu2, Jianbai Li2
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China

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.

Keywords: data-driven, electric vehicles, Charging loads, diffusion estimator, slice sampling, system reliability

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Publication history

Received: 31 March 2020
Revised: 06 September 2020
Accepted: 08 December 2020
Published: 21 December 2020
Issue date: September 2023

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© 2020 CSEE.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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