Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data

The penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Deter...

Full description

Bibliographic Details
Main Authors: Abdulaziz Almutairi, Saeed Alyami
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9402264/
id doaj-08410df783234978bafbbfed89230fbb
record_format Article
spelling doaj-08410df783234978bafbbfed89230fbb2021-04-23T23:01:00ZengIEEEIEEE Access2169-35362021-01-019596375964810.1109/ACCESS.2021.30729829402264Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test DataAbdulaziz Almutairi0https://orcid.org/0000-0003-2374-9754Saeed Alyami1https://orcid.org/0000-0003-0647-6993Electrical Engineering Department, College of Engineering, Majmaah University, Al Majma’ah, Saudi ArabiaElectrical Engineering Department, College of Engineering, Majmaah University, Al Majma’ah, Saudi ArabiaThe penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Determining realistic PEV load profiles is challenging due to the involvement of serval uncertainties and complex interdependencies among different factors and to date, there are no benchmark load profiles of PEVs. In this paper, realistic and ready-to-use load profiles for PEVs are developed by considering vehicle mobility, charging infrastructure, and the market share of PEVs. Firstly, the U.S. National Household Travel Survey (NHTS) data is filtered to remove vehicles with unrealistic, duplicate, and missing data. Secondly, a set of relevant parameters is extracted to estimate different features of PEVs, such as arrival time, departure time, and daily mileage. Then, all the commercially available PEVs are grouped into four clusters using the K-means algorithm. Finally, the per unit (per PEV) load profiles are estimated using the information of the available PEVs in the market, charging levels in the residential sector, and features extracted in the previous step. A large set of scenarios are considered for each PEV cluster in determining the load profiles. The pre-unit profiles estimated in this study are ready-to-use for researchers and planners in the PEV industry and are realistic due to consideration of different relevant factors and a large traveling database of vehicles. The developed per-unit load profiles are used to estimate and analyze the PEV load profiles of the top four countries with the highest penetration percentage of PEVs.https://ieeexplore.ieee.org/document/9402264/Load profilepeak demand estimationPEV demand estimationPEV loadPEV policymakersplug-in electric vehicles
collection DOAJ
language English
format Article
sources DOAJ
author Abdulaziz Almutairi
Saeed Alyami
spellingShingle Abdulaziz Almutairi
Saeed Alyami
Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
IEEE Access
Load profile
peak demand estimation
PEV demand estimation
PEV load
PEV policymakers
plug-in electric vehicles
author_facet Abdulaziz Almutairi
Saeed Alyami
author_sort Abdulaziz Almutairi
title Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
title_short Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
title_full Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
title_fullStr Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
title_full_unstemmed Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data
title_sort load profile modeling of plug-in electric vehicles: realistic and ready-to-use benchmark test data
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description The penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Determining realistic PEV load profiles is challenging due to the involvement of serval uncertainties and complex interdependencies among different factors and to date, there are no benchmark load profiles of PEVs. In this paper, realistic and ready-to-use load profiles for PEVs are developed by considering vehicle mobility, charging infrastructure, and the market share of PEVs. Firstly, the U.S. National Household Travel Survey (NHTS) data is filtered to remove vehicles with unrealistic, duplicate, and missing data. Secondly, a set of relevant parameters is extracted to estimate different features of PEVs, such as arrival time, departure time, and daily mileage. Then, all the commercially available PEVs are grouped into four clusters using the K-means algorithm. Finally, the per unit (per PEV) load profiles are estimated using the information of the available PEVs in the market, charging levels in the residential sector, and features extracted in the previous step. A large set of scenarios are considered for each PEV cluster in determining the load profiles. The pre-unit profiles estimated in this study are ready-to-use for researchers and planners in the PEV industry and are realistic due to consideration of different relevant factors and a large traveling database of vehicles. The developed per-unit load profiles are used to estimate and analyze the PEV load profiles of the top four countries with the highest penetration percentage of PEVs.
topic Load profile
peak demand estimation
PEV demand estimation
PEV load
PEV policymakers
plug-in electric vehicles
url https://ieeexplore.ieee.org/document/9402264/
work_keys_str_mv AT abdulazizalmutairi loadprofilemodelingofpluginelectricvehiclesrealisticandreadytousebenchmarktestdata
AT saeedalyami loadprofilemodelingofpluginelectricvehiclesrealisticandreadytousebenchmarktestdata
_version_ 1714660933992710144