Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar
To avoid the operational consequence of thermal rating exceedance and the financial consequence of excessive reinforcement, the impact of domestic charging of electric vehicles (EVs) on power distribution networks must be accurately assessed prior to accepting vehicle electrification at the communit...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10285483/ |
| _version_ | 1797652683374460928 |
|---|---|
| author | Fulin Fan I. Safak Bayram Usman Zafar Sertac Bayhan Bruce Stephen Stuart Galloway |
| author_facet | Fulin Fan I. Safak Bayram Usman Zafar Sertac Bayhan Bruce Stephen Stuart Galloway |
| author_sort | Fulin Fan |
| collection | DOAJ |
| description | To avoid the operational consequence of thermal rating exceedance and the financial consequence of excessive reinforcement, the impact of domestic charging of electric vehicles (EVs) on power distribution networks must be accurately assessed prior to accepting vehicle electrification at the community-scale. Although driven by routine, charging behaviour patterns are also influenced by geography, meteorological conditions and season, hence will have a localised element to them that could reduce the diversity of charging load profiles. To model this uncertainty, this article develops a probabilistic methodology to quantify EV home charging demands based on vehicle mobility data and underlying trip characteristics. Models articulate the departure time distribution using a mixture of von Mises distributions, and incorporate non-negative conditional distributions of trip durations, distances and parking durations, which in turn generalise localised charging behaviours. The resulting load profiles are used to drive a community electric network model based on a distribution feeder in Qatar, a country with high per km energy consumption, to quantify impact scenarios of high temperature and driving habit in terms of voltage and thermal stability. Results indicate that overnight domestic charging is sufficient to support daily trips and local networks are capable of hosting high EV penetration despite peaks. |
| first_indexed | 2024-03-11T16:33:33Z |
| format | Article |
| id | doaj.art-c9669c4f368f48aaaa5b838d4e514463 |
| institution | Directory Open Access Journal |
| issn | 2644-1330 |
| language | English |
| last_indexed | 2024-03-11T16:33:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj.art-c9669c4f368f48aaaa5b838d4e5144632023-10-23T23:00:39ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302023-01-01479680810.1109/OJVT.2023.332362610285483Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in QatarFulin Fan0https://orcid.org/0000-0003-2450-6877I. Safak Bayram1https://orcid.org/0000-0001-8130-5583Usman Zafar2https://orcid.org/0000-0001-5819-6024Sertac Bayhan3https://orcid.org/0000-0003-2027-532XBruce Stephen4https://orcid.org/0000-0001-7502-8129Stuart Galloway5https://orcid.org/0000-0003-1978-993XSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, ChinaDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, QatarQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, QatarDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.To avoid the operational consequence of thermal rating exceedance and the financial consequence of excessive reinforcement, the impact of domestic charging of electric vehicles (EVs) on power distribution networks must be accurately assessed prior to accepting vehicle electrification at the community-scale. Although driven by routine, charging behaviour patterns are also influenced by geography, meteorological conditions and season, hence will have a localised element to them that could reduce the diversity of charging load profiles. To model this uncertainty, this article develops a probabilistic methodology to quantify EV home charging demands based on vehicle mobility data and underlying trip characteristics. Models articulate the departure time distribution using a mixture of von Mises distributions, and incorporate non-negative conditional distributions of trip durations, distances and parking durations, which in turn generalise localised charging behaviours. The resulting load profiles are used to drive a community electric network model based on a distribution feeder in Qatar, a country with high per km energy consumption, to quantify impact scenarios of high temperature and driving habit in terms of voltage and thermal stability. Results indicate that overnight domestic charging is sufficient to support daily trips and local networks are capable of hosting high EV penetration despite peaks.https://ieeexplore.ieee.org/document/10285483/Electric network stabilityelectric vehicle demand synthesisprobabilistic assessmentvehicle mobility data |
| spellingShingle | Fulin Fan I. Safak Bayram Usman Zafar Sertac Bayhan Bruce Stephen Stuart Galloway Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar IEEE Open Journal of Vehicular Technology Electric network stability electric vehicle demand synthesis probabilistic assessment vehicle mobility data |
| title | Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar |
| title_full | Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar |
| title_fullStr | Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar |
| title_full_unstemmed | Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar |
| title_short | Probabilistic Assessment of Community-Scale Vehicle Electrification Using GPS-Based Vehicle Mobility Data: A Case Study in Qatar |
| title_sort | probabilistic assessment of community scale vehicle electrification using gps based vehicle mobility data a case study in qatar |
| topic | Electric network stability electric vehicle demand synthesis probabilistic assessment vehicle mobility data |
| url | https://ieeexplore.ieee.org/document/10285483/ |
| work_keys_str_mv | AT fulinfan probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar AT isafakbayram probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar AT usmanzafar probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar AT sertacbayhan probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar AT brucestephen probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar AT stuartgalloway probabilisticassessmentofcommunityscalevehicleelectrificationusinggpsbasedvehiclemobilitydataacasestudyinqatar |