Planning of electric vehicle charging stations and distribution system with highly renewable penetrations
Abstract With the increasing prevalence of electric vehicles (EVs), the EV charging station (EVCS) and power distribution have become a coupled physical system. A multi‐objective planning model is developed herein for the sizing and siting of EVCSs and the expansion of a power distribution network w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | IET Electrical Systems in Transportation |
| Online Access: | https://doi.org/10.1049/els2.12022 |
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| _version_ | 1832546729656844288 |
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| author | Jingqi Zhang Shu Wang Cuo Zhang Fengji Luo Zhao Yang Dong Yingliang Li |
| author_facet | Jingqi Zhang Shu Wang Cuo Zhang Fengji Luo Zhao Yang Dong Yingliang Li |
| author_sort | Jingqi Zhang |
| collection | DOAJ |
| description | Abstract With the increasing prevalence of electric vehicles (EVs), the EV charging station (EVCS) and power distribution have become a coupled physical system. A multi‐objective planning model is developed herein for the sizing and siting of EVCSs and the expansion of a power distribution network with high wind power penetration. The objectives of the planning model are to minimise the total cost of investment and energy losses of the distribution system while maximising the total captured traffic flow. The uncertainties associated with wind power sources are considered. Additionally, the uncertainties in EV daily charging loads are also important concerns in the optimisation of the planning model. To model the EV load uncertainties, a recent scenario generation (SG) method is adopted. Further, a multi‐objective optimisation tool, Multi‐Objective Natural Aggregation Algorithm (MONAA), is introduced to obtain the final solutions of the planning model. The simulations based on coupled 54‐node distribution network and 25‐node traffic network systems are conducted to verify the efficiency of the proposed model and the effectiveness of SG‐based MONAA. |
| format | Article |
| id | doaj-art-55b4f420f1834d8fbdef7727f8e6a2cd |
| institution | Kabale University |
| issn | 2042-9738 2042-9746 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj-art-55b4f420f1834d8fbdef7727f8e6a2cd2025-02-03T06:47:18ZengWileyIET Electrical Systems in Transportation2042-97382042-97462021-09-0111325626810.1049/els2.12022Planning of electric vehicle charging stations and distribution system with highly renewable penetrationsJingqi Zhang0Shu Wang1Cuo Zhang2Fengji Luo3Zhao Yang Dong4Yingliang Li5School of Electrical Engineering and Telecommunications University of New South Wales Sydney AustraliaSchool of Automobile Chang'an University Xi'an ChinaSchool of Electrical Engineering and Telecommunications University of New South Wales Sydney AustraliaSchool of Civil Engineering University of Sydney Sydney AustraliaSchool of Electrical Engineering and Telecommunications University of New South Wales Sydney AustraliaSchool of Electronic Engineering Xi'an Shiyou University Xi'an ChinaAbstract With the increasing prevalence of electric vehicles (EVs), the EV charging station (EVCS) and power distribution have become a coupled physical system. A multi‐objective planning model is developed herein for the sizing and siting of EVCSs and the expansion of a power distribution network with high wind power penetration. The objectives of the planning model are to minimise the total cost of investment and energy losses of the distribution system while maximising the total captured traffic flow. The uncertainties associated with wind power sources are considered. Additionally, the uncertainties in EV daily charging loads are also important concerns in the optimisation of the planning model. To model the EV load uncertainties, a recent scenario generation (SG) method is adopted. Further, a multi‐objective optimisation tool, Multi‐Objective Natural Aggregation Algorithm (MONAA), is introduced to obtain the final solutions of the planning model. The simulations based on coupled 54‐node distribution network and 25‐node traffic network systems are conducted to verify the efficiency of the proposed model and the effectiveness of SG‐based MONAA.https://doi.org/10.1049/els2.12022 |
| spellingShingle | Jingqi Zhang Shu Wang Cuo Zhang Fengji Luo Zhao Yang Dong Yingliang Li Planning of electric vehicle charging stations and distribution system with highly renewable penetrations IET Electrical Systems in Transportation |
| title | Planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| title_full | Planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| title_fullStr | Planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| title_full_unstemmed | Planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| title_short | Planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| title_sort | planning of electric vehicle charging stations and distribution system with highly renewable penetrations |
| url | https://doi.org/10.1049/els2.12022 |
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