Optimal Urban EV Charging Station Site Selection and Capacity Determination Considering Comprehensive Benefits of Vehicle-Station-Grid
Article
Article Title | Optimal Urban EV Charging Station Site Selection and Capacity Determination Considering Comprehensive Benefits of Vehicle-Station-Grid |
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Article Category | Article |
Authors | Li, Hongwei, Song, Yufeng, Tan, Jiuding, Cui, Yi, Li, Shuaibing, Kang, Yongqiang and Dong, Haiying |
Journal Title | iEnergy |
Journal Citation | 3 (3), pp. 162-174 |
Number of Pages | 13 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Tsinghua University Press | |
Place of Publication | China |
ISSN | 2771-9197 |
Digital Object Identifier (DOI) | https://doi.org/10.23919/IEN.2024.0021 |
Web Address (URL) | https://ieeexplore.ieee.org/document/10703177 |
Abstract | This paper presents an optimization model for the location and capacity of electric vehicle (EV) charging stations. The model takes the multiple factors of the “vehicle-station-grid” system into account. Then, ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost, which includes the operator cost, user charging cost, and power grid loss. An immune particle swarm optimization algorithm (IPSOA) is proposed in this paper to obtain the optimal coupling strategy. The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems. It also balances the interests of users, operators, and the power grid. Compared with other schemes, the grid loss cost is reduced by 11.1% and 17.8%, and the total social cost decreases by 9.96% and 3.22%. |
Keywords | EVs; charging station; site selection and capacity determination; ArcScene; immune particle swarm optimization algorithm (IPSOA); road electrical coupling |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Public Notes | The accessible file is the submitted version of the paper. Please refer to the URL for the published version. |
Byline Affiliations | Lanzhou Jiaotong University, China |
School of Engineering |
https://research.usq.edu.au/item/zqzzv/optimal-urban-ev-charging-station-site-selection-and-capacity-determination-considering-comprehensive-benefits-of-vehicle-station-grid
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