Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands
Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295043/full |
| _version_ | 1827379835076345856 |
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| author | Zhang Linjuan Fu Han Zhou Zhiheng Wang Shangbing Wang Shangbing Zhang Jinbin |
| author_facet | Zhang Linjuan Fu Han Zhou Zhiheng Wang Shangbing Wang Shangbing Zhang Jinbin |
| author_sort | Zhang Linjuan |
| collection | DOAJ |
| description | Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified. |
| first_indexed | 2024-03-08T13:22:54Z |
| format | Article |
| id | doaj.art-ae7d92e373ae4169bd4f914de64950b8 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T13:22:54Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-ae7d92e373ae4169bd4f914de64950b82024-01-17T16:16:41ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-01-011110.3389/fenrg.2023.12950431295043Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demandsZhang Linjuan0Fu Han1Zhou Zhiheng2Wang Shangbing3Wang Shangbing4Zhang Jinbin5Economics and Technology Research Institute of State Grid Henan Electric Power Company, Zhengzhou, Henan, ChinaHenan Society of Electrical Engineering, Zhengzhou, Henan, ChinaEconomics and Technology Research Institute of State Grid Henan Electric Power Company, Zhengzhou, Henan, ChinaDepartment of Automation, North China Electric Power University, Baoding, Hebei, ChinaBaoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System, Baoding, ChinaBeijing Qunling Energy Technology Co., Ltd., Beijing, ChinaAiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295043/fullcharging stationssite selectioncapacity determinationrobust optimizationweighted voronoi diagramadaptive simulated annealing particle swarm algorithm |
| spellingShingle | Zhang Linjuan Fu Han Zhou Zhiheng Wang Shangbing Wang Shangbing Zhang Jinbin Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands Frontiers in Energy Research charging stations site selection capacity determination robust optimization weighted voronoi diagram adaptive simulated annealing particle swarm algorithm |
| title | Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands |
| title_full | Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands |
| title_fullStr | Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands |
| title_full_unstemmed | Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands |
| title_short | Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands |
| title_sort | site selection and capacity determination of charging stations considering the uncertainty of users dynamic charging demands |
| topic | charging stations site selection capacity determination robust optimization weighted voronoi diagram adaptive simulated annealing particle swarm algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1295043/full |
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