Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities
Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from op...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/16/4933 |
| _version_ | 1797524032353992704 |
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| author | Miguel Campaña Esteban Inga Jorge Cárdenas |
| author_facet | Miguel Campaña Esteban Inga Jorge Cárdenas |
| author_sort | Miguel Campaña |
| collection | DOAJ |
| description | Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research. |
| first_indexed | 2024-03-10T08:51:33Z |
| format | Article |
| id | doaj.art-4d628095940f42a48c26efeb232595fa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:51:33Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4d628095940f42a48c26efeb232595fa2023-11-22T07:29:30ZengMDPI AGEnergies1996-10732021-08-011416493310.3390/en14164933Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart CitiesMiguel Campaña0Esteban Inga1Jorge Cárdenas2Master of Electricity Program, Universidad Politécnica Salesiana, Quito 170525, EcuadorMaster of Electricity Program, Universidad Politécnica Salesiana, Quito 170525, EcuadorInclusive Education Research Group, Universidad Politécnica Salesiana, Quito 170525, EcuadorAchieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.https://www.mdpi.com/1996-1073/14/16/4933electric vehiclesgeo-referenced systemheterogeneous transportationtraffic analysisurban traffic system |
| spellingShingle | Miguel Campaña Esteban Inga Jorge Cárdenas Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities Energies electric vehicles geo-referenced system heterogeneous transportation traffic analysis urban traffic system |
| title | Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities |
| title_full | Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities |
| title_fullStr | Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities |
| title_full_unstemmed | Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities |
| title_short | Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities |
| title_sort | optimal sizing of electric vehicle charging stations considering urban traffic flow for smart cities |
| topic | electric vehicles geo-referenced system heterogeneous transportation traffic analysis urban traffic system |
| url | https://www.mdpi.com/1996-1073/14/16/4933 |
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