scholarly journals Decision Model of Electric Vehicle Charging Station Configuration: A Case Study of the United States

2018 ◽  
Vol 394 (4) ◽  
pp. 042120
Author(s):  
Chuxi Li ◽  
Qiqi Wang ◽  
Zhuofan Shen ◽  
Qiang He
Keyword(s):  
United States ◽  
Electric Vehicle ◽  
Decision Model ◽  
The United States ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

2021 ◽  
Vol 9 ◽  
Author(s):  
Elias Hartvigsson ◽  
Niklas Jakobsson ◽  
Maria Taljegard ◽  
Mikael Odenberger
Keyword(s):  
United States ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
The United States ◽  
Power Demand ◽  
Driving Patterns ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact ◽  
Charging Demand

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

scholarly journals Payback Period and Life Cycle Emissions of a Commercial Solar Carport with a Virtual Case Study

2021 ◽  
Vol 335 ◽  
pp. 02008
Author(s):  
Kameswara Satya Prakash Oruganti ◽  
Chockalingam Aravind Vaithilingam ◽  
Agileswari Ramasamy ◽  
Gowthamraj Rajendran
Keyword(s):  
Life Cycle ◽  
Electric Vehicle ◽  
System Analysis ◽  
Payback Period ◽  
Vehicle To Grid ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Charging Stations

The solar carport is a significant technology-oriented infrastructural concept for facilitating electric vehicle charging stations (EVCS). The EVCS predominantly utilise the onsite solar photovoltaic energy for the charging of EVs. Moreover, EVCS can act as multipurpose CS to enable Grid to Vehicle (G2V) and Vehicle to Grid(V2G). Photovoltaic Electric vehicle charging station (PEVCS) can feed both EVs, traditional consumer loads, and can also feed power to the grid. Thus, enabling PEVCs across the various organisations and institutions can meet the local as well as dynamic demands incurred during charging of EVs. In this paper, a detailed economic and system analysis for the PEVCS is carried out using PVSyst and Helioscope for the area planning and shadow analysis. The normalised results of PEVCS is analysed along with the payback period and life cycle emissions are calculated for a virtual case study in Taylor’s University. At the end of the 25th year, based on the analysis, the overall payback and revenue for 25 years is 2,653.6 kMYR will be generated by selling energy at 0.58 MYR / kWh.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

scholarly journals An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

2021 ◽  
Vol 12 (3) ◽  
pp. 107
Author(s):  
Tao Chen ◽  
Peng Fu ◽  
Xiaojiao Chen ◽  
Sheng Dou ◽  
Liansheng Huang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Current Fluctuation ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Three Phase ◽  
Optimization Control ◽  
Dc Bus ◽  
Vehicle Charging

This paper presents a systematic structure and a control strategy for the electric vehicle charging station. The system uses a three-phase three-level neutral point clamped (NPC) rectifier to drive multiple three-phase three-level NPC converters to provide electric energy for electric vehicles. This topology can realize the single-phase AC mode, three-phase AC mode, and DC mode by adding some switches to meet different charging requirements. In the case of multiple electric vehicles charging simultaneously, a system optimization control algorithm is adopted to minimize DC-bus current fluctuation by analyzing and reconstructing the DC-bus current in various charging modes. This algorithm uses the genetic algorithm (ga) as the core of computing and reduces the number of change parameter variables within a limited range. The DC-bus current fluctuation is still minimal. The charging station system structure and the proposed system-level optimization control algorithm can improve the DC-side current stability through model calculation and simulation verification.

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