scholarly journals Deploying Electric Vehicle Charging Stations Considering Time Cost and Existing Infrastructure

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2436 ◽  
Author(s):  
Yuan Qiao ◽  
Kaisheng Huang ◽  
Johannes Jeub ◽  
Jianan Qian ◽  
Yizhou Song
Keyword(s):  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Central Area ◽  
Time Cost ◽  
Battery Electric Vehicles ◽  
Electric Vehicle Charging ◽  
Service Areas ◽  
Harsh Criticism ◽  
The Right ◽  
Charging Stations

Under the challenge of climate change, fuel-based vehicles have been receiving increasingly harsh criticism. To promote the use of battery electric vehicles (BEVs) as an alternative, many researchers have studied the deployment of BEVs. This paper proposes a new method to choose locations for new BEV charging stations considering drivers’ perceived time cost and the existing infrastructure. We construct probability equations to estimate drivers’ demanding time for charging (and waiting to charge), use the Voronoi diagram to separate the study area (i.e., Shanghai) into service areas, and apply an optimization algorithm to deploy the charging stations in the right locations. The results show that (1) the probability of charging at public charging stations is 39.6%, indicating BEV drivers prefer to charge at home; (2) Shanghai’s central area and two airports have the busiest charging stations, but drivers’ time costs are relatively low; and (3) our optimization algorithm successfully located two new charging stations surrounding the central area, matching with our expectations. This study provides a time-efficient way to decide where to build new charging stations to improve the existing infrastructure.

Impact of Electric Vehicle Charging Infrastructure on the Distribution Network Power Quality

2022 ◽  
pp. 114-132
Author(s):  
Gagandeep Sharma ◽  
Vijay K. Sood
Keyword(s):  
Power Quality ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Control Strategies ◽  
Charging Infrastructure ◽  
Battery Electric Vehicles ◽  
Electric Vehicle Charging ◽  
Fast Charging ◽  
Charging Stations

This chapter discusses the available charging systems for electric vehicles (EV) which include battery electric vehicles (BEV) and plugged hybrid electric vehicles (PHEV). These architectures are categorized as common DC bus charging (CDCB) station and common AC bus charging (CACB) station. CACB charging stations are generally used as slow chargers or semi-fast chargers (on-board chargers). CDCB charging stations are used as fast chargers (off-board chargers). These chargers are vital to popularize the electric vehicles (EVs) as a green alternative to the internal combustion engine (ICE) vehicles. Further, this chapter covers the power quality problems related to the grid-connected fast charging stations (FCS), AC-DC converter, control strategies for converters, proposed system of architectures, methodology, system results with comparisons, and finally, a conclusion.

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.

The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

2019 ◽  
Vol 33 (08) ◽  
pp. 1959027 ◽  
Author(s):  
Jing-min Wang ◽  
Yan Liu ◽  
Yi-fei Yang ◽  
Wei Cai ◽  
Dong-xuan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Robust Optimization ◽  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Site Selection ◽  
Queuing Theory ◽  
Location Model ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Charging Demand

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

scholarly journals Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2820 ◽  
Author(s):  
Hui Sun ◽  
Peng Yuan ◽  
Zhuoning Sun ◽  
Shubo Hu ◽  
Feixiang Peng ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Network Congestion ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Large Numbers ◽  
Pricing Scheme ◽  
Multiple Charging ◽  
Charging Stations

With the popularization of electric vehicles, free charging behaviors of electric vehicle owners can lead to uncertainty about charging in both time and space. A time-spatial dispatching strategy for the distribution network guided by electric vehicle charging fees is proposed in this paper, which aims to solve the network congestion problem caused by the unrestrained and free charging behaviors of large numbers of electric vehicles. In this strategy, congestion severity of different lines is analyzed and the relationship between the congested lines and the charging stations is clarified. A price elastic matrix is introduced to reflect the degree of owners’ response to the charging prices. A pricing scheme for optimal real-time charging fees for multiple charging stations is designed according to the congestion severity of the lines and the charging power of the related charging stations. Charging price at different charging station at different time is different, it can influence the charging behaviors of vehicle owners. The simulation results confirmed that the proposed congestion dispatching strategy considers the earnings of the operators, charging cost to the owners and the satisfaction of the owners. Moreover, the strategy can influence owners to make judicious charging plans that help to solve congestion problems in the network and improve the safety and economy of the power grid.

scholarly journals Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2479 ◽  
Author(s):  
Yue Wang ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Rui Wang
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimal Policy ◽  
Practical Case ◽  
Battery Electric Vehicle ◽  
Battery Electric Vehicles ◽  
Endurance Range ◽  
Highway Network ◽  
Charging Stations ◽  
The Impact

The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the market share of battery electric vehicles. In this paper, we investigate the joint optimal policy for a subsidy on electric vehicles and infrastructure construction in a highway network, where the impact of siting and sizing of fast charging stations and the impact of subsidy on the potential electric vehicle flows is considered. A new specified local search (LS)-based algorithm is developed to maximize the overall number of available battery electric vehicles in the network, which can get provide better solutions in most situations when compared with existed algorithms. Moreover, we firstly combined the existing algorithms to establish a multi-stage optimization method, which can obtain better solutions than all existed algorithms. A practical case from the highway network in Hunan, China, is studied to analyze the factors that impact the choice of subsidy and the deployment of charging stations. The results prove that the joint policy for subsidy and infrastructure construction can be effectively improved with the optimization model and the algorithms we developed. The managerial analysis indicates that the improvement on the capacity of charging facility can increase the proportion of construction fees in the total budget, while the improvement in the endurance range of battery electric vehicles is more efficient in expanding battery electric vehicle adoption in the highway network. A more detailed formulation of the battery electric vehicle flow demand and equilibrium situation will be studied in the future.

scholarly journals Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

2019 ◽  
Vol 9 (6) ◽  
pp. 5586
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicles ◽  
Service Providers ◽  
Real Life ◽  
Price Effect ◽  
Effect Analysis ◽  
Electric Vehicle Charging ◽  
Demand Patterns ◽  
Charging Stations ◽  
Occupancy Rates ◽  
Charging Demand

<p>This paper investigates the price effect to the charging demand coming from electric vehicles and then evaluates the performance of a pre-reservation mechanism using the real-life demand patterns. On the charging network in Jeju city, the occupancy rates for 3 price groups, namely, free, medium-price, and expensive chargers, are separated almost evenly by about 9.0 %, while a set of chargers dominates the charging demand during hot hours. The virtual pre-reservation scheme matches electric vehicles to a time slot of a charger so as not only to avoid intolerable waiting time in charging stations systematically but also to increase the revenue of service providers, taking into account both bidding levels specified by electric vehicles and preference criteria defined by chargers. The performance analysis results obtained by prototype implementation show that the proposed pre-reservation mechanism improves the revenue of service providers by up to 9.5 % and 42.9 %, compared with the legacy FCFS and reservation-less walk-in schemes for the given performance parameter sets.</p>

scholarly journals Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV

2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Igna Vermeulen ◽  
Jurjen Rienk Helmus ◽  
Mike Lees ◽  
Robert van den Hoed
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Early Years ◽  
Charging Infrastructure ◽  
Agent Based ◽  
Electric Vehicle Charging ◽  
Subsidy Scheme ◽  
Infrastructure Performance ◽  
Charging Stations

The Netherlands is a frontrunner in the field of public charging infrastructure, having one of the highest number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012–2015), a large percentage of the EV fleet were plugin hybrid electric vehicles (PHEV) due to the subsidy scheme at that time. With an increasing number of full electric vehicles (FEVs) on the market and a current subsidy scheme for FEVs only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have an effect on the charging behavior of the complete fleet, and is reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing agent-based model. Results show important effects of this transition on charging infrastructure performance.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

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