scholarly journals Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1964
Author(s):  
Hong Gao ◽  
Kai Liu ◽  
Xinchao Peng ◽  
Cheng Li
Keyword(s):  
Electric Vehicles ◽  
Rapid Development ◽  
User Equilibrium ◽  
Hybrid Network ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Gasoline Vehicles ◽  
Level Model ◽  
Elastic Demands ◽  
Charging Stations

With the rapid development of electric vehicles (EVs), one of the urgent issues is how to deploy limited charging facilities to provide services for as many EVs as possible. This paper proposes a bilevel model to depict the interaction between traffic flow distribution and the location of charging stations (CSs) in the EVs and gasoline vehicles (GVs) hybrid network. The upper level model is a maximum flow-covering model where the CSs are deployed on links with higher demands. The lower level model is a stochastic user equilibrium model under elastic demands (SUE-ED) that considers both demands uncertainty and perceived path constraints, which have a significant influence on the distribution of link flow. Besides the path travel cost, the utility of charging facilities, charging speed, and waiting time at CSs due to space capacity restraint are also considered for the EVs when making a path assignment in the lower level model. A mixed-integer nonlinear program is constructed, and the equivalence of SUE-ED is proven, where a heuristic algorithm is used to solve the model. Finally, the network trial and sensitivity analysis are carried out to illustrate the feasibility and effectiveness of the proposed model.

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 Locating Multiple Size and Multiple Type of Charging Station for Battery Electricity Vehicles

2018 ◽  
Vol 10 (9) ◽  
pp. 3267 ◽  
Author(s):  
Shaohua Cui ◽  
Hui Zhao ◽  
Huijie Wen ◽  
Cuiping Zhang
Keyword(s):  
Travel Time ◽  
Electric Vehicles ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Range Limit ◽  
Mixed Integer Linear Program ◽  
Charging Station ◽  
Different Types ◽  
Initial Charge ◽  
Charging Stations

As environmental and energy issues have attracted more and more attention from the public, research on electric vehicles has become extensive and in-depth. As driving range limit is one of the key factors restricting the development of electric vehicles, the energy supply of electric vehicles mainly relies on the building of charging stations, battery swapping stations, and wireless charging lanes. Actually, the latter two kinds of infrastructure are seldom employed due to their immature technology, relatively large construction costs, and difficulty in standardization. Currently, charging stations are widely used since, in the real world, there are different types of charging station with various levels which could be suitable for the needs of network users. In the past, the study of the location charging stations for battery electric vehicles did not take the different sizes and different types into consideration. In fact, it is of great significance to set charging stations with multiple sizes and multiple types to meet the needs of network users. In the paper, we define the model as a location problem in a capacitated network with an agent technique using multiple sizes and multiple types and formulate the model as a 0–1 mixed integer linear program (MILP) to minimize the total trip travel time of all agents. Finally, we demonstrate the model through numerical examples on two networks and make sensitivity analyses on total budget, initial quantity, and the anxious range of agents accordingly. The results show that as the initial charge increases or the budget increases, travel time for all agents can be reduced; a reduction in range anxiety can increase travel time for all agents.

scholarly journals Operating Charging Infrastructure in China to Achieve Sustainable Transportation: The Choice between Company-Owned and Franchised Structures

2019 ◽  
Vol 11 (6) ◽  
pp. 1549 ◽  
Author(s):  
Lin Ma ◽  
Yuefan Zhai ◽  
Tian Wu
Keyword(s):  
Electric Vehicles ◽  
Rapid Development ◽  
Sustainable Transportation ◽  
Sustainable Infrastructure ◽  
Charging Infrastructure ◽  
Initial Stage ◽  
Charging Stations ◽  
Operation Cost ◽  
Optimal Quality

The rapid development of electric vehicles (EVs) is conducive to clean transportation, which is an important aspect of sustainable infrastructure. However, the introduction of EVs is constrained by the lagging development of EV chargers. To optimally promote the development of charging stations, we analyzed the differences in the optimal quality and quantity of EV chargers between company-owned and franchised enterprises by constructing a theoretical model, and the changes in the quality and quantity of EV chargers in different market environments are discussed. We found that the total number of franchised charging stations was larger in general, but that the quality of the franchised charging stations was worse compared with the company-owned stations. The supervision cost, operation cost, and the investment return affect the quality and quantity of EV chargers. Although franchised structures are more conducive in the initial stage to increasing the number of charging stations to meet the needs of EVs, company-owned structures perform better and will be needed to improve the quality of the EV chargers as the market becomes more saturated, necessitating a higher quality of EV chargers.

scholarly journals Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3056 ◽  
Author(s):  
Shaohua Cui ◽  
Hui Zhao ◽  
Cuiping Zhang
Keyword(s):  
Electric Vehicles ◽  
Location Problem ◽  
Capacity Constraints ◽  
Sustainable Transportation ◽  
Market Potential ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Link Capacity ◽  
Battery Electric Vehicles ◽  
Charging Stations

Compared with traditional fuel vehicles, battery electric vehicles (BEVs) as a sustainable transportation form can reduce carbon dioxide emissions and save energy, so its market share has great potential. However, there are some problems, such as: Their limited range, long recharging time, and scarce charging facilities, hindering improvement in the market potential of BEVs. Therefore, perfect and efficient charging facility deployment for BEVs is very important. For this reason, the optimal locations for charging stations for BEVs are investigated in this paper. Instead of flow-based formulation, this paper is based on agents under strictly imposed link capacity constraints, where all agents can select their routes and decide on the battery recharging plan without running out of charge. In our study, not only the locations of charging stations, but also the size of charging stations with the different number of chargers, would be taken into consideration. Then, this problem is formulated as a location problem for BEV charging stations of multiple sizes based on agents under link capacity constraints. This problem is referred to as the agent-refueling, multiple-size location problem with capacitated network (ARMSLP-CN). We formulate the ARMSLP-CN as a 0–1 mixed-integer linear program (MILP) with the aim to minimize the total trip time for all agents, including four parts, namely, the travel time, queue time, fixed time for recharging, and variable recharging time depending on the type of charger and the amount of power recharged, in which commercial solvers can solve the linearized model directly. To demonstrate this model, two different numerical instances are designed, and sensitivity analyses are also presented.

scholarly journals Location and Capacity Manufacturing Calculation of Electric Vehicle Charging Station Based on Queuing Theory

CONVERTER ◽  
2021 ◽  
pp. 529-542
Author(s):  
Qian Qian Mao, Et al.
Keyword(s):  
Electric Vehicles ◽  
Queuing Theory ◽  
Rapid Development ◽  
Theory Model ◽  
Construction Site ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
The World ◽  
Charging Stations ◽  
Minimum Objective Function

With the rapid development of industry and manufacturing, the world vigorously promotes green environmental protection, the construction of charging station is particularly important. It is urgent to study the location and capacity of charging station. In this paper, we will analyze the charging behavior of electric vehicles, and combined with the queuing theory model of electric vehicles, optimize the number of chargers by using the minimum objective function of the comprehensive cost of building charging stations. According to the maximum access power allowed by the distribution network, the service radius and scale of the target charging station are finally determined, and then 0-1 planning is used to finally determine the construction site. At the end, we give a specific example analysis to verify the effectiveness of the research results.

scholarly journals Charging station planning based on the accumulation prospect theory and dynamic user equilibrium

2021 ◽  
Author(s):  
Qiu Heting ◽  
Dou Shuihai ◽  
Shang Huayan ◽  
Zhang Jun
Keyword(s):  
Travel Time ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Traffic Equilibrium ◽  
Charging Station ◽  
Level Model ◽  
Upper Level ◽  
Generalized Cost ◽  
Range Limitation

AbstractThis study investigates the electric vehicle (EV) traffic equilibrium and optimal deployment of charging locations subject to range limitation. The problem is similar to a network design problem with traffic equilibrium, which is characterized by a bilevel model structure. The upper level objective is to optimally locate charging stations such that the total generalized cost of all users is minimized, where the user’s generalized cost includes two parts, travel time and energy consumption. The total generalized cost is a measure of the total societal cost. The lower level model seeks traffic equilibrium, in which travelers minimize their individual generalized cost. All the utilized paths have identical generalized cost while satisfying the range limitation constraint. In particular, we use origin-based flows to maintain the range limitation constraint at the path level without path enumeration. To obtain the global solution, the optimality condition of the lower level model is added to the upper level problem resulting in a single level model. The nonlinear travel time function is approximated by piecewise linear functions, enabling the problem to be formulated as a mixed integer linear program. We use a modest-sized network to analyze the model and illustrate that it can determine the optimal charging station locations in a planning context while factoring the EV users’ individual path choice behaviours.

scholarly journals Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

2021 ◽  
Vol 10 (5) ◽  
pp. 320
Author(s):  
Jing Kang ◽  
Changcheng Kan ◽  
Zhongjie Lin
Keyword(s):  
Urban Space ◽  
Electric Vehicles ◽  
Urban Form ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
Rapid Development ◽  
Interaction Patterns ◽  
Rapid Urbanization ◽  
Charging Stations ◽  
The City

With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

scholarly journals An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles

2019 ◽  
Vol 11 (24) ◽  
pp. 6945
Author(s):  
Qun Niu ◽  
Kecheng Jiang ◽  
Zhile Yang
Keyword(s):  
Electric Vehicles ◽  
Unit Commitment ◽  
Low Voltage ◽  
Polynomial Optimization ◽  
Rapid Development ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Unit Commitment Problem ◽  
Charging Mode ◽  
The Impact

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further mass roll-out of electric vehicles. To assess the impact of charging of substantial number of electric vehicles on the grid, a model of 30000 PEVs integrated with unit commitment (UCEV) was investigated in this study. The unit commitment was a large-scale, mixed-integer, nonlinear, NP-Hard (non-deterministic polynomial) optimization problem, while the integration of PEVs further increased the complexity of the model. In this paper, a global best inspired negatively correlated search (GBNCS) method which extends the evolutionary logic of negatively correlated search is proposed to tackle the UCEV problem. In the proposed algorithm, a rounding transfer function in GBNCS, is deployed to convert real-valued variables into binary ones; further, the global best information is combined in the population to improve the efficiency of the algorithm. Numerical results confirmed that the proposed GBNCS can achieve good performance in both a basic IEEE 10 unit commitment problem and the UCEV problem. It was also shown that, among four charging modes, the off-peak charging mode and EPRI (Electric Power Research Institute) charging mode are more economical in PEV charging.

scholarly journals A Pattern Analysis of Daily Electric Vehicle Charging Profiles: Operational Efficiency and Environmental Impacts

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ranjit R. Desai ◽  
Roger B. Chen ◽  
William Armington
Keyword(s):  
Electric Vehicles ◽  
Pattern Analysis ◽  
Ghg Emissions ◽  
Free Access ◽  
Cluster Membership ◽  
The Public ◽  
Electric Vehicle Charging ◽  
Gasoline Vehicles ◽  
Charging Stations ◽  
Specific Cluster

Plug-in Electric Vehicles (PEVs) are considered one solution to reducing GHG emissions from private transport. Additionally, PEV adopters often have free access to public charging facilities. Through a pattern analysis, this study identifies five distinct clusters of daily PEV charging profiles observed at the public charging stations. Empirically observed patterns indicate a significant amount of operational inefficiency, where 54% of the total parking duration PEVs do not consume electricity, preventing other users from charging. This study identifies the opportunity cost in terms of GHG emissions savings if gasoline vehicles are replaced with potential PEV adopters. The time spent in parking without charging by current PEV users can be used by these potential PEV users to charge their PEVs and replace the use of gasoline. The results suggest that reducing inefficient station use leads to significant reductions in emissions. Overall, there is significant variability in outcomes depending on the specific cluster membership.

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