Location Of Electric Vehicle Charging Stations Research Articles

A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location

This work focuses on a hybrid preference-based electric vehicle charging station location problem, which considers multiple optimization preferences of distribution network operators, charge station owners, and electric vehicle users. The problem is formulated by an uncertain mixed-integer programming model. Due to the multi-fold uncertainty of the charging process, the uncertain model parameters are expressed as Type-2 fuzzy variables (T2-FVs). The critical value-based type reduction method is adopted to handle the high computational complexity. The proposed uncertain model is converted to its equivalent deterministic chance-constrained programming model. The deterministic counterpart is solved by General Algebraic Modeling System (GAMS). At last, numerical simulations are performed to demonstrate the proposed location strategy as well as some sensitivity analyses. The results indicate that for any given parameters, the equivalent deterministic model follows the general form of mixed-integer programming one that can be easily solved by GAMS. The proposed methodology can effectively handle the multi-fold uncertainty of the charging process. Compared with crisp models, the proposed location strategy can provide more robust location decisions for electric vehicle charging stations (EVCSs). In addition, we also found that different interest groups have conflicting preferences for the locations of EVCSs, so considering multiple hybrid optimization preferences is necessary.

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.

Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning

To accommodate the increased demand for electric vehicle(EV) charging in China, this paper examines electric vehicle charging station(EVCS) location strategies from the perspective of private investors, taking full account of the competitive environment of existing EVCSs in the region. First of all, a three-level location model considering dynamic pricing is developed, which includes user decisions, EVCS pricing, and EVCS location decisions. Then, the soft actor-critic(SAC) reinforcement learning algorithm is used to train the optimal pricing strategy for EVCS to guarantee the maximum cumulative revenue. The proposed methodology is verified through case studies based on an industrial park in China. The results show that the proposed methodology can make more economical and scientific location decisions than the traditional method. The dynamic pricing method based on reinforcement learning can provide a reference for the location and operation of more EVCSs.

An approach for selecting optimal locations for electric vehicle solar charging stations

AbstractElectric vehicles (EVs) are seen as a solution to reduce transport‐related greenhouse gas emissions. A major obstacle to wider adoption is the insufficient amount of charging stations. Furthermore, supplying charging stations with renewable energy is still in its infancy. The selection of optimal locations for charging stations is important to best serve the users and maximise the possibilities of renewable energy use. Given this background, this study developed an approach for Solar‐supplied Electric Vehicle Charging Station (EVCS) location selection by combining EVCS and solar farm site selection studies using Geographical Information System (GIS) and Analytic Hierarchy Process (AHP). The study determined the most important criteria for site selection based on previous solar and EVCS site selection studies and expert opinions. The 10 most important criteria according to the survey results were: availability of power, solar energy potential, solar panel installation cost, number of EVs, operation and management costs, land cost, distance from roads/highways, distance from current EVCSs, industrial capability of installation and distance to high population density centres. The importance weights of these criteria were assigned using AHP method. The findings are expected to benefit urban planners, decision‐makers, and researchers designing solar‐supplied EV charging infrastructure.

A novel normal wiggly hesitant fuzzy multi-criteria group decision making method and its application to electric vehicle charging station location

Electric vehicle charging station location (EVCSL) is critical to promoting the electric vehicle industry and how it is evaluated is typically a MCDM issue. In this paper, an applicable multi-criteria group decision support model is developed to evaluate the electric vehicle charging station location. To do this, we used normal wiggly hesitant fuzzy set to describe the fuzzy decision-making information, which can further dig the potential uncertainty. Considering that the experts’ preference can lead to significant changes in the results, k-means clustering method is used to group the experts in different fields. Moreover, the improved correlation coefficient and standard deviation (CCSD) method is used to determine the criterion weights, and considering that prospect theory can reflect the mental behavior of experts and the simplicity and efficiency of weighted aggregated sum product assessment (WASPAS) method, a combined ranking model is proposed to evaluate the alternatives comprehensively. The procedure of the model is given in detail, and the example of electric vehicle charging station location evaluation is employed to demonstrate our results. Comparative analyses and discussion show that the proposed model has powerful effectiveness, performs better than other existing methods, and largely extends the applicability of the method with theoretical and practical implications.

Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety

Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better.

A two-stage ambiguous stochastic program for electric vehicle charging station location problem with valet charging service

This paper introduces a new charging service mode that is emerging in the market for electric vehicles called the valet charging service. Through this service mode, a company provides free valet charging services to users that are far away from charging stations, and users can request the fee-based service when they are unwilling to charge electric vehicles themselves. The valet charging service is a new service mode that promises to alleviate users’ worries regarding the lack of nearby charging stations. However, whether such a mode truly benefits a specified system remains open to careful study. We build the charging station locating and sizing model with the valet charging service under demand uncertainty. We first formulate this problem as a two-stage stochastic mixed-integer model (TSMIP). Then, considering that the demand probability distribution is difficult to estimate, we reformulate TSMIP into a risk-averse two-stage stochastic mixed-integer model (RTSMIP) with the ambiguity set. Next, we propose an SAA-based hybrid-cut L-shaped algorithm to solve this model. By comparing the total cost of the system with and without the valet charging service under different scenarios through hypothetical numerical experiments and a realistic case study in Shanghai, meaningful insights are provided that include identifying the conditions in which the valet charging service works well and how it influences the location and capacity of charging stations. These insights provide some understanding of this new service mode that can help companies make rational decisions.

Optimal Allocation for Electric Vehicle Charging Stations

Currently, more than 30% of the fine dust generated in the Seoul metropolitan area is a pollutant emitted from automobiles such as diesel vehicles, and air pollution caused by this is becoming increasingly serious. In addition, the importance of electric vehicle distribution is increasing due to the strengthening of international environmental regulations on automobile exhaust gas and increasing the possibility of depletion of petroleum resources. This manuscript proposes a method for selecting an optimal electric vehicle charging station location in expanding charging facilities to activate electric vehicle distribution. For the sake of illustration, directions will be provided on how to select the best location for electric vehicle charging stations using data from Seoul, which has the best access. As the features, the number of living population and work force people and the number of guest facilities, which are determined to affect demand for quick charging, are considered. The missing values of the observed data are imputed based on the kriging technique from spatial correlation, and by segmenting the data through clustering, a representative technique of unsupervised learning, the characteristics of each cluster are examined and the characteristics of the clusters are identified. In addition, machine learning techniques such as the elastic net, random forest, support vector machine, and extreme gradient boosting are applied to examine the influence of the features used in predicting classes of data. In clustering analysis, the optimal number of clusters was determined to be 3 based on the heuristic and information-theoretic methods, and all the machine learning techniques considered showed that the number of work force population is the most important feature in predicting classes of data. All things considered from our results, it is reasonable to install quick electric vehicle charging stations in the places with the highest concentration of work force population and guest facility.

Electric vehicle charging station location determination with consideration of routing selection policies and driver’s risk preference

Nowadays, petroleum resources reduction along with increased fossil fuels price and severe pollution in large cities have invoked the consumption of clean energy. The advancement of modern electricity generation infrastructures and substantial improvement in automobile industry make electric vehicles become the promising alternative in the common transportation fleet. Motivating people to use electric vehicles and removing its development obstacles, require to identify appropriate locations for the installation of public charging stations. Having one reserve battery as the complement is one of the primary options to reduce the electric driving range limitation for inter-city travels. This paper proposes a three-stage model consisting of: (1) regional traffic load network development using different route policies and Monte Carlo simulation, (2) vehicle battery capacity/driving range determination with consideration of drivers’ risk preference towards reserve battery, and (3) mixed integer linear programming model for charging station location selection and socket count determination. A novel metaheuristic algorithm based on scatter search and variable neighborhood search algorithm is proposed to identify non-dominated solutions. A numerical example and charging stations in the state of California are used as the case studies to validate the proposed model and algorithm performance. The results confirm that the low traffic regions near large cities would satisfy the charge demands and reduce the installation cost simultaneously.

Robust model of electric vehicle charging station location considering renewable energy and storage equipment

The location of electric vehicle charging station (EVCS) is one of the critical problems that restricts the popularization of electric vehicle (EV), and the combination of EVCS and distributed renewable energy can stabilize the fluctuation of renewable energy output. This article takes a micro-grid composed of the power distribution such as wind power and photovoltaic (PV), EVCSs and energy storage systems (ESS) as the research object. The uncertainties of EVs’ charging demand and distributed renewable energy output are considered. A robust optimization model for the location of charging stations with distributed energy is proposed based on the combination of the road network and the grid. Load fluctuation rate is used to evaluate the degree of fit between the renewable energy uncertain output curve and the charging demand curve to determine the appropriate capacity of the wind and PV generation system and ESS. The method of kernel density estimation is used to improve the issue of over-conservatism of the robust optimization. Finally, a simulation is carried out on a network which consists of an IEEE 33-node power distribution network and a 25-node transportation system. The robustness and economy of the model are demonstrated by the results of simulation given.

Electric Vehicle Charging Station Location Decision Analysis for a Two-Stage Optimization Model Based on Shapley Function

The promotion of electric vehicles and their charging facilities to achieve carbon emission reduction is a research hotspot in the field of transportation. Aiming at the comprehensive decision of electric vehicle charging station (EVCS) location, this paper constructs an EVCS location evaluation index system that includes five indexes of grid load, traffic facilities, user preference, construction cost, and service radius. Firstly, we convert the exact number into interval judgment matrix, introduce Shapley fuzzy measure to calculate the weight of factors, and use the two-stage optimization model to further optimize the weight. Then, we combine the multiple criteria decision-making (MCDM) method in the Pythagorean fuzzy environment with partitioned normalized weighted Bonferroni mean (PFPNWBM) operator, and calculate the optimal ranking of alternatives according to the performance function and the accuracy function. Finally, a numerical example is used to analyze the difference between first-order linear optimization and two-stage optimization in alternative scheme evaluation, and the practical value of using model to evaluate EVCS location is verified.

Determining Electric Vehicle Charging Station Location Suitability: A Qualitative Study of Greek Stakeholders Employing Thematic Analysis and Analytical Hierarchy Process

Shifting from a fossil-fueled to an eco-friendly vehicle fleet in cities could pave the way towards a more sustainable future. Electric Vehicles (EVs) should thus be prioritized, so that they could replace conventional vehicles gradually. In this context, an EV-accommodating infrastructure, which ensures the functionality of the entire system, is essential. This study aims to develop a methodological framework to identify suitable locations for the deployment of EV charging points in urban environments. To meet this objective, we acquired a mixed method approach including a systematic literature review, 12 semistructured stakeholder interviews which were thematically analyzed, and an Analytical Hierarchy Process (AHP). The outcome is a spatial model function, which consists of parameters and weights for estimating the suitability of each urban road link that will allow the establishment of EV charging points. Results show that the key location selection factors are: transport hubs, marked or controlled parking spaces, and points of interest. The less significant factor is public services. Therefore, there is a preference, in stakeholder level, for transport features over the land use ones (69% over 31%). Although this research is conducted in Greece, we intend to suggest methods and generate valuable findings that may be valid and generalizable for a more global context.

Location planning of electric vehicle charging station based on HPSO-TS

Aiming at the optimization problem of electric vehicle (EV) charging station layout, an optimization model is constructed which takes the cost of all charging stations within the planning range as the objective function. Combining particle swarm optimization (PSO) and tabu search algorithm (TS), a hybrid algorithm of particle swarm optimization and tabu search (HPSO-TS) was proposed to solve the problem of EV charging station location. This algorithm increases the particle diversity by mutating the individual extremum, and uses TS in the later iteration to improve the late search ability of PSO and overcome premature convergence. Simulation results show that when solving the layout optimization problem of EV charging stations with service requirements and user demand constraints, HPSO-TS is superior to the computational results of PSO and mutation particle swarm optimization (MPSO). Compared with the basic algorithm, the improved algorithm has better efficiency and convergence, which proves the effectiveness and feasibility of the improved algorithm.

Electric Vehicle Charging Station Location towards Sustainable Cities.

Electric vehicles, a significant part of sustainable transport, are attracting increasing attention with the development of sustainable cities. However, as supporting facilities of electric vehicles, public charging stations are of great significance to the promotion of electric vehicles. This paper proposes an electric vehicle charging station location model to improve the resource utilization of electric vehicles for sustainable cities. In this model, reservation services, idle rates during off-peak periods, and waiting time during peak periods are considered. Finally, a case from Chengdu, China, is used to examine the effectiveness of the proposed model. Then, further analyses of reservation ratios and penetration rates are conducted. The results show that the introduction of a reservation service has a positive effect on reducing the total cost, which would provide further support for sustainable cities and have an even greater impact on healthier lives.

Increasing electric vehicle adoption through the optimal deployment of fast-charging stations for local and long-distance travel

We present a new strategic multi-period optimization problem for the siting of electric vehicle (EV) charging stations. One main novelty in this problem is that EV adoption over time is influenced by the availability of charging opportunities, as well as by local EV diffusion. Furthermore, to the best of our knowledge, this is the first contribution where the distribution of charging demand is modeled with a combination of node-based - more appropriate for urban or suburban settings - and flow-based approaches - with which we can model the needs of EVs to recharge on intermediary stops on long-haul travels. We propose a mixed-integer linear programming (MILP) formulation for this problem. Our computational experiments show that by simply implementing it in state-of-art MILP solvers, we are unable to obtain feasible solutions for realistically-sized instances. As such, we propose a rolling horizon-based heuristic that efficiently provides provably good solutions to instances based on much larger territories (namely the province of Quebec and the state of California) than those tackled by the methods proposed in the literature for the location of EV charging stations.

Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction

The development of electric vehicles has significant value for the sustainable utilization of energy resources. However, the unreasonable construction of charging stations causes problems such as low user satisfaction, waste of land resources, unstable power systems, and so on. Reasonable planning of the location and capacity of charging stations is of great significance to users, investors and power grids. This paper synthetically considers three indicators of user satisfaction: charging convenience, charging cost and charging time. Considering the load and charging requirements, the model of electric vehicle charging station location and volume is established, and the model based on artificial immune algorithm is used to optimize the solution. An empirical analysis was conducted based on a typical regional survey. The research results show that increasing the density of charging stations, lowering the charging price and shortening the charging time can effectively improve user satisfaction. The constructed site and capacity selection optimization solving model can scientifically guide charging station resource allocation under the constraints of the optimal user comprehensive satisfaction target, improve the capacity of scientific planning and resource allocation of regional electric vehicle charging stations, and support the large-scale promotion and application of electric vehicles.

Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range

This paper investigates the optimal locations for refueling stations for electric vehicles. Electric vehicles have been successfully introduced into the market. However, their use seems to be limited to urban transport since recharging facilities are readily available only near home and work. Planning recharging infrastructure for electric vehicles is highly relevant because this will enable longer trips, including inter-state travel which requires multiple battery charges. Among various models to determine optimal locations of recharging stations, a flow refueling location model (FRLM) is considered in this study. It determines locations for recharging stations to maximize the flow that can travel between origin and destination pairs by refueling at built facilities. FRLM is extended by introducing a probabilistic consideration of the travel range which might vary depending on various factors including road conditions. We develop a mixed integer nonlinear programming formulation and propose a Benders-and-Price algorithm by combining the Benders decomposition and column generation to solve the proposed formulation. The proposed algorithm is validated using extensive computational experiments on two transport networks, including a real-life Texas highway network.

The Location Model of the Electric Vehicle Charging Station Based on Simulated Annealing Algorithm

The model of the electric vehicle charging station location is built in this paper. The objective function is charging station utility maximization, the constraint condition is traffic flow, and simulated annealing algorithm is used to solve this model. The result shows that this model is reasonable to provide the electric vehicle project managers with an optimal or approximate optimal location solution.

Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal

Growing concerns about environmental issues have led to the consideration of alternatives to current mobility. Electric mobility is one such alternative that is receiving a great deal of attention in Europe. In particular, a new legal framework for the introduction of an electric mobility system in Portugal has recently been set up by the government. A key issue in this system is recharging the batteries and, consequently, the location of charging stations. This paper presents a study on the location of electric-vehicle charging stations for an area of Lisbon, the capital city of Portugal, characterized with a strong concentration of population and employment. This type of area is appropriate for slow charging because vehicles stay parked for several hours within a 24-h period. The methodology used here is based on a maximal covering model to optimize the demand covered within an acceptable level of service and to define the number and capacity of the stations to be installed. The results clearly indicate that this methodology can be useful in the future planning of electric mobility systems.