Location Of Charging Stations Research Articles

FRLM 기반 다기간 고속도로 화물차 전용 수소충전소 입지 선정 모형

Hydrogen is the eco-friendly energes which can replace fossil fuels In transportation/logistics Korea is the leading country about hydrogen-powered vehicles. For example, it is revealed that there is the highest penetration rate of new hydrogen cars and the greatest numbers for the existing vehicles in 2022 all over the worlds. In addition, hydrogen-fueled trucks have been invented and commercialized for the first time. However, the hydrogen charging network to support them seems really poor. Especially, there is only one station for the trucks, which may be the main obstacle for establishing the hydrogen-based logistics system in the country. The purpose of this study is to suggest an optimization model selecting the station locations for the long-distance eco-friendly transportation/logistics. This model is based on FRLM and reflects the trucks’ traffic characteristics. Besides, the model comprehensively considers the elements of the hydrogen refueling network. Based on the proposed model, the stations with natural gas reforming facilities account for the largest portion and all cost items increased, but hydrogen procurement cost is the fastest one. Academically, this study proposes a new FRLM-based model for the stations during the periods. Practically, the research provides the useful information for establishing hydrogen-charging network policies.

Bilevel optimization approach to fast charging station planning in electrified transportation networks

Unplanned en-route charging of electric vehicles (EVs) can unexpectedly cripple traffic conditions. As less-effective location of fast charging station (FCS) may exacerbate this issue, the effects of FCS on EVs' charging behaviors should be incorporated at the system planning stage. This paper proposes a strategic-charging-behavior awared model in the context of electrified transportation network environment. This model is formulated into a bilevel mixed-integer programming problem. A newly designed network equilibrium model targets on the low-level problem, which is to model the drivers' charging reaction to a certain FCS layout. In considering the EV self-serving routing and charging behaviors as well as power network constraints, the upper-level problem is formulated for location and sizing decision-makings, of which the objective is to minimize the overall traffic time and investment cost. To handle the proposed bilevel problem, a descent algorithm is further developed. To examine the effectiveness of the proposed approach, extensive numerical experiments are conducted, through which the obtained results demonstrate that the EV charging demand can perfectly be met while traffic congestions can be alleviated to a great extent.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.

Optimal planning and allocation of Plug-in Hybrid Electric Vehicles charging stations using a novel hybrid optimization technique.

India's expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila's behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function.

Mathematical program with equilibrium constraints approach with genetic algorithm for joint optimization of charging station location and discrete transport network design

ABSTRACT This paper focuses on the joint optimization of the charging station location problem (CSLP) and discrete network design problem (DNDP) in a transportation network. We present a variational inequality (VI) formulation to describe the user equilibrium (UE) state of gasoline vehicles (GVs) and electric vehicles (EVs). Based on the mixed-UE model, a mathematical program with equilibrium constraints (MPEC) model is formulated for integrating the decisions of deploying EV charging stations (EVCSs) and adding new links to minimize the total travel cost (TTC) of all vehicles. A modified genetic algorithm is developed to tackle the MPEC model with an adaptive path generation procedure to address the mixed-UE model. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. Specifically, we propose a two-step optimization model and explore a performance comparison between the joint and two-step optimization approaches, while the joint optimization exhibits superiority in minimizing the TTC.

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.

Spatial adaptability evaluation and optimal location of electric vehicle charging stations: A win-win view from urban travel dynamics

The contradiction between charging stations (CSs) and electric vehicles (EVs) is a “chicken-and-egg” problem. Multiple factors and stakeholders, including the goals of the government's industry planning, CS operators' profits, and EV consumers' charging demand, influence the CS layout. Using multisource open data, this study proposes a time series optimization layout model based on the supply-demand balance by constructing EV customers' urban travel dynamics index and CS adaptability index. The results show that: the supply-demand imbalance is widespread in Shenzhen, a Chinese city. The “mismatch”, “unmatch” and “overmatch” of CSs are prevalent. Addressing this supply-demand imbalance will help reduce consumer concerns regarding “range anxiety”. Facing potential future power supply constraints, the government needs to expand the existing CS development strategy to include battery swap stations (BSs) and mobile charging stations (MCSs).

O explorador: desenvolvimento de um jogo digital inclusivo

Aplicativos estão em todos os lugares e ganham cada vez mais espaço na vida das pessoas. Junto com o crescimento da tecnologia, as relações sociodigitais também crescem, como esferas e pontes de integração e desenvolvimento humano. O presente projeto tem como objetivo propor um jogo acessível ao grupo de pessoas com deficiência visual, visando incluí-los socialmente e oferecer, como em qualquer outro jogo, diversão e lazer. Para isso, foram realizadas várias pesquisas sobre o tema da deficiência, inclusão e restrição da participação social, bem como pesquisas sobre o desenvolvimento de jogos, recursos de acessibilidade e meios de inclusão em jogos. Aqui apresentamos um jogo criado especificamente para pessoas cegas, demonstrando as funcionalidades do mesmo e suas potencialidades.

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.

A bibliometric analysis and research agenda of the location of electric vehicle charging stations

Developing technology and population growth increase energy demand. The limited natural resources and their negative environmental effects increase the interest in alternative energy sources. The decrease in oil resources and the environmental problems caused by these resources have increased the studies for electric vehicles. This study presents descriptive statistics on the charging station locations for electric vehicles and relationships between studies by author, country, citation, and occurrence. This will provide a way for researchers to obtain detailed information about their studies into the locations of electric vehicle charging stations. This study examined 212 studies in the “Web of Science” database from 2011 to 2022. Network analysis was conducted in this research using the Vosviewer and R software. In this context, descriptive statistics are first provided. In the section on descriptive statistics, basic facts about the studies are shown along with the annual scientific output, the most productive countries, the total number of citations per country, country collaboration, the most productive countries and authors, and the total number of citations per country. Since the second half of 2010, there has been an increasing trend in the number of publications on the positioning of electric vehicle charging stations. It is seen that most of the studies on the positioning of electric vehicle charging stations were also carried out in China, India and the USA. The analyses of document co-occurrence, bibliographic coupling, and co-citation are presented in the second section. It has been observed that the most frequently used keywords in studies related to the location of electric vehicle charging stations are optimization, sustainability and distribution systems.

Charging Station Location and Sizing for Electric Vehicles Under Congestion

This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .

Robust Design of Electric Charging Infrastructure Locations under Travel Demand Uncertainty and Driving Range Heterogeneity

The rising demand for electric vehicles (EVs), motivated by their environmental benefits, is generating an increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. Such investment decisions, which include EV charging station locations and capacities, and the timing of such investments require robust estimates of future travel demand and EV battery range constraints. This paper develops and implements a framework to establish an optimal schedule and locations for new charging stations and decommissioning gasoline refueling stations over a long-term planning horizon, considering the uncertainty in future travel demand forecasts and the driving range heterogeneity of EVs. A robust mathematical model is proposed to solve the problem by minimizing not only the worst-case total system travel cost but also the total penalty for unused capacities of charging stations. This study uses an adaptation of the cutting-plane method to solve the proposed model. Based on two key decision criteria (travelers’ cost and charging supply sufficiency), the results indicate that the robust scheme outperforms its deterministic counterpart.

Optimal Location and Line Assignment for Electric Bus Charging Stations

This article considers an optimization problem aiming at planning the service stations to recharge a fleet of electric buses (EBs) for public transportation. The planning problem includes the selection of the sites of the stations (among a set of pre-specified eligible sites) and their sizing (both in terms of the number of sockets and maximum output power). The optimization problem also integrates issues related to the assignment of the various lines to the activated stations and the bus fleet's sizing. The formalization of the optimization problem is carried out by taking into account the nonlinear charging process characteristic of the batteries of EBs. The optimization problem is defined as a minimum cost design while guaranteeing a minimum quality level of the service, represented in this case by a minimum frequency for each of the lines. The overall decision problem turns out to be a mixed nonlinear one. It can be efficiently solved by common commercial software tools for moderate-medium problem sizes, as demonstrated in the presented application.

Incentivizing Electric Vehicle Adoption Through State and Federal Policies: Reviewing influential policies

All-Electric Vehicles (EV <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</small> ), BATTERY-powered EVs (BEVs), and plug-in hybrid EVs (PHEVS) are gaining market share and increasing in popularity with the buying public because the battery range (longer) and cost (lower) have reached sweet spots, the charging infrastructure is more robust, and concern with global climate change is high. In 2013, only 100,000 EVs were sold in the United States, but by 2022, approximately 800,000 have been purchased. A similar growth is seen in EV supply equipment (EVSE), i.e., EV charging stations, with 19,742 documented EV charging station locations in the United States in 2013 to 50,054 documented EV charging station locations, with approximately 130,000 ports, by the end of 2022.

Selection of Charging Station Location based on Sustainability Perspective using AHP Method

The acceleration of electric vehicle (EV) development in Indonesia to support energy and environmental sustainability has taken over and attracted the attention of many parties. Various policies, programs, and research outputs have made the public aware of EVs. However, the availability of charging stations (CS) in the community and the optimal placement are matters of great concern as CS is an essential infrastructure that enhances EV development. Therefore, to support sustainable urban development, CS location selection must consider a sustainability perspective in decision-making. In this study, we employed Analytical Hierarchy Process (AHP) method to assess the best placement of CS in Surakarta based on a sustainability perspective. Ten sub-criteria were identified based on a literature review to establish a hierarchy structure of the problem. We distributed a questionnaire to five experts in different fields to assess the importance scale for each sub-criteria and five alternatives location. The priority value and rank of each sub-criteria and alternative were generated. We found that CS 1 obtains the highest-ranking of preferable sites. The level of water and vegetation damage, service capacity, and impact on society are the most critical parameters that must be considered carefully in choosing a CS location. This study supplements literature for location selection and the application of the AHP method.

Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm

Transportation is one of the critical sectors worldwide, mainly based on fossil fuels, especially internal combustion engines. In a developing country, heightened dependence on fossil fuels affected energy sustainability issues, greenhouse gas emissions, and increasing state budget allocation towards fuel subsidies. Moreover, shifting to electric vehicles (EVs) with alternative energy, primely renewable energy sources, is considered a promising alternative to decreasing dependence on fossil fuel consumption. The availability of a sufficient EV charging station infrastructure is determined as an appropriate strategy and rudimentary requirement to optimize the growth of EV users, especially in urban cities. This study aims to utilize the k-mean algorithm’s clustering method to group and select a potential EV charging station location in Jakarta an urban city in Indonesia. This study proposed a method for advancing the layout location’s comprehensive suitability. An iterative procedure determines the most suitable value for K as centroids. The K value is evaluated by cluster silhouette coefficient scores to acquire the optimized numeral of clusters. The results show that 95 potential locations are divided into 19 different groups. The suggested initial EV charging station location was selected and validated by silhouette coefficient scores. This research also presents the maps of the initially selected locations and clustering.

A facility location problem in a mixed duopoly on networks

This paper studied a facility location problem (FLP) between a public firm and a private firm on a network space. Diverse situations are presented, and the corresponding mathematical formulations are modeled by implementing the optimization approach. The relationships between the models are then analyzed mathematically, showing the existence of complementarity and dominance. Considering the increasing interest in electric vehicle (EV) charging stations and the relevance to our models, computational experiments for a charging station location problem (CSLP) follow this in order to validate the presented models and verify the analyses. The trade-off between the stakeholders’ objectives is demonstrated, providing policy implications for the public sector and managerial insights for private investors.

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.

Optimal allocation of transmission fixed costs to electric vehicle charging stations and wind farms using an analytical method and structural decomposition

Renewable energies have found a suitable place in the electricity grid due to their non-pollution and cheapness. From the point of view of the grid operator, the allocation of costs should be appropriately allocated to producers and customers in competitive electricity markets. A fair allocation strategy can lead to more efficient use of current transmission lines while providing economic signals to guide future generation planning and load shedding. In this paper, first, using a powerful structural decomposition method, the optimal location of electric vehicle charging stations (EVCSs) and wind farms is determined from the flow aspect of grid lines. Then, the transmission fixed cost is assigned to each item placed in the network. The proposed method is implemented on the IEEE 24-bus network, and its results are analyzed. The fair allocation of transmission fixed costs prevents collusion, and the proposed method can be an effective tool for the network operator.

Incorporating time-dependent demand patterns in the optimal location of capacitated charging stations

A massive use of electric vehicles is nowadays considered to be a key element of a sustainable transportation policy and the availability of charging stations is a crucial issue for their extensive use. Charging stations in an urban area have to be deployed in such a way that they can satisfy a demand that may dramatically vary in space and time. In this paper we present an optimization model for the location of charging stations that takes into account the main specific features of the problem, in particular the different charging technologies, and their associated service time, and the fact that the demand depends on space and time. To measure the importance of incorporating the time dependence in an optimization model, we also present a simpler model that extends a classical location model and does not include the temporal dimension. A worst-case analysis and extensive computational experiments show that ignoring the temporal dimension of the problem may lead to a substantial amount of unsatisfied demand.