Location Of Charging Stations Research Articles

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome.

ADDRESSING INFRASTRUCTURE GAPS IN MUMBAI’S 2-WHEELER EV CHARGING NETWORK: A FOCUS ON ACCESSIBILITY, USABILITY, AND USER FEEDBACK

Electric vehicles (EVs) are transforming transportation in Mumbai, with 2-wheeler EVs gaining significant traction among commuters. However, the insufficient availability and accessibility of charging infrastructure remain critical challenges. This study investigates the experiences of 2- wheeler EV users in Mumbai, focusing on the barriers they encounter when charging their vehicles. Given Mumbai’s dense traffic and urban complexities, limited charging points, long waiting times, and infrastructural constraints can hinder the user experience and discourage further adoption of EVs. The findings provide key insights into optimal charging station locations based on a comprehensive analysis of population density, traffic flow, and urban planning frameworks. Additionally, the study addresses the need for standardized charging protocols, advanced technologies, and user-friendly interfaces to enhance the usability of charging stations. The study emphasizes that addressing these challenges is crucial not only for the user experience but also for policymakers, urban planners, and stakeholders aiming to expand Mumbai’s EV infrastructure. Moreover, the environmental benefits of 2-wheeler EV adoption are examined, including reductions in energy consumption, carbon emissions, and overall environmental impact. By analyzing these factors, the research highlights the positive environmental outcomes of investing in robust charging infrastructure. Key challenges, such as high costs, feasibility concerns, and policy limitations, are also explored, offering a holistic understanding of the practical barriers to improving the EV charging network. The study concludes with actionable recommendations aimed at overcoming these challenges, fostering sustainable growth, and ensuring the efficient deployment of 2-wheeler EV charging infrastructure across Mumbai.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Method of Determining New Locations for Electric Vehicle Charging Stations Using GIS Tools

The growing awareness of environmental issues, climate policies, and rapidly developing technologies is contributing to the increasing number of battery electric vehicles (BEVs) around the world. A key requirement for their widespread implementation is providing a charging infrastructure that allows users to operate these vehicles comfortably. Lack of access to charging stations can be a major barrier to the development of electromobility in a given area. Therefore, each additional charging infrastructure can support a change in the structure of the vehicle fleet. One of the key challenges facing this transformation is the selection of suitable locations for charging stations. It is necessary to ensure that they are uniformly distributed so that range anxiety for EV users is reduced and equal access to charging infrastructure is provided to all residents. One of the most important stakeholders in this market is local authorities. Therefore, the objective of this research was to develop a method of determining optimal locations for electric vehicle charging stations (EVCSs) from the perspective of local authorities that also takes into account equal access to the charging infrastructure for all residents, which seems to be a unique approach to this problem. We used commonly available spatial data as input to enable the method to be applied on a larger scale and over an urban area. We carried out our research using a case study: the city of Gliwice in Poland. The city area was divided into hexagonal basic fields, for which potentials for locations of new charging stations were calculated. The analysis was carried out using the geographic information system (GIS) QGIS (ver. 3.34).

CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective.

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Decision Support System for Determining the Location of Public Electric Charging Stations (SPLU) with Machine Learning

Electric vehicles are becoming increasingly popular as an environmentally friendly alternative, but the main challenge is the availability of adequate charging infrastructure. This research aims to develop a decision making system for determining the best public electric vehicle charging station (SPLU) locations utilizing machine learning. The purpose of this research is to find out the process of determining the location of Public Electric Charging Stations (SPLU) using a Machine Learning-based decision making system. This research uses quantitative methods with AHP techniques to determine the criteria weights and machine learning to recommend the best SPLU locations based on spatial data, surveys, and social media. The location recommendations are displayed in the form of an interactive digital map with visualizations of the suitability level to facilitate decision making.

Location and capacity optimization of EV charging stations using genetic algorithms and fuzzy analytic hierarchy process

Location and capacity optimization of EV charging stations using genetic algorithms and fuzzy analytic hierarchy process

Identifying locations for electric vehicle charging stations in urban areas through the application of multicriteria techniques

The incorporation of electric vehicles (EVs) into urban mobility is increasing daily, driving the need to incorporate urban charging stations (EVCSs) additionally. This study proposes a methodology for identifying adequate EVCS locations within an urban area. This methodology is based on the joint application of multicriteria decision-making methods (MCDMs) to solve urban planning problems related to the placement of EVCSs. As a starting point, the locations of the existing fossil fuel supply stations in each city are used as alternatives. The city of Cuenca, Ecuador, is used as a case study. Criteria and subcriteria have also been determined based on the available information and are used to propose three scenarios based on their value variation, which is established as preponderance weights. Among the results obtained, three alternatives scored better than the rest in most of the evaluations. As there was no conclusive result, a statistical analysis was conducted to strengthen the decision-making process and determine the most favourable potential locations.

Optimal site selection for electric vehicle charging stations: Analysis with hybrid FUCOM and geographic information systems

One of the biggest disadvantages of electric vehicles, which have gained popularity due to reasons such as high energy efficiency and low emissions, is the lack of sufficient charging stations and limited access to suitable charging options. For this reason, in the study, the criteria and their weights that are effective in determining the location of electric vehicle charging stations were determined using FUCOM, which reduces the number of pairwise comparisons, allows less subjective data to be used during the comparison, and provides reliable results because it checks the consistency with the mathematical model. Then, the determined criteria were used in the overlap analysis in GBS to select the most suitable sites that can be used in daily life. The data obtained were compared using the hybrid Best-Worst Method (BWM)-GIS and the Analytical Hierarchy Process (AHP)-GIS. All three hybrid methods determined that the southeast part of the European side and the southwest part of the Anatolian side were the most suitable areas. Therefore, it was seen that the hybrid FUCOM-GIS method should be preferred due to its advantages. In addition, the proposed method was compared with the current EVCS data and the suitability of the method was emphasized.

Two-stage stochastic planning for distribution networks considering uncertainties of electric vehicles and distributed generators

Abstract A significant quantity of electric vehicles and distributed generator (DG) sources are connected to the distribution network (DN), resulting in the formation of a new power-transportation network (PTN). This integration introduces source-load uncertainty to the system. This research presents a stochastic planning model for PTN that focuses on making decisions on charging station locations. Initially, a model for the distribution network is created, which incorporates energy storage devices, demand response devices, and DG devices. The transportation network utilizes a P-median siting determination model. Ultimately, a two-stage stochastic planning approach is employed to address system uncertainty. The primary goal is to minimize both the investment cost and operation cost of the system while also considering several common situations. The efficacy of the proposed approach has been proven using a combined system consisting of a 33-node DN and a 25-node TN.

Novel Methodology to Measure the Reliability of Public DC Fast Charging Stations

A network of reliable corridor charging stations is essential to building driver confidence in long-distance battery electric vehicle trips. Here, we propose a detailed methodology to measure station reliability based on charging infrastructure data. By assigning charging events to unique charging visits, our methodology can capture a holistic overview of the driver’s charging experience. We use real world charging data collected between 2019 and 2022 from 54 Direct Current Fast Chargers (DCFCs) in 36 corridor charging stations across California to demonstrate that our overarching reliability framework is close to the experience of users. Our analysis of two different charging networks shows that users of these networks have an average chance of 83% and 77% generally successful outcomes, respectively, after one or more tries at a charging station location. We also find significant variation in station performance within the same network (i.e., 79%–87% and 13%–95%, respectively). Furthermore, we observe that at least 3% of users are facing unexpected charging interruptions. In addition, we demonstrate a practical application of our framework for deep diagnostics of the charging eco-system using error codes to identify common issues such as vehicle/charger communication issues, safety issues, payment issues, and cable/connector issues. We compare how error codes alone are not a good proxy to diagnose charging failures. As more data from DCFCs becomes available, our methodology can become a mainstream tool for evaluating station reliability.

Optimal location of electric vehicle charging stations using proximity diagrams

The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.

The Cooperative Maximal Covering Location Problem with ordered partial attractions

The Maximal Covering Location Problem (MCLP) is a classical location problem where a company maximizes the demand covered by placing a given number of facilities, and each demand node is covered if the closest facility is within a predetermined radius. In the cooperative version of the problem (CMCLP), it is assumed that the facilities of the decision maker act cooperatively to increase the customers’ attraction towards the company. In this sense, a demand node is covered if the aggregated partial attractions (or partial coverings) of open facilities exceed a threshold. In this work, we generalize the CMCLP introducing an Ordered Median function (OMf), a function that assigns importance weights to the sorted partial attractions of each customer and then aggregates the weighted attractions to provide the total level of attraction. We name this problem the Ordered Cooperative Maximum Covering Location Problem (OCMCLP). The OMf serves as a means to compute the total attraction of each customer to the company as an aggregation of ordered partial attractions and constitutes a unifying framework for CMCLP models.We introduce a multiperiod stochastic non-linear formulation for the CMCLP with an embedded assignment problem characterizing the ordered cooperative covering. For this model, two exact solution approaches are presented: a MILP reformulation with valid inequalities and an effective approach based on Generalized Benders’ cuts. Extensive computational experiments are provided to test our results with randomly generated data and the problem is illustrated with a case study of locating charging stations for electric vehicles in the city of Trois-Rivières, Québec (Canada).

Decarbonizing transportation: A data-driven examination of ICE vehicle to EV transition

Transportation is one of the sectors with the highest CO2 emissions, accounting for 23% globally and significantly contributing to climate change. To address this challenge, the authorities have proposed new stringent policies that lead to decarbonization. From this perspective, this work proposes a multi-scenario analysis for the electrification of a fleet of private users. The scenarios differ on the type of charging mode adopted: slow charging (charging modes 1 and 2) and fast charging (charging modes 3 and 4). The model aims to identify the percentage of potential users who can shift from Internal Combustion Engine (ICE) to Electric Vehicles (EVs) in different scenarios. Furthermore, the model will highlight the average expenditure of users for charging, highlighting how the cost of energy could be a driver for the electrification of the sector. Finally, the model will allow us to evaluate the savings of up to 220 tons of CO2/year thanks to the electrification of the sector with Long Range vehicles, in best case scenario. The use of a multi-scenario analysis allowed several possible electrification solutions to be explored, highlighting the strengths and weaknesses of the charging mode used, supported by quantitative results. This data-driven approach allows us to identify optimal locations for public charging stations in region of northern Italy region, where the data was sourced, which will help to encourage the switch to EVs.

Capability identification for new charging station into distribution system with electric, economic and traffic considerations

The shift of conventional vehicles to electric vehicles (EVs) is on pace nowadays. Increment in EVs adoption eventually results as increment of load on the existing power distribution system. This increment can adversely affect the performance of the distribution system. Location of electric vehicles charging station (EVCS) in the distribution system requires more technical finding before moving on into it. Implementation needs attention for several aspects like electric profile, economics, and traffic management and so, this work considers all of them. The impact of charging stations (EVCS) on operating parameters of the distribution network such as voltage stability, reliability and power losses are investigated by the proposed formulation of a Voltage Stability-Reliability-Power Loss (VRP) indices in this work. This index has the capabilities of considering voltage stability, reliability, and power loss under a single frame. The capabilities of the system with random load changes are compared with the base system without increments in load. This presented work gives assurance about the EV based load increment capabilities in the existing system without compromising with distribution system constraints. The main contribution of the proposed work is to examine the impact of voltage sensitivity, system stability, power losses, reliability measures, and a complex VRP index on choosing locations for EV charging stations and its effects on the existing system. Also, to ensure accurate results, these factors are assessed across seven distinct load scenarios, contributing significantly to the ongoing research in this field.