Placement Of Electric Vehicle Charging Stations Research Articles

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

This paper presents a novel and practical data-driven approach to sub-optimally allocate charging stations for electric vehicles (EVs) in an early-stage setting. Specifically, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed in order to promote the transition of EVs from traditional cars? We develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -nearest model and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest model that can capture people's satisfaction towards a certain design and formulate the early-stage EV charging station placement problem as a monotone submodular maximization problem utilizing fine-grained population, trip, transportation network and POI data. A greedy-based algorithm is proposed to solve the problem efficiently with a provable approximation ratio. A case study of Haikou is provided to demonstrate the effectiveness of our approach.

Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project.

Minimizing the impact of electric vehicle charging station with distributed generation and distribution static synchronous compensator using PSR index and spotted hyena optimizer algorithm on the radial distribution system

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

Comparative analysis of unidirectional and bidirectional electric vehicle charging stations (EVCS) optimal configuration in an IEEE 37-bus feeder system using Genetic Algorithm

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.

The application challenge of electric vehicles

The growing demand for sustainable transportation has prompted an accelerated transition toward electric vehicles (EVs) as a promising solution to mitigate the environmental impact of conventional internal combustion engine (ICE) vehicles. This research analyzes the key challenges and technologies associated with EVs, aiming to produce insights into this rapidly evolving field. The challenges surrounding EVs encompass various aspects, including limited driving range, lack of access to electric vehicle charging stations (EVCS), charging times for EVs, and safety considerations. To overcome these challenges, several technological advancements have emerged. Battery technology has advanced to extend the driving range per charge of an electric vehicle. Various models and algorithms to study the EVs charging station placement problem (EVCSPP) have been proposed to find the optimal location to build the charging stations. The advancement of extreme fast charging (XFC) technology and the intelligent transport system (ITS) shorten the charging time to improve the practicality of EVs adoption. Furthermore, the EVs charging standard and risk management have been introduced to ensure a secure charging system. By addressing these challenges and leveraging innovative technologies, a comprehensive and sustainable charging network can be realized, facilitating the transition towards a cleaner and greener transportation system.

Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging

Policymakers have structured legislation and set targets to increase the adoption of electric vehicles and the deployment of charging stations. As questions arise on how to expand the electric vehicle infrastructure, advanced computational models are attempting to quantify the future placement of charging stations. These models, however, do not necessarily incorporate the variability of user behavior as a subgroup of consumer behavior, as this information is not well-understood. This paper explores a state-wide network of 68 public charging stations' data representing 58,273 charging events to determine users' charging patterns. The methodological approach applied k-means clustering with Minkowski embedded distances to distinguish unique charging subgroups from 608 frequent users. The results indicate four user types: convenient (n = 319, 53%), gradual (n = 147, 24%), anxious (n = 104, 17%), and urgent (n = 38, 6%). Quantifying these users’ unique patterns supports the human variability knowledge required for more comprehensive and holistic models of electric vehicle charging station placement. More broadly, these results support the user-centric design of public charging infrastructure, which assists in reducing the electric vehicle adoption threshold in the long run and facilitating the transition to a low-carbon, sustainable transportation system.

Improvement of Distribution Network Performance by Optimally Allocating EV Charging Station

With zero emissions to the environment, electric vehicles (EVs) are the most environmentally friendly mode of transportation. The placement of EV charging stations (EVCS) in the Radial Distribution Network (RDN) is necessary to satisfy the demand of charging in various places while minimizing power loss on the power system networks. In the distribution system, distributed generation (DG) not only reduces power loss but also enhances power quality. To fully utilize the benefits of DG, it is necessary to find the optimal location and size in the distribution system. In this work, the ideal installation of EVCS has been consistently demonstrated in the IEEE 33 bus distribution network. In order to provide widespread charging facilities, the RDN has been split into three regions, and it has been established that each area has one charging station placed. The main purpose is to minimize the Active Power Loss and Voltage Deviation Index (VDI) to maintain a healthy power system network. Adding DG to the appropriate EV Station is obtained through optimization. This problem has been formulated as a problem of optimization for finding the best location to install EVCS in the IEEE 33 bus RDN by using the Symbiotic Organisms Search (SOS) algorithm. The obtained results have been validated and compared using the Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA).

A map matching-based method for electric vehicle charging station placement at directional road segment level

• A method of electric vehicle charging station placement at the road segment level. • A map matching-based method for locating electric vehicle charging stations. • A new charging demand estimation model incorporating trip purposes. • Traffic speed and arrival trips are key factors in placing charging stations. • Optimal charging station placement shows regional clustering characteristics. This paper proposes a method for electric vehicle charging station (EVCS) placement problem at the directional road segment (DRS) level for large urban road networks, which integrates a multi-criteria decision-making model with a new map matching technique called “segment-wise matching based on MRI”. The charging demand of DRS is estimated based on a novel prediction method which considers the arrival trips and the variation of charging demand for different trip purposes. Traffic attributes, charging demand attributes, and land price are incorporated into the TOPSIS model to determine the optimal EVCS placement. Finally, the proposed method is demonstrated using the road network of Xi'an in China as a case study. The results show the proposed method can be well applied to the EVCS placement problem at the DRS level for large-scale urban road networks. It is found that EVCS installation potentials of road segments approximately follow a normal distribution. The road segments with a high installation potential exhibit regional clustering characteristics due to the level of well-developed land use in the surrounding area. Sensitivity analyses suggest that it is important to include multiple criteria for modeling the EVCS placement problem and that traffic speed and arrival trips are key factors.

Examining spatial disparities in electric vehicle charging station placements using machine learning

• A framework for predicting equitable future EVCS placements is developed for Orange County, CA. • Santa Ana, Anaheim, Costa Mesa and La Habra experience the highest inequities in terms of EVCS placements. • Low-income areas with low car ownership and median age of population between 25-45 years, experience least EVCS access. • Random Forests achieve the highest predictive accuracy of 94.9% for EVCS density classes at 3 km resolution. • 11.04% of predicted EVCS placements lie within a high inequity zone. • 11.2% of the high inequity zones will require low to medium density of EVCS placements. Electric vehicles (EV) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors which may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 11.04% of predicted EVCS placements in Orange County will lie within a high spatial inequity zone – indicating populations with the lowest accessibility may require the greater investments in EVCS placements. 69.52% of the study area experience moderate accessibility issues and the remaining 19.11% face least accessibility issues w.r.t EV charging stations. Within least accessible areas, 7.8% of the area will require low density of predicted EVCS placements, 3.4% will require a medium density of predicted EVCS placements, and 0.55% will require high density of EVCS placements. The moderately accessible areas would require the highest placements of EVCS but mostly with low density placements covering 54.42% of the area. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.

Optimal Allocation of EV Charging Stations in a Radial Distribution Network Using Probabilistic Load Modeling

Electric vehicle (EV) charging stations (CSs) are the coupling point between the power grid and the transport network. Operational behaviour of EVs will affect both the networks simultaneously. Thus, the optimal placement of CSs in a distribution network plays a vital role. This paper proposes a novel method for optimal placement of EV charging stations considering loss minimization of the electrical grid, as well as EVs’ power loss during travel towards charging station. CS’s utilization factor helps to determine the number of CSs required in a network by ensure the effective usage of the investment in the CS infrastructure. Instead of a constant load/static load demand, the proposed work uses a queueing model for the dynamic behaviour of a CS serviceability. To capture the uncertainty in electrical demand and EV behaviour, a probabilistic load modelling (PLM) method is employed. With the recognized modelling, this work can optimally allocate CSs in a distribution network. The problem is structured as a multi-objective optimization problem. Simulation experiments are conducted on a test system to illustrate the proposed method. The influence on voltage profile, CS utilization, and load uncertainty are analyzed and presented to demonstrate the effectiveness of the proposed method.

Charging Station Planning for Electric Vehicles

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.

Multiobjective Planning Strategy for the Placement of Electric-Vehicle Charging Stations Using Hybrid Optimization Algorithm

Electric Vehicle (EV) charging station placement problem is a facility location problem. The EV charging station placement problem concerns about the total coverage in traffic network, the system losses and node voltage deviations in electric distribution system. To address the loss reduction and voltage profile improvement, the distribution systems are normally equipped with shunt capacitors for reactive power compensation. In this paper mathematical model comprising three objective functions, maximization of coverage and minimization of loss and node voltage deviations subjected to constraints is proposed for the simultaneous placement of EV charging stations and shunt capacitors. The control variables for optimization are the rating and location of charging stations and shunt capacitors. A hybrid optimization algorithm (PSO-DS) combining particle swarm optimization algorithm and direct search method is proposed for the solution of the mathematical model. The performance of PSO-DS is justified by comparing it with other state-of-the-art algorithms in solving the standard benchmark functions. Simulations are carried out on a 33-bus distribution system and a 25-node traffic network system to determine the different planning strategy for the placement of charging stations.

A multi-objective mathematical model for the electric vehicle charging station placement problem in urban areas

ABSTRACT As the popularity of electric vehicle (EV) technology continues to develop rapidly, so does the demand for charging stations, which are expected to be the main source of energy for electric vehicles in smart cities. An ideal charging network of stations would facilitate easy access to charging stations for drivers by ensuring that there is always a station nearby, and do that with the minimum number of stations to reduce infrastructure costs. Motivated by striking a balance between these two, we propose a novel multi-objective model for the EV charging station placement problem under a renewed constraint of a network that aims to make it easy for drivers to reach a station from anywhere in a city within a reasonable distance. We apply the model to Eskisehir in Turkey and then solved it with the weighted sum scalarisation method. We demonstrate and discuss the results which show practicality of the model.

Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques

This paper presents a state-of-the-art review of electric vehicle technology, charging methods, standards, and optimization techniques. The essential characteristics of Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) are first discussed. Recent research on EV charging methods such as Battery Swap Station (BSS), Wireless Power Transfer (WPT), and Conductive Charging (CC) are then presented. This is followed by a discussion of EV standards such as charging levels and their configurations. Next, some of the most used optimization techniques for the sizing and placement of EV charging stations are analyzed. Finally, based on the insights gained, several recommendations are put forward for future research.