Placement Of Charging Stations Research Articles

Resolving Charging Station Placement Issues for Electric Vehicles: Hybrid Optimization-Assisted Multi-Objective Framework

As new energy technology has advanced to a respectable level, Electric Vehicles (EV) have gained recognition from people all over the world and have become increasingly popular in a number of nations. Effective charging station placement is critical for the rapid growth of electric vehicles, as it is vital to provide convenience for electric vehicles while also ensuring the effectiveness of traffic networks. The location of an EV charging station is simply an application situation for the facility location problem. Traditional works often pay attention to the mileage concerns of electric vehicle customers while ignoring their competitive and strategic charging tactics. This research aims to frame the allocation of charging stations issue as a multi-faceted venture by assessing the aspects economically along with the characteristics of the power grid like “cost, Voltage stability, Reliability, and Power loss (VRP) index, waiting time, and accessibility index”. Further, we proposed a hybrid artificial intelligence to resolve the allocation issue. The proposed new Artificial Intelligence (AI) based algorithm is termed as Hybridized Salp and Harris Algorithm (HS-HA). The effectiveness and scalability of the presented algorithm for the charging station placement issue are also assessed through different plans in the IEEE 33-bus distribution systems. Finally, the efficiency of the presented approach is proved over the existing approaches with respect to various measures.

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.

REGENERATIVE SHOCK ABSORBERS

Electric vehicles (EVs) have gained popularity as a solution to the fossil fuel crisis and carbon emissions. However, limited range due to battery constraints has been a major obstacle to widespread adoption. Battery management strategies, fast charging, and strategic charging station placement are some of the solutions being explored to address this issue. Another solution is to harness energy from the shock absorbers, which are responsible for filtering vehicle vibrations on rough roads. Research has been conducted since the 1980s on harnessing energy from shock absorbers, and REGENERATIVE SHOCK ABSORBERS have been proposed as a way to harvest the kinetic energy dissipated by suspension vibrations. Studies have shown that REGENERATIVE SHOCK ABSORBERS can produce up to 400W of power at 100 km/h, and can improve the energy harvesting and ride comfort of regenerative vehicle suspensions. Some studies have used electromagnetic methods to generate electric power, while others have used hydraulic methods. The use of regenerative shock absorbers in EVs is a promising solution that has the potential to significantly extend the range of these vehicles. KEYWORDS: Regenerative shock absorber; vehicle suspension; vehicle kinetic energy recovery.

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.

Electric bus arrival and charging station placement assessment using machine learning techniques

ABSTRACT The goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

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.

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.

Joint Optimization of Charging Station Placement and UAV Trajectory for Fresh Data Collection

Joint Optimization of Charging Station Placement and UAV Trajectory for Fresh Data Collection

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

EV Charging Station Placement Considering V2G and Human Factors in Multi-Energy Systems

EV Charging Station Placement Considering V2G and Human Factors in Multi-Energy Systems

PSO‐based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method

AbstractThe transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

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.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

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.

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit.

A novel modified Archimedes optimization algorithm for optimal placement of electric vehicle charging stations in distribution networks

The number of electric vehicle charging stations (EVCSs) is increasing alongside electric vehicles (EVs). The unplanned rollout of EVCSs can lead to problems such as deterioration of the voltage profile, increased active power losses, and maximum load demand in distribution networks (DNs). Therefore, EVCSs should be placed in optimum locations on the DNs. The complexity of the problem requires efficiency and performance. In this paper, a novel modified Archimedes optimization algorithm (MAOA) is proposed for optimal placement of EVCSs in DNs. Five parameters of the AOA were modified using the Honey Badger Algorithm (HBA). This placement is based on a combination of indexes: power loss, voltage deviation, and voltage stability index (VSI). Next, Newton-Raphson based load flow analysis is used to compute the minimum cost. MAOA, AOA and eight other optimization algorithms are compared. The results provide the most appropriate buses in the DN. The efficiency of the proposed algorithm was tested on IEEE 33, 69, and 10-bus Hatay power network systems. In all test systems, the proposed MAOA provided the best results with consistently low standard deviation values. The results suggest that the proposed method is more effective in the integration of EVCSs into the DN. © 2017 Elsevier Inc. All rights reserved.

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.