Статті в журналах: "Charging Station Placement"

1

Mortimer, Benedict J., Christopher Hecht, Rafael Goldbeck, Dirk Uwe Sauer, and Rik W. De Doncker. "Electric Vehicle Public Charging Infrastructure Planning Using Real-World Charging Data." World Electric Vehicle Journal 13, no. 6 (May 24, 2022): 94. http://dx.doi.org/10.3390/wevj13060094.

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The current increase of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage. To make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. An essential factor for the degree of utilization is the placement of a charging station. Therefore, the initial site selection plays a critical role in the planning process. This paper proposes a charging station placement procedure based on real-world data on charging station utilization and places of common interest. In the first step, we correlate utilization rates of existing charging infrastructure with places of common interest such as restaurants, shops, bars and sports facilities. This allows us to estimate the untapped potential of unexploited areas across Germany in a second step. In the last step, we employ the resulting geographical extrapolation to derive two optimized expansion strategies based on the attractiveness of locations for electric vehicle charging.

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2

El Hafdaoui, Hamza, Hamza El Alaoui, Salma Mahidat, Zakaria El Harmouzi, and Ahmed Khallaayoun. "Impact of Hot Arid Climate on Optimal Placement of Electric Vehicle Charging Stations." Energies 16, no. 2 (January 9, 2023): 753. http://dx.doi.org/10.3390/en16020753.

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Electric vehicles (EVs) are becoming more commonplace as they cut down on both fossil fuel use and pollution caused by the transportation sector. However, there are a number of major issues that have arisen as a result of the rapid expansion of electric vehicles, including an inadequate number of charging stations, uneven distribution, and excessive cost. The purpose of this study is to enable EV drivers to find charging stations within optimal distances while also taking into account economic, practical, geographical, and atmospheric considerations. This paper uses the Fez-Meknes region in Morocco as a case study to investigate potential solutions to the issues raised above. The scorching, arid climate of the region could be a deterrent to the widespread use of electric vehicles there. This article first attempts to construct a model of an EV battery on MATLAB/Simulink in order to create battery autonomy of the most widely used EV car in Morocco, taking into account weather, driving style, infrastructure, and traffic. Secondly, collected data from the region and simulation results were then employed to visualize the impact of ambient temperature on EV charging station location planning, and a genetic algorithm-based model for optimizing the placement of charging stations was developed in this research. With this method, EV charging station locations were initially generated under the influence of gas station locations, population and parking areas, and traffic, and eventually through mutation, the generated initial placements were optimized within the bounds of optimal cost, road width, power availability, and autonomy range and influence. The results are displayed to readers in a node-link network to help visually represent the impact of ambient temperatures on EV charging station location optimization and then are displayed in interactive GIS maps. Finally, conclusions and research prospects were provided.

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3

Islam, Md Mainul, Hussein Shareef, and Azah Mohamed. "Optimal Quick Charging Station Placement for Electric Vehicles." Applied Mechanics and Materials 785 (August 2015): 697–701. http://dx.doi.org/10.4028/www.scientific.net/amm.785.697.

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Environmental concerns, dependency on imported petroleum and lower cost alternative to gasoline always motivated policymakers worldwide to introduce electric vehicles in road transport system as a solution of those problems. The key issue in this system is recharging the electric vehicle batteries before they are exhausted. Thus, the charging station should be carefully located to make sure the vehicle users can access the charging station within its driving range. This paper therefore proposes a multi-objective optimization method for optimal placement of quick charging station. It intends to minimize the integrated cost of grid energy loss and travelling of vehicle to quick charging station. Due to contrary objectives, weighted sum method is assigned to generate reference Pareto optimal front and optimized the overture by genetic algorithm. The results show that the proposed method can find the optimal solution of quick charging station placement that can benefit electric vehicle users and power grid.

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4

Singh, Praveen Prakash, Fushuan Wen, Ivo Palu, Sulabh Sachan, and Sanchari Deb. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions." Energies 16, no. 1 (December 20, 2022): 7. http://dx.doi.org/10.3390/en16010007.

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Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper.

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Bacanli, Salih Safa, Enas Elgeldawi, Begümhan Turgut, and Damla Turgut. "UAV Charging Station Placement in Opportunistic Networks." Drones 6, no. 10 (October 9, 2022): 293. http://dx.doi.org/10.3390/drones6100293.

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Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.

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6

Mishra, Partha, Eric Miller, Shriram Santhanagopalan, Kevin Bennion, and Andrew Meintz. "A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles." Energies 15, no. 10 (May 21, 2022): 3788. http://dx.doi.org/10.3390/en15103788.

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Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.

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7

Mohanty, Ajit Kumar, Perli Suresh Babu, and Surender Reddy Salkuti. "Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System." Energies 15, no. 22 (November 19, 2022): 8702. http://dx.doi.org/10.3390/en15228702.

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Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented.

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8

Houssein, Essam H., Sanchari Deb, Diego Oliva, Hegazy Rezk, Hesham Alhumade, and Mokhtar Said. "Performance of Gradient-Based Optimizer on Charging Station Placement Problem." Mathematics 9, no. 21 (November 6, 2021): 2821. http://dx.doi.org/10.3390/math9212821.

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The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

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9

Ahmed, Ahmed Jassim, Mohammed H. Alkhafaji, and Ali Jafer Mahdi. "DECISION-MAKING METHOD FOR THE OPTIMUM ALLOCATION OF CHARGING STATIONS Of ELECTRIC VEHICLE IN DISTRIBUTION NETWORKS." Tekhnichna Elektrodynamika 2023, no. 1 (January 9, 2023): 67–75. http://dx.doi.org/10.15407/techned2023.01.067.

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Electric vehicles are becoming prominent nowadays and playing an important role in the transportation sector as conventional vehicles affect the environment. The rising number of vehicles requires increasing the charging stations, which affects the distribution network if placed randomly. Therefore, selecting the optimal place for these charging stations is very important to mitigate the effect on the distribution system. This paper presents a decision-making method to select the location of the charging station in a radial distribution system optimally. The fixed point algorithm was used for the analysis of load flow. The analysis was carried outand tested on the 33 bus IEEE and a real case study in Iraq was used for the study. The result of the charging station placement is compared with other research and showed its efficiency in work. The analysis showed the effectiveness of the proposed method in reducing the effect of charging stations on voltages and losses under different conditions. References 27, Figures 10, Tables 6.

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10

Kalakanti, Arun Kumar, and Shrisha Rao. "Charging Station Planning for Electric Vehicles." Systems 10, no. 1 (January 2, 2022): 6. http://dx.doi.org/10.3390/systems10010006.

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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.

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11

Machado, Cláudia A. Soares, Harmi Takiya, Charles Lincoln Kenji Yamamura, José Alberto Quintanilha, and Fernando Tobal Berssaneti. "Placement of Infrastructure for Urban Electromobility: A Sustainable Approach." Sustainability 12, no. 16 (August 6, 2020): 6324. http://dx.doi.org/10.3390/su12166324.

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Over the last few years, electric vehicles (EVs) have turned into viable urban transportation alternatives. Charging infrastructure is an issue, since high investment is needed and there is a lot of demand uncertainty. Seeking to fill gaps in past studies, this investigation proposes a set of procedures to identify the most adequate places for implementing the EV charging infrastructure. In order to identify the most favorable districts for the installation and operation of electric charging infrastructure in São Paulo city, the following public available information was considered: the density of points of interest (POIs), distribution of the average monthly per capita income, and number of daily trips made by transportation mode. The current electric vehicle charging network and most important business corridors were additionally taken into account. The investigation shows that districts with the largest demand for charging stations are located in the central area, where the population also exhibits the highest purchasing power. The charging station location process can be applied to other cities, and it is possible to use additional variables to measure social inequality.

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12

Golla, Naresh Kumar, Suresh Kumar Sudabattula, and Velamuri Suresh. "Optimal Placement of Electric Vehicle Charging Station in Distribution System Using Meta-Heuristic Techniques." Mathematical Modelling of Engineering Problems 9, no. 1 (February 28, 2022): 60–66. http://dx.doi.org/10.18280/mmep.090108.

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Technological findings recommend that Electric Vehicles (EVs) play a vital role in the road transportation system. EV's are becoming more prominent as formal vehicles have a substantial effect on the atmosphere. The rising adoption of EVs will lead to an increase in the number of charging stations that would profoundly impact the power grid. The inappropriate forecasting of EV Charging Stations (EVCSs) has a detrimental effect on the distribution system. Therefore, the selection of the optimum placement of EVCS in the power grid is a significant problem. In the proposed approach, an IEEE 33 Bus system is considered for optimal placement of EV charging station, with the account of optimal loads of the buses. The analysis was carried for an IEEE 33 BUS system using the Loss Sensitivity Factor (LSF) and power flow by Newton Raphson method. LSF was determined for various buses considering the system voltage, load (real and reactive power), and losses in the system. Also, the results are compared with the conventional method, Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) algorithms. Finally, the reliability test was carried out for optimal placement of EVCS in an IEEE 33 BUS system.

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13

Arif, Syed Muhammad, Tek Tjing Lie, Boon Chong Seet, Soumia Ayyadi, and Kristian Jensen. "Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques." Electronics 10, no. 16 (August 9, 2021): 1910. http://dx.doi.org/10.3390/electronics10161910.

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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.

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14

Lam, Albert Y. S., Yiu-Wing Leung, and Xiaowen Chu. "Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions." IEEE Transactions on Smart Grid 5, no. 6 (November 2014): 2846–56. http://dx.doi.org/10.1109/tsg.2014.2344684.

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15

Cui, Qiushi, Yang Weng, and Chin-Woo Tan. "Electric Vehicle Charging Station Placement Method for Urban Areas." IEEE Transactions on Smart Grid 10, no. 6 (November 2019): 6552–65. http://dx.doi.org/10.1109/tsg.2019.2907262.

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16

Deb, Sanchari. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review." Energies 14, no. 23 (November 23, 2021): 7833. http://dx.doi.org/10.3390/en14237833.

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As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%.

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17

Vargas Tamayo, Luis, Christopher Thron, Jean Louis Kedieng Ebongue Fendji, Shauna-Kay Thomas, and Anna Förster. "Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range." Sustainability 12, no. 21 (October 26, 2020): 8878. http://dx.doi.org/10.3390/su12218878.

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Drones are used increasingly for agricultural surveillance. The limited flight range of drones poses a problem for surveillance of large, inaccessible areas. One possible solution is to place autonomous, solar-powered charging stations within the area of interest, where the drone can recharge during its mission. This paper designs and implements a software system for planning low-cost drone coverage of large areas. The software produces a feasible, cost-minimizing charging station placement, as well as a drone path specification. Multiple optimizations are required, which are formulated as integer linear programs. In extensive simulations, the resulting drone paths achieved 70–90 percent of theoretical optimal performance in terms of minimizing mission time for a given number of charging stations, for a variety of field configurations.

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18

Pineda Sandoval, Joseph Daniel, José Antonio Arciniega-Nevárez, Xitlali Delgado-Galván, Helena M. Ramos, Modesto Pérez-Sánchez, P. Amparo López-Jiménez, and Jesús Mora Rodríguez. "Street Lighting and Charging Stations with PATs Location Applying Artificial Intelligence." Water 15, no. 4 (February 4, 2023): 616. http://dx.doi.org/10.3390/w15040616.

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This research proposes a methodology with multi-objective optimization for the placement of Pumps operating As Turbines (PATs), energizing street lighting, devices for monitoring the water network, and charging stations for small electric vehicles such as bikes and scooters. This methodology helps to find the most profitable project for benefiting life quality and energy recovery through pumps operating as turbines, replacing virtual pressure reduction valves to locate the best point for decreasing pressure. PATs are selected by maximizing power recovery and minimizing pressure in the system as well as maximizing recoverable energy. Benefits analyzed include the reduction of carbon dioxide emissions and fuel use, as well as the saving of electricity consumption and benefiting socio-economic impact with street lighting, monitoring, and charging station. It was considered that each PAT proposed by the methodology will supply a street light pole, a station for monitoring the water network, and a charging station; under these established conditions, the return on investment is up to 1.07 at 12 years, with a power generation of 60 kWh per day.

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19

NODA, Yoshichika, and Masafumi MIYATAKE. "3E24 Rational placement of charging-station for Lithium-ion battery onboard of rail vehicles(Electrical-Vehicle)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _3E24–1_—_3E24–11_. http://dx.doi.org/10.1299/jsmestech.2015._3e24-1_.

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20

Gao, Xiang, Sebastian Brueske, Markus Andresen, and Marco Liserre. "Optimization of EV-Fast Charging Station Placement for Grid Support." IFAC-PapersOnLine 53, no. 2 (2020): 13769–74. http://dx.doi.org/10.1016/j.ifacol.2020.12.884.

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21

Wang, Xiumin, Chau Yuen, Naveed Ul Hassan, Ning An, and Weiwei Wu. "Electric Vehicle Charging Station Placement for Urban Public Bus Systems." IEEE Transactions on Intelligent Transportation Systems 18, no. 1 (January 2017): 128–39. http://dx.doi.org/10.1109/tits.2016.2563166.

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22

Bi, Ran, Jiajian Xiao, Vaisagh Viswanathan, and Alois Knoll. "Influence of Charging Behaviour Given Charging Station Placement at Existing Petrol Stations and Residential Car Park Locations in Singapore." Procedia Computer Science 80 (2016): 335–44. http://dx.doi.org/10.1016/j.procs.2016.05.347.

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23

Motoaki, Yutaka. "Location-Allocation of Electric Vehicle Fast Chargers—Research and Practice." World Electric Vehicle Journal 10, no. 1 (March 6, 2019): 12. http://dx.doi.org/10.3390/wevj10010012.

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This paper conducts a comparative analysis of academic research on location-allocation of electric vehicle fast chargers into the pattern of the actual fast-charger allocation in the United States. The work aims to highlight the gap between academic research and actual practice of charging-station placement and operation. It presents evidence that the node-serving approach is, in fact, applied in the actual location-allocation of fast charging stations. However, little evidence suggests that flow-capturing, which has been much more predominantly applied in research, is being applied in practice. The author argues that a large-scale location-allocation plan for public fast chargers should be formulated based on explicit consideration of stakeholders, the objective, practical constraints, and underlining assumptions.

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24

Mirzaeinia and Hassanalian. "Minimum-Cost Drone‒Nest Matching through the Kuhn‒Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement." Aerospace 6, no. 11 (November 17, 2019): 125. http://dx.doi.org/10.3390/aerospace6110125.

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The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn‒Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25‒80% can be achieved by applying the Kuhn‒Munkres and greedy algorithms in drone‒nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone‒station matching through the Kuhn‒Munkres and greedy algorithms.

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25

Xiong, Yanhai, Jiarui Gan, Bo An, Chunyan Miao, and Ana L. C. Bazzan. "Optimal Electric Vehicle Fast Charging Station Placement Based on Game Theoretical Framework." IEEE Transactions on Intelligent Transportation Systems 19, no. 8 (August 2018): 2493–504. http://dx.doi.org/10.1109/tits.2017.2754382.

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26

Deb, Sanchari, Kari Tammi, Karuna Kalita, and Pinakeswar Mahanta. "Charging Station Placement for Electric Vehicles: A Case Study of Guwahati City, India." IEEE Access 7 (2019): 100270–82. http://dx.doi.org/10.1109/access.2019.2931055.

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27

Deb, Sanchari, Xiao-Zhi Gao, Kari Tammi, Karuna Kalita, and Pinakeswar Mahanta. "Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison." Archives of Computational Methods in Engineering 28, no. 1 (November 19, 2019): 91–106. http://dx.doi.org/10.1007/s11831-019-09374-4.

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28

Dharavat, Nagaraju, Suresh Kumar Sudabattula, Suresh Velamuri, Sachin Mishra, Naveen Kumar Sharma, Mohit Bajaj, Elmazeg Elgamli, Mokhtar Shouran, and Salah Kamel. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm." Energies 15, no. 18 (September 13, 2022): 6698. http://dx.doi.org/10.3390/en15186698.

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This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (PLoss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes PLoss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

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Krishnamurthy, Nandini K., Jayalakshmi N. Sabhahit, Vinay Kumar Jadoun, Dattatraya Narayan Gaonkar, Ashish Shrivastava, Vidya S. Rao, and Ganesh Kudva. "Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method." Energies 16, no. 4 (February 10, 2023): 1781. http://dx.doi.org/10.3390/en16041781.

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In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

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Erdelić, Tomislav, and Tonči Carić. "Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge." Energies 15, no. 1 (January 1, 2022): 285. http://dx.doi.org/10.3390/en15010285.

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With the rise of the electric vehicle market share, many logistic companies have started to use electric vehicles for goods delivery. Compared to the vehicles with an internal combustion engine, electric vehicles are considered as a cleaner mode of transport that can reduce greenhouse gas emissions. As electric vehicles have a shorter driving range and have to visit charging stations to replenish their energy, the efficient routing plan is harder to achieve. In this paper, the Electric Vehicle Routing Problem with Time Windows (EVRPTW), which deals with the routing of electric vehicles for the purpose of goods delivery, is observed. Two recharge policies are considered: full recharge and partial recharge. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) metaheuristic based on the ruin-recreate strategy is coupled with a new initial solution heuristic, local search, route removal, and exact procedure for optimal charging station placement. The procedure for the O(1) evaluation in EVRPTW with partial and full recharge strategies is presented. The ALNS was able to find 38 new best solutions on benchmark EVRPTW instances. The results also indicate the benefits and drawbacks of using a partial recharge strategy compared to the full recharge strategy.

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Jamian, J. J., M. W. Mustafa, H. Mokhlis, and M. A. Baharudin. "Minimization of Power Losses in Distribution System via Sequential Placement of Distributed Generation and Charging Station." Arabian Journal for Science and Engineering 39, no. 4 (January 29, 2014): 3023–31. http://dx.doi.org/10.1007/s13369-013-0922-5.

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Kathiravan, K., and P. N. Rajnarayanan. "Application of AOA algorithm for optimal placement of electric vehicle charging station to minimize line losses." Electric Power Systems Research 214 (January 2023): 108868. http://dx.doi.org/10.1016/j.epsr.2022.108868.

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Shabbar, Rawan, Anemone Kasasbeh, and Mohamed M. Ahmed. "Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization." Sustainability 13, no. 6 (March 17, 2021): 3314. http://dx.doi.org/10.3390/su13063314.

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Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

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34

Hashemian, Seyed Nasrollah, Mohammad Amin Latify, and G. Reza Yousefi. "PEV Fast-Charging Station Sizing and Placement in Coupled Transportation-Distribution Networks Considering Power Line Conditioning Capability." IEEE Transactions on Smart Grid 11, no. 6 (November 2020): 4773–83. http://dx.doi.org/10.1109/tsg.2020.3000113.

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35

Mohanty, Ajit Kumar, Perli Suresh Babu, and Surender Reddy Salkuti. "Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach." Sustainability 14, no. 22 (November 8, 2022): 14731. http://dx.doi.org/10.3390/su142214731.

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The usage of Electric Vehicles (EVs) for transportation is expected to continue growing, which opens up new possibilities for creating new smart grids. It offers a large-scale penetration of Fast Charging Stations (FCE) in a local utility network. A severe voltage fluctuation and increased active power loss might result from the inappropriate placement of the FCE as it penetrates the Distribution System (DST). This paper proposes a multi-objective optimisation for the simultaneous optimal allocation of FCEs, Distributed Generators (DGs), and Shunted Capacitors (SCs). The proposed Pareto dominance-based hybrid methodology incorporates the advantages of the Grey Wolf Optimiser and Particle Swarm Optimisation algorithm to minimise the objectives on 118 bus radial distribution systems. The proposed method outperforms some other existing algorithms in terms of minimising (a) active power loss costs of the distribution system, (b) voltage deviations, (c) FCE development costs, (d) EV energy consumption costs, and (e) DG costs, as well as satisfying the number of FCEs and EVs in all zones based on transportation and the electrical network. The simulation results demonstrate that the simultaneous deployment technique yields better outcomes, such as the active power loss costs of the distribution system being reduced to 53.21%, voltage deviations being reduced to 68.99%, FCE development costs being reduced to 22.56%, EV energy consumption costs being reduced to 19.8%, and DG costs being reduced to 5.1%.

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36

Islam, Md Mainul, Hussain Shareef, and Azah Mohamed. "Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm." PLOS ONE 12, no. 12 (December 8, 2017): e0189170. http://dx.doi.org/10.1371/journal.pone.0189170.

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Phonrattanasak, Prakornchai, and Nopbhorn Leeprechanon. "Multiobjective Optimal Placement of Public Fast Charging Station on Power Distribution Network Using Hybrid Ant Colony Optimization and Bees Algorithm." International Journal of Engineering and Technology 8, no. 6 (December 31, 2016): 2431–42. http://dx.doi.org/10.21817/ijet/2016/v8i6/160806405.

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38

Syed Nasir, S. N., J. J. Jamian, and M. W. Mustafa. "Minimizing Harmonic Distortion Impact at Distribution System with Considering Large-Scale EV Load Behaviour Using Modified Lightning Search Algorithm and Pareto-Fuzzy Approach." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/6587493.

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This research is focusing on optimal placement and sizing of multiple variable passive filter (VPF) to mitigate harmonic distortion due to charging station (CS) at 449 bus distribution network. There are 132 units of CS which are scheduled based on user behaviour within 24 hours, with the interval of 15 minutes. By considering the varying of CS patterns and harmonic impact, Modified Lightning Search Algorithm (MLSA) is used to find 22 units of VPF coordination, so that less harmonics will be injected from 415 V bus to the medium voltage network and power loss is also reduced. Power system harmonic flow, VPF, CS, battery, and the analysis will be modelled in MATLAB/m-file platform. High Performance Computing (HPC) is used to make simulation faster. Pareto-Fuzzy technique is used to obtain sizing of VPF from all nondominated solutions. From the result, the optimal placements and sizes of VPF are able to reduce the maximum THD for voltage and current and also the total apparent losses up to 39.14%, 52.5%, and 2.96%, respectively. Therefore, it can be concluded that the MLSA is suitable method to mitigate harmonic and it is beneficial in minimizing the impact of aggressive CS installation at distribution network.

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39

Pal, Arnab, Aniruddha Bhattacharya, and Ajoy Kumar Chakraborty. "Placement of Public Fast-Charging Station and Solar Distributed Generation with Battery Energy Storage in Distribution Network Considering Uncertainties and Traffic Congestion." Journal of Energy Storage 41 (September 2021): 102939. http://dx.doi.org/10.1016/j.est.2021.102939.

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40

Tounsi Fokui, Willy Stephen, Livingstone Ngoo, and Michael Saulo. "Optimal Integration of Electric Vehicle Charging Stations and Compensating Photovoltaic Systems in a Distribution Network Segregated into Communities." Journal of Advanced Engineering and Computation 6, no. 4 (December 31, 2022): 260. http://dx.doi.org/10.55579/jaec.202264.380.

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This paper proposes a method of optimally utilizing electric vehicles (EVs) in the distribution network. The method is firstly based on segregating the distribution network into communities and then optimally placing an EV charging station (EVCS) in each community using the backward forward sweep (BFS) technique. The Second phase uses particle swarm optimization (PSO) to size and allocates photovoltaic systems in the network for power loss minimization and voltage improvement. The proposed method is tested on an IEEE 33 node test feeder and simulation results showed the effectiveness of the BFS in finding the best nodes for the placement of EVCS in each community as well as the effectiveness of the PSO in allocating the photovoltaic systems. To validate the effectiveness of the BFS technique, its results obtained are compared with those obtained when the EVCSs are placed on some nodes other than those chosen by the BFS technique.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Falah, Muhammad Yusuf, Adib Muhammad Arrasyid, Afrida Nurul Ulfa, Rizal Zulfiqri Ahmad, and Jimmy Trio Putra. "DAMPAK DISTRIBUTED ENERGY RESOURCES TERHADAP PROFIL TEGANGAN DAN RUGI DAYA PENYULANG BANTUL 05." Jurnal Edukasi Elektro 5, no. 2 (November 30, 2021): 70–79. http://dx.doi.org/10.21831/jee.v5i2.41090.

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ABSTRACT:To realize Indonesia's commitment to cut greenhouse effect, it is important to use environmentally friendly vehicles. On August 12, 2019, the Presidential Regulation on the velocity of Battery-Based Electric Motor Vehicle Program for land vehicle as a sign of the seriousness of the government applying this vehicle. This research is specific in terms of the provision of Public Electric Vehicle Charging Station (SPKLU) in Bantul refinery area, Yogyakarta after the installation of Photovoltaic (PV) plant as Distributed Energy Resources (DER). This study used a simulation of the Open Distribution System Simulator (OpenDSS) software distribution network in analyzing the comparison of voltage profiles and power losses before and after spklu installation. By optimizing the placement of SPKLU using Flower Pollination Algorithm (FPA) method using Matlab software that results in spklu placement on bus 58 by 300 kW, bus 37 by 301 kW, and bus 36 by 300 kW. The voltage profile changed from 0.9695 p.u to 0.9688 p.u and power losses from 53.3 kW to 56.1 kW.ABSTRAK:Untuk mewujudkan tanggungjawab Indonesia dalam mengurangi efek rumah kaca maka harus menggunakan angkutan ramah lingkungan. Pada tanggal 12 Agustus 2019 telah disahkan Perpres Percepatan Program Kendaraan Bermotor Listrik Berbasis Baterai sebagai transportasi jalan merupakan tanda seriusnya pemerintah mengaplikasikan kendaraan ini. Penelitian ini spesifik pada hal penyediaan Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) pada area penyulang Bantul, Yogyakarta setelah pemasangan pembangkit Photovoltaic (PV) sebagai Distributed Energy Resources (DER). Penelitian ini menggunakan simulasi jaringan distribusi software Open Distribution System Simulator (OpenDSS) untuk mempelajari perbandingan profil tegangan dan rugi daya sebelum dan sesudah pengaplikasian SPKLU. Dengan optimasi penempatan SPKLU menggunakan metode Flower Pollination Algorithm (FPA) menggunakan software Matlab yang menghasilkan penempatan SPKLU pada bus 58 sebesar 300 kW, bus 37 sebesar 301 kW, dan bus 36 sebesar 300 kW. Profil tegangan berubah dari 0,9695 p.u menjadi 0,9688 p.u dan rugi-rugi daya dari 53,3 kW menjadi 56,1 kW.

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42

CESAR AZEREDO SILVA, DANILO, and MÁRIO MESTRIA. "LOCALIZAÇÃO EFICIENTE DE ESTAÇÕES DE CARREGAMENTO DE VEÍCULOS ELÉTRICOS NUMA REGIÃO METROPOLITANA UTILIZANDO METAHEURÍSTICA CRO E TRAJETOS VEICULARES REAIS." Revista SODEBRAS 14, no. 159 (March 2019): 142–48. http://dx.doi.org/10.29367/issn.1809-3957.14.2019.159.142.

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43

Zang, Haixiang, Yuting Fu, Ming Chen, Haiping Shen, Liheng Miao, Side Zhang, Zhinong Wei, and Guoqiang Sun. "Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route." Applied Sciences 8, no. 7 (July 12, 2018): 1130. http://dx.doi.org/10.3390/app8071130.

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The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user’s travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.

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Skaloumpakas, Panagiotis, Evangelos Spiliotis, Elissaios Sarmas, Alexios Lekidis, George Stravodimos, Dimitris Sarigiannis, Ioanna Makarouni, Vangelis Marinakis, and John Psarras. "A Multi-Criteria Approach for Optimizing the Placement of Electric Vehicle Charging Stations in Highways." Energies 15, no. 24 (December 13, 2022): 9445. http://dx.doi.org/10.3390/en15249445.

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The electric vehicle (EV) industry has made significant progress but, in many markets, there are still barriers holding back its advancement. A key issue is the anxiety caused to the drivers by the limited range of current EV models and the inadequate access to charging stations in long-distance trips, as is the case on highways. We propose an intuitive multi-criteria approach that optimally places EV charging stations on highways that (partially) lack such points. The approach, which is applied in an iterative fashion to dynamically evaluate the alternatives, considers a set of practical criteria related to the traffic intensity and the relative location of the charging stations with interchanges, major cities, and existing stations, thus supporting decisions in a pragmatic way. The optimal locations are determined by taking into consideration constraints about the EV driving range and installation preferences to improve the operation of the highway while ensuring reasonable cost of investment. The proposed approach is showcased in the Egnatia Motorway, the longest highway in Greece that runs a total of 670 km but currently involves a single EV charging point. Our findings illustrate the utility of the proposed approach and highlight its merits as a decision-support tool.

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45

Yi, Xinyi. "Modeling the Placement of Charging Stations for All-Electric Vehicles." IOP Conference Series: Materials Science and Engineering 612 (October 19, 2019): 042029. http://dx.doi.org/10.1088/1757-899x/612/4/042029.

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46

Tarandushka, Ludmyla, Nataliia Kostian, Ivan Tarandushka, Stepan Kurko, Eduard Klimov, and Maksym Melnychenko. "Method of Determining and Locating the Optimal Number of Charging Stations for Electric Transportation in Settlement." Central Ukrainian Scientific Bulletin. Technical Sciences 2, no. 6(37) (2022): 57–67. http://dx.doi.org/10.32515/2664-262x.2022.6(37).2.57-67.

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The problem of determining the optimal number and location of charging stations for electric transport within the settlement is being studied. To solve this problem, the criteria, factors and limitations of the optimal placement of charging stations were defined. Since the task included ensuring the comfortable operation of electric cars by citizens until 2025, it was necessary to forecast the number of electric cars that will be registered in the town of Cherkasy. For this, a portrait of the potential consumer was drawn up, and the potential capacity of the transport market was determined. Also, the key points of concentration of potential customers were determined, the level of charging stations for electric vehicles that will ensure their operation in the town of Cherkasy was selected, and the calculation of the optimal number of charging stations for the South-Western district of Cherkasy was performed. Since there are no regulations regarding the required number of charging stations for a certain fleet, it was proposed to adapt the regulations for gas stations by making corrections regarding the features of the technical charging process and the number of electric vehicle charges per unit of time. With the help of the method of hierarchical clustering, demand points in charging stations for electric cars were determined and a map of their location was developed for the residents of the South-Western district of Cherkasy. The cost of this project was also calculated. It can be concluded that the implementation of the project to provide charging stations for electric cars is profitable both from the point of view of material investments and from the point of view of the occupied useful area of town parking lots. The administration of Cherkasy may be interested in the implementation of this project under the terms of the city development program. This is due to the fact that the implementation of this project is expected to improve the town's environmental situation, generate profit from charging electric cars and sell new electric cars at car dealerships.

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MILJANIC, Z., V. RADULOVIC, and B. LUTOVAC. "Efficient Placement of Electric Vehicles Charging Stations using Integer Linear Programming." Advances in Electrical and Computer Engineering 18, no. 2 (2018): 11–16. http://dx.doi.org/10.4316/aece.2018.02002.

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48

Morro-Mello, Igoor, Antonio Padilha-Feltrin, Joel D. Melo, and Aida Calviño. "Fast charging stations placement methodology for electric taxis in urban zones." Energy 188 (December 2019): 116032. http://dx.doi.org/10.1016/j.energy.2019.116032.

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49

Kim, YeoJin, and Jin Hur. "A Study on the Placement Determination of New EV Charging Stations for EV Charging Demand Dispersion." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 33, no. 5 (May 31, 2019): 28–34. http://dx.doi.org/10.5207/jieie.2019.33.5.028.

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Zeb, Muhammad Zulqarnain, Kashif Imran, Abraiz Khattak, Abdul Kashif Janjua, Anamitra Pal, Muhammad Nadeem, Jiangfeng Zhang, and Sohail Khan. "Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network." IEEE Access 8 (2020): 68124–34. http://dx.doi.org/10.1109/access.2020.2984127.

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