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Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

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Green Energy and Intelligent Transportation 2023年第3期2卷 53-64页

作者： Ebunle Akupan Rene Willy Stephen Tounsi Fokui Paule Kevin Nembou Kouonchie Department of Electrical and Electronic Engineering University of BueaP.O Box 63BueaCameroon Teleconnect GmbH Am Lehmberg 5401157DresdenGermany Department of Electrical Engineering Pan African University Institute for Basic SciencesTechnology and InnovationP.O.Box 62000-00200NairobiKenya

The transportation sector is characterized by high emissions of greenhouse gases(GHG)into the ***,electric vehicles(EVs)have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products,which are fast *** are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging *** study proposes the use of the hybrid genetic algorithm and particle swarm optimization(GA-PSO)for the optimal allocation of plug-in EV charging stations(PEVCS)into the distribution network with distributed generation(DG)in high volumes and at selected ***(PV)systems with a power factor of 0.95 are used as *** PVs are penetrated into the distribution network at 60%and six penetration cases are considered for the optimal placement of the *** optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation *** IEEE 33 and 69 bus distribution networks are used as test *** simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid *** example,the integration of PEVCSs results in the minimum bus voltage still within accepted *** the IEEE 69 bus network,the resulting minimum voltage is 0.973 p.u in case 1,0.982 p.u in case 2,0.96 p.u in case 3,0.961 p.u in case 4,0.954 p.u in case 5,and 0.965 p.u in case *** are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.

关键词： Electric vehicles charging stations Distributed generation Photovoltaic Genetic algorithm Particle swarm optimization

The Study on Electric Vehicle Routing Problem with Customer's Time Windows and charging stations

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The Study on Electric Vehicle Routing Problem with Customer'...

Estimation of charging demand for electric vehicles by discrete choice models and numerical simulations: Application to a case study in Turin

2019中国汽车工程学会年会

作者： Sun Chenyang Hei Wenjie Xue Xuyang Ye Yong Fan Qianwang Shi Ling CRRC Yongji Electric Co. Ltd. Tianhua College Shanghai Normal University

Compared with traditional fuel vehicles,electric vehicles have the characteristics of short range and frequent charging,which brings difficulties in vehicle routing problem,such as increased delivery time and ***,this article will first add charging stations to the distribution network to build electric vehicle routing planning and charging policy model with time windows and multiple distribution centers,considering the vehicle cost,transportation cost,power supply cost and time windows penalty *** then designed a improved particle swarm optimization algorithm to search *** used the classical data of Solomon to test and analyze algorithm which is designed in this paper,we can give yield satisfactory electric car vehicle routing planning scheme.

关键词： electric vehicles vehicle route planning particle swarm optimization charging stations time window

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Green Energy and Intelligent Transportation 2023年第2期2卷 94-104页

作者： Lorenzo Sica Francesco Deflorio Politecnico di Torino-DIATI Corso Duca degli Abruzzi2410129TorinoItaly

The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate *** meet user needs,electric vehicle(EV)management for charging operations is *** study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging *** conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system *** of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect *** objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a *** these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.

关键词： Electric vehicles charging demand charging stations Discrete choice models User preference

Analysis of Electric and Hybrid Vehicle Usage in Proximity to charging Infrastructure in Indiana

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Journal of Transportation Technologies 2021年第4期11卷 577-596页

作者： Jairaj Desai Jijo K. Mathew Howell Li Darcy M. Bullock Purdue University West Lafayette USA

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

关键词： Electric Vehicles Connected Vehicles Trajectory Data Driver Behavior charging stations