Electric Vehicle Charging Stations Coverage: A Study of Slovenia

To promote the penetration of electric vehicles (EVs), it is of great importance to plan and construct charging stations rationally. In this sense, the state of Slovenia\u27s charging station coverage was analysed. Using discrete and network geographic information system (GIS) models, with ArcGIS software, the density of electric vehicle charging stations (EVCSs), geographic distribution, nearness along a street network, and clustering analyses were performed. A survey conducted among Slovenian users of EVs supported the GIS analysis. It was found out that the distribution of EVCSs has an east-northeast to west-southwest directional trend. Only 13% of EVCSs are accessible from the nearest motorway at a distance of 500 meters or less. An insight into intrinsic clustering structure revealed 11 clusters of EVCSs from which the most distinct is the cluster on the area of Ljubljana. The scientific contribution of the research is in the integration of GIS, spatial analysis and the results of a survey to study the coverage of EVCSs in a certain region. Spatial analyses are carefully selected, and, in complementarity, give a comprehensive picture of EVCSs coverage. The research is important for further spatial planning of EVCSs

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd

EV charging stations and RES-based DG: A centralized approach for smart integration in active distribution grids

Renewable Energy Sources based (RES-based) Dispersed Generation (DG) and Electrical Vehicles (EVs) charging systems diffusion is in progress in many Countries around the word. They have huge effects on the distribution grids planning and operation, particularly on MV and LV distribution grids. Many studies on their impact on the power systems are ongoing, proposing different approaches of managing. The present work deals with a real application case of integration of EVs charging stations with ES-based DG. The final task of the integration is to be able to assure the maximum utilization of the distribution grid to which both are connected, without any upgrading action, and in accordance with Distribution System Operators (DSOs) needs. The application of the proposed approach is related to an existent distribution system, owned by edistribuzione, the leading DSO in Italy. Diverse types of EVs supplying stations, with diverse diffusion scenarios, have been assumed for the case study; various Optimal Power Flow (OPF) models, based on diverse objective functions, reflecting DSO necessities, have been applied and tried. The obtained results demonstrate that a centralized management approach by the DSO, could assure the respect of operation limits of the system in the actual asset, delaying or avoiding upgrading engagements and charges

Ready To Roll: Southeastern Pennsylvania's Regional Electric Vehicle Action Plan

On-road internal combustion engine (ICE) vehicles are responsible for nearly one-third of energy use and one-quarter of greenhouse gas (GHG) emissions in southeastern Pennsylvania.1 Electric vehicles (EVs), including plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (AEVs), present an opportunity to serve a significant portion of the region's mobility needs while simultaneously reducing energy use, petroleum dependence, fueling costs, and GHG emissions. As a national leader in EV readiness, the region can serve as an example for other efforts around the country."Ready to Roll! Southeastern Pennsylvania's Regional EV Action Plan (Ready to Roll!)" is a comprehensive, regionally coordinated approach to introducing EVs and electric vehicle supply equipment (EVSE) into the five counties of southeastern Pennsylvania (Bucks, Chester, Delaware, Montgomery, and Philadelphia). This plan is the product of a partnership between the Delaware Valley Regional Planning Commission (DVRPC), the City of Philadelphia, PECO Energy Company (PECO; the region's electricity provider), and Greater Philadelphia Clean Cities (GPCC). Additionally, ICF International provided assistance to DVRPC with the preparation of this plan. The plan incorporates feedback from key regional stakeholders, national best practices, and research to assess the southeastern Pennsylvania EV market, identify current market barriers, and develop strategies to facilitate vehicle and infrastructure deployment

On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.Comment: Accepted by IEEE Energycon 201