scholarly journals Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

2019 ◽  
Vol 11 (8) ◽  
pp. 2301 ◽  
Author(s):  
Raphaela Pagany ◽  
Anna Marquardt ◽  
Roland Zink
Keyword(s):  
Neighborhood Effects ◽  
Energy Demand ◽  
Electric Energy ◽  
Transportation Systems ◽  
Walking Distance ◽  
Vehicle Ownership ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Charging Demand

In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

A Tale of Two Entities

2021 ◽  
Vol 2 (2) ◽  
pp. 1-21
Author(s):  
Hossam ElHussini ◽  
Chadi Assi ◽  
Bassam Moussa ◽  
Ribal Atallah ◽  
Ali Ghrayeb
Keyword(s):  
Power Flow ◽  
Power Grid ◽  
Communication Protocols ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Public Data ◽  
Simulation Based ◽  
Charging Stations ◽  
The Impact ◽  
Ev Charging

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

2019 ◽  
Vol 33 (08) ◽  
pp. 1959027 ◽  
Author(s):  
Jing-min Wang ◽  
Yan Liu ◽  
Yi-fei Yang ◽  
Wei Cai ◽  
Dong-xuan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Robust Optimization ◽  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Site Selection ◽  
Queuing Theory ◽  
Location Model ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Charging Demand

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

scholarly journals Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7992
Author(s):  
Dominik Husarek ◽  
Vjekoslav Salapic ◽  
Simon Paulus ◽  
Michael Metzger ◽  
Stefan Niessen
Keyword(s):  
Peak Reduction ◽  
Charging Infrastructure ◽  
Infrastructure Networks ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Economic Framework ◽  
Infrastructure Investments ◽  
Grid Operators ◽  
The Impact ◽  
Charging Demand

Since e-Mobility is on the rise worldwide, large charging infrastructure networks are required to satisfy the upcoming charging demand. Planning these networks not only involves different objectives from grid operators, drivers and Charging Station (CS) operators alike but it also underlies spatial and temporal uncertainties of the upcoming charging demand. Here, we aim at showing these uncertainties and assess different levers to enable the integration of e-Mobility. Therefore, we introduce an Agent-based model assessing regional charging demand and infrastructure networks with the interactions between charging infrastructure and electric vehicles. A global sensitivity analysis is applied to derive general guidelines for integrating e-Mobility effectively within a region by considering the grid impact, the economic viability and the Service Quality of the deployed Charging Infrastructure (SQCI). We show that an improved macro-economic framework should enable infrastructure investments across different types of locations such as public, highway and work to utilize cross-locational charging peak reduction effects. Since the height of the residential charging peak depends up to 18% on public charger availability, supporting public charging infrastructure investments especially in highly utilized power grid regions is recommended.

scholarly journals Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

2019 ◽  
Vol 9 (6) ◽  
pp. 5586
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicles ◽  
Service Providers ◽  
Real Life ◽  
Price Effect ◽  
Effect Analysis ◽  
Electric Vehicle Charging ◽  
Demand Patterns ◽  
Charging Stations ◽  
Occupancy Rates ◽  
Charging Demand

<p>This paper investigates the price effect to the charging demand coming from electric vehicles and then evaluates the performance of a pre-reservation mechanism using the real-life demand patterns. On the charging network in Jeju city, the occupancy rates for 3 price groups, namely, free, medium-price, and expensive chargers, are separated almost evenly by about 9.0 %, while a set of chargers dominates the charging demand during hot hours. The virtual pre-reservation scheme matches electric vehicles to a time slot of a charger so as not only to avoid intolerable waiting time in charging stations systematically but also to increase the revenue of service providers, taking into account both bidding levels specified by electric vehicles and preference criteria defined by chargers. The performance analysis results obtained by prototype implementation show that the proposed pre-reservation mechanism improves the revenue of service providers by up to 9.5 % and 42.9 %, compared with the legacy FCFS and reservation-less walk-in schemes for the given performance parameter sets.</p>

scholarly journals Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV

2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Igna Vermeulen ◽  
Jurjen Rienk Helmus ◽  
Mike Lees ◽  
Robert van den Hoed
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Early Years ◽  
Charging Infrastructure ◽  
Agent Based ◽  
Electric Vehicle Charging ◽  
Subsidy Scheme ◽  
Infrastructure Performance ◽  
Charging Stations

The Netherlands is a frontrunner in the field of public charging infrastructure, having one of the highest number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012–2015), a large percentage of the EV fleet were plugin hybrid electric vehicles (PHEV) due to the subsidy scheme at that time. With an increasing number of full electric vehicles (FEVs) on the market and a current subsidy scheme for FEVs only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have an effect on the charging behavior of the complete fleet, and is reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing agent-based model. Results show important effects of this transition on charging infrastructure performance.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

scholarly journals Studying the Formation of the Charging Session Number at Public Charging Stations for Electric Vehicles

2020 ◽  
Vol 12 (14) ◽  
pp. 5571
Author(s):  
Anastasia Gorbunova ◽  
Ilya Anisimov ◽  
Elena Magaril
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Electric Energy ◽  
Hydrocarbon Fuel ◽  
Energy System ◽  
Road Transport ◽  
Preliminary Calculation ◽  
Charging Infrastructure ◽  
Charging Stations

The energy industry is a leader of introduction and development of energy supply technologies from renewable energy sources. However, there are some disadvantages of these energy systems, namely, the low density and inconsistent nature of the energy input, which leads to an increase in the cost of the produced electric energy in comparison to the traditional energy complexes using hydrocarbon fuel resources. Therefore, the smart grid technology based on preliminary calculation parameters of the energy system develops in cities. This area should also be used to organize the charging infrastructure of electric vehicles, as the electrification of road transport is one of the global trends. As a result, a current task of the transport and energy field is the development of scientifically based approaches to the formation of the urban charging infrastructure for electric vehicles. The purpose of the article is to identify the features of the application flow formation for the charge of the electric vehicle battery. The results obtained provide a basis for building a simulation model for determining the required number of charging stations in the city, taking into account the criteria of minimizing operating costs for electric vehicle owners and energy companies.

scholarly journals Demand Calculation Method for Electric Vehicle Charging Station Locating and Deployment

2019 ◽  
Author(s):  
Csaba Csiszár
Keyword(s):  
Electric Vehicle ◽  
Calculation Method ◽  
Energy Demand ◽  
Charging Infrastructure ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Territorial Unit ◽  
Mobility System ◽  
Potential Demand ◽  
Vehicle Charging

To develop, plan, implement and operate the electric road mobility system, especially the charging infrastructure, the existing and potential demand must be revealed for several time horizons. Accordingly, the aim of the research was to elaborate a calculation method for electric vehicle charging demand and to determine the public charging infrastructure locating principles. The research questions were: how many and what kind of vehicles will be used; where, when and how long they will be charged; what aspects and how influence the charging station deployment. The number of charging points to be installed, the energy demand and capacity management parameters can be also determined using the revealed correlations. The calculation method is adaptable to any territorial unit and any time horizon. It is the basis of charging station locating methods, which is demonstrated through two novel geoinformatics applications.

Feasibility Study of Renewable Energy Integrated Electric Vehicle Charging Infrastructure

2016 ◽  
pp. 313-349
Author(s):  
Azhar Ul-Haq ◽  
Marium Azhar
Keyword(s):  
Renewable Energy ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Area Network ◽  
Charging Infrastructure ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Charging Stations

This chapter presents a detailed study of renewable energy integrated charging infrastructure for electric vehicles (EVs) and discusses its various aspects such as siting requirements, standards of charging stations, integration of renewable energy sources for powering up charging stations and interfacing devices between charging facilities and smart grid. A smart charging station for EVs is explained along with its essential components and different charging methodologies are explained. It has been recognized that the amalgamation of electric vehicles in the transportation sector will trigger power issues due to the mobility of vehicles beyond the stretch of home area network. In this regard an information and communication technology (ICT) based architecture may support EVs management with an aim to enhance the electric vehicle charging and energy storage capabilities with the relevant considerations. An ICT based solution is capable of monitoring the state of charge (SOC) of EV batteries, health and accessible amount of energy along with the mobility of EVs.

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