scholarly journals Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1577
Author(s):  
Shuang Gao ◽  
Jianzhong Wu ◽  
Bin Xu
Keyword(s):  
Power Distribution ◽  
Queuing Theory ◽  
Distribution Networks ◽  
Temporal And Spatial Distribution ◽  
Charging Infrastructure ◽  
Optimal Dispatch ◽  
Gas Stations ◽  
Charging Stations ◽  
The Impact ◽  
Ev Charging

A considerable market share of electric vehicles (EVs) is expected in the near future, which leads to a transformation from gas stations to EV charging infrastructure for automobiles. EV charging stations will be integrated with the power grid to replace the fuel consumption at the gas stations for the same mobile needs. In order to evaluate the impact on distribution networks and the controllability of the charging load, the temporal and spatial distribution of the charging power is calculated by establishing mapping the relation between gas stations and charging facilities. Firstly, the arrival and parking period is quantified by applying queuing theory and defining membership function between EVs to parking lots. Secondly, the operational model of charging stations connected to the power distribution network is formulated, and the control variables and their boundaries are identified. Thirdly, an optimal control algorithm is proposed, which combines the configuration of charging stations and charging power regulation during the parking period of each individual EV. A two-stage hybrid optimization algorithm is developed to solve the reliability constrained optimal dispatch problem for EVs, with an EV aggregator installed at each charging station. Simulation results validate the proposed method in evaluating the controllability of EV charging infrastructure and the synergy effects between EV and renewable integration.

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.

scholarly journals Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

2018 ◽  
Vol 8 (7) ◽  
pp. 1130 ◽  
Author(s):  
Haixiang Zang ◽  
Yuting Fu ◽  
Ming Chen ◽  
Haiping Shen ◽  
Liheng Miao ◽  
...  
Keyword(s):  
Queuing Theory ◽  
Planning Model ◽  
Travel Pattern ◽  
Charging Infrastructure ◽  
Factors Affecting ◽  
Satisfaction Index ◽  
Fast Charging ◽  
Total Social Cost ◽  
Charging Stations ◽  
The Impact

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.

scholarly journals Modelling charge profiles of electric vehicles based on charges data

2021 ◽  
Vol 1 ◽  
pp. 156
Author(s):  
Natascia Andrenacci ◽  
Federigo Karagulian ◽  
Antonino Genovese
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Net Present Value ◽  
Optimal Size ◽  
Renewable Energy Resources ◽  
Economic Criterion ◽  
Charging Infrastructure ◽  
Charging Stations ◽  
The Impact ◽  
Ev Charging

Background: The correct design of electric vehicle (EV) charging infrastructures is of fundamental importance to maximize the benefits for users and infrastructure managers. In addition, the analysis and management of recharges can help evaluate integration with auxiliary systems, such as renewable energy resources and storage systems. EV charging data analysis can highlight informative behaviours and patterns for charging infrastructure planning and management. Methods: We present the analysis of two datasets about the recorded energy and duration required to charge Electric Vehicles (EV) in the cities of Barcelona (Spain) and Turku (Finland). In particular, we investigated hourly, daily and seasonal patterns in charge duration and energy delivered. Simulated scenarios for the power request at charging stations (CSs) were obtained using statistical parameters of the Barcelona dataset and non-parametric distributions of the arrivals. Monte Carlo simulations were used to test different scenarios of users’ influx at the CSs, and determine the optimal size of an integrated renewable energy system (RES). Results: This study highlighted the difference between fast and slow charging users’ habits by analysing the occupancy at the charging stations. Aside from the charge duration, which was shorter for fast charges, distinct features emerged in the hourly distribution of the requests depending on whether slow or fast charges are considered. The distributions were different in the two analysed datasets. The investigation of CS power fluxes showed that results for the investment on a RES could substantially vary when considering synthetic input load profiles obtained with different approaches. The influence of incentives on the initial RES cost were investigated. Conclusions: The novelty of this work lies in testing the impact of different simulated profiles as input in the economic criterion of the net present value (NPV) for determining the size of a photovoltaic (PV) system installed at a charging infrastructure.

scholarly journals Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1869 ◽  
Author(s):  
Alexandre Lucas ◽  
Giuseppe Prettico ◽  
Marco Flammini ◽  
Evangelos Kotsakis ◽  
Gianluca Fulli ◽  
...  
Keyword(s):  
Urban Areas ◽  
Energy Demand ◽  
Energy Use ◽  
Spatial Density ◽  
Carbon Intensity ◽  
Impact Indicator ◽  
Charging Infrastructure ◽  
Smart Charging ◽  
The Impact ◽  
Ev Charging

Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

A Comprehensive Review of Blockchain Technology Implementation in the EV Charging Infrastructure

2022 ◽  
pp. 38-67
Author(s):  
Toni Zhimomi ◽  
Mohammad Saad Alam ◽  
Hafiz Malik
Keyword(s):  
Technology Implementation ◽  
Security And Privacy ◽  
Comprehensive Review ◽  
Charging Infrastructure ◽  
Blockchain Technology ◽  
Key Factor ◽  
Communication Flow ◽  
Electric Vehicle Adoption ◽  
Charging Stations ◽  
Ev Charging

Charging infrastructure is a key factor in successful electric vehicle adoption. Charging stations are still a fragmented market in terms of ownership, lack of standards, and charging protocols. The increasing decentralised grid has made energy and communication flow bi-directional. Challenges arise in maintaining the increasing decentralised structure, security, and privacy of the network. Blockchain facilitates the interconnectedness of such a distributed and decentralised network. Blockchain's versatility lies in its transparent and immutable decentralized architecture that enables direct transactions between users without the need of a middleman. It provides powerful safeguards against cyberattacks with its advanced cryptography enabling privacy-preserving authentication. This chapter presents a comprehensive review on the application of blockchain technology in EV charging infrastructure such as facilitating the peer-to-peer energy exchange, increased security and privacy, immutable transactions, and mitigating trust issues among the participants in the charging infrastructure.

scholarly journals Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 686 ◽  
Author(s):  
Bruno Canizes ◽  
João Soares ◽  
Zita Vale ◽  
Juan Corchado
Keyword(s):  
Dynamic Pricing ◽  
Distribution System ◽  
Smart City ◽  
User Behavior ◽  
Distribution Grid ◽  
Charging Infrastructure ◽  
Energy Pricing ◽  
Electric Vehicle Charging ◽  
The Impact ◽  
Ev Charging

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

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