scholarly journals Estimation of Harmonic Emission of Electric Vehicles and Their Impact on Low Voltage Residential Network

2021 ◽  
Vol 13 (15) ◽  
pp. 8551
Author(s):  
Muhammad Naveed Iqbal ◽  
Lauri Kütt ◽  
Kamran Daniel ◽  
Bilal Asad ◽  
Payam Shams Ghahfarokhi
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Nonlinear Loads ◽  
Charging Infrastructure ◽  
Current Harmonic ◽  
Load Model ◽  
Harmonic Emission ◽  
Battery Chargers ◽  
The Impact ◽  
Ev Charging

The EV penetration in the low voltage residential grids is expected to increase rapidly in the coming years. It is expected that EV consumers will prefer overnight home charging because of its convenience and lack of charging infrastructure. The EV battery chargers are nonlinear loads and likely to increase the current harmonic emission in the distribution network. The imminent increase of EV load requires upgrading or managing the existing power system to support the additional charging load. This paper provides the estimation of the current harmonic emission of the EV charging load at different voltage distortions using the stochastic EV load model. The impact of EV charging on the distribution transformer is also presented.

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 Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1688 ◽  
Author(s):  
C. Birk Jones ◽  
Matthew Lave ◽  
William Vining ◽  
Brooke Marshall Garcia
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Low Voltage ◽  
Peak Load ◽  
Electric Power Systems ◽  
Primary System ◽  
Maximum Increase ◽  
Actual Distribution ◽  
The Impact ◽  
Ev Charging

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

Time-dependent variations in current harmonic emission by LED lamps in the low-voltage network

2021 ◽  
Author(s):  
Muhammad Naveed Iqbal ◽  
Lauri Kütt ◽  
Bilal Asad ◽  
Noman Shabbir ◽  
Iftikhar Rasheed
Keyword(s):  
Low Voltage ◽  
Time Dependent ◽  
Current Harmonic ◽  
Harmonic Emission

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 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.

Drivers of EV Charging Infrastructure Entrepreneurship in India

2022 ◽  
pp. 208-219
Author(s):  
Mohd Yasir Arafat ◽  
Imran Saleem ◽  
Thoudam Prabha Devi
Keyword(s):  
Developing Countries ◽  
Electric Vehicles ◽  
Crucial Role ◽  
Entrepreneurship Policy ◽  
Charging Infrastructure ◽  
Personal Attitude ◽  
Self Confidence ◽  
Policy Measures ◽  
Ev Charging

The existing research advocating entrepreneurship as an important way to increase the uptake of electric vehicles (EVs) in developing countries and EV charging business is also playing a crucial role in increasing the adoption of EVs. EV charging is important for EV adoption, and entrepreneurship is also important for EV adoption; therefore, it is important that we must understand what mobilizes or prevents EV charging entrepreneurship. This chapter aimed at explaining drivers of EV charging entrepreneurship. A survey of 121 potential entrepreneurs shows that personal attitude, self-confidence, and opportunity perceptions shape the decision to engage in EV charging entrepreneurship. Policy measures to boost EV charging entrepreneurship have been suggested.

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.

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