scholarly journals Operation of Battery Storage as a Temporary Equipment During Grid Reinforcement Caused by Electric Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 888
Author(s):  
Lukas Held ◽  
Sebastian Baumann ◽  
Michael R. Suriyah ◽  
Thomas Leibfried ◽  
Levin Ratajczak ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Input Data ◽  
Battery Storage ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Grid Reinforcement ◽  
Distribution Grids

Electric vehicle charging stresses distribution grids significantly with high penetrations of electric vehicles. This will lead to grid reinforcement works in several distribution grids. Battery storage is a possible solution to bypass times of grid reinforcement due to electric vehicle charging. In this paper, different operation strategies for such a battery storage are tested at first in simulations. The main difference between the strategies is the necessary input data. Following the simulations, selected strategies are tested in reality in the project ”Netzlabor E-Mobility-Allee”. It is proved that battery storage is a functioning possibility to bypass times of grid reinforcement.

scholarly journals Voltage-Based Droop Control of Electric Vehicles in Distribution Grids under Different Charging Power Levels

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3905
Author(s):  
Muhandiram Arachchige Subodha Tharangi Ireshika ◽  
Ruben Lliuyacc-Blas ◽  
Peter Kepplinger
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Mechanism ◽  
Droop Control ◽  
Distribution Grid ◽  
Electric Vehicle Charging ◽  
Charging Control ◽  
Vehicle Charging ◽  
The Impact ◽  
Distribution Grids

If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.

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 Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

scholarly journals An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

2021 ◽  
Vol 12 (3) ◽  
pp. 107
Author(s):  
Tao Chen ◽  
Peng Fu ◽  
Xiaojiao Chen ◽  
Sheng Dou ◽  
Liansheng Huang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Current Fluctuation ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Three Phase ◽  
Optimization Control ◽  
Dc Bus ◽  
Vehicle Charging

This paper presents a systematic structure and a control strategy for the electric vehicle charging station. The system uses a three-phase three-level neutral point clamped (NPC) rectifier to drive multiple three-phase three-level NPC converters to provide electric energy for electric vehicles. This topology can realize the single-phase AC mode, three-phase AC mode, and DC mode by adding some switches to meet different charging requirements. In the case of multiple electric vehicles charging simultaneously, a system optimization control algorithm is adopted to minimize DC-bus current fluctuation by analyzing and reconstructing the DC-bus current in various charging modes. This algorithm uses the genetic algorithm (ga) as the core of computing and reduces the number of change parameter variables within a limited range. The DC-bus current fluctuation is still minimal. The charging station system structure and the proposed system-level optimization control algorithm can improve the DC-side current stability through model calculation and simulation verification.

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

Smart Electric Vehicle Charging

ITNOW ◽  
2019 ◽  
Vol 61 (4) ◽  
pp. 12-13
Author(s):  
Harry Leeks
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Pivotal Role ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Abstract What does IT have to do with the charging of electric vehicles? In this article, Harry Leeks, a graduate IT Analyst at National Grid, explains how IT plays a pivotal role in the electric vehicle charging market.

Study on the Harmonic Impact of Large Scale Electric Vehicles to Grid

2013 ◽  
Vol 443 ◽  
pp. 273-278 ◽  
Author(s):  
Ceng Ceng Hao ◽  
Yue Jin Tang ◽  
Jing Shi
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Flow Distribution ◽  
Electric Grid ◽  
Nonlinear Loads ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Charging Stations ◽  
Harmonic Problem

Large scale electric vehicles integration into power grid, as nonlinear loads, will pose inevitable impacts on the operation of power system, one of which the harmonic problem will affect the power quality greatly. Firstly, the article analyzes the characteristics of harmonic caused by electric vehicle charging. And then, the harmonic flow distribution is analyzed based on the IEEE standard node systems. During transient analyses, the electric vehicle charging stations connected to electric grid are represented as harmonic sources. Results show that structure and voltage grade of electric grid, capacity and access points of electric vehicle charging load will have different effects on harmonic problem. At last, a few conclusions are given for connecting electric vehicles to electric grid.

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