Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data

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.

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

Sustainability ◽

10.3390/su13116163 ◽

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.

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

World Electric Vehicle Journal ◽

10.3390/wevj12030107 ◽

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.

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

Energies ◽

10.3390/en12142692 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2692 ◽

Cited By ~ 12

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.

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Smart Electric Vehicle Charging

ITNOW ◽

10.1093/itnow/bwz092 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.443.273 ◽

2013 ◽

Vol 443 ◽

pp. 273-278 ◽

Cited By ~ 4

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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Discussion on Operation Modes to the Electric Vehicle Charging Station

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.875-877.1827 ◽

2014 ◽

Vol 875-877 ◽

pp. 1827-1830 ◽

Cited By ~ 2

Author(s):

Xian Qiu Tan ◽

Sheng Chun Yang ◽

Yan Ping Fang ◽

Dong Xue

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Power Supply ◽

Charging Station ◽

Advantages And Disadvantages ◽

Developing Trend ◽

Electric Vehicle Charging ◽

Operation Modes ◽

The Future ◽

Vehicle Charging

Electric vehicle charging station provides power supply for electric vehicles running, and it is the most important supporting infrastructure of electric vehicles. The article analyses three modes of electric vehicle charging station charging methods, discusses the advantages and disadvantages of each model, gives the developing trend of the pattern of the operation of electric vehicles, and provides some effective suggestions for electric vehicle charging station for the future.

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Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

Frontiers in Energy Research ◽

10.3389/fenrg.2021.730242 ◽

2021 ◽

Vol 9 ◽

Author(s):

Elias Hartvigsson ◽

Niklas Jakobsson ◽

Maria Taljegard ◽

Mikael Odenberger

Keyword(s):

United States ◽

Electric Vehicle ◽

Electric Vehicles ◽

The United States ◽

Power Demand ◽

Driving Patterns ◽

Electric Vehicle Charging ◽

Vehicle Charging ◽

The Impact ◽

Charging Demand

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

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Novel Fuzzy Controller for a Standalone Electric Vehicle Charging Station Supplied by Photovoltaic Energy

Applied System Innovation ◽

10.3390/asi4030063 ◽

2021 ◽

Vol 4 (3) ◽

pp. 63

Author(s):

Sherif A. Zaid ◽

Hani Albalawi ◽

Khaled S. Alatawi ◽

Hassan W. El-Rab ◽

Mohamed E. El-Shimy ◽

...

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Fuzzy Controller ◽

Electric Utility ◽

Low Noise ◽

System Response ◽

Charging Station ◽

Electric Vehicle Charging ◽

Vehicle Charging ◽

Charging Stations

The electric vehicle (EV) is one of the most important and common parts of modern life. Recently, EVs have undergone a big development thanks to the advantages of high efficiency, negligible pollution, low maintenance, and low noise. Charging stations are very important and mandatory services for electric vehicles. Nevertheless, they cause high stress on the electric utility grid. Therefore, renewable energy-sourced charging stations have been introduced. They improve the environmental issues of the electric vehicles and support remote area operation. This paper proposes the application of fuzzy control to an isolated charging station supplied by photovoltaic power. The system is modeled and simulated using Matlab/Simulink. The simulation results indicate that the disturbances in the solar insolation do not affect the electric vehicle charging process at all. Moreover, the controller perfectly manages the stored energy to compensate for the solar energy variations. Additionally, the system response with the fuzzy controller is compared to that with the PI controller. The comparison shows that the fuzzy controller provides an improved response.

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Ordered Electric Vehicles Charging Scheduling Algorithm Based on Bidding in Residential Area

Information ◽

10.3390/info11010049 ◽

2020 ◽

Vol 11 (1) ◽

pp. 49

Author(s):

Xiao Cheng ◽

Jinma Sheng ◽

Xiuting Rong ◽

Hui Zhang ◽

Lei Feng ◽

...

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Dynamic Scheduling ◽

Scheduling Algorithm ◽

Utilization Efficiency ◽

Residential Areas ◽

Allocation Policy ◽

Electric Vehicle Charging ◽

Vehicle Charging ◽

Priority Needs

With the rise of electric vehicles, the key of electric vehicle charging is how to charge them in residential areas and other closed environments. Addressing this problem is extremely important for avoiding adverse effects on the load and stability of the neighboring grids where multi-user centralized charging takes place. Therefore, we propose a charging dynamic scheduling algorithm based on user bidding. First, we determine the user charging priority according to bidding. Then, we design a resource allocation policy based on game theory, which could assign charge slots for users. Due to users leaving and urgent user needs, we found an alternate principle that can improve the flexibility slot utilization of charging. Simulation results show that the algorithm could meet the priority needs of users with higher charging prices and timely responses to requests. Meanwhile, this algorithm can ensure orderly electric vehicle charging, improve power utilization efficiency, and ease pressure on grid loads.

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