A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques

Ahmadian, Ali; Mohammadi‐Ivatloo, Behnam; Elkamel, Ali

doi:10.35833/mpce.2018.000802

Cited by 100 publications

(52 citation statements)

References 65 publications

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“…However, most of the nonlinear models are developed via empirical-based models that are not proper for optimal power-system research. Therefore, in this work, because of the simplicity, a linear model of the SoC calculation is adjusted like many of the other works [9,14]. A linear form of the SoC can be stated as below:…”

Section: Battery Soc Modelmentioning

confidence: 99%

“…PEV load demand mainly depends on its battery capacity, battery efficiency and traveling distance. Details of the most popular EV models can be found in [9]. For instance, a Nissan Leaf's battery capacity is 30 kWh, which means nearly four times more than a residential building's demand is required.…”

Section: Introductionmentioning

confidence: 99%

“…4-The fourth method is based on traffic studies and origin-destination (OD) concepts [18][19][20]. In addition, a comprehensive review and comparison of load-modeling techniques is available in [9]. All these mentioned works suffer from considering the correlation between datasets.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based PEVs Load Extraction and Analysis

et al. 2020

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Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

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Section: Battery Soc Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based PEVs Load Extraction and Analysis

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“…The impact of the charging infrastructure of EV on the grid in terms of power quality is reviewed in Reference [15]. An overview of EV modelling techniques is presented in References [16][17][18] with an emphasis on modelling methods for EV loads and charging stations.…”

Section: Introductionmentioning

confidence: 99%

Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

2020

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Electric vehicles (EVs) are one of a prominent solution for the sustainability issues needing dire attention like global warming, depleting fossil fuel reserves, and greenhouse gas (GHG) emissions. Conversely, EVs are shown to emit higher emissions (measured from source to tailpipe) for the fossil fuel-based countries, which necessitates renewable energy sources (RES) for maximizing EV benefits. EVs can also act as a storage system, to mitigate the challenges associated with RES and to provide the grid with ancillary services, such as voltage regulation, frequency regulation, spinning reserve, etc. For extracting maximum benefits from EVs and minimizing the associated impact on the distribution network, modelling optimal integration of EVs in the network is required. This paper focuses on reviewing the state-of-the-art literature on the modelling of grid-connected EV-PV (photovoltaics) system. Further, the paper evaluates the uncertainty modelling methods associated with various parameters related to the grid-connected EV-PV system. Finally, the review is concluded with a summary of potential research directions in this area. The paper presents an evaluation of different modelling components of grid-connected EV-PV system to facilitate readers in modelling such system for researching EV-PV integration in the distribution network.

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“…Nowadays, the use of electric vehicles (EVs) has expanded due to various environmental and economic benefits. However, given that EVs are considered as loads that are not fixed in one place and can be connected to different parts of the power system at different times, challenges are undeniable [1]. In fact, contrary to the benefits such as reduced fuel costs and reduced pollution of EVs compared to conventional vehicles, the presence of EVs, particularly the accidental and their unplanned connection to a distribution network, can have adverse effects on the system, and be an issue for the power system operators.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

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A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques

Cited by 100 publications

References 65 publications

Machine Learning Based PEVs Load Extraction and Analysis

Machine Learning Based PEVs Load Extraction and Analysis

Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

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