Autonomous electric vehicle charging management over real time digital simulator

Akhtar, G. M. Asim; Al‐Awami, Ali T.; Sortomme, E.; Abido, M. A.; Ahmed, M. Waqar

doi:10.1109/pesgm.2014.6939355

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…To date, most EVs operate in the unidirectional energy flow only, where the energy is coming from the grid or the source to charge the EVs in what is known as Grid-to-Vehicles (G2V) [16,17]. However, in the future, with more EVs on the road and more development in the communication infrastructure, other operational modes that allow for bidirectional energy flow to and from the vehicles can be feasible.…”

Section: Control and Operation Of Electric Vehiclesmentioning

confidence: 99%

Charge Control and Operation of Electric Vehicles in Power Grids: A Review

2018

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Electric Vehicles (EVs) and hybrid Electric vehicles (HEVs) are going to reshape the future of the transportation sector. However, adopting large numbers of EVs and HEVs will impact the electric utilities as well. Managing the charging/discharging of substantial numbers of distributed batteries will be critical for the successful adoption of EVs and HEVs. Therefore, this paper presents a review study about the recent control and optimization strategies for managing the charging/discharging of EVs. The paper covers different control and operation strategies reported in the literature as well as issues related to the real time dispatching of EVs in the smart grids. In addition, challenges related to the stochastic nature of the driving characteristics of EVs are considered. Finally, some open problems related to the energy management of EVs will be presented.

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Section: Control and Operation Of Electric Vehiclesmentioning

confidence: 99%

Charge Control and Operation of Electric Vehicles in Power Grids: A Review

2018

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“…The applied load profile is based on historical Residential High Winter Ratio (ResHiWR) load profiles found in the ERCOT system with five minutes resolution [21].To the ERCOT loading profiles, Gaussian distributed random noise is added to introduce changes in the individual use. One EV for each two houses in the system was adopted.…”

Section: Testing System Descriptionmentioning

confidence: 99%

“…It controls generator active and reactive power output to overcome voltage and thermal issues near the point of connection. In [20] and [21], the authors have proposed a voltage-based control scheme for charging EVs. This control, though requires no real-time communication, effectively coordinates charging among the EVs connected to the distribution nodes in a good manner.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic-based autonomous controller for electric vehicles charging under different conditions in residential distribution systems

Faddel

Mohamed

Mohammed

2017

Electric Power Systems Research

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Integrating a fleet of electric vehicles (EVs) in the distribution system without a communication infrastructure exhibits several issues that need to be handled by electric utilities. Robust autonomous controllers are crucial in this case to manage the charging operation without violating the grid standard limits. This paper proposes a communication-free autonomous charging controller for EVs in distribution systems. The controller is based on fuzzy logic considering both the system voltage profile and the EV's battery state of charge (SOC). The fuzzy inference system is designed to avoid under voltage issues in the grid, flatten the system loading profile and accomplish fair charging among different EVs connected to the system. An 18-bus distribution system driven by the proposed controller was implemented in a simulation environment. The proposed controller performance was investigated under different distribution system operating conditions such as different loading, different levels of EV penetration, system reconfiguration as well as with distributed generation. In all cases, the controller demonstrated faster and better charging performance without violating the standard voltage limits compared to other research reported in the literature.

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“…The EV charge controller takes local measurements at a point-of-charging (POC) and decides on an optimal charging rate in an efficient manner. Many researchers have developed and discussed disparate autonomous strategies [26][27][28][29][30][31][32][33][34]. A bidirectional frequency-dependent EV charge controller is presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Some voltage-based charge controllers are also discussed in Refs. [28][29][30][31]. For example, a proportional EV charge controller's output is proportional to the difference between the POC voltage and the reference voltage.…”

Section: Introductionmentioning

confidence: 99%

A novel communication‐free charge controller for electric vehicles using machine learning

et al. 2021

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Despite many advantages, wide‐spread integration of electric vehicles (EVs) in the power systems is challenging. Large‐scale uncontrolled EV charging load may lead to under‐voltage violations, higher power losses, overloading of transformers and transmission lines. In this research work, a machine learning‐based communication‐free EV charge control strategy is developed to mitigate the issues caused by the uncontrolled EV charging. Another notable feature of the proposed controller is to ensure the fairness among the EVs available at different locations in the distribution system. Moreover, two indices are proposed, that is system utilisation index (SUI) and system average voltage violation duration index (SAVVDI), to assess the performance of any EV charge controller. In the proposed control structure, a nodal voltage and the voltage‐to‐load sensitivity are measured at each load node which are fed to the EV charge controller. In fact, an upstream node is generally less sensitive to the changes in load, since it is closer to the feeding point, as compared to a downstream node. The output of the charge controller is the charging rate of an EV. In order to validate the robustness of the proposed controller, light and heavy loading conditions are considered which mimic the daily, monthly, yearly and seasonal load variations. Simulation results prove that the proposed controller effectively improves the voltage profiles and ensures fairness among the EVs connected at various charging points in the distribution system. Moreover, it effectively utilises the power system resources.

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Autonomous electric vehicle charging management over real time digital simulator

Cited by 11 publications

References 18 publications

Charge Control and Operation of Electric Vehicles in Power Grids: A Review

Charge Control and Operation of Electric Vehicles in Power Grids: A Review

Fuzzy logic-based autonomous controller for electric vehicles charging under different conditions in residential distribution systems

A novel communication‐free charge controller for electric vehicles using machine learning

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