Evaluation of the impact of plug-in electric vehicle loading on distribution system operations

Taylor, Jason; Maitra, Arindam; Alexander, Marcus; Brooks, D.; Duvall, Mark

doi:10.1109/pes.2009.5275317

Cited by 317 publications

(134 citation statements)

References 1 publication

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“…EVs can be modelled as mobility loads whose main characteristics are charging time, power demand and the rate of charge [33,34]. In a dynamic analysis, such as EV control, dynamic models of the EV batteries must be considered [16] where EV batteries can be approximated by a voltage source V OC in series with an equivalent constant resistance of the battery cell R i in which the active power characteristic of the EV battery represents the charging process.…”

Section: Electric Vehicle Battery Chargingmentioning

confidence: 99%

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

et al. 2014

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Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers' behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.

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Section: Electric Vehicle Battery Chargingmentioning

confidence: 99%

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

et al. 2014

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“…According to related data, we can get the last trip ending and return time distribution probability density [6] and electric vehicle driving distance distribution probability density [7][8]. Continuous charging time is calculated as follow:…”

Section: Conventional Charging Modelingmentioning

confidence: 99%

Research of Charging(Discharging) Orderly and Optimizing Load Curve for Electric Vehicles Based on Dynamic Electric Price and V2G

Yang

Luan

Qin

2016

MATEC Web of Conferences

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Abstract. Firstly, using the Monte Carlo method and simulation analysis, this paper builds models for the behaviour of electric vehicles, the conventional charging model and the fast charging model. Secondly, this paper studies the impact that the number of electric vehicles which get access to power grid has on the daily load curve. Then, the paper put forwards a dynamic pricing mechanism of electricity, and studies how this dynamic pricing mechanism guides the electric vehicles to charge orderly. Last but not the least, the paper presents a V2G mechanism. Under this mechanism, electric vehicles can charge orderly and take part in the peak shaving. Research finds that massive electric vehicles' access to the power grid will increase the peak-valley difference of daily load curve. Dynamic pricing mechanism and V2G mechanism can effectively lead the electric vehicles to take part in peak-shaving, and optimize the daily load curve.

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“…The controller then optimises the charge rate of each vehicle in order to deliver the maximum amount of power delivered to all EVs on the feeder, and thereby making best use of the network capacity. The process occurs at each time step and is independent of all other time steps with the exception of the rate of change of charge constraint (3).…”

Section: E Centralised Controlmentioning

confidence: 99%

“…The maximum variation allowable for the rate of charge between time steps, i.e. ∆ in (3), is set at 1 kW for both control strategies. Values for the voltage sensitivities, µ in (6), were calculated to be in the range -0.02 to -0.045 V/kW.…”

Section: B Controlled Ev Chargingmentioning

confidence: 99%

Local versus centralized charging strategies for electric vehicles in low voltage distribution systems

Richardson

Flynn²,

Keane³

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Controlled charging of electric vehicles offers a potential solution to accommodating large numbers of such vehicles on existing distribution networks without the need for widespread upgrading of network infrastructure. Here, a local control technique is proposed whereby individual electric vehicle charging units attempt to maximise their own charging rate for their vehicle while maintaining local network conditions within acceptable limits. Simulations are performed to demonstrate the benefits of the technique on a test distribution network. The results of the method are also compared to those from a centralised control method whereby EV charging is controlled by a central controller. The paper outlines the advantages and disadvantages of both strategies in terms of capacity utilisation and total energy delivered to charging EVs.

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Evaluation of the impact of plug-in electric vehicle loading on distribution system operations

Cited by 317 publications

References 1 publication

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Research of Charging(Discharging) Orderly and Optimizing Load Curve for Electric Vehicles Based on Dynamic Electric Price and V2G

Local versus centralized charging strategies for electric vehicles in low voltage distribution systems

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