Controlled charging of electric vehicles in residential distribution networks

O'Connell, Alison; Flynn, Damian; Richardson, Peter; Keane, Andrew

doi:10.1109/isgteurope.2012.6465676

Cited by 10 publications

(13 citation statements)

References 10 publications

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“…So, in this paper, both 3.3 kW‐slow charging and 6.6 kW‐fast charging are compared. The scaled residential grid load profile and electricity price of the residential grid, which is used for calculating EV charging cost, are taken from [10]. EVs are schedule one by one.…”

Section: Resultsmentioning

confidence: 99%

“…Such strategies demonstrate that EV can be integrated into the existing system without increasing the available generating capacity. Comparison of multiple objective functions for smart charging, that is, maximum power objective, minimum cost objective, and maximum wind objective to optimise the charging of EV, is investigated in [10]. Smart charging schemes from both customer and local distribution company's perspective were compared with the uncontrolled charging scheme by Sharma et al [11].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of EV smart charging strategies from multiple stakeholders' perception

Rawat

Niazi

2017

J. eng.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of EV smart charging strategies from multiple stakeholders' perception

Rawat

Niazi

2017

J. eng.

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“…Literature [17] developed a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Literature [18] presented an optimization method that focuses on controlling the rate at which EVs charge over a 24-hour time horizon. Literature [19] proposed a local control scheme whereby individual electric vehicle charging units attempt to maximize their own charging rate while maintaining local network conditions within acceptable limits.…”

Section: Introductionmentioning

confidence: 99%

“…that the charging load mainly concentrates in node1,2,5,11,18,19,20,21,26, 31 and 32. The discharging load mainly concentrates in node 4, 15, 16, 17, 24, 30 and 32.…”

mentioning

confidence: 98%

A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles

Yang

Yan

et al. 2016

Applied Energy

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Electric vehicle (EV) is a promising, environmental friendly technique for its potential to reduce the using of fossil fuels. Massive EVs pose both opportunities and challenges for power systems, especially with the growing amount of wind-power integration. This paper investigates the problem of collaborative optimization scheduling of generators, EVs and wind power. A novel bi-layer optimization of transmission and distribution system is proposed to solve the scheduling problem of EVs charging and discharging load from respective time and space domain in the presence of wind-power. The upper layer optimization in transmission grid coordinates EVs with thermal generators, base load, with the consideration of wind power, to optimize load periods of EVs in the time domain. The lower layer optimization in distribution grid then spatially schedules the location of EVs load. Based on a power system benchmark with a 10-unit transmission grid and an IEEE 33-bus distribution grid, the performance of the proposed bi-layer optimization strategy is evaluated. The impacts of electricity price profile, EVs penetration and EVs load location are analyzed. Simulation results show that the proposed bi-layer optimization strategy can accommodate wind power and improve both the economics of grid operation and benefits of EV users by scheduling EVs charging and discharging temporally and spatially. Also, the results have shown that the location of EVs charging and discharging load is critical for the distribution network planning.

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“…Several centralised coordination strategies of this nature have been developed in [15,[27][28][29][30][31]. Vehicle-to-grid (V2G) techniques for grid regulation have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

Residential electrical vehicle charging strategies: the good, the bad and the ugly

Liu

McNamara

Shorten

et al. 2015

J. Mod. Power Syst. Clean Energy

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In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

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Controlled charging of electric vehicles in residential distribution networks

Cited by 10 publications

References 10 publications

Comparison of EV smart charging strategies from multiple stakeholders' perception

Comparison of EV smart charging strategies from multiple stakeholders' perception

A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles

Residential electrical vehicle charging strategies: the good, the bad and the ugly

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