2019
DOI: 10.3390/su12010008
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Abstract: Growing environmental concerns have contributed to urban transit alternatives, such as Electric Vehicles (EVS). As a result, the market for EVs is growing each year, which are a solution to mitigate these concerns. Although EVs present several environmental advantages, a massive introduction of them could generate power systems issues. Several works have proposed strategies to mitigate those issues. Since EVs posses batteries with significant capacity, they could provide services to the power grid, such as anc… Show more

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Cited by 24 publications
(19 citation statements)
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“…For a single user, demand charge management via V1G can synchronize the charging to the over-generation of the roof-mounted photovoltaic plant so to maximize self-consumption [47]; similarly, V1G can apply a time-of-use tariff in order to reduce the electricity bill [48]. Similarly, demand charge management via V1G can coordinate the charging of electric vehicles in a car park [49,50] or in a narrow geographical border [51], applying machine learning methods [52,53], taking into account the users' preferences [54] or the batteries' state of health [55], thus limiting the demand during peak hours and, in general, providing valuable grid services to network operators. Given the different impact of V1G and V2G on the battery charging infrastructure and economics, today's investments are mainly aimed at supporting the massive deployment of electric vehicles and to ensure the extensive presence of charging points with one-way chargers.…”
Section: Smart Chargingmentioning
confidence: 99%
“…For a single user, demand charge management via V1G can synchronize the charging to the over-generation of the roof-mounted photovoltaic plant so to maximize self-consumption [47]; similarly, V1G can apply a time-of-use tariff in order to reduce the electricity bill [48]. Similarly, demand charge management via V1G can coordinate the charging of electric vehicles in a car park [49,50] or in a narrow geographical border [51], applying machine learning methods [52,53], taking into account the users' preferences [54] or the batteries' state of health [55], thus limiting the demand during peak hours and, in general, providing valuable grid services to network operators. Given the different impact of V1G and V2G on the battery charging infrastructure and economics, today's investments are mainly aimed at supporting the massive deployment of electric vehicles and to ensure the extensive presence of charging points with one-way chargers.…”
Section: Smart Chargingmentioning
confidence: 99%
“…Likewise, the EVs integration in micro-grids also have been studied [24,25]. In [26,27], charging methodologies are proposed based on EV users' preferences.…”
Section: Related Workmentioning
confidence: 99%
“…The decentralized method can deal with larger-scale EVs, but with the typical distributed algorithm, it is not easy to consider the ADN constraints, which will produce negative effects to the optimization process. To cope with the mentioned challenges, a new entity, the EV aggregator (EVA), is introduced to the ADN [18]. From the perspective of ADNO, EVA can be used as a fully controllable resource (this is also convenient for considering grid constraints) [19], and each EVA can independently optimize the process of EVs under its jurisdiction.…”
Section: Introductionmentioning
confidence: 99%