Profile of Charging Load on the Grid Due to Plug-in Vehicles

Shahidinejad, Soheil; Filizadeh, Shaahin; Bibeau, Eric

doi:10.1109/tsg.2011.2165227

Cited by 155 publications

(75 citation statements)

References 14 publications

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“…As previously commented, and as demonstrated in [27], the load profile of the EVs batteries charging systems has high importance to the power grid management. Depending on the design of the electrical grid and the type of charging and discharging processes, EVs can be a problem or a benefit to the power grid.…”

Section: Batteries Charging Processmentioning

confidence: 81%

Batteries Charging Systems for Electric and Plug-In Hybrid Electric Vehicles

Monteiro¹,

Goncalves²,

Ferreira³

et al. 2012

New Advances in Vehicular Technology and Automotive Engineering

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Section: Batteries Charging Processmentioning

confidence: 81%

Batteries Charging Systems for Electric and Plug-In Hybrid Electric Vehicles

Monteiro¹,

Goncalves²,

Ferreira³

et al. 2012

New Advances in Vehicular Technology and Automotive Engineering

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“…In this study, it is assumed that EVs charge with domestic chargers. The battery capacity for each EV is 24 kWh and the charging efficiency is 0.9 and it takes about 4 hours to be charged fully [19]. The amount of energy that each EV requires depends on its driving profile as well as its trip distance.…”

Section: Test Casementioning

confidence: 99%

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

et al. 2017

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This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners' viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty-including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators-are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush-Kuhn-Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

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“…Specifically in V2G, a number of algorithms are proposed to deal with different types of uncertainties in V2G amid uncertainty in the production of renewable power (Pinson et al, 2009) (Panagopoulos et al, 2012), together with that of EV driving behaviour (Ghiasnezhad Omran and Filizadeh, 2014) (Shahidinejad et al, 2012). Moreover, several studies discuss uncertainty in power market prices, for instance the work by (Shi and Wong, 2011).…”

Section: Related Workmentioning

confidence: 99%

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

Almansour¹,

Gerding²,

Wills³

2017

Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

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Abstract:Due to the limited availability of fuel resources, there is an urgent need for converting to use renewable sources efficiently. To achieve this, power consumers should participate actively in power production and consumption. Consumers nowadays can produce power and consume a portion of it locally, and then could offer the rest of the power to the grid. Vehicle-to-grid (V2G) which is one of the most effective sustainable solutions, could provide these opportunities. V2G can be defined as a situation where electric vehicles (EVs) offer electric power to the grid when parked. We developed an agent to trade on behalf of V2G users to maximize their profits in a day-ahead price market. We then ran the proposed model in three different scenarios using an optimal algorithm and compared the results of our solution to a benchmark. We show that our solution outperforms the benchmark strategy in the proposed three scenarios 49%, 51%, and 10% respectively in terms of profit.

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Profile of Charging Load on the Grid Due to Plug-in Vehicles

Cited by 155 publications

References 14 publications

Batteries Charging Systems for Electric and Plug-In Hybrid Electric Vehicles

Batteries Charging Systems for Electric and Plug-In Hybrid Electric Vehicles

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

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