Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data

Brady, John; O’Mahony, Margaret

doi:10.1016/j.scs.2016.06.014

Cited by 154 publications

(64 citation statements)

References 40 publications

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“…Khoo et al [13] derive the impact of EV charging on peak load based on around 5k sessions from an Australian field trial and establish the expected impact on the total power demand in 2032-33 for the state of Victoria. Brady et al [14] use a probabilistic charging module to translate the travel patterns of EVs into the respective power demand of 95 the vehicles. Quiròs-Tortòs et al [15] and Navarro-Espinosa et al [16] use the probability distribution of start charging time and energy demanded during a connection of charging sessions in a one-year EV trial in Ireland to obtain the EV load demand and assess their impact in the low voltage distribution grid.…”

Section: Related Workmentioning

confidence: 99%

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Sadeghianpourhamami

Refa

Strobbe

et al. 2018

International Journal of Electrical Power & Energy Systems

162

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Section: Related Workmentioning

confidence: 99%

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Sadeghianpourhamami

Refa

Strobbe

et al. 2018

International Journal of Electrical Power & Energy Systems

162

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“…Modelling the charging process itself is a complicated task and is influenced by different variables such as battery type, charging power, single-or multi-phase charging, and user behaviour. A variety of different stochastic approaches and techniques have been published: probabilistic approaches [27][28][29][30], Monte-Carlo [11,31,32], and Markov Chain [11,33,34].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

Vopava¹,

Koczwara²,

Traupmann³

et al. 2019

Energies

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To achieve climate goals, it is necessary to decarbonise the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery powered vehicles). This change will lead to an increase in the demand for electrical energy, which will cause additional stress on power grids. It is therefore necessary to evaluate energy and power requirements of a future society using e-mobility. Therefore, we present a new approach to investigate the influence of increasing e-mobility on a distribution grid level. This includes the development of a power grid model based on a cellular approach, reducing computation efforts, and allowing time and spatially resolved grid stress analysis based on different load and renewable energy source scenarios. The results show that by using the simplified grid model at least seven times, more scenarios can be calculated in the same time. In addition, we demonstrate the capability of this novel approach by analysing the influence of different penetrations of e-mobility on the grid load using a case study, which is calculated using synthetic charging load profiles based on a real-life mobility data. The results from this case study show an increase on line utilisations with increasing e-mobility and the influence of producers at the same connection point as e-mobility.

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“…However, these assumptions can lead to inaccuracy of evaluation by ignoring the stochastic nature of driving patterns. To improve the accuracy of evaluation, Qian K et al use the probability method to model the arrival time, departure time, and daily mileage in [5][6][7]. In addition, a method to estimate the electric vehicle integration patterns in distributed power systems considering their dispersion in different locations is proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Domestic Electric Vehicle Charging Strategy for Reducing Network Transmission Loss While Taking Seasonal Factors into Consideration

et al. 2018

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Abstract:With the rapid growth of domestic electric vehicle charging loads, the peak-valley gap and power fluctuation rate of power systems increase sharply, which can lead to the increase of network losses and energy efficiency reduction. This paper tries to regulate network loads and reduce power system transmission loss by optimizing domestic electric vehicle charging loads. In this paper, a domestic electric vehicle charging loads model is first developed by analyzing the key factors that can affect users' charging behavior. Subsequently, the Monte Carlo method is proposed to simulate the power consumption of a cluster of domestic electric vehicles. After that, an optimal electric vehicle charging strategy based on the 0-1 integer programming is presented to regulate network daily loads. Finally, by taking the IEEE33 distributed power system as an example, this paper tries to verify the efficacy of the proposed optimal charging strategy and the necessity for considering seasonal factors when scheduling electric vehicle charging loads. Simulation results show that the proposed 0-1 integer programming method does have good performance in reducing the network peak-valley gap, voltage fluctuation rate, and transmission loss. Moreover, it has some potential to further reduce power system transmission loss when seasonal factors are considered.

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Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data

Cited by 154 publications

References 40 publications

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

An Optimal Domestic Electric Vehicle Charging Strategy for Reducing Network Transmission Loss While Taking Seasonal Factors into Consideration

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