Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data

Pareschi, Giacomo; Küng, Lukas; Georges, Gil; Boulouchos, Konstantinos

doi:10.1016/j.apenergy.2020.115318

Cited by 83 publications

(35 citation statements)

References 53 publications

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“…These activity-based modeling approaches can provide the user's charging behavior that may help to observe the effect of EV charging on the electric power network. The EV models based on the trip behavior can be divided into two categories; direct use of observed activity-travel schedules (DUOATS) and activity-based models (ABS) [12,16].…”

Section: Overview Of the Existing Modelsmentioning

confidence: 99%

“…The EV model provides charging profiles to estimate additional electricity load introduced by the EV charging in the distribution grid. The researchers have used different approaches to construct EV charging profiles [12]. In the first approach, simulation models of EV usage are made that are based on available data related to transportation patterns.…”

Section: Introductionmentioning

confidence: 99%

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Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

et al. 2021

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The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.

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Section: Overview Of the Existing Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

et al. 2021

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“…The case study for the resident group models the charging demand only. The charging demand is based on the study [35] focusing on the PDF of daily distance driven by car (Figure 8). The assumed demand for charging characterizes the residents' behaviour.…”

Section: Users Input Data Analysismentioning

confidence: 99%

Optimization of Electric Vehicle Charging Points Based on Efficient Use of Chargers and Providing Private Charging Spaces

et al. 2020

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Electric vehicles are a mobility innovation that can help significantly reduce greenhouse gas emissions and mitigate climate change. However, increasing numbers of electric vehicles require the construction of a dense charging infrastructure with a sufficient number of chargers. Based on the identified requirements for existing electric vehicle users and potential new customers, the paper proposes a charging point model for an urban area equipped with a local transformer station and a sufficient number of low-power chargers. In particular, the model focuses on efficient use of chargers throughout the day, considering private rental of chargers paid by residents in the evening. The model uses an optimization method that compares the non-covered fixed costs due to unsold electricity to nonresidents and the annualized costs of building an additional transformer. The proposed optimal charging point solution was tested in a case study using real data capturing users’ habits and their arrivals in and departures from the car park. As our model results show, the great benefit of a park-and-ride car park equipped with chargers consists of a simple increase in car park efficiency, ensuring sufficient numbers of private charging lots, optimizing operating costs, and supporting the development of electromobility.

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“…The study did not employ real charging data; rather the used data was based on a survey of parking times and driving distances in Beijing. As the used data was synthetic, the accuracy of the results may have not reflected the reality [13]. As such, the study did not provide insights into charging behavior between charging sites.…”

Section: Introductionmentioning

confidence: 96%

Electric Vehicles as a Flexibility Provider: Optimal Charging Schedules to Improve the Quality of Charging Service

et al. 2021

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The sub-aggregation of electric vehicles provides significant potential to power systems in the form of ancillary services. This means with smart charging it is possible to shift loads from peak to off-peak hours. For the flexibility from privately owned electric vehicles to be offered to the electricity market, customer participation is crucial; however, the impacts of sub-aggregation on customers have not been studied thoroughly. In this paper, charging data covering over 80,000 real-world charging sessions from various commercial charging sites are introduced and the charging characteristics are analyzed. Importantly, a method for finding an optimal pattern for load reduction with the least impact on customers is presented. We suggest an optimal schedule for load reduction from the customer viewpoint at different types of charging sites, including public car parks, offices, residential sites, and shopping centers. The findings indicate that residential and office charging sites offer the greatest potential for load reduction with the least impact on customers. The most flexibility is available during peak charging hours, which on average are at 08:00 at car parks, 07:30 at office sites, 19:00 at residential sites, and 10:00 at shopping centers.

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Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data

Cited by 83 publications

References 53 publications

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

Optimization of Electric Vehicle Charging Points Based on Efficient Use of Chargers and Providing Private Charging Spaces

Electric Vehicles as a Flexibility Provider: Optimal Charging Schedules to Improve the Quality of Charging Service

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