European electric vehicle fleet: driving and charging data analysis

Corchero, Cristina; Gonzalez-Villafranca, S.; Sanmarti, M.

doi:10.1109/ievc.2014.7056144

Cited by 26 publications

(23 citation statements)

References 3 publications

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“…Between 2011 and 2013 data about driving and EV demand patterns were collected on a study conducted in Europe [9]. More than 230,000 charges have been registered.…”

Section: Charging Behaviourmentioning

confidence: 99%

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Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

et al. 2019

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The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.

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“…Between 2011 and 2013 data about driving and EV demand patterns were collected on a study conducted in Europe [9]. More than 230,000 charges have been registered.…”

Section: Charging Behaviourmentioning

confidence: 99%

“…To ensure that each user always has sufficient charge to make the trips, it was considered a critical battery level. Following the results of [9] this value is set to 20%. Whenever a user reaches a level lower than this, regardless of the trip, the car must be charged.…”

Section: Charging Decisionsmentioning

confidence: 99%

Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

et al. 2019

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“…A few related studies evaluated the general ability of EV fleet for power system operation, collected or surveyed large-scale EV charging data through demonstration projects, and analyzed the charging duration and amount of EVs [11][12][13][14]. However, to the knowledge of the authors, there are no reported studies evaluating the flexibility of EVCs with a valid formula that reflects EVC-data-driven characteristics, especially in a typical demand side resource perspective for grid network security.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector

et al. 2021

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The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis.

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“…Time to start charging at home, work, and other locations (extracted and revised fromCorchero et al, 2015) …”

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confidence: 99%

Estimating charging demand by modelling EV drivers' parking patterns and habits

Macaluso¹

2023

European Transport/Trasporti Europei

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The diffusion of battery electric vehicles (BEVs) requires a proper charging infrastructure to supply users the chance to charge their vehicles according to energy, time, and space needs. Thus, city planners and stakeholders need decision support tools to estimate the impacts of potential charging activities and compare alternative scenarios. The paper proposes a modelling approach to represent parking activities in urban areas and obtain key indicators of the electric energy required. The agent-based model reproduces the dynamics of user parking and assesses the impacts on the electricity grid during the day. Since the focus is on parking activities, no detailed data on vehicle trips are required to apply the standard demand modelling approach, which would require Origin-Destination matrices to simulate traffic flows on the road network. Preliminary results concerning the city of Turin are presented for simulated scenarios to identify zones where charging demand can be critical and peak events in electric power over the day. The model is designed to be scalable for all European cities because, as the case study shows, it uses available data. The results obtained can be used for the design of charging infrastructure (power and type) by zones.

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European electric vehicle fleet: driving and charging data analysis

Cited by 26 publications

References 3 publications

Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector

Estimating charging demand by modelling EV drivers' parking patterns and habits

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