Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Wolbertus, Rick; Kroesen, Maarten; Hoed, Robert van den; Chorus, Caspar

doi:10.1016/j.trd.2018.03.012

Cited by 104 publications

(56 citation statements)

References 47 publications

(59 reference statements)

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“…Second, users predominantly charge where they already park. These assumptions are consistent with other research [30,31] and the anticipated user behavior for a mass-market of electric vehicles. For our case study, we use the 2008 German household travel survey (MiD) [23].…”

Section: Vector21supporting

confidence: 89%

“…We assume that electric vehicle users do not alter their travel patterns. This assumption is supported by the literature, but we are also limited by the lack of data on detailed travel information of electric vehicle users [30,31]. Second, electricity demand from BEVs is temperature and seasonally dependent, which was not considered in this model experiments but suggested e.g.…”

Section: Modelmentioning

confidence: 74%

See 1 more Smart Citation

Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

Wulff¹,

Steck²,

Gils³

et al. 2020

Preprint

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Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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Section: Vector21supporting

confidence: 89%

Section: Modelmentioning

confidence: 74%

Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

Wulff¹,

Steck²,

Gils³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The results of Mau et al [36] support the relevance of a range of vehicle attributes beyond the purchase price in shaping consumer preferences toward EVs. Wolbertus et al [37] showed that offering free parking has a positive effect on the purchase intention of EVs. Many drivers were concerned about the financial implications of purchasing.…”

Section: Literature Reviewmentioning

confidence: 99%

Attitudes Toward Electric Vehicles: The Case of Perugia Using a Fuzzy Set Analysis

Bigerna

Micheli

2018

Sustainability

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Sustainable mobility has received increasing attention in recent years. The transport sector contributes to almost a quarter of Europe's greenhouse gas emissions. The development of electric vehicles (EVs) may help the shift toward sustainable mobility, reducing oil vulnerability and greenhouse gas emissions in road transport. Poor penetration of EVs might be explained, moreover, by consumer resistance to EVs. The aim of our paper is to determine consumers' attitude and preferences for EVs, investigating which conditions influence consumer decision-making to purchase an EV. Using a fuzzy set qualitative comparative analysis of 421 highly-educated individuals, involving students and faculty members, we identify several configurations of conditions of the variables which lead to the outcomes, supporting their equifinality and asymmetric nature. Our findings indicate attitudes related to EVs differ across age and groups. Our paper offers public decision makers new useful insights for understanding the importance of specific determinants, and for designing effective strategies for EVs' development worldwide.system, due to their high efficiency throughout the fuel supply chain and vehicle fuel consumption [7,8]. A number of positive impacts can be expected from the development of EVs. They are an alternative approach to contribute to a more sustainable mobility, reducing fuel consumption and GHG emissions [9]. EVs are the best way to tap into renewable energy sources, such as solar and wind power [10], thus indicating that the deployment of EVs and renewable energy sources should take place concomitantly [11]. Moreover, EVs have the potential to make major contributions to the electric supply system, as storage or generation resources, or both [12]. Specifically, EVs can absorb excess energy production (for instance from wind power) that would be wasted or curtailed [13].However, the emerging EV "industry" still faces technological challenges in terms of vehicle and infrastructure design, and EVs are expensive to obtain. There are unresolved issues about the viability of the current generation of EVs, such as rapid charging, range, lack of infrastructure, and load of the grid. Also, off peak-load impacts may arise from the integration of EVs with electricity grids [14].Despite the fact that the Paris Declaration on Electro-Mobility and Climate Change and Call to Action has forecast a threshold of 100 million EVs by 2030 [15], it has to be recognized that large scale EV deployment is still not a reality. There were 2 million EVs in circulation worldwide in 2016, i.e., 1.4% of the total passenger car market.Transitions to green technologies such as EVs often require significant shifts not only in technology, but in political regulations, tariffs and pricing regimes, and the behavior of users and adopters [16]. Consumer resistance to EVs is an obstacle to the adoption of this technology. Potential EV purchasers identify the cost premium, range limitation and recharging time of EVs as disadvantages compared to conventiona...

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“…Findings from a national survey showed that recharging times have a considerable influence on consumers' preferences [21]. The effects of policies on charging behavior and EV adoption were studied in [59]. The authors used a large data set to investigate the influence of daytime and free parking policies on EV drivers' charging behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

Fazeli

Venkatachalam

Chinnam

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking location, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

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Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Cited by 104 publications

References 47 publications

Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

Attitudes Toward Electric Vehicles: The Case of Perugia Using a Fuzzy Set Analysis

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

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