The number of Electric Vehicles (EVs) is expected to increase exponentially in the coming years. The growing presence of charging points generates a multitude of interactions between EV users, particularly in metropolitan areas where a charging infrastructure is largely part of the public domain. There is a current knowledge gap as to how current decisions on charging infrastructure deployment affect both current and future infrastructure performance. In the thesis an attempt is made to bridge this knowledge gap by creating a deeper understanding of the relation between charging behavior, charging infrastructure deployment, and performance.The results demonstrate shown how both strategic and demand-drive deployment strategies have an effect on performance metrics. In a case study in the Netherlands it was found that during the initial deployment phase, strategic Charging Points (CPs) facilitate EV users better than demand driven deployment. As EV user adoption increased, demand-driven CPs show to outperform strategic CPs.This thesis further shows that there are 9 EV user types each with distinct difference distinct behavior in terms of charging frequency and mean energy uptake, both of which relate to aggregate CP performance and that user type composition, interactions between users and battery size play an important role in explaining performance of charging infrastructure.A validated data-driven agent-based model was developed to explore effects of interactions in the EV system and how they influence performance. The simulation results demonstrate that there is a non-linear relation between system utilization and inconvenience even at the base case scenario. Also, a significant rise of EV user population will lead to an occupancy of non-habitual charging at the expense of habitual EV users, which leads to an expected decline of occupancy for habitual EV users.Additional simulations studies support the hypothesis that several Complex Systems properties are currently present and affecting the relation between performance and occupation.
In tourism and recreation management it is still common practice to apply traditional input-output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input-output (NLIO) model, in which price-induced input substitution is accounted for. For large changes in final demand, a NLIO model is more useful than a traditional IO model, leading to higher or lower impacts. For small changes in final demand input substitution is less likely. In that case the application of the NLIO may lead to the same results as a traditional IO model. To analyse changes of subsidies, a traditional IO model is not an option. A more flexible model, such as the NLIO, is required. The NLIO model forces researchers to make choices about capacity constraints, factor mobility and the substitution elasticity, which can be difficult but create flexibility and allow for more realism.
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In tourism and recreation management it is still common practice to apply traditional input-output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input-output (NLIO) model, in which price-induced input substitution is accounted for. For large changes in final demand, a NLIO model is more useful than a traditional IO model, leading to higher or lower impacts. For small changes in final demand input substitution is less likely. In that case the application of the NLIO may lead to the same results as a traditional IO model. To analyse changes of subsidies, a traditional IO model is not an option. A more flexible model, such as the NLIO, is required. The NLIO model forces researchers to make choices about capacity constraints, factor mobility and the substitution elasticity, which can be difficult but create flexibility and allow for more realism.
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