Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study

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

doi:10.1016/j.tra.2021.04.010

Cited by 26 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This creates a feedback loop that may reinforce deployment decisions, even though policy makers may not have the full information on the system state [50]. We also observe a feedback loop within the system, related to the demand side population growth, as the existence of CPs in an area has found attract new EV users [1]. An increase of EV users will result in an increase of interactions and, hence, affect the other feedback loops.…”

Section: Feedback Loopsmentioning

confidence: 87%

“…As the EV system matures, and the density of an EV system increases, emergent patterns of competition between EV users for CPs or emergent patterns of scheduling may appear. Patterns of competition were found in earlier research that analyzes unsuccessful connection attempts of an EV user to a CP [1]. The EV system may also exhibit a tendency to react to perturbations similar to critical infrastructures, as it is connected to other systems (i.e., electricity and road infrastructure).…”

Section: Emergencementioning

confidence: 90%

“…The number of electric vehicles (EVs) is expected to increase exponentially in the coming years [1,2]. To meet this rise in EV growth, policy makers and market parties are increasingly deploying several private, semi-public, and public charging points, both AC and DC.…”

Section: Introductionmentioning

confidence: 99%

“…Planning methods involve (i) planning using spatial operational research methods [14,15], (ii) data-driven methods using spatiotemporal presence of vehicles (mostly non-EV) data [16,17], and (iii) models focused on charging behavior [18]. The problem with these models is that they are unable to fully capture the complexity of charging behavior and EV user interactions [1]. The use of these models induces a linear scaling between the system's supply (CPs) and demand (EVs).…”

Section: Introductionmentioning

confidence: 99%

“…The use of these models induces a linear scaling between the system's supply (CPs) and demand (EVs). As a result, predictions from linear models may lead to overestimation of charging infrastructure performance and underestimation of its convenience for EV users [1]. Inaccurate predictions can result in the under-and oversupply of CPs, leading to ineffective deployment strategies and inefficient use of CI.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Understanding Complexity in Charging Infrastructure through the Lens of Social Supply–Demand Systems

Helmus

Lees

Hoed

2022

WEVJ

Self Cite

View full text Add to dashboard Cite

Since the first release of modern electric vehicles, researchers and policy makers have shown interest in the deployment and utilization of charging infrastructure. Despite the sheer volume of literature, limited attention has been paid to the characteristics and variance of charging behavior of EV users. In this research, we answer the question: which scientific approaches can help us to understand the dynamics of charging behavior in charging infrastructures, in order to provide recommendations regarding a more effective deployment and utilization of these infrastructures. To do so, we propose a conceptual model for charging infrastructure as a social supply–demand system and apply complex system properties. Using this conceptual model, we estimate the rate complexity, using three developed ratios that relate to the (1) necessity of sharing resources, (2) probabilities of queuing, and (3) cascading impact of transactions on others. Based on a qualitative assessment of these ratios, we propose that public charging infrastructure can be characterized as a complex system. Based on our findings, we provide four recommendations to policy makers for taking efforts to reduce complexity during deployment and measure interactions between EV users using systemic metrics. We further point researchers and policy makers to agent-based simulation models that capture interactions between EV users and the use complex network analysis to reveal weak spots in charging networks or compare the charging infrastructure layouts of across cities worldwide.

show abstract

Section: Feedback Loopsmentioning

confidence: 87%

Section: Emergencementioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%