An agent-based electric vehicle ecosystem model: San Francisco case study

Adepetu, Adedamola; Keshav, Srinivasan; Arya, Vijay

doi:10.1016/j.tranpol.2015.11.012

Cited by 54 publications

(41 citation statements)

References 13 publications

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“…Whilst PEVs can be recharged from standard plug sockets, these sockets charge PEVs slowly and are not always easily accessible by vehicles. Developing dedicated infrastructure can encourage more consumers to purchase PEVs and allow them to drive more electric miles (Adepetu et al, 2016;Ajanovic and Haas, 2016;Bonges and Lusk, 2016;Caperello et al, 2015;Egbue and Long, 2012;Graham-Rowe et al, 2012;Javid and Nejat, 2017;Mersky et al, 2016;Ozaki and Sevastyanova, 2011;Plötz et al, 2016;Zhang et al, 2016;Zheng et al, 2012). The development of this infrastructure needs to be carefully considered so that the benefits of infrastructure development can be maximised.…”

Section: Introductionmentioning

confidence: 99%

A review of consumer preferences of and interactions with electric vehicle charging infrastructure

Hardman

Jenn

Tal

et al. 2018

Transportation Research Part D: Transport and Environment

494

211

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This is the autho s e sio of a o k that was published in the following source: Hardman, S.; Jenn, A.; Beard, G.; Daina, N.;Figenbaum, E.; Jochem, P. E. P.; Kinnear, N. A. D.; Pontes, J. P.;Refa, N.;Turrentine, T. S.; Witkamp, B. (2018). AbstractThis paper presents a literature review of studies that investigate infrastructure needs to support the market introduction of plug-in electric vehicles (PEVs). It focuses on literature relating to consumer preferences for charging infrastructure, and how consumers interact with and use this infrastructure. This includes studies that use questionnaire surveys, interviews, modelling, GPS data from vehicles, and data from electric vehicle charging equipment. These studies indicate that the most important location for PEV charging is at home, followed by work, and then public locations. Studies have found that more effort is needed to ensure consumers have easy access to PEV charging and that charging at home, work, or public locations should not be free of cost. Research indicates that PEV charging will not impact electricity grids on the short term, however charging may need to be managed when the vehicles are deployed in greater numbers. In some areas of study the literature is not sufficiently mature to draw any conclusions from. More research is especially needed to determine how much infrastructure is needed to support the roll out of PEVs. This paper ends with policy implications and suggests avenues of future research.Next, we provide background information on charging modes and levels and then introduce the approach to the literature review. Section 2 then summarises the literature, whilst Section 3 concludes with insights for policymakers and literature gaps.

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Section: Introductionmentioning

confidence: 99%

A review of consumer preferences of and interactions with electric vehicle charging infrastructure

Hardman

Jenn

Tal

et al. 2018

Transportation Research Part D: Transport and Environment

494

211

View full text Add to dashboard Cite

show abstract

“…Consumers' behaviors while adopting EVs can be modeled using an agent-based modeling approach, which is known as a bottom-up simulation methodology [21]. ABM has its roots in Complex Adaptive Systems.…”

Section: Agent-based Modeling and Its Applications In Ev Diffusionmentioning

confidence: 99%

“…Recently, this methodology has been used to model innovation diffusion. In the field of vehicle technology adoption, there are some studies that used ABM to study the diffusion of EVs as well [21,34,35].…”

Section: Agent-based Modeling and Its Applications In Ev Diffusionmentioning

confidence: 99%

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An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

Ahkamiraad¹,

Wang²

2018

Energies

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Current power grids in many countries are not fully prepared for high electric vehicle (EV) penetration, and there is evidence that the construction of additional grid capacity is constantly outpaced by EV diffusion. If this situation continues, then it will compromise grid reliability and cause problems such as system overload, voltage and frequency fluctuations, and power losses. This is especially true for densely populated areas where the grid capacity is already strained with existing old infrastructure. The objective of this research is to identify the zip-code level electricity consumption that is associated with large-scale EV adoption in New York City, one of the most densely populated areas in the United States (U.S.). We fuse the Fisher and Pry diffusion model and Rogers model within the agent-based simulation to forecast zip-code level EV diffusion and the required energy capacity to satisfy the charging demand. The research outcomes will assist policy makers and grid operators in making better planning decisions on the locations and timing of investments during the transition to smarter grids and greener transportation.

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“…In [11] an agent-based model using San Francisco as a test city is presented considering how different policies and battery technologies might affect the uptake and usage of EVs. The model includes a set of agents with socio-demographic properties and attitudes and EV ecosystem with costs of gas, electricity, rebates and public charging stations.…”

Section: Agent Based Modelling Of Pvs and Evs Uptakementioning

confidence: 99%

Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles

Hattam

Greetham

2016

J. Mod. Power Syst. Clean Energy

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In the near future, various types of low-carbon technologies (LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage (LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods, where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood. This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.

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An agent-based electric vehicle ecosystem model: San Francisco case study

Cited by 54 publications

References 13 publications

A review of consumer preferences of and interactions with electric vehicle charging infrastructure

A review of consumer preferences of and interactions with electric vehicle charging infrastructure

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles

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