EVBBmendeleyrefs.bib

@article{speidel_driving_2014,
	title = {Driving and charging patterns of electric vehicles for energy usage},
	volume = {40},
	issn = {1364-0321},
	url = {http://www.sciencedirect.com/science/article/pii/S1364032114006297},
	doi = {10.1016/j.rser.2014.07.177},
	abstract = {This paper presents findings from the Western Australian Electric Vehicle Trial (2010–2012) and the ongoing Electric vehicle (EV) charging research network in Perth. The University of Western Australia is collecting the data from eleven locally converted EVs and 23 charging stations. The data confirms most charging is conducted at business and home locations (55\%), while charging stations were only used for 33\% of charging events. The EV charging power over time-of-day and aggregated over all charging stations closely resembles a solar PV curve, which means that EV charging stations can ideally be offset by solar PV. Another important finding is that EVs spend significantly more time at a charging station than what is technically required for the charging process. Also on average, EVs have more than 50\% battery charge remaining when they plug in. This tells us parking spaces are in higher demand than Level-2 charging facilities.},
	urldate = {2019-04-12},
	journal = {Renewable and Sustainable Energy Reviews},
	author = {Speidel, Stuart and Bräunl, Thomas},
	month = dec,
	year = {2014},
	keywords = {Electric vehicle charging, Electricity Grid, Home charging, Pool vehicles},
	pages = {97--110},
	file = {ScienceDirect Snapshot:/Users/ben/Zotero/storage/4XIG6XE6/S1364032114006297.html:text/html}
}

@article{langbroek2017,
	title = {When do you charge your electric vehicle? {A} stated adaptation approach},
	volume = {108},
	issn = {0301-4215},
	shorttitle = {When do you charge your electric vehicle?},
	url = {http://www.sciencedirect.com/science/article/pii/S0301421517303774},
	doi = {10.1016/j.enpol.2017.06.023},
	abstract = {A large scale deployment of electric vehicles (EVs) is likely to contribute to a more sustainable transport system. However, charging EVs will increase the load on the electricity network. The maximum load may be minimized by coordinating the timing of charging activities, in order to spread electricity demand more equally over the course of a day. In this study, based on a stated-choice experiment, the effects of two different temporal price differentiation strategies on stated charging time are investigated, including socio-demographic, behavioural and socio-psychological variables.
In a situation without charging time coordination, a peak in charging events is likely to occur during the early evening. Temporal price differentiation has a significant influence on charging time and in particular the level of price differentiation matters. The likelihood to change charging time differs and different alternative time slots are chosen when comparing high to low levels of price differentiation. People that have more knowledge about EVs have a higher chance to change their charging time, whereas people that have the tendency to plan their trips long time beforehand are less likely to adjust their charging time in the scenarios with temporal price differentiation.},
	journal = {Energy Policy},
	author = {Langbroek, Joram H. M. and Franklin, Joel P. and Susilo, Yusak O.},
	month = sep,
	year = {2017},
	keywords = {Charging, Electric vehicles, Temporal price differentiation},
	pages = {565--573}
}

@article{rezvani_advances_2015,
	title = {Advances in consumer electric vehicle adoption research: {A} review and research agenda},
	volume = {34},
	issn = {1361-9209},
	shorttitle = {Advances in consumer electric vehicle adoption research},
	url = {http://www.sciencedirect.com/science/article/pii/S1361920914001515},
	doi = {10.1016/j.trd.2014.10.010},
	abstract = {In spite of the purported positive environmental consequences of electrifying the light duty vehicle fleet, the number of electric vehicles (EVs) in use is still insignificant. One reason for the modest adoption figures is that the mass acceptance of EVs to a large extent is reliant on consumers’ perception of EVs. This paper presents a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in EVs, as well as an overview of the theoretical perspectives that have been utilized for understanding consumer intentions and adoption behavior towards EVs. In addition, we identify gaps and limitations in existing research and suggest areas in which future research would be able to contribute.},
	urldate = {2019-03-26},
	journal = {Transportation Research Part D: Transport and Environment},
	author = {Rezvani, Zeinab and Jansson, Johan and Bodin, Jan},
	month = jan,
	year = {2015},
	keywords = {Adoption behavior, Consumer behavior, Electric vehicles, Intention, Literature review},
	pages = {122--136},
	file = {ScienceDirect Snapshot:/Users/ben/Zotero/storage/Q8FG75D3/S1361920914001515.html:text/html}
}

@article{li_review_2017,
	title = {A review of factors influencing consumer intentions to adopt battery electric vehicles},
	volume = {78},
	issn = {1364-0321},
	url = {http://www.sciencedirect.com/science/article/pii/S1364032117305798},
	doi = {10.1016/j.rser.2017.04.076},
	abstract = {Despite reducing environmental pollution and the excessive consumption of fossil fuels, the number of battery electric vehicles (BEVs) on the road is still low. Why is this so? Why is the mass adoption of BEVs so difficult to realize? One important reason is that the adoption of BEVs is, to a large extent, dependent on the acceptance of private consumers, and their willingness to adopt this mode of transport is insufficient. This study is a systematic overview of peer-reviewed journal articles to identify the reasons for and against consumer intentions to adopt BEVs. A total of 1846 papers were retrieved and after a two-step identification, 40 papers were finally identified and analyzed in detail. The influencing factors were categorized into three main types, namely demographic, situational and psychological, and they were reviewed separately. In addition, the shortcomings and deficiencies in the current studies were also noted.},
	urldate = {2019-03-26},
	journal = {Renewable and Sustainable Energy Reviews},
	author = {Li, Wenbo and Long, Ruyin and Chen, Hong and Geng, Jichao},
	month = oct,
	year = {2017},
	keywords = {Battery electric vehicles, Influencing factors, Intention to adopt, Literature review},
	pages = {318--328},
	file = {ScienceDirect Snapshot:/Users/ben/Zotero/storage/NMK5SES7/S1364032117305798.html:text/html}
}
@article{TranspowerNewZealand2017,
abstract = {Electricity is a convenient means of transferring and using energy. In New Zealand, our hydro lakes store energy on a large scale. However, until now we have had limited options to store electricity cost-effectively close to where it is used. Around the world, battery technology now offers opportunities to store electricity economically, close to where it is used. It can also store local sources of generation, such as rooftop solar, and smooth out the impacts that variable generation can have on the power system. Widespread, distributed storage could, and most probably will, fundamentally change the way that power systems will be operated in the future. Long-term, we expect that battery or other storage technologies installed in homes, businesses, vehicles, distribution networks and grid substations could alter our transmission business by covering short-term imbalances in supply and demand. We will be able to operate the power system differently, having more flexibility to schedule energy transfers and grid outages to optimise the use of the grid, grid generation and distributed energy resources. We explore these future possibilities in depth in our perspective document, Transmission Tomorrow. Despite these changes, the services that the national grid provides will be enduring. New Zealand's remotely located renewable generation will continue to be an economic, low-carbon electricity source. Our focus on resilience will continue to deliver essential services to New Zealand communities, households and businesses. As a critical infrastructure provider, these expectations need to be incorporated into our investment decisions over the short and long term. Developing a realistic view of the future will ensure we continue to provide attractive, cost-effective services that meet our customers' changing needs. We considered hosting our own trial of grid-connected battery storage, but first we chose to investigate the benefits of battery storage across the electricity supply chain. We did this by investigating the costs, benefits, regulatory, technical and commercial implications of battery storage located in different regions of New Zealand and at each point in the electricity supply chain. We developed various applications for battery storage and considered how these could also provide the services that are required to operate the electricity system. These applications were applied to separate case studies which were evaluated for a range of highlevel assumptions using a range of industry metrics.},
author = {{Transpower New Zealand}},
file = {:home/parra358/Documents/Battery Storage in New Zealand.pdf:pdf},
number = {September},
pages = {41},
title = {{Battery Storage in New Zealand}},
url = {https://www.transpower.co.nz/sites/default/files/publications/resources/Battery Storage in New Zealand.pdf},
year = {2017}
}
@article{TranspowerNZ2015,
author = {{Transpower New Zealand}},
file = {:home/parra358/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Transpower NZ - 2015 - Transmission Planning Report.pdf:pdf},
number = {July},
pages = {320},
title = {{Transmission Planning Report}},
year = {2015}
}
@article{Khan2018,
abstract = {Greenhouse gas (GHG) emissions from electricity generation are generally assessed using a yearly average carbon intensity (in carbon dioxide equivalent emissions per unit of energy). This masks the variability of emissions associated with different forms of generation over different timescales. Variability is a characteristic of electricity systems with high levels of renewable generation, where fossil fuels are typically used to meet any shortfall in supply. In this paper we argue that quantification of the time variability of carbon intensity is necessary to understand the detailed patterns of carbon emissions in electricity systems, particularly as future systems are likely to increasingly rely on a mix of time-variable generation types such as wind, hydro and solar. We analysed the time-varying carbon intensity of New Zealand's electricity sector, which has approximately 80{\%} renewable generation. In contrast to many other nations, we found that carbon intensity did not consistently follow daily peak demand, and was only weakly correlated with demand. This result, and the finding that carbon intensity has significant seasonal variation, stems from the dominance of hydro (albeit with limited storage capacity) in New Zealand's generation mix. Further investigation of the operating regimes of the fossil fuel generators, using time-varying analysis, indicates that New Zealand's electricity system is sub-optimal from a GHG emission perspective, with more coal generation than would seem to be required. Two policy measures, which also generalize to other countries, are proposed to address this issue: (i) the creation of an electricity capacity market – providing revenue for standby fossil fuel generation capacity without the need for continual generation; (ii) use of time-varying carbon intensity to inform demand-side measures and decisions about new renewable generation.},
author = {Khan, Imran and Jack, Michael W. and Stephenson, Janet},
doi = {10.1016/j.jclepro.2018.02.309},
file = {:home/parra358/Downloads/1-s2.0-S0959652618306474-main.pdf:pdf},
issn = {09596526},
journal = {Journal of Cleaner Production},
keywords = {Electricity generation,GHG emissions,Peak demand,Time-varying carbon intensity},
pages = {1091--1101},
publisher = {Elsevier Ltd},
title = {{Analysis of greenhouse gas emissions in electricity systems using time-varying carbon intensity}},
url = {https://doi.org/10.1016/j.jclepro.2018.02.309},
volume = {184},
year = {2018}
}
@article{Stephenson2017,
abstract = {A B S T R A C T Globally, renewable generation is growing rapidly, and the next few decades are likely to see many consumers adopting new grid-connected technologies such as electric vehicles, photovoltaics and energy management systems. However, these 'greener' and smarter' changes could create significant challenges for power quality, safety and other aspects of grid management. We describe how New Zealand is an ideal research environment for combining smart grid capability with integration of high levels of renewables, as it already has around 80{\%} renewable generation, and advanced metering infrastructure in over 62{\%} of households. Challenges for achieving a greener, smarter grid identified in the GREEN Grid research programme include managing the increased variability in supply, especially from the growing use of wind and solar generation; the potential for power quality and congestion issues from high levels of small scale distributed generation; the need for increased frequency keeping and instantaneous reserves as variability increases; and the relatively low level of consumer engagement in demand response which could ideally assist with variability. In this paper we describe the methodology and approach used in the research programme, and note some initial findings that may help address these issues, including the benefits of geographically distributed wind farms to reduce overall wind variability; the development of a hosting capacity tool for small scale distributed generation; a proposal for new ancillary services to help manage (and cover the costs of) increased variability; and the increased use of hot water cylinders for demand response. As the research programme continues to move forward with developing mechanisms for managing a smart green grid, the findings are likely to have widespread relevance to other nations that are seeking high levels of renewable generation.},
author = {Stephenson, Janet and Ford, Rebecca and Nair, Nirmal-Kumar and Watson, Neville and Wood, Alan and Miller, Allan},
doi = {10.1016/j.rser.2017.07.010},
file = {:home/parra358/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Stephenson et al. - 2017 - Smart grid research in New Zealand – A review from the GREEN Grid research programme.pdf:pdf},
keywords = {Consumer behaviour,Grid management,Photovoltaic,Renewable energy,Smart grid},
title = {{Smart grid research in New Zealand – A review from the GREEN Grid research programme}},
url = {https://ac-els-cdn-com.ezproxy.otago.ac.nz/S1364032117310675/1-s2.0-S1364032117310675-main.pdf?{\_}tid=14ef5d30-a0bc-11e7-864c-00000aacb35f{\&}acdnat=1506211712{\_}5dad0c49362d4ab6c14c0109fa66f8c7},
year = {2017}
}
@article{Eyers2018,
author = {Eyers, Lisa},
file = {:home/parra358/Downloads/electric-chargepoint-analysis-2017-domestics.pdf:pdf},
number = {December},
title = {{Electric Chargepoint Analysis 2017 : Domestics Key findings :}},
year = {2018}
}
@article{ConceptConsulting2018,
author = {{Concept Consulting}},
file = {:home/parra358/Downloads/ev{\_}study{\_}v1.0.pdf:pdf},
number = {March},
title = {{“ Driving change ” – Issues and options to maximise the opportunities from large-scale electric vehicle uptake in New Zealand}},
year = {2018}
}
@article{Azadfar2015,
abstract = {All major vehicle manufacturers now have, or plan to have, an electric vehicle model (EVs) on the market. Current EV take up rates are relatively slow, but the main factors that will determine take up rates are complex and unpredictable. A rapid and large increase in the take up rates over the coming years is therefore possible and probable. Such a rapid take up rate, if it occurs, would impact on electricity load and load profiles. Determining what the impacts will be, however, is made difficult as recharging behaviours of EV drivers are not well known or understood in advance. While a number of research studies have reviewed the methods that can be used to control the recharging profiles of EVs, this paper focuses on EV driver recharging behaviours and charging patterns and reviews and presents the major technical, environmental and economical factors that will influence these.},
author = {Azadfar, Elham and Sreeram, Victor and Harries, David},
doi = {10.1016/j.rser.2014.10.058},
file = {:home/parra358/Downloads/1-s2.0-S1364032114008831-main.pdf:pdf},
isbn = {1364-0321},
issn = {13640321},
journal = {Renewable and Sustainable Energy Reviews},
keywords = {Charging behaviour,Charging regimes,Distribution grid,Electric vehicle,Electric vehicle battery system,Electric vehicles (EVs),Grid reliability},
pages = {1065--1076},
publisher = {Elsevier},
title = {{The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour}},
url = {http://dx.doi.org/10.1016/j.rser.2014.10.058},
volume = {42},
year = {2015}
}
@article{Brady2016,
author = {Brady, John and Mahony, Margaret O},
doi = {10.1016/j.scs.2016.06.014},
file = {:home/parra358/Downloads/1-s2.0-S221067071630124X-main.pdf:pdf},
issn = {2210-6707},
journal = {Sustainable Cities and Society},
keywords = {Charging patterns,Electric vehicles},
pages = {203--216},
publisher = {Elsevier B.V.},
title = {{Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data}},
url = {http://dx.doi.org/10.1016/j.scs.2016.06.014},
volume = {26},
year = {2016}
}
@article{Daina2017,
abstract = {The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers' response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers' charging and travel behaviour. We propose a random utility model for joint EV drivers' activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers.},
author = {Daina, Nicol{\`{o}} and Sivakumar, Aruna and Polak, John W.},
doi = {10.1016/j.trc.2017.05.006},
file = {:home/parra358/Downloads/1-s2.0-S0968090X17301365-main.pdf:pdf},
isbn = {0968-090X},
issn = {0968090X},
journal = {Transportation Research Part C: Emerging Technologies},
keywords = {Charging choices,Charging service provider,Electric vehicles,Smart charging},
pages = {36--56},
title = {{Electric vehicle charging choices: Modelling and implications for smart charging services}},
volume = {81},
year = {2017}
}