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Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.
James Dixon; Sofia Koukoura; Christian Brand; Malcolm Morgan; Keith Bell. Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks. Future Transportation 2021, 1, 113 -133.
AMA StyleJames Dixon, Sofia Koukoura, Christian Brand, Malcolm Morgan, Keith Bell. Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks. Future Transportation. 2021; 1 (1):113-133.
Chicago/Turabian StyleJames Dixon; Sofia Koukoura; Christian Brand; Malcolm Morgan; Keith Bell. 2021. "Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks." Future Transportation 1, no. 1: 113-133.
This paper presents an investigation of the potential for coordinated charging of electric vehicles to i) reduce the CO2 emissions associated with their charging by selectively charging when grid carbon intensity (gCO2/kWh) is low and ii) absorb excess wind generation in times when it would otherwise be curtailed. A method of scheduling charge events that seeks the minimum carbon intensity of charging while respecting EV and network constraints is presented via a time-coupled linearised optimal power flow formulation, based on plugging-in periods derived from a large travel dataset. Schedules are derived using real half-hourly grid intensity data; if charging in a particular event can be done entirely through use of renewable energy that would otherwise have been curtailed, its carbon intensity is zero. It was found that if ‘dumb’ charged from the current UK mainland (GB) grid, average emissions related to electric vehicle (EV) charging are in the range 35–56 gCO2/km; this can be reduced to 28–40 gCO2/km by controlled charging – approximately 20–30% of the tailpipe emissions of an average new petrol or diesel car sold in Europe. There is potential for EVs to absorb excess wind generation; based on the modelled charging behaviour, 500,000 EVs (20% of Scotland's current car fleet) could absorb around three quarters of curtailment at Scotland's largest onshore wind farm.
James Dixon; Waqquas Bukhsh; Calum Edmunds; Keith Bell. Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment. Renewable Energy 2020, 161, 1072 -1091.
AMA StyleJames Dixon, Waqquas Bukhsh, Calum Edmunds, Keith Bell. Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment. Renewable Energy. 2020; 161 ():1072-1091.
Chicago/Turabian StyleJames Dixon; Waqquas Bukhsh; Calum Edmunds; Keith Bell. 2020. "Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment." Renewable Energy 161, no. : 1072-1091.
The battery electric vehicle (EV) market is in a state of continuing rapid evolution, both in terms of the battery capacities and charger power ratings that vehicle manufacturers are bringing to the market and the increasingly widespread penetration of charging infrastructure. As a result, individuals’ charging behaviours may change substantially and be different from what has been expected or observed to date. This could have a significant effect on the resulting electrical demand from EV charging. Aside from these technical parameters, the demographics of the population served by any given network – and how that might affect their travel habits, particularly car use – must be considered. In this paper, statistical analysis of a large travel survey dataset is carried out to support the hypothesis that car use is likely to vary according to population demographics. Car-based travel diaries disaggregated on key demographic traits of the drivers are assigned to vehicles in the network according to Census data pertaining to those same demographic traits. Charging schedules are derived from these travel diaries for different battery capacities, charger power ratings and level of access to charging to investigate the likely effect of changing these parameters on the resulting charging behaviour and electricity demand. It is found that out of the key emerging patterns identified in the evolving EV market, increasing battery capacities and the establishment of more widespread charging opportunities may reduce the peak demand from EV charging or shift it to a time less likely to coincide with peak domestic demand, hence making it easier for the network to cope with increasing penetrations of EVs. On the other hand, increasing charging power may increase the peak and bring it closer to a time where it is more likely to coincide with peak domestic demand.
James Dixon; Keith Bell. Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks. eTransportation 2020, 4, 100059 .
AMA StyleJames Dixon, Keith Bell. Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks. eTransportation. 2020; 4 ():100059.
Chicago/Turabian StyleJames Dixon; Keith Bell. 2020. "Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks." eTransportation 4, no. : 100059.
‘Destination charging’ – in which drivers charge their battery electric vehicles (EVs) while parked at amenities such as supermarkets, shopping centres, gyms and cinemas – has the potential to accelerate EV uptake. This study presents a Monte Carlo-based method for the characterisation of EV destination charging at these locations based on smartphone users' anonymised positional data captured in the Google Maps Popular Times feature. Unlike the use of household and travel surveys, from which most academic works on the subject are based, these data represent individuals' actual movements rather than how they might recall or divulge them. Through a fleet EV charging approach proposed in this study, likely electrical demand profiles for EV destination charging at different amenities are presented. Use of the method is presented first for a generic characterisation of EV charging in the car parks of gyms, based on a sample of over 2000 gyms in around major UK cities, and second for a specific characterisation of hypothetical EV charging infrastructure installed at a large UK shopping centre to investigate the impact of varying the grid and converter capacity on the expected charging demand and level of service provision to the vehicles charging there.
James Dixon; Ian Elders; Keith Bell. Evaluating the likely temporal variation in electric vehicle charging demand at popular amenities using smartphone locational data. IET Intelligent Transport Systems 2020, 14, 504 -510.
AMA StyleJames Dixon, Ian Elders, Keith Bell. Evaluating the likely temporal variation in electric vehicle charging demand at popular amenities using smartphone locational data. IET Intelligent Transport Systems. 2020; 14 (6):504-510.
Chicago/Turabian StyleJames Dixon; Ian Elders; Keith Bell. 2020. "Evaluating the likely temporal variation in electric vehicle charging demand at popular amenities using smartphone locational data." IET Intelligent Transport Systems 14, no. 6: 504-510.
This paper presents a quantitative investigation of the inconvenience of electric vehicle (EV) charging relative to internal combustion engine vehicle (ICEV) fuelling in terms of the time penalty likely to be experienced by drivers. A heuristic approach to deriving idealised charging schedules from over 39,000 week-long travel diaries from the UK National Travel Survey is presented in order to quantify the expected convenience parity — the point at which EV charging and ICEV fuelling are of comparable convenience — for combinations of battery capacity, charger power and access to charging at different locations (home, workplace and public destinations). It was found that although the majority — up to 95% — of individuals who can charge at home are expected to be able to reach convenience parity with battery sizes currently available in EV models at the ‘affordable’ end of the market, this is significantly less likely for those who rely on workplace or public charging — and particularly for those who must rely solely on en route charging. These individuals are expected to suffer considerable inconvenience associated with EV charging relative to ICEV fuelling, and although greater battery capacities and charger power ratings are expected to lessen this inconvenience, there remains a significant gap in the convenience of EV ownership between those who can charge while parked at home and those who cannot. Further analysis is carried out to long journeys that cannot be made on a single charge; ‘range anxiety’ being a major obstacle to widespread EV adoption. It was found that if drivers are compliant with the UK Highway Code in taking regular breaks on long journeys, fewer than 0.01% of trips are expected to be delayed by charging when using battery capacities of 40–60 kWh.
James Dixon; Peter Bach Andersen; Keith Bell; Chresten Træholt. On the ease of being green: An investigation of the inconvenience of electric vehicle charging. Applied Energy 2019, 258, 114090 .
AMA StyleJames Dixon, Peter Bach Andersen, Keith Bell, Chresten Træholt. On the ease of being green: An investigation of the inconvenience of electric vehicle charging. Applied Energy. 2019; 258 ():114090.
Chicago/Turabian StyleJames Dixon; Peter Bach Andersen; Keith Bell; Chresten Træholt. 2019. "On the ease of being green: An investigation of the inconvenience of electric vehicle charging." Applied Energy 258, no. : 114090.
Not all Electric Vehicle (EV) charging in future will take place at drivers' homes or on-street; at least some will take place at fast-charging `forecourts' analogous to today's petrol stations. This paper presents a Monte Carlo (MC)-based method for the characterization of the likely demand profile of EV fast charging forecourts based on activity profiles of existing petrol stations, derived from smartphone users' anonymised positional data captured in the `Popular Times' feature in Google Maps. Unlike most academic works on the subject to date which rely on vehicle users' responses to surveys, these data represent individuals' actual movement patterns rather than how they might recall or divulge them. Other inputs to the model are generated from probability distributions derived from EV statistics in the UK and existing academic work. A queuing model is developed to simulate busy periods at charging forecourts. The output from the model is a set of expected time series of electrical demand for an EV forecourt and statistical analysis of the variation in results. Finally, a method is presented for the probabilistic evaluation of the combined loading of an EV forecourt and existing demand; this could be used to assess the sufficiency of existing network capacity and the potential for innovative smart grid technologies to facilitate increasing penetration of EVs.
James Dixon; Ian Elders; Keith Bell. Characterization of Electric Vehicle Fast Charging Forecourt Demand. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) 2018, 1 -9.
AMA StyleJames Dixon, Ian Elders, Keith Bell. Characterization of Electric Vehicle Fast Charging Forecourt Demand. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). 2018; ():1-9.
Chicago/Turabian StyleJames Dixon; Ian Elders; Keith Bell. 2018. "Characterization of Electric Vehicle Fast Charging Forecourt Demand." 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) , no. : 1-9.
This paper presents an investigation into the potential benefits of interconnecting adjacent 33 kV demand groups in the GB distribution network by presentation of two case studies. Results presented are, firstly, a comparison of load profiles of adjacent groups and, secondly, following application of a series of credible future scenarios, the potential reduction in loss of load and generation curtailment achievable from interconnection and the proportion of time for which interconnection would be utilised. It was found that there is significant dissimilarity between load profiles of the adjacent groups analysed and interconnection could be valuable for the future distribution system. The value of interconnection could be increased with the use of storage, though more analysis is needed to quantify the economic viability of this.
James Dixon; Keith Bell; Ian Elders. Opportunities for interconnection of adjacent distribution feeders in GB networks. 2017 52nd International Universities Power Engineering Conference (UPEC) 2017, 1 -6.
AMA StyleJames Dixon, Keith Bell, Ian Elders. Opportunities for interconnection of adjacent distribution feeders in GB networks. 2017 52nd International Universities Power Engineering Conference (UPEC). 2017; ():1-6.
Chicago/Turabian StyleJames Dixon; Keith Bell; Ian Elders. 2017. "Opportunities for interconnection of adjacent distribution feeders in GB networks." 2017 52nd International Universities Power Engineering Conference (UPEC) , no. : 1-6.