In accordance with the main aim of the study, a widely accessible, modifiable tool was created for parties interested in maintaining the national electricity supply network and parties interested in informing policy on plug-in vehicle adoption schemes and recharging behaviour control. The Parry Tool enables the user to incorporate present limits to plug-in vehicle recharging demand scheduling as imposed by the state of present technology (no third party mechanism for monitoring and control of recharging), present human travel behaviour needs and existing patterns in electricity usage; into the investigation of the impacts of recharging demand impacts and the design of mitigation measures for deflecting (parrying) worst case scenarios. The second aim of the project was to demonstrate the application of the Parry Tool. The multidisciplinary/interdisciplinary information gathered by the Parry Tool was used to produce national demand profiles for plug-in vehicle recharging demand, calculated using socioeconomic and travel behaviour-estimated population sizes for plug-in eligible vehicles and vehicle usage patterns, which were added to existing national electricity demand for a chosen test week – this was the first scenario subsequently tested. The information gathered by the Parry Tool was then used to inform the design of two demand management methods for plug-in vehicle recharging: Recharging Regimes and weekly recharging load-shifting – these were the second and third scenarios subsequently tested. Unmitigated simultaneous recharging demand in scenario 1 (all vehicles assumed to recharge at home upon arrival home every day) severely exacerbated peak demand, raising it by 20% above the highest peak in existing demand for the year 2009 over half an hour from 58,554 MW to 70,012 MW – a challenge to the generation sector. This increased the difference between daily demand minima and maxima and made the new total demand have sharper peaks – a challenge for grid regulators. Recharging Regimes in scenario 2 split the estimated national plug-in vehicle populations into groups of different sizes that started recharging at different times of the day, with the word ‘regime’ being applied because the spread of start times changed over the course of the test week from workdays to weekend. This avoided exacerbation of the peak and reduced the difference between daily demand minima and maxima by raising minima, providing a load-levelling service. Scenario 3 embellished the Recharging Regimes with workday-to-weekend recharging load-shifting that therefore took better advantage of the often overlooked weekly pattern in existing demand (demand being higher on workdays than weekends), by allowing partial recharging of a segment of the plug-in vehicle population. Limited consideration of the impact of changing vehicle energy usage (for which distance travelled was assumed to proxy in this study) showed that the more vehicles used their batteries during the day, the better the levelling effect offered by Recharging Regimes. Greater utilisation of battery capacity each day, however, can also be assumed to lessen the potential for workday-to-weekend load levelling, because load-shifting depends upon vehicles being able to partially recharge or defer recharging to later days and still meet their travel needs plus keep a reserve State Of Charge (SOC) for emergency and other unplanned travel. Whilst altering vehicle energy usage did not change the finding that unmitigated simultaneous recharging exacerbated existing peak demand, it was noted that when limited mileage variation was considered this sharpened the profile of total demand – the rise and fall of the new peak far steeper than that of the original peak in existing demand. The Parry Tool combines a series of integrated methods, several of which are new contributions to the field that use UK data archives but may potentially be adapted by researchers looking at energy issues in other nations. It presents a novel fossil-fuel based justification for targeting road transport – acknowledging energy use of fossil fuel as the originator of many global and local problems, the importance of non-energy use of petroleum products and subsequent conflicts of interest for use, and a fossil fuel dependency based well-to-wheel assessment for UK road transport for the two energy pathways: electricity and petroleum products. It presents a method for the recalculation and ranking of top energy use/users using national energy use statistics that better highlights the importance of the electricity industry. It also presents the first publicly documented method for the direct consultation and extraction of vehicle-focused statistics from the people-focused National Travel Survey database, including a travel behaviour and household income-based assessment of plug-in vehicle eligibility, used to scale up to national estimates for battery electric and plug-in electric hybrid vehicle (BEV and PHEV) national population sizes. The work presented here is meant to allow the reader to perceive the potential benefits of using several resources in combination. It details the Parry Tool, a framework for doing so, and where necessary provides methods for data analysis to suit. It should however be noted that methods were kept as simple as possible so as to be easily followed by non-specialists and researchers entering the field from other disciplines. Methods are also predominantly data-exploratory in nature: strong conclusions therefore should not be drawn. Rather, the work here should be seen as a guideline for future work that may more rigorously study these combined topics and the impacts they may have upon plug-in vehicle ownership, usage behaviour, impacts of recharging upon the national network and the design of mitigation measures to cope with this new demand.

The quest towards resolving concerns about transportation energy consumption and emissions across nations has created more interests to investigate factors responsible for households’ car ownership. While literature holds an extensive body of investigation usually compartmentalised in individual different disciplines, limited efforts have been made to promote inter-linkages of this strand of research across different disciplines. To fill this gap, this study developed an integrating Multinomial logit (MNL) model to examine the impact of some rarely-investigated and conventional explanatory variables, including: ethnicity, accommodation tenure, settlement nature, mental belief, environmental concern, geographical regions, household structure, driving licence, number of household income earners and household income, on car ownership. Analysis based on rich data sets of British Household Survey and US Consumer Expenditure Survey found not only the conventional explanatory variables to be significantly linked to the number of cars in the US and UK households, but also the rarely-investigated psychological variables were found to be significantly linked as well. As Socio-demography, Geography and Psychology impact on how people and households process information and assess market offers (e.g., products and services), this study presents findings which have beneficial implications for policymakers and transportations planners, including those who would like to alter people’s behaviour from private car ownership to public transportation use, car sellers in terms of how to identify and reach potential customers, provision of alternative forecasting approaches to car ownership scholars as well as possible consideration for general car ownership decision making. Caution should be taken when interpreting the relationship between psychological factors and car ownership since the psychological factors adopted are measure representatives from databases used with limitations in the factor structure for a representative sample of the countries’ population.