Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Scenarios of technology adoption towards low-carbon cities

Technological change has often been presented as a readily accepted means by which long-term greenhouse gas (GHG) emission reductions can be achieved. Cities are the future centers of economic growth, with the global population becoming predominantly urban; hence, increases or reductions of GHG emissions are tied to their energy strategies. This research examines the likelihood of a developed world city (the Greater Toronto Area) achieving an 80% reduction in GHG emissions through policy-enabled technological change. Emissions are examined from 3 major sources: light duty passenger vehicles, residential buildings and commercial/institutional buildings. Logistic diffusion curves are applied for the adoption of alternative vehicle technologies, building retrofits and high performance new building construction. This research devises high, low and business-as-usual estimates of future technological adoption and finds that even aggressive scenarios are not sufficient to achieve an 80% reduction in GHG emissions by 2050. This further highlights the challenges faced in maintaining a relatively stable climate. Urban policy makers must consider that the longer the lag before this transition occurs, the greater the share of GHG emissions mitigation that must addressed through behavioural change in order to meet the 2050 target, which likely poses greater political challenges.