Transport is expected to become electrified in coming decades, bringing new challenges and opportunities for commuters and electricity distributors. This thesis presents analysis of Household Travel Survey (2014/15) and Journey to Work (2011) census datasets from the New South Wales (NSW) with the aim of;(i) investigating whether electric vehicles (EVs) could meet the daily commuting needs, and(ii) quantifying the potential impact of EVs on the electricity distribution grid as a function of location and time.It was found that 87% of commuter vehicle trips could be provided using affordable EVs and that the resulting electricity demand would increase by more than 10% in only 9 out of 35 local government areas (LGAs) in NSW, Australia. We also quantified the potential spatiotemporal electric energy available for vehicle-to-grid services.It was found that greenhouse gas emissions across NSW would reduce by 26% CO2(eq) even if all EVs were recharged from non-renewable coal-fired power plants, due to greater efficiency of EVs. The results demonstrated the potential for wide-scale adoption of EVs in Australia. Lastly, to facilitate analysis and prediction of key variables, the travel data was modelled using regression trees (RTs) and artificial neural networks (ANNs).

This paper proposes two electric energy management systems (EMSs) in the context of a gridconnected residential neighbourhood with electric vehicles (EVs), battery storage, and solar photovoltaic (PV) generation. The EMSs were developed to minimize the cost of electricity whilst having no impact on routine individual energy needs and travel patterns. The EMSs were evaluated using common sets of real data with the aim to compare the effectiveness of a centralized EMS with decentralized EMS. The models also accounted for the battery capacity degradation and the associated costs. Simulation studies and numerical analyses were presented to validate the effectiveness of the proposed EMSs considering a high-density residential building in Sydney, Australia. The simulation results indicate that the centralized EMS is more effective compared to the decentralized EMS in terms of cost savings. It is also observed that the energy management strategies significantly reduce the energy drawn from the grid compared to un-optimized energy management schemes.

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