GIS-BASED MODELING OF ELECTRIC VEHICLES AND THE AUSTRALIAN ELECTRICTY GRID

The decarbonisation of transport and power supply sectors is key to achieving global and national emissions cut targets in line with Paris Agreement’s limiting global warming goals. Electric vehicles (EVs), coupled with large adoption of renewable energy (RE) resources in the power system, offer such carbon mitigation solutions. However, due to the unknown spatio-temporal variability of EV charging load, introducing large quantities of EVs and high shares of variable wind and solar energy poses challenges to the load balance management. Against this background, this thesis examines the potential role of flexible EV loads and diverse energy resources in decarbonisation of the transport and electricity supply sectors. The main content of this thesis includes: First, based on real-world vehicle driving survey data, I present a deterministic and a probabilistic model to quantitatively investigate the spatio-temporal distribution of EV charging load for Australia. Second, I present a cross-sectoral integrated EV-grid model for accessing various energy supply and demand scenarios with high spatio-temporal resolution. I quantify the impacts of EV charging demand on the current fossil-based power system in terms of its electricity generation, LOLP and levelized cost of electricity (LCOE) in Australia. Third, I further investigate spatio-temporal configurations of the least-cost 100% renewable power supply in Australia, at various levels of biomass resource use and concentrated solar power (CSP) penetration. Fourth, I utilize the EV-grid integrated model to examine the spatio-temporal interactions of widespread EV charging with a future, 100% renewable electricity system in Australia. I obtain least-cost grid configurations that include both RE generators and EVs, the latter under both uncontrolled and controlled charging, and under adoption rates between 0 and 100%