Spatial-temporal Modeling of Electric Vehicles Charging Infrastructure and Management for a Sustainable Energy System
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Spatial-temporal Modeling of Electric Vehicles Charging Infrastructure and Management for a Sustainable Energy System

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    Transportation electrification is playing an increasingly essential role in mitigating climate change, especially coupled with a sustainable energy system. However, proper placement of charging infrastructures and management of charging activities is the key to ensuring the environmental benefits from the widespread adoption of electric vehicles. Existing literature on the emissions implications of vehicle electrification is often limited by neglecting the spatial and temporal diversity of the electricity grid, or by failing to respect individual heterogeneity. This dissertation research demonstrates the importance of assessing BEV charging infrastructure in an integrated perspective, focusing on key interactions between transportation, energy, and economy across individual patterns of travel behavior, dwelling constraints, pricing elasticity of consumers with regards to charging, and the temporal and spatial diversity in price and GHG intensity of electricity through three studies. Results from the charging infrastructure optimization study show that higher non-home charging opportunity informed by the empirical travel and dwelling patterns offers more potentials for a shared public charging system in San Diego, resulting in 14% - 30% lower in total system cost and 21% - 25% lower in emissions. This indicates that the heterogeneity in spatial and temporal travel and dwelling patterns substantially affect the design of the charging infrastructure system, and substantially change the energy, economic and environmental impacts of the system. The charging price strategies study considers the price elasticity of charging demand while investigating how different charging price strategies can affect the spatial and temporal distribution of charging activities and their energy, environmental and economic impacts. The results show that the ability of changing charging behavior to obtain environmental benefits depends on charging price strategies largely and the charging load profile is the result of various determinants including the dynamic electricity price, travel, and dwelling constraints, carbon price clustering effect, as well as exclusive home and shared non-home charging patterns. Lastly, results from the shared autonomous electric vehicle study indicate that SAEVs with exogenous charging would reduce GHG emissions by at least 75% compared to the internal combustion vehicles fleet in 2030, and the advantage expands to 97% if charging activities can interact with the grid when smart charging is available. The emission benefits of SAEVs are mainly dominated by vehicle electrification and grid development.
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