An integrated optimization platform for spatial-temporal modeling of electric vehicle charging infrastructure - Li, Xinwei; Jenn, Alan (Transportation research. Part D, Transport and environment, 2022)
•An integrated platfrom is developed to inform EV chargers placement and their impacts.•Home and non-home chargers are optimally distributed at the census tract level simultaneously.•Charging opportunity in California demonstrates the dominance of home charging.•Travel and dwelling patterns, and electricity dynamics enhance non-home charging.•Heterogeneity in spatial and temporal travel and dwelling patterns determines the system impacts. Vehicle electrification has been identified as one of the most important roles in decreasing greenhouse gas (GHG) emissions in transportation. 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 (EVs). By employing empirical travel trajectory data, this paper investigates how individual travel and dwelling patterns can affect the distribution of spatial and temporal opportunities for electric vehicle charging, as well as charging infrastructure installation across regions. We formulate an integrated optimization platform for estimating electric vehicle charging infrastructure placement in home and non-home locations simultaneously that include infrastructure costs and dynamic electricity prices with a mixed-integer linear programming. We provide two case studies in the Great Sacramento Area and San Diego, California. The results 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 significantly change the energy, economic and environmental impacts of the system. We also observe sensible timing of charging in non-home locations that correspond to daytime hours and a secondary peak in charging at home locations during nighttime hours in both regions, emphasizing the importance of integrating grid dynamics into EV charging infrastructures planning process. Our model platform provides new insights on how to properly allocate EV charging infrastructures and manage charging activities from a comprehensive and disaggregated perspective combined with power grid smoothing.