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

This research provides insights to policymakers and academics on how to properly allocate electric vehicle charging infrastructure and manage charging activities. The high spatial resolution activity-based travel diary data will show much more information on travel and dwelling patterns of people than the aggregated data, which provides the opportunities to develop new spatial and temporal optimization models for EV charging infrastructure planning and charging management. Using California as a case study, this research will use the mobility and dwelling information of 10,913 residents for one day to explore the benefits of a comprehensive spatial-temporal optimization model for detecting the best strategy of BEV infrastructure placement and charging management with minimum system cost, GHG emissions, and renewable curtailment. California is leading the revolution towards transportation electrification in the US and the world, and Governor Jerry Brown signed Executive Order B-48-18 in January 2018 setting a state target of having 5 million ZEVs on California roads by 2030 and deploying 250,000 charging stations, including 10,000 fast-charging stations, by 2025. Results of this research not only provide policy guidance for charging infrastructure planning in California but can be applied to other regions for which similar data are available. This research represents the first of its kind to consider dwelling time limits and price elasticity of charging demand in exploring the economic, environmental and energy implements for EV charging infrastructure planning and management. This study is based on the mobility of current vehicle drivers, but it can be easily converted to new mobility with changing vehicle occupation rates under different scenarios such as shared mobility or/and autonomous vehicle.

Research Area