Abstract:While utilizing price signals to affect charging behaviors has been identified as a promising strategy to manage charging loads, few studies discuss their impacts comprehensively. We investigate how different charging price strategies can affect the spatial and temporal distribution of charging activities at the individual level and the required charging infrastructure system. We utilize an integrated optimization platform for electric vehicle (EV) charging management and infrastructure placement in home and nonhome locations in San Diego, CA, that include charging price strategies, infrastructure costs, and mobility demand patterns. We evaluate three pricing scenarios and demonstrate that the time-of-use pricing scheme results in the highest emissions and the real-time one the lowest, which are 20.2% higher and 0.7% lower than the annual emissions under the flat rate scenario, which is about 8,787 MtCO2e. Our results show that the charging load profile is the result of various determinants including the dynamic electricity price, price elasticity of charging demand, travel and dwelling constraints, carbon price, as well as exclusive home and shared nonhome charging patterns. The effectiveness of changing charging behavior through internalizing climate damage to obtain environmental benefits depends largely on charging price strategies, implying that policymakers should consider charging price strategies in conjunction with carbon pricing rather than independently.

Key words: charging behavior, price elasticity optimization, GHG emissions, grid impact