Electric Vehicle Battery Swapping-Charging System in Power Generation Scheduling for Managing Ambient Air Quality and Human Health Conditions

The proliferation of electric vehicles (EVs) offers additional opportunities for improving the ambient air quality and reducing the associated health impacts. However, such opportunities could become unattainable if the electricity for charging EVs is mainly supplied by coal-fired units. This paper aims to enhance the environmental benefits of EVs by differentiating the marginal impacts of coal-fired unit emissions at various times and locations on ambient air pollutant concentration (AAPC) and human health conditions. The proposed cost-effective approach will assign EV battery charging loads and other electricity demands to generation units with low health impacts. The proposed air pollution dispersion model maps the emission into spatial AAPC increments (Δ AAPCs), and concentration-response function maps Δ AAPCs into health impacts. As such, we estimate differentiating health impacts of emissions at various times and locations without executing large-scale atmospheric models. We also integrate differentiating emission regulations with the joint optimization of security-constrained unit commitment (SCUC) and EV battery swapping-charging system (BSCS). BSCS refers to the optimal scheduling of an aggregated number of EV batteries which are centrally charged and then dispatched via delivery trucks to battery swapping stations (BSSs) to supply local EVs. BSCS determines optimal schedules for battery charging, swapping, and truck routing. A Lagrangian decomposition method decouples the formulated large-scale mixed-integer linear programming model into SCUC and BSCS subproblems. The case studies demonstrate the effectiveness of the proposed approach for managing ambient air quality and human health conditions.