Learning EV Placement Factors with Social Welfare and Economic Variation Modeling

The past few years have witnessed significant growth on the possession rate of electric vehicles (EV). Such growth urgently requires well-designed plans on charging station placement for sustainable EV growth. Existing solutions ignore many practical factors and lack a systematic method prioritizing them. Through constructive learning, we propose an urban EV charging station planning method with the deployment of levelized cost. This method incorporates four practical costs, considering the convexification of the constraints, economic parameter variation, and the interconnected electric and transportation networks. To better quantify the charging demand, the nested logit model is deployed. Meanwhile, we relate the public information of house prices with EV growth when assigning the weights. Furthermore, we also design the software that enables EV charging station placement. Numerical results reveal the trade-off in EV charger planning, as well as a promising system-level optimization performance.