Allocate electric vehicles’ public charging stations with charging demand uncertainty

Nowadays, electric vehicles (EVs) have developed rapidly due to their great advantages in fuel savings, reduced greenhouse gas emissions, and low air pollution. However, charging imbalances and shortages have limited the prevalence of EVs. We optimize the siting and sizing of public charging stations under uncertain urban en-route recharging demand. Considering charging congestion, we introduce the Erlang's loss formula to represent the service level requirement of each charging station. First, we determine the optimal locations and sizes of those stations by a robust optimization model, where we figure out its deterministic dual following linear approximation of the loss rate in queueing models. We further use a discrete-event simulation to relax the assumption of time-independent charging demand in the optimization approach and model real-time traffic based on real-world traffic and power grid information in Nanjing, China. Thus, we verify the robust optimum outperforming deterministic and stochastic optimums.