Charging Station Siting and Sizing Considering Uncertainty in Electric Vehicle Charging Demand Distribution

In the past decade, the demand for Electric Vehicle (EV) charging has increased, leading to an irregular fluctuation in EV inflow at the Charging Stations (CS). The policy-makers, therefore, need to have an infrastructure planning mechanism that addresses this fluctuation by estimating the ideal location and capacity of these CSs. This problem is known as the Charging Station Siting and Sizing Problem (CSSSP). Due to the uncertainty in EV inflow, the possible non-availability of charging ports, and the resulting unpredictability in the queueing time, a limited number of EVs can get charged. To minimize this dissatisfaction with charging, we model the uncertainty in the EV demand in terms of a statistical distribution varying over time. We propose a queueing mechanism that accounts for the demand distribution over time but restricts the waiting time by a given threshold. With this mechanism, we propose an iterative heuristic algorithm where an initial allocation of ports is obtained using a policy. Then a statistical approximation approach is proposed to estimate the total unsatisfied EVs within a CS for the given port allocation as a derived random variable. Finally, an approach is proposed to intelligently rearrange the charging ports across the CSs, resulting in a fresh allocation. We repeat the steps until there is no further reduction in the unsatisfied demands. We validate the performance of the proposed method against the Monte Carlo simulation and provide a comparative analysis.