Grid-ECO: Grid Aware Electric Vehicle Charging Stations Placement Optimizer

Abstract

The paper develops a methodology, Grid-ECO, to optimally allocate electric vehicle charging stations (EVCS) within a distribution feeder, while considering EV charging demand at census-level granularity. The underlying problem is NP-hard and requires satisfying nonlinear, nonconvex, three-phase unbalanced AC network constraints while including integer decision variables. Existing works cannot guarantee AC feasibility nor optimality of this problem without either i) relaxing the integer decision variable space or ii) convexifying AC constraints. Proposed Grid-ECO exactly solves the underlying mixed-integer nonlinear program (MINLP) to near-zero optimality gap while prioritizing candidate locations based on grid voltage and current sensitivities. To solve the MINLP exactly, Grid-ECO exactly reformulates it into mixed-integer bilinear program (MIBLP), enabling global optimization using the spatial branch-and-bound algorithm (sBnB). To ensure computational tractability for large-scale feeders, we develop and include a novel presolving strategy based on Sequential Bound Tightening (SBT) with variable filtering and decomposition. Case studies demonstrate that Grid-ECO outperforms the off-the-shelf Gurobi sBnB solver by solving cases where no feasible solution is found within 167 hours. When feasible solution is found by off-the-shelf solver, Grid-ECO reduces solution time by up to 73\% and sBnB node exploration by up to 97\%, while achieving a 0\% optimality gap and guaranteed AC feasibility.