An Exact Solution Algorithm for the Bi-Level Optimization Problem of Electric Vehicles Charging Station Placement

TL;DR

This paper introduces the first exact, scalable algorithm for large-scale EV charging station placement, guaranteeing global optimality and outperforming heuristic methods in speed and solution quality.

Contribution

Develops a novel Branch-and-Price-and-Cut algorithm with value function cuts for solving large bi-level EV charging station placement problems exactly.

Findings

Algorithm terminates within minutes on large networks.

Achieves optimality gaps below 1% in all tested cases.

Outperforms genetic algorithms by 3-50 times in computation time.

Abstract

This work addresses electric vehicle (EV) charging station placement through a bi-level optimization model, where the upper-level planner maximizes net revenue by selecting station locations under budget constraints, while EV users at the lower level choose routes and charging stations to minimize travel and charging costs. To account for range anxiety, we construct a battery-expanded network and apply a shortest path algorithm with Frank-Wolfe traffic assignment. Our primary contribution is developing the first exact solution algorithm for large scale EV charging station placement problems. We propose a Branch-and-Price-and-Cut algorithm enhanced with value function cuts and column generation. Our exact algorithm delivers globally optimal solutions with mathematical certainty. Computational experiments on the Eastern Massachusetts network (74 nodes, 248 links), the Anaheim network (416 nodes, 914 links), and the Barcelona network (110 zones, 1,020 nodes, and 2,512 links) demonstrate exceptional performance. Our algorithm terminates within minutes, while achieving optimality gaps below 1% across all instances. Controlled benchmarks against two genetic algorithms on identical instances confirm that the proposed algorithm finds equal or better solutions in 3-50 times less computation time across all tested networks. The algorithm successfully handles problems with over 300,000 feasible combinations, transforming EV charging infrastructure planning into a tractable optimization suitable for practical decision making on real-world networks with optimality guaranteed.