Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition

Abstract

This study investigates the planning problem of fast-charging stations for electric vehicles with the consideration of uncertain charging demands. This research aims to determine where to build fast-charging stations and how many charging piles to be installed in each fast-charging station. Based on the multicommodity flow model, a chance-constrained programming model is established to address this planning problem. A scenario-based approach as well as a big-M coefficients generation algorithm are applied to reformulate the programming model into tractable one, then the Dantzig–Wolfe decomposition method is leveraged to find its optimal solution. Finally, a numerical experiment is conducted in a 25-node network to assess the efficiency of the proposed model and solution approach.

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

“…Zhang et al [25] proposed a three-time charging station siting and capacity model based on high-resolution spatial and temporal charging demand distribution based on the M/M/c/N charging queuing theory and the actual operation data of electric vehicles in Beijing. Wang et al [26] planned the charging station installation locations and charging station capacities using neural networks and chance constraints, taking into account the uncertainty of charging demand, and found their optimal solutions using the Dantzig-Wolfe decomposition method. Woo et al [27] proposed a minimum genetic algorithm combining game theory and the genetic algorithm to solve the optimal charging station layout model and minimize the peak charging demand.…”