Data-Driven Robust Dynamic Wireless Charging Lane Planning with Tunable Profit–Risk Trade-off and Modified User Equilibrium

Abstract

Dynamic wireless charging (DWC) has attracted increasing attention as a complement to conventional plug-in charging for electric vehicles (EVs). However, existing planning methods for dynamic wireless charging lanes (DWCLs) overlook realistic EV routing and charging behavior and lack explicit out-of-sample risk control under traffic-demand and renewable-energy uncertainties. To address these limitations, this paper proposes a data-driven two-stage robust planning framework that coordinates DWCL deployment with the operation of coupled traffic–power networks (CTPNs). A data-driven uncertainty set is constructed from joint historical scenarios to preserve spatio-temporal correlations without imposing parametric distributional assumptions. A modified user-equilibrium (MUE) model is proposed to capture realistic EV routing and charging behavior by incorporating state-of-charge (SoC) dynamics and endogenous charging decisions. The resulting two-stage robust model with mixed-integer recourse is reformulated as an equivalent single-stage scenario-approach problem and solved using a customized nested column-and-constraint generation algorithm. An a posteriori feasibility guarantee is established to quantify out-of-sample risk. Based on this guarantee, the scenario set is iteratively enriched to meet a prescribed risk tolerance. Case studies on a 69-node power distribution network and a 24-node traffic network demonstrate that the proposed MUE model prevents overinvestment in DWCLs, while the proposed risk-quantification procedure enables a tunable profit–risk trade-off.