Integrated scheduling of battery EV charging and mobile storage logistics in solar-driven expressway systems

This paper develops a two-stage optimization framework for coordinating battery electric vehicle (BEV) charging and mobile energy storage truck (MEST) scheduling within expressway networks powered by distributed photovoltaic (PV) systems. The proposed model addresses spatiotemporal mismatches between solar generation and vehicle charging demand by representing the expressway as a directed space-time graph that includes both grid-connected service areas (ESAs) and slope-based PV supply points (SPESPs) without grid access. The first stage formulates a bilevel problem, where the expressway operator sets dynamic charging prices and allocates charging slots to maximize revenue, while BEVs respond by minimizing individual energy and time-related costs. A scenario-based decomposition approach is used to handle uncertainties in PV output and vehicle routes, incorporating dual updates to ensure feasibility and convergence. In the second stage, MESTs are dispatched to transport surplus PV energy from SPESPs and underutilized ESAs to high-demand areas. This stage incorporates constraints on energy storage dynamics, routing feasibility, and charging infrastructure limitations, while enforcing a hierarchical supply logic that prioritizes PV, followed by MEST discharge, and lastly grid imports. The framework introduces a novel integration of mobile storage logistics, decentralized solar utilization, and driver-operator interaction within a unified expressway energy system. Numerical experiments demonstrate improved PV energy usage, reduced grid dependence, and cost-effective coordination of charging infrastructure and mobile storage operations under realistic traffic and generation scenarios.