Robust Joint Planning of EV and eBus Charging Infrastructure with PV Self-Consumption under Demand Uncertainty
This paper presents a mixed-integer linear programming (MILP) framework for joint electric vehicle (EV) and electric bus (eBus) charging-infrastructure planning with photovoltaic (PV) self-consumption. The model co-optimizes charger siting, sizing, technology selection, eBus-to-depot assignment, and hourly charging schedules. A deterministic MILP is first formulated as a nominal benchmark and then extended to a scenario-based robust min--max formulation under vehicle energy-demand uncertainty. The robust model uses shared first-stage infrastructure decisions and scenario-specific operating decisions, and minimizes infrastructure cost plus the worst-case scenario operating cost through an epigraph reformulation. Soft-feasibility penalties quantify unmet charging energy, terminal state-of-charge (SOC) shortfall, and capacity violations under stressed scenarios. A 50-node case study shows that route-segment eBus demand and PV weighting alter charger deployment, technology mix, and grid import, while V2G enables hard-feasible robust operation at sufficiently large penalty weights. These results highlight that effective long-term infrastructure planning must simultaneously account for diverse vehicle fleets, operational strategies that adapt to different scenarios, and node-level constraints on charging capacity.