A new optimization framework addresses a critical challenge in transportation electrification: planning charging infrastructure for both private electric vehicles and electric buses while maximizing on-site solar energy consumption. Researchers developed a mixed-integer linear programming model that makes infrastructure decisions robust against uncertain vehicle energy demands.
The framework operates in two stages. First, it determines where chargers should be located, their capacity, and technology type—decisions that remain fixed across different demand scenarios. Second, it optimizes hourly charging schedules and bus-to-depot assignments for each specific scenario, allowing operational flexibility while protecting infrastructure investments.
The robust formulation minimizes total costs by combining infrastructure capital expenditure with the worst-case operating cost across demand scenarios. Rather than assuming perfect demand forecasts, the model acknowledges that vehicle charging needs vary, and it protects against realistic stress cases through soft penalties that quantify unmet energy, inadequate state-of-charge at trip end, and capacity violations.
A 50-node case study reveals several practical insights. Bus route characteristics significantly influence where chargers concentrate and what technology mix proves optimal. Vehicle-to-grid (V2G) capability—allowing vehicles to discharge to the network—enables fully feasible solutions under uncertain conditions when penalty weights are sufficiently large, reducing grid import and supporting local solar utilization.
The work emphasizes that effective long-term infrastructure planning must balance multiple objectives: accommodating diverse vehicle types, adapting operations to different demand scenarios, and respecting node-level charging constraints. Traditional deterministic planning risks either underinvestment, creating bottlenecks during peak demand, or overinvestment in redundant capacity. This scenario-based robust approach provides a practical middle ground for regulated utilities and private operators planning charging networks in an uncertain transportation future.
