Integrated Electric Vehicle Highway Charging Infrastructure Planning and Grid Reinforcement Under Alternative Scenarios: A Multi-Objective Optimization Approach and Application

Planning charging infrastructure is essential to support the growing use of electric vehicles (EVs) and is particularly critical for long-distance travel, where reliable access along the route cannot be assumed. Because meeting the diverse goals of electromobility stakeholders at once requires balancing economic, environmental, and social priorities, this study develops a multi-objective mixed-integer linear program that jointly optimizes investment cost, CO2 avoided through photovoltaic (PV) generation, and regional demand coverage for EV charging infrastructure along intercity highway corridors. As charging demand grows substantially over the planning horizon, the study adopts a multi-period formulation to support adaptive planning under Business-as-Usual (BAU) and Net Zero scenarios that represent different EV penetration and demand trajectories. The model is solved via the augmented ϵ-constraint method (AUGMECON2) and applied to the Istanbul–Ankara highway corridor, one of Türkiye’s busiest intercity motorway routes, through 2035. Results show that achieving the maximum environmental benefit raises investment cost by about 63% (BAU) and 30% (Net Zero) relative to the cost-minimal plan, while the highest coverage regime raises served demand by a factor of 2.9 and 3.4 at cost increases of about 40% and 33%, respectively. Across the resulting solutions, grid capacity is extended exclusively through PV rather than transformer upgrades, and economic and environmental optimization converge on a minimal, centralized footprint of five stations, whereas coverage-oriented planning requires opening all sixteen candidate locations. These findings suggest planners should first set a coverage target as a policy commitment, then treat PV as the default, lower-cost grid-reinforcement pathway under the cost assumptions of this study.