Adaptive multi-stage planning of electric vehicle charging infrastructure with photovoltaic and energy storage systems: a transportation-aware optimization approach

The electrification of urban transportation is essential for achieving carbon neutrality, yet large-scale electric vehicle integration poses significant challenges to power distribution networks requiring coordinated infrastructure planning. Current approaches exhibit four critical limitations: static planning horizons inadequately capture dynamic electric vehicle adoption patterns; user satisfaction metrics remain simplistic, neglecting multidimensional service quality; transportation-power system integration is insufficient, with frameworks selecting locations based on electrical nodes rather than mobility dynamics; and solution methodologies lack problem-specific customization for complex optimization landscapes.To address these gaps, this research develops a multi-stage co-planning framework integrating electric vehicle charging infrastructure with photovoltaic and energy storage systems. Three methodological innovations are proposed. First, a dynamic adaptive planning paradigm divides a 15-year horizon into three five-year stages with objective weights shifting from economic efficiency (0.50, 0.25, 0.25) to user satisfaction (0.25, 0.50, 0.25) and environmental benefits (0.25, 0.25, 0.50). Second, a comprehensive four-component satisfaction model integrates voltage quality, waiting time, traffic-based coverage, and temporal availability into a composite utility function. Third, traffic flow matrices establish data-driven coupling between transportation and power networks, deriving node importance directly from aggregate traffic throughput. Validation on a modified IEEE 33-bus system integrated with a 12-node transportation network demonstrates framework effectiveness. User satisfaction peaks at 0.580 in Stage 2, while carbon emissions decrease to 1.385 × 107 kg in Stage 3. Total cumulative investment reaches 328.8 × 104 CNY with 5.0-year payback periods. Traffic analytics concentrate 38 chargers at each of Nodes 6, 20, and 31, demonstrating effective translation of transportation data into technically feasible, economically viable infrastructure deployment strategies.