Boosting EVCS profitability via strategic optimized PEV charging with integrated on-site PV and BESS support in constrained distribution systems

The rapid proliferation of plug-in electric vehicles (PEVs) is straining distribution networks and challenging the profitability of Electric Vehicle Charging Stations (EVCSs). This paper introduces a hierarchical control model to overcome these challenges by strategically coordinating on-site photovoltaic (PV) generation, a Battery Energy Storage System (BESS), and dynamic grid interactions. The core contributions of this work are threefold: first, the development of a two-stage optimization framework that integrates day-ahead scheduling with real-time adjustments based on locational marginal price (LMP) signals; second, a novel incentive-based flexibility scheme that categorizes PEVs into distinct groups (zero, medium, and high flexibility) to enable strategic load shifting; and third, the use of scenario-based stochastic modeling to incorporate uncertainties in PV output and PEV behavior. Simulation results demonstrate that the proposed strategy yields substantial improvements over traditional uncontrolled charging, including a peak demand reduction of over 50 %, a 10 % reduction in PEV charging costs, and approximately 37.63 % higher net operational savings for the EVCS. Furthermore, the integration of PV and BESS ensures all PEVs achieve their target state-of-charge before departure. These findings conclusively show that the proposed strategy provides a scalable and practical solution for enhancing the economic viability, grid stability, and renewable energy integration of next-generation smart charging infrastructures. • A novel two-stage stochastic framework optimizes EVCS operations with PV-BESS. • A tiered flexibility mechanism for PEVs cuts peak grid demand by over 50 %. • The strategy boosts EVCS net operational savings by 37.63 %. • PEV charging costs are reduced by 10 % while ensuring 100 % target SOC. • The method eliminates grid import violations and is validated as scalable.