Smart DC Fast Charging Management Using Genetic Algorithms in Public Area Car Parks: A Hybrid Renewable Energy and Dynamic Tariff Approach for Operator Profit and User Cost Reduction

Aligned with the requirements of efficient charging infrastructure operation and user satisfaction, this study treats smart charging scheduling for the DC fast-charging infrastructure of a shopping mall (SM) parking lot as an operator profit maximization problem. The proposed approach simultaneously enforces with realistic station-level constraints, that plug uniqueness, plug reservation, vehicle/plug matching continuity, plug-power limitation and capacity limits and with vehicle and user level requirements, namely the attainment of the target battery state of energy (SoE), the time-of-use (ToU) tariff, and an incentive-based dynamic user tariff derived from renewable energy generation. A genetic algorithm (GA) based optimization framework is established; solutions that maximize operator profit are sought. Defined on a one-day time series comprising the photovoltaic (PV) and wind-turbine (WT) generation profiles integrated on the SM rooftop, together with grid buy-sell prices, the model enforces that all vehicles attain their end-of-day target state of energy (SoE), targets higher operator profit through enhanced user incentives during periods of renewable generation, and reduces peak loading on the grid. The study framework is structured around cases in which system capabilities and scale are varied, and resources are commissioned sequentially. The results of case simulations indicate that, without violating any constraints, operator profit can be increased, peak demand can be reduced, the load profile can be smoothed, and user cost can be lowered through the proposed incentive mechanism.