Hybrid Renewable Energy System for Electric Vehicle Charging Stations with Adaptive Energy Fairness Scheduling in Semi-Urban Microgrids

The accelerating global adoption of electric vehicles (EVs) has introduced substantial pressure on electrical infrastructure, particularly in semi-urban and peri-urban regions where grid capacity is constrained and supply reliability remains inconsistent. Addressing this challenge necessitates a departure from conventional, grid-dependent charging paradigms toward architectures that leverage locally available renewable energy resources. This paper presents a comprehensively designed hybrid renewable energy-powered EV charging station tailored for semi-urban microgrid environments. The proposed system integrates solar photovoltaic (PV) arrays, wind turbine generators, a lithium-ion battery energy storage subsystem, and a controlled grid-interconnection module, all governed by an intelligent multi-layer control framework. Three novel conceptual contributions distinguish this work from prior art. First, a Self-Learning Energy Behaviour Model (SLEBM) continuously assimilates historical data on renewable generation patterns and EV utilisation trends to refine short-horizon demand forecasts. Second, a Carbon-Aware Decision Engine (CADE) quantifies the real-time carbon intensity of each available energy source and constructs a priority dispatch sequence that minimises greenhouse gas emissions. Third, an Energy Fairness Scheduling Index (EFSI) governs charging-slot allocation by simultaneously accounting for vehicle state-of-charge (SoC), queue residence time, and prevailing energy availability, thereby preventing systemic inequities in service delivery. Simulation experiments conducted in MATLAB/Simulink under seasonally varying semi-urban meteorological profiles demonstrate statistically significant improvements in renewable energy utilisation, reductions in grid dependency, diminished carbon output, and equitable charging distribution relative to both grid-only and rule-based hybrid baselines. The architecture is designed for modular scalability and is well-suited for deployment in energy-constrained developing-region contexts.