Multi-objective coordinated optimization of renewable-based EV charging stations with user-centric scheduling and grid support in distribution networks

• Simultaneous optimization of grid performance, EVCS profitability, and user cost including battery degradation using a unified multi-objective framework. • Deployment of a novel Cheetah Optimization Algorithm (COA) for dynamic, real-time scheduling of EV charging and discharging. • Integration of solar PV, wind energy, and BESS into EVCS design to reduce grid dependence and support grid services like V2G and peak shaving. • Inclusion of stochastic EV user behavior and real-time electricity pricing in the optimization model for realistic scheduling. • Comprehensive scenario-based validation on IEEE 33-bus RDS demonstrating system resilience under varying load, renewable, and pricing conditions. The rapid proliferation of Electric Vehicles (EVs) imposes significant challenges on modern distribution networks, including increased power losses, voltage instability, peak demand stress, and economic impacts on both grid operators and EV users. Existing EV charging strategies often fail to address these challenges simultaneously while considering technical, economic, and user-centric objectives. This study proposes a novel multi-objective optimization framework for coordinated EV charging management that simultaneously minimizes power losses and voltage deviations, maximizes EV charging station (EVCS) profitability through dynamic pricing and Vehicle-to-Grid (V2G) support, and reduces EV user costs while accounting for battery degradation. EVCSs are supplied via a hybrid renewable system integrating photovoltaic (PV), wind generation, and battery energy storage systems (BESS), reducing grid dependency and enhancing local energy sustainability. A recently developed Cheetah Optimization Algorithm (COA) is employed as an energy management system (EMS) to optimally schedule EV charging/discharging under realistic operational constraints, while Monte Carlo Simulation (MCS) models uncertainties in renewable DG generation and stochastic EV arrivals to ensure robust operation under variable conditions. Comparative benchmarking against GA, PSO, and NSGA-II demonstrates COA’s superior solution quality, faster convergence, and computational efficiency. Simulation results on IEEE 33-bus and 118-bus systems indicate that, under coordinated optimal conditions (Scenario 1), network losses reduce by 15–17%, voltage profiles improve by 40–45%, EVCS profitability increases by 55–60%, and total EV operating costs decrease by 40–45% compared to uncoordinated stressed operations (Scenario 2). Even under dynamic, price-responsive operations (Scenario 3), COA maintains 10–12% lower losses, 25–30% improved voltage stability, 20–25% higher EVCS profitability, and 20–25% reduced EV costs. These results highlight COA’s ability to simultaneously enhance technical performance, economic efficiency, and user-centric operation, providing a scalable, resilient, and practical solution for integrating high EV penetration with renewable energy in future smart grids.