Smart energy strategies for dynamic EV charging and renewable integration in distribution systems : a multi-objective optimization framework

The accelerating penetration of electric vehicles (EVs) is reshaping modern distribution networks by introducing substantial, time-varying charging demand and new operational uncertainties. At the same time, Vehicle-to-Grid (V2G) capable EVs offer unprecedented flexibility to support grid stability when coordinated effectively. However, utilities still face major challenges in jointly managing large-scale EV charging, intermittent renewable generation, and feeder-level operational constraints. To address these gaps, this study develops an integrated scheduling framework that simultaneously optimizes solar PV, wind-based renewable distributed generators (RDGs), and V2G-enabled EV charging stations within the IEEE 85-bus radial distribution system. A user-priority-based charging strategy is formulated to accommodate diverse mobility behaviors, including stochastic arrival/departure patterns, state-of-charge variability, and urgency-based service requirements. A multi-objective optimization model is constructed to minimize charging costs, peak demand, charging duration, active power losses, and voltage deviations, while maximizing net grid-trading revenue over a 24-h horizon. The problem is solved using the Cheetah Optimization Algorithm (COA) and benchmarked against Particle Swarm Optimization (PSO), Honey Badger Optimization Algorithm (HPOA), and the Grey Wolf Optimizer (GWO). System uncertainties are rigorously examined through sensitivity tests, Monte Carlo-based error-band analysis, and stability assessments. The coordinated EV-RDG framework yields substantial technical and economic improvements: a peak load reduction of nearly 50%, voltage deviation reduction exceeding 25%, active power loss reduction of 20-30%, and a total operational cost reduction of above 40% compared to the uncoordinated baseline. COA demonstrates rapid and stable convergence, delivering consistently high-quality solutions across scenarios. Overall, the findings highlight the value of priority-aware EV integration and advanced optimization in developing scalable, cost-effective, and renewable-supportive charging infrastructures for future smart distribution networks.