Electric vehicles with renewables integration in electrical power systems: A review of technologies, uncertainties and optimization allocations

-Electric vehicles (EVs) are becoming a key part of future transportation and energy systems as the world moves towards decarbonization and sustainable mobility. This review explores the evolution, classification, and technical architecture of EVs, emphasizing their integration within renewable energy-powered electrical networks. It examines the main EV technologies, such as battery systems, drivetrains, and charging infrastructure, highlighting recent advancements such as fast-charging capabilities and bidirectional energy flows (e.g., V2G, V2B). A critical analysis of energy storage technologies and battery management systems (BMS) is presented, addressing their influence on vehicle performance and grid interaction. To deal with the unpredictability that comes with EVs and renewables, the study examines many ways to simulate uncertainty, such as Monte Carlo simulation, Markov chains, copula functions, point estimate methods, and neural networks. These tools are essential for forecasting load demand, charging behavior, and battery performance in dynamic grid conditions. Furthermore, the paper surveys recent optimization frameworks developed for planning and operation of EV infrastructure, focusing on objectives such as loss minimization, voltage profile improvement, cost reduction, and environmental impact mitigation. This study observes common research gaps by considering a number of different studies, including limited treatment of unbalanced distribution networks, insufficient real-time control strategies, and the underutilization of advanced optimization methods for large-scale deployment. The study reveals that EVs can enhance electrical systems by integrating with renewable energy sources, and suggests future research to overcome technical hurdles and expedite their adoption in modern power grids. • Reviews EV technologies: batteries, drivetrains, and charging infrastructure. • Covers uncertainty modeling using MCS, Markov chains, and neural networks. • Analyzes optimization methods for EV planning in power distribution networks. • Identifies research gaps in electrical distribution systems and real-time control. • Supports cleaner grids by integrating EVs with renewable energy sources.