Saudi Arabia is making the transition from an electricity system based mainly on hydrocarbons to one in which solar photovoltaic and wind power make up a much larger share, and this is leading to a demand for simulation tools that are able to account for variability, converter-related dynamics, storage, demand flexibility, and network constraints all within a single, coherent decision-making process. This review brings together peer-reviewed research carried out between 2020 and 2025 on the integration of renewable energy, smart-grid modelling, forecasting, hybrid systems, storage, inverter control, hosting capacity, and applications specific to Saudi Arabia. The findings indicate that no single type of simulation is adequate; quasi-static time-series and techno-economic models are useful for planning and for determining capacity sizes; probabilistic methods are able to take into account uncertainties relating to weather and demand; phasor-domain dynamic models are suitable for studies on electromechanical stability; electromagnetic-transient models are required when inverter controls and interactions with weak grids are the main factors; and data-driven or digital-twin approaches can enhance forecasting and enable online updating of the models. Studies specific to Saudi Arabia show that there is a considerable resource potential but also highlight sensitivity to high ambient temperatures, dust, the uneven distribution of wind resources across the geographical area, demand that is dominated by cooling needs, the economics of storage, and the operating assumptions made in the optimisation process. The review presents an integrated, multi-timescale framework that connects resource forecasting, component models, network constraints, energy management, uncertainty analysis, and validation. It contends that future research into smart grids in Saudi Arabia should go beyond separate optimal-sizing exercises and instead focus on reproducible, grid-aware, uncertainty-aware simulations that have been validated under a range of seasonal and disturbance conditions. The key areas that should receive priority include the assessment of grid-forming inverters, probabilistic hosting capacity, hybrid storage, dynamic demand response, high-resolution resource forecasting, and digital-twin-assisted operation. Pursuing these avenues will help to strengthen the technical credibility of the decisions regarding the deployment of renewable energy while at the same time supporting reliability, flexibility, and economic efficiency.