Accurate forecasting of solar power generation is critical for the efficient operation of microgrids, particularly those integrating electric charging stations. This chapter classifies models for forecasting solar generation in microgrids, examining physical, statistical, machine learning, hybrid, and ensemble methods, highlighting their respective strengths and weaknesses. It emphasizes the importance of selecting appropriate forecasting methods and accuracy metrics, along with benchmarking against reference models, for optimizing energy balance and decision-making within microgrids. The chapter also discusses ongoing advancements in hybrid and ensemble models, combined with intelligent systems, as promising avenues for improving forecast accuracy and adapting to the dynamic conditions of microgrids.