This paper presents an energy scheduling and output smoothing scheme for storage aided utility scale photovoltaic systems. A weighted energy scheduling approach is adopted for the peak load periods, and this ensures enhanced performance with well-fitted supply-demand curve and flat net load variation. A novel smoothing method is proposed by blending double grid search support vector machine power prediction with first-in-first-out robust smoothing. The actual hourly and minute interval data sets for Australia are used for case studies, demonstrating the effectiveness and efficiency of the proposed scheme.