This chapter provides an in-depth discussion of a scalable solution for running solar energy production ensemble simulations. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are then used to simulate 11 10 KW photovoltaic solar power systems to study the simulation uncertainty under a wide range of panel configurations and weather conditions. This workflow has been developed and tested at scale on the National Center for Atmospheric Research supercomputer, Cheyenne, with more than 7000 concurrent cores. Results show that spring and summer are typically associated with greater simulation uncertainty. Optimizing the panel configuration based on the individual performance of simulations under changing weather conditions can improve the accuracy of simulations by more than 12%. This work also shows how panel configuration can be optimized based on geographic locations.