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This chapter proposes and provides an in-depth discussion of a scalablesolution for running ensemble simulation for solar energy production.Generating a forecast ensemble is computationally expensive. But with the helpof Analog Ensemble, forecast ensembles can be generated with a singledeterministic run of a weather forecast model. Weather ensembles are then usedto simulate 11 10 KW photovoltaic solar power systems to study the simulationuncertainty under a wide range of panel configuration and weather conditions.This computational workflow has been deployed onto the NCAR supercomputer,Cheyenne, with more than 7,000 cores.  Results show that, spring and summer are typically associated with a largersimulation uncertainty. Optimizing the panel configuration based on theirindividual performance under changing weather conditions can improve thesimulation accuracy by more than 12%. This work also shows how panelconfiguration can be optimized based on geographic locations.

A Scalable Solution for Running Ensemble Simulations for Photovoltaic Energy