Background/Objectives

Renewable and low-carbon energy sources should power the grid to minimize future climate change. This project seeks to predict climate dependent generation availability, such as from solar and wind farms, under various future climate projections.

Approach/Activities

A computational pipeline was created for working with large numeric objects such as scientific array data. Features include streamlining inconsistent netCDF files while performing various validation checks, lazy evaluation, eager garbage collection, and automatic unit conversion. The pipeline is used to run renewable power simulations with the System Advisor Model (SAM) from the National Renewable Energy Laboratory (NREL) for different netCDF climate datasets, from past to future data, with various gridding schemes, spatial and temporal resolutions, variables, units, climate projection scenarios, and data sources. The pipeline builds upon numerous climate simulations, which themselves include just climatological information. It generates future power simulation data that can inform sustainable energy policy investment decisions.

Results/Lessons Learned

I will present empirical results on real climate datasets and implications for future renewable power availability.