This case study presents the underlying motivation and representative insights from an electric cooperative net demand planning exercise that utilized a long-term (15-year) correlated climate simulation and machine learning-based energy resource modeling. The approach described here overcomes several challenges related to net demand planning and resource adequacy analysis, namely– 1) the creation of a large sample of properly jointly distributed weather, 2) the incorporation of climate change trends, 3) the creation of a typical hourly net demand path, and 4) the creation (and curation) of extreme–but realistic–weather and energy scenarios across a utility footprint.

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Leeward Renewable Energy used the Sunairio platform to select new wind and solar projects that best diversified risks across different regions and different technologies. Sunairio enabled Leeward to simulate thousands of correlated future production and revenue outcomes at dozens of current and prospective sites. The Sunairio platform applied modern portfolio theory to pick an optimal combination of new projects.

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