Operationally Perfect Solar Power Forecasts: A Scalable Strategy to Lowest-Cost Firm Solar Power Generation
Richard Perez1, Marc Perez2, Marco Pierro3, James Schlemmer1, Sergey Kivalov1, John Dise2, Mark Grammatico2, Agate Swierc2, Philipp Schmidd2, Patrick Keelin2, Jorge Ferreira2, Morgan Putnam2, Thomas E. Hoff2
1University at Albany, Albany, NY, United States
/2Clean Power Research, Kirkland, WA, United States
/3University of Rome Tor Vergata, Rome, Italy

The SUNY solar irradiance forecast model is implemented in the software SolarAnywhere®. In this article, we introduce its latest version and present fully independent performance validations for climatically distinct individual US locations as well as one extended region.

In addition to standard performance metrics such as mean absolute error or forecast skill, we apply an operational metric that quantifies the lowest cost of operationally achieving perfect forecasts. This cost represents the amount of solar production curtailment and backup storage necessary to correct all over/under-prediction situations. This perfect forecast metric applies a recently developed algorithm to optimally transform intermittent renewable power generation into firm power generation with the optimal – lowest-cost – amount of curtailment and energy storage.
We discuss how operational logistics to deliver perfect solar power forecasts can logically evolve into firm solar power generation logistics, with the objective of cost-optimally displacing conventional [dispatchable] power generation.