Stochastic modeling of solar irradiance during hurricanes

Authors of this research, led by Luis Ceferino, professor of civil and urban engineering and member of the Center for Urban Science and Progress, include Ning Lin and Dazhi Xi of Princeton University.

Despite solar’s growing criticality for electricity generation, few studies have proposed models to assess solar generation during extreme natural events. In particular, hurricanes bring environmental conditions that may drastically reduce solar generation even if solar infrastructure remains fully functional. 

In a new paper in researchers present a stochastic model to quantify irradiance decay during hurricanes, using a dataset that analyzes historical data on Global Horizontal Irradiance and 22 landfalling storms from the Atlantic in the North American basin, which reached a category of at least three during their lifetime. The data showed higher irradiance decays for higher hurricane categories and closer to the hurricane center due to optically thick clouds that absorb and reflect light. 

Specifically, their model describes the irradiance decay as a function of hurricane category and the distance to the hurricane center normalized by the hurricane size. Their analysis, based on an examination and performance ranking of four irradiance decay functions with varying complexities, demonstrates that the hurricane’s radius of outermost closed isobar performs best as the size metric for normalizing distance. 

To showcase the methodology’s applicability, they used it to generate spatiotemporal models of irradiance during storms from genesis to dissipation, based on probable storm behavior in 839 counties in the United States’ southern region. Among the results were that solar-powered electricity generation in Miami-Dade, Florida, can decrease beyond 70% in large regions during a category-4 hurricane even if the solar infrastructure is undamaged. 

They found that, furthermore, generation losses can also last beyond three days, and this timeframe will be exacerbated if solar panels become non-functional. The team plans a follow-up study integrating the proposed model with panel fragility functions to offer analysis capabilities for forecasting time-varying solar generation during hurricanes.