Sunairio is proud to join EPRI's Climate READi Affinity Group. Climate READi is the industry's collaborative approach to managing climate risk in the power sector. Sunairio will be supporting the effort by providing our technical expertise in predictive climate and energy analytics.
Solar production risk assessment is traditionally based on hypothetical power generation on Typical Meteorological Years (TMYs) or historical weather data. These approaches ignore climate trends and extrapolate from small datasets, leading to production estimates that are unreliable and drift from reality over time. Sunairio addresses these shortcomings via a system of stochastic climate simulations to create arbitrary amounts of realistic local- climate-adjusted weather data.
On 100 representative US solar sites, we first verified that the Sunairio simulation method generated realistic weather. We then found TMYs to be unrepresentative and to overpredict power generation at 85% of the sites. We found site-dependent local-climate-trend production adjustments to range from -6.16% to 2.77% of predicted production in 2022 — adjustments that grew in magnitude to -13.48% to 4.63% when extrapolated out to 2034. An overall negative GHI trend of −0.225 W/m² per year (83% of the sites had negative GHI trends) caused overall production losses of 2.43% in 2022 and 4.98% in 2034 with respect to TMY estimates. Finally, the degree of uncertainty in production estimates was at least four times lower in the Sunairio simulation data compared to production estimates using historical samples.
Check out the latest contributors to Sunairio's tech: Nick, Natalie, Kim, Katie and Gabe--engineering students at Northwestern University who just finished their junior year. Sunairio sponsored a student project (IEMS 394) and asked this team to help us understand wind turbine icing dynamics in real life. Icing events can be catastrophic for wind farm energy production--and they're really hard to predict. But now we have a probabilistic wind turbine icing model that quantifies the risk of these events (in terms of probability of capacity factor loss) as a function of weather. All thanks to the hard work of this Northwestern University engineering student team!