Predicting Solar Photovoltaic Generation Impacted by Severe Wildfire Smoke

ALI AMADEH, Fenya Bartram, Bo Yuan, K. Max Zhang, Cornell University

     Abstract Number: 610
     Working Group: Burning Questions of Aerosol Emissions, Chemistry, and Impacts from Wildland-Urban Interface (WUI) Fires

Abstract
The negative impact of wildfire smoke on solar photovoltaic (PV) generation by reducing the amount of solar irradiance reaching the modules has been observed worldwide. In the summer of 2023, smoke from Canadian wildfires spread to the northeastern U.S., impacting solar PV output in the region. The New York Independent System Operator (NYISO) day-ahead forecasts for this period significantly overpredicted PV output. We presents novel machine learning-based models for predicting the hourly solar capacity factor (CF), focusing on improving predictive performance during periods of severe wildfire smoke. The results demonstrate a R2 value of up to 0.85 for the severe wildfire periods (aerosol optical depth (AOD) above the 99.99th percentile) from our models, significantly outperforming NYISO’s R2 value of 0.50 across six load zones included in the analysis. The greatly enhanced performance arises from two innovations. First, we adopted a series of data products, newly available in the public domain, from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) weather forecasting system. These include predictions of the AOD and the downward shortwave radiation flux (DSWRF) incorporating aerosol impacts. Our study marks the first time the HRRR-Smoke wildfire AOD product has been used in solar electricity forecasts. Second, we employed upsampling strategies to address the data imbalance issues due to the inherently infrequent nature of wildfire events. As the data products are publicly available, our methodology can be readily adopted by power system operators to enhance predictions of solar electricity production during periods of wildfire smoke, ensuring the reliability of power grids with high penetration of solar energy.