Characterization and forecasting of national-scale solar power ramp events
2026-03-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied two years of solar power data from thousands of solar stations to understand sudden big changes in power output, called solar ramp events. They found that these events happen mostly when clouds clear up in the morning or cover the sky more in the afternoon. They tested several advanced forecasting models and discovered that while some models were better than others, none could predict these sudden changes very well. Their work shows that better detailed forecasting methods are needed to help keep the power grid stable as more solar energy is used.
solar ramp eventsphotovoltaic (PV) generationgrid stabilitycloud coverspatiotemporal forecastingnowcastingdeep learning modelsCRPS (Continuous Ranked Probability Score)power grid operationsrenewable energy integration
Authors
Luca Lanzilao, Angela Meyer
Abstract
The rapid growth of solar energy is reshaping power system operations and increasing the complexity of grid management. As photovoltaic (PV) capacity expands, short-term fluctuations in PV generation introduce substantial operational uncertainty. At the same time, solar power ramp events intensify risks of grid instability and unplanned outages due to sudden large power fluctuations. Accurate identification, forecasting and mitigation of solar ramp events are therefore critical to maintaining grid stability. In this study, we analyze two years of PV power production from 6434 PV stations at 15-minute resolution. We develop quantitative metrics to define solar ramp events and systematically characterize their occurrence, frequency, and magnitude at a national scale. Furthermore, we examine the meteorological drivers of ramp events, highlighting the role of mesoscale cloud systems. In particular, we observe that ramp-up events are typically associated with cloud dissipation during the morning, while ramp-down events commonly occur when cloud cover increases in the afternoon. Additionally, we adopt a recently developed spatiotemporal forecasting framework to evaluate both deterministic and probabilistic PV power forecasts derived from deep learning and physics-based models, including SolarSTEPS, SHADECast, IrradianceNet, and IFS-ENS. The results show that SHADECast is the most reliable model, achieving a CRPS 10.8% lower than that of SolarSTEPS at a two-hour lead time. Nonetheless, state-of-the-art nowcasting models struggle to capture ramp dynamics, with forecast RMSE increasing by up to 50% compared to normal operating conditions. Overall, these results emphasize the need for improved high-resolution spatiotemporal modelling to enhance ramp prediction skill and support the reliable integration of large-scale solar generation into power systems.