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.

翻译：太阳能发电的快速增长正在重塑电力系统运行方式，并加剧电网管理的复杂性。随着光伏（PV）装机容量持续扩张，光伏出力的短期波动带来了显著的运行不确定性。与此同时，太阳能功率的陡升陡降事件因突然的大幅功率波动，加剧了电网失稳和非计划停电的风险。因此，精准识别、预测和缓解太阳能陡升陡降事件对于维持电网稳定性至关重要。本研究分析了6434个光伏电站两年期间15分钟分辨率的光伏发电出力数据。我们建立了量化指标以定义太阳能陡升陡降事件，并在国家尺度上系统地表征了其发生频率和幅度。此外，我们探究了陡升陡降事件的气象驱动因素，揭示了中尺度云系的关键作用。特别地，我们观测到爬坡事件通常与早晨云层消散相关，而滑坡事件则常发生于午后云量增多之时。进而，我们采用新近开发的时空预测框架，评估了基于深度学习和物理模型的确定性及概率性光伏功率预测方法，包括SolarSTEPS、SHADECast、IrradianceNet和IFS-ENS。结果表明，SHADECast是最可靠的模型，在2小时预测时效下其连续分级概率评分（CRPS）较SolarSTEPS低10.8%。然而，当前最先进的临近预报模型在捕捉爬坡动力学特征方面仍存在困难，在非正常运行条件下，其预测均方根误差（RMSE）最高增加50%。总体而言，这些结果强调了改进高分辨率时空建模的必要性，以提升陡升陡降预测能力，并支撑大规模太阳能发电在电力系统中的可靠整合。