Characterization and forecasting of national-scale solar power ramp events

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.

Figures (13)

• Figure 1: (a) Satellite-based SSI field and (b) PV power output of the 6434 PV stations, observed on 26 June 2020 at 14:00 UTC. Note that the PV power output is normalized using the station-specific 95th percentile of the power time series. (c) Elevation map showing values in meters above sea level. The black and red lines denote national borders.
• Figure 2: (a) Time series of aggregated PV power production from 6434 PV stations, normalized by the maximum value observed over the displayed period, and (b) the corresponding magnitude of power variations, normalized by the ramp threshold $\Delta P_{\mathrm{tr}}$. Results are shown for the first two weeks of July 2020. The horizontal dashed red line indicates the threshold used to identify ramp events, while the vertical dashed grey lines mark midnight of each day.
• Figure 3: (a) Empirical probability density of magnitude of the power variation over a time interval of 15 minutes normalized by the threshold $\Delta P_\mathrm{tr}$, measured over the year 2019-2020 for the aggregate PV power generation of 6434 PV stations. (b) Number of ramp events aggregated by hour of the day, with distinction between ramp-up and ramp-down events.
• Figure 4: Hexagonal bin distribution of ramp magnitude normalized with the threshold $\Delta P_\mathrm{tr}$ as a function of hour of the day for (a) MAM, (b) JJA, (c) SON, and (d) DJF. Color shading indicates the number of samples per hexagonal bin on a logarithmic scale. The dashed white line traces the locus of maximum sample density at each hour, highlighting the most probable ramp magnitude throughout the diurnal cycle. The dashed red line marks the threshold used to identify ramp events. The bin widths are 15 minutes along the x-axis and 0.028 in normalized ramp units along the y-axis.
• Figure 5: (a-c) Satellite-based SSI fields and (d-f) PV power output of the 6434 PV stations, observed on 23 May 2020 at three time stamps: 09:00 UTC, 10:00 UTC and 11:00 UTC. The black lines denote national borders. Note that the PV power output is normalized using the station-specific 95th percentile of the power time series.