Surface solar irradiance (SSI) plays a crucial role in tackling climate change — as an abundant, non-fossil energy source, exploited primarily via photovoltaic (PV) energy production. With the growing contribution of SSI to total energy production, the stability of the latter is challenged by the intermittent character of the former, arising primarily from cloud effects. Mitigating this stability challenge requires accurate, uncertainty-aware, near real-time, regional-scale SSI forecasts with lead times of minutes to a few hours, enabling robust real-time energy grid management. State-of-the-art nowcasting methods typically meet only some of these requirements. Here we present SHADECast, a deep generative diffusion model for the probabilistic spatiotemporal nowcasting of SSI, conditioned on deterministic aspects of cloud evolution to guide the probabilistic ensemble forecast, and based on near real-time satellite data. We demonstrate that SHADECast provides improved forecast quality, reliability, and accuracy in all weather scenarios. Our model produces realistic and spatiotemporally consistent predictions extending the state-of-the-art forecast horizon by 26 min over different regions with lead times of 15-120 min. Our physics-informed generative approach leads to up to 60% performance improvement in extreme value prediction over the state-of-the-art deterministic models, showcasing the advantage of probabilistic modeling of cloudiness over the classical deterministic approach. It also surpasses the probabilistic benchmarks in predicting extreme values. Finally, SHADECast empowers grid operators and energy traders to make informed decisions, ensuring stability and facilitating the seamless integration of PV energy across multiple locations simultaneously.

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Renewable generation variability over multiple days is a key challenge in decarbonizing the European power system. Weather regimes are one way to quantify this variability, but so far, their applications to energy research have focused on wind power generation in winter. However, the projected growth of solar photovoltaic (PV) capacity implies that its absolute variability across the continent will grow substantially. Here we combine weather regimes based on ERA5 reanalysis data with country-specific capacity factors to investigate multiday PV generation variability in Europe. With current installed capacity (131 GW), total PV production in Europe (52.3 GW) varies by 0.9 GW on average, with a maximum change of 3.0 GW, upon transition from one weather regime to another. Using projected PV capacity for 2050 (1.94 TW), variability would rise to 13.9 and 43.8 GW. We present optimized spatial distributions of capacity additions in three scenarios that substantially reduce variability by up to 40%. One of them ascertains a large local PV production, thereby minimizing the need for long-range power transmission while still reducing variability by about 30%, highlighting that optimized siting and local generation can be reconciled. Our results emphasize the value of leveraging climate information in decarbonizing power systems.@en

China is the largest worldwide consumer of solar photovoltaic (PV) electricity, with 130 GW of installed capacity as of 2017. China’s PV capacity is expected to reach at least 400 GW by 2030, to provide 10% of its primary energy. However, anthropogenic aerosol emissions and changes in cloud cover affect solar radiation in China. Here, we use observational radiation data from 119 stations across China to show that the PV potential decreased on average by 11–15% between 1960 and 2015. The relationship between observed surface radiation and emissions of sulfur dioxide and black carbon suggests that strict air pollution control measures, combined with reduced fossil fuel consumption, would allow surface radiation to increase. We find that reverting back to 1960s radiation levels in China could yield a 12–13% increase in electricity generation, equivalent to an additional 14 TWh produced with 2016 PV capacities, and 51–74 TWh with the expected 2030 capacities. The corresponding economic benefits could amount to US$1.9 billion in 2016 and US$4.6–6.7 billion in 2030.@en

In the version of this Article originally published, the units of ‘Total electricity yield’ and ‘Potential electricity gain’ in Table 1 were incorrectly presented as GWh yr^–1; they should have been TWh yr^–1. These errors have now been corrected.

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Using projections of surface solar radiation and temperature from 23 CMIP5 global climate models for two climate change scenarios (RCP4.5 & 8.5) we quantify the average change in PV electricity production expected in the years 2060–2080 compared to the present (2007–2027). We upsample daily radiation data to hourly resolution with a sinusoidal diurnal cycle model and split it into direct and diffuse radiation with the semi-empirical BRL model as input to a PV electricity generation model. Locally, changes in PV potential from −6% to +3% in annual and −25% to +10% in monthly means are shown. These projections are combined with a PV deployment scenario and show countries benefitting from increased PV yields include Spain, France, Italy and Germany. We also calculate uncertainties when calculating PV yield with input data at daily or lower resolution, demonstrating that our method to derive synthetic hourly profiles should be of use for other researchers using input data with low temporal resolution. We conclude that PV is an attractive and no-regrets investment in Europe irrespective of future climate change, and can continue to play a key role in energy system decarbonisation.@en