用生成式深度学习预测德国未来电力黑暗期风险
Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
- 用生成式模型降尺度气候数据，模拟未来风电光伏低发情景
- 预计21世纪德国电力黑暗期频率和持续时间基本不变
- 对能源规划者有参考价值，尤其关注可再生能源稳定性
欧洲电力系统正向风能和太阳能等可再生能源转型，但其依赖天气的特性带来了电网不稳的风险，其中‘Dunkelflaute’事件——即风力和太阳能发电量极低的时期——尤为令人担忧。本研究利用生成式深度学习框架对CMIP6气候集合模拟结果进行降尺度处理，将其与历史气象数据（ERA5）对比，并评估在低排放（SSP2-4.5）和高排放（SSP5-8.5）情景下，德国未来可能发生的大规模电力短缺事件。分析表明，在集合平均情形下，德国的电力黑暗期事件频率和持续时间相较于历史时期变化不大，提示在所考虑的气候情景下，相关风险在本世纪内预计将保持稳定。
原文摘要 · Abstract (English)
The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the weather dependency of these energy sources poses a challenge to grid stability, with so-called Dunkelflaute events -- periods of low wind and solar power generation -- being of particular concern due to their potential to cause electricity supply shortages. In this study, we investigate the impact of these events on the German electricity production in the years and decades to come. For this purpose, we adapt a recently developed generative deep learning framework to downscale climate simulations from the CMIP6 ensemble. We first compare their statistics to the historical record taken from ERA5 data. Next, we use these downscaled simulations to assess plausible future occurrences of Dunkelflaute events in Germany under the optimistic low (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Our analysis indicates that both the frequency and duration of Dunkelflaute events in Germany in the ensemble mean are projected to remain largely unchanged compared to the historical period. This suggests that, under the considered climate scenarios, the associated risk is expected to remain stable throughout the century.
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