基于检索增强概率降尺度的气象-风光出力联合场景生成方法OA

The operation and planning of power systems with high penetrations of renewable energy highly depend on high-temporal-resolution meteorological data and wind-solar power output scenarios.However,most existing climate models only provide daily-scale meteorological information,which is insufficient to directly support the hourly-level operational analysis of power systems.Therefore,this paper proposes a joint scenario generation method for meteorology-wind-solar power outputs based on retrieval-augmented probabilistic downscaling(RAPD).First,a similar-day retrieval mechanism based on the rank-sum distance is constructed to extract historical high-resolution meteorological baseline sequences.Second,a Transformer-based conditional variational autoencoder(T-CVAE)learning network is built to realize the probabilistic generation of meteorological fluctuation residuals.Finally,a physical mapping model from meteorological variables to wind-solar power outputs is established to generate continuous hourly wind-solar power output scenarios for a whole year.Validations based on historical meteorological data demonstrate that the mean absolute error(MAE)values for temperature and wind speed are reduced by 30.17%and 28.84%,respectively,compared with the traditional statistical analog method.Furthermore,taking the daily-scale data from climate models as input,the corresponding hourly-level meteorology-wind-solar power output scenarios are generated through downscaling,and the consistency between the downscaled results and the daily-scale inputs is verified.The results indicate that the proposed method can effectively accomplish temporal downscaling and scenario generation,providing a high-temporal-resolution scenario foundation for renewable energy output analysis under future scenarios.