This work proposes a method of wind farm scenario generation to support real-time optimization tools and presents key findings therein. This work draws upon work from the literature and presents an efficient and scalable method for producing an adequate number of scenarios for a large fleet of wind farms while capturing both spatial and temporal dependencies. The method makes probabilistic forecasts using conditional heteroscedastic regression for each wind farm and time horizon. Past training data is transformed (using the probabilistic forecasting models) into standard normal samples. A Gaussian copula is estimated from the normalized samples and used in real-time to enforce proper spatial and temporal dependencies. The method is evaluated using historical data from MISO and performance within the MISO real-time look-ahead framework is discussed.

翻译：这项工作提出了风力农场情景生成方法,以支持实时优化工具,并介绍了其中的关键结果。这项工作借鉴了文献中的工作,提出了一种高效和可扩展的方法,为庞大的风力农场制造数量充足的情景,同时捕捉空间和时间依赖性。该方法对每个风力农场和时间前景使用有条件的异变回归法进行概率预测。以往的培训数据(使用概率预测模型)转换为标准正常样本。一个高斯大交织机是根据正常样本估算的,并实时用于实施适当的空间和时间依赖性。该方法使用来自最低运作指标的历史数据以及MISO实时外观框架的性能进行了评估。