论文标题
可再生能源生产的大规模概率模拟
Large Scale Probabilistic Simulation of Renewables Production
论文作者
论文摘要
我们开发了一个概率框架，用于从可再生资产中对短期发电的联合模拟。在本文中，我们描述了一种在数百个资产中在网格规模上产生每小时的日常临时场景的方法。这些方案是基于指定预测的条件，并且在边际资产级和跨资产集合中产生了完全不确定性的定量。我们的仿真管道首先应用资产校准，以使每小时，每日和季节性生成概况正常化，并将高斯化预测分布。然后，我们开发了一种新颖的聚类方法，以稳定地估计跨资产的协方差矩阵。聚类是在层次上完成的，以实现可伸缩性。使用近500个太阳能和风电场的ERCOT样系统进行的扩展案例研究用于说明。
We develop a probabilistic framework for joint simulation of short-term electricity generation from renewable assets. In this paper we describe a method for producing hourly day-ahead scenarios of generated power at grid-scale across hundreds of assets. These scenarios are conditional on specified forecasts and yield a full uncertainty quantification both at the marginal asset-level and across asset collections. Our simulation pipeline first applies asset calibration to normalize hourly, daily and seasonal generation profiles, and to Gaussianize the forecast--actuals distribution. We then develop a novel clustering approach to stably estimate the covariance matrix across assets; clustering is done hierarchically to achieve scalability. An extended case study using an ERCOT-like system with nearly 500 solar and wind farms is used for illustration.
