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.
翻译:我们为利用可再生资产联合模拟短期发电开发了一个概率框架。我们在本文件中描述了一种在数百个资产中生成电网规模上生成每小时日头发电情景的方法。这些情景以具体的预测为条件,并在边际资产一级和资产收集之间产生充分的不确定性量化。我们的模拟管道首先将资产校准应用于每小时、每日和季节生成概况的正常化,并用于预测-事实分布。然后我们开发了一种新的组合法,以对资产之间的共变矩阵进行细微估计;集群按等级进行,以实现可扩缩性。我们用近500个太阳能和风力农场的ERCOT系统进行扩展案例研究,作为例证。