论文标题
电力负载，太阳能和风能的关节随机模型，以及蒙特卡洛情景发电机Carmona \＆xinshuo yang
Joint Stochastic Model for Electric Load, Solar and Wind Power at Asset Level and Monte Carlo Scenario GenerationRené Carmona \& Xinshuo Yang
论文作者
论文摘要
出于蒙特卡洛场景的产生目的，我们提出了一个图形模型，用于给定区域中风能和电力需求的联合分布。为了符合电力行业的实践，我们假设外源提供了点预测，并专注于与这些预测的偏差建模，而不是对实际关注数量进行建模。我们发现，这些偏差的边际分布可以具有沉重的尾部，在将图形高斯模型拟合到数据之前，我们需要处理这些特征。我们使用图形套索程序的扩展来估算协方差和精度矩阵，该过程使我们能够以单独的依赖图的形式识别时间和地理（条件）依赖性。我们对NREL提供的数据实施了算法，并确认算法确定的依赖项与生产资产的相对位置以及收集数据的地理负载区的相对位置一致。
For the purpose of Monte Carlo scenario generation, we propose a graphical model for the joint distribution of wind power and electricity demand in a given region. To conform with the practice in the electric power industry, we assume that point forecasts are provided exogenously, and concentrate on the modeling of the deviations from these forecasts instead of modeling the actual quantities of interest. We find that the marginal distributions of these deviations can have heavy tails, feature which we need to handle before fitting a graphical Gaussian model to the data. We estimate covariance and precision matrices using an extension of the graphical LASSO procedure which allows us to identify temporal and geographical (conditional) dependencies in the form of separate dependence graphs. We implement our algorithm on data made available by NREL, and we confirm that the dependencies identified by the algorithm are consistent with the relative locations of the production assets and the geographical load zones over which the data were collected.
