论文标题
通过非参数贝叶斯网络对多尺度可变的可再生能源和流入方案进行建模
Modeling Multiscale Variable Renewable Energy and Inflow Scenarios in Very Large Regions with Nonparametric Bayesian Networks
论文作者
论文摘要
在本文中，我们提出了一种非参数贝叶斯网络方法，以生成可变可再生能源（VRE）植物的小时生成的合成场景。该方法包括对VRE生成的概率分布的非参数估计，然后是反向概率积分变换，以获得VRE生成的正态分布变量。然后，我们基于对变量之间的空间相关性的评估（VRE生成和水力流入和水力流入，但是负载预测，温度和其他类型的随机变量也可以与提议的框架一起使用），以生成未来的合成场景，同时保持历史上的空间相关结构。最后，我们提出了一项现实生活中的案例研究，该研究使用巴西电力系统中的真实数据，以展示当前方法允许现实研究的改进。
In this paper, we propose a non-parametric Bayesian network method to generate synthetic scenarios of hourly generation for variable renewable energy(VRE) plants. The methodology consists of a non-parametric estimation of the probability distribution of VRE generation, followed by an inverse probability integral transform, in order to obtain normally distributed variables of VRE generation. Then, we build a Bayesian network based on the evaluation of the spatial correlation between variables (VRE generation and hydro inflows, but load forecast, temperature, and other types of random variables could also be used with the proposed framework), to generate future synthetic scenarios while keeping the historical spatial correlation structure. Finally, we present a real-life case study, that uses real data from the Brazilian power system, to show the improvements that the present methodology allows for real-life studies.
