论文标题
高斯过程学习基于概率的最佳功率流
Gaussian Process Learning-based Probabilistic Optimal Power Flow
论文作者
论文摘要
在这封信中，我们提出了一种基于新型的高斯过程学习的概率最佳功率流（GP-POPF），用于在可再生和任意分布的不确定性下解决POPF。提出的方法依赖于非参数贝叶斯基于推理的不确定性传播方法，称为高斯过程（GP）。我们还使用有关GP-POPF超参数的解释性观察，建议一种称为子空间灵敏度的新型灵敏度。与蒙特 - 卡洛模拟（MCS）溶液相比，在不同水平的不确定的可再生渗透以及负载不确定性的情况下，该方法在14类和30个总线系统上的仿真结果表明，该方法提供了相当准确的溶液，同时需要较少的样品和经过的时间。
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary distribution. The proposed method relies on a non-parametric Bayesian inference-based uncertainty propagation approach, called Gaussian Process (GP). We also suggest a new type of sensitivity called Subspace-wise Sensitivity, using observations on the interpretability of GP-POPF hyperparameters. The simulation results on 14-bus and 30-bus systems show that the proposed method provides reasonably accurate solutions when compared with Monte-Carlo Simulations (MCS) solutions at different levels of uncertain renewable penetration as well as load uncertainties, while requiring much less number of samples and elapsed time.
