论文标题
数据驱动的稀疏多项式混乱扩展方法，用于评估具有可再生能源的电力系统的概率总传输能力
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
论文作者
论文摘要
可再生能源（RES）和老化传输网络引起的不确定性水平的提高在评估总传输能力（TTC）和可用传输能力（ATC）方面构成了巨大挑战。在本文中，提出了一种新型的数据驱动的稀疏多项式混乱扩展（DDSPCE）方法，用于估计概率TTC（PTTC）的概率特征（例如，平均值，方差，概率分布）。具体而言，提出的方法不需要随机输入的预先提出的概率分布，它直接利用数据集来估计PTTC。此外，集成了稀疏方案以提高计算效率。对修改的IEEE 118-BUS系统的数值研究表明，提出的DDSPCE方法可以以高效率来实现PTTC概率特征的准确估计。此外，数值结果揭示了在PTTC和ATC评估中纳入离散随机输入的重要性，尽管如此，这些输入尚未得到足够的关注。
The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this paper, a novel data-driven sparse polynomial chaos expansion (DDSPCE) method is proposed for estimating the probabilistic characteristics (e.g., mean, variance, probability distribution) of probabilistic TTC (PTTC). Specifically, the proposed method, requiring no pre-assumed probabilistic distributions of random inputs, exploits data sets directly in estimating the PTTC. Besides, a sparse scheme is integrated to improve the computational efficiency. Numerical studies on the modified IEEE 118-bus system demonstrate that the proposed DDSPCE method can achieve accurate estimation for the probabilistic characteristics of PTTC with a high efficiency. Moreover, numerical results reveal the great significance of incorporating discrete random inputs in PTTC and ATC assessment, which nevertheless was not given sufficient attention.
