A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration
A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration
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DOI：
10.1109/tpwrs.2020.2987900
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发表时间：
2020-11
影响因子：
6.6
通讯作者：
Yijun Xu;L. Mili;M. Korkali;Kiran Karra;Zongsheng Zheng;Xiao Chen
中科院分区：
文献类型：
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作者：
Yijun Xu;L. Mili;M. Korkali;Kiran Karra;Zongsheng Zheng;Xiao Chen
现代电力系统的特点是风力发电的渗透率不断提高，这导致其状态存在很大的不确定性。这些不确定性必须适当地量化；否则，可能会对系统安全造成威胁。面对这一挑战，我们提出了一种具有成本效益的、数据驱动的方法来评估电力系统的负荷裕度。利用实际风数据，利用核密度估计器推断出非参数风速分布，并将其合并到一个藤联结的框架中。后者使我们能够模拟复杂的多变量和高度依赖的模型输入，使用各种精确表示相关样本中尾部依赖性的二元联公式。此外，为了减少处理大量样本的传统蒙特卡罗模拟的令人难以置信的计算时间，我们建议通过贝叶斯学习框架使用非参数、基于高斯过程模拟器的降阶模型来取代原始复杂的连续潮流模型。为了加快贝叶斯算法的收敛速度，提出了截断多项式混沌代理作为高效的参数贝叶斯先验。该仿真器允许我们以可忽略不计的计算成本在采样值上执行耗时的连续潮流求解器。在几个测试系统上进行的仿真结果表明，所提出的方法在概率负载裕度评估方面具有令人印象深刻的性能。
A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.