基于高斯过程回归与不确定性耦合关系的电力系统规划典型场景提取技术
DOI:
作者:
作者单位:

1.国网山东省电力公司威海供电公司;2.国网山东省电力公司经济技术研究院

作者简介:

通讯作者:

基金项目:


Extraction of Typical Scenarios for Power System Planning Based on Gaussian Process Regression and Uncertainty Coupling Relationship
Author:
Affiliation:

1.State Grid Shandong Power Supply Company,Weihai;2.State Grid Shandong Power Supply Company,Economic Research Institute,Jinan

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    为解决典型场景生成方法未能全面考虑风、光、负荷等不确定因素间的耦合关系、传统聚类方法在高维数据集上表现较差、提取的典型场景不能很好体现原数据特征等问题。在改进核函数的基础上,结合贝叶斯公式和多元高斯分布,利用高斯过程回归(GPR)对电力系统中的多种不确定因素的耦合关系进行建模,生成模拟运行数据;采用时序分段典型场景提取方法,划分总调度区间为若干子区间并分别进行中心点聚类,得出子区间带权典型场景并用笛卡尔积连接,生成全调度区间典型场景集;应用Earth Mover’s Distance (EMD)方法,进行典型场景提取效果评价;算例验证了提取的典型场景能更好保留原始基础场景集合的概率分布特性,充分体现原始数据集合中不确定因素之间的耦合关系;验证了所述方法提取的典型场景能更好体现原数据特征。

    Abstract:

    Existing typical scenarios extraction methods fail to fully consider the coupling relationship between uncertain factors, and traditional clustering methods perform poorly on high-dimensional data sets, and the extracted typical scenes cannot well reflect the characteristics of the original data. In view of this situation, on the basis of improving the kernel function, combine Bayesian formula and multivariate Gaussian distribution, use Gaussian Process Regression (GPR) to model the coupling relationship of multiple uncertain factors in the power system, and generate simulating data ;Use the time sequence segmentation typical scenarios extraction method, divide the total scheduling interval into several sub-intervals and perform central point clustering respectively, obtain the weighted typical scenarios of the sub-intervals and connect them with the Cartesian product to generate the typical scenario set of the entire scheduling interval; Evaluate the extraction effect of typical scenarios on the basis of Earth Mover's Distance (EMD) method; The example verifies that the extracted typical scenarios can better retain the probability distribution characteristics of the original basic scenario set, and fully reflect the coupling between different uncertain factors in the original data set and proves that the typical scenarios extracted by the method can better reflect the characteristics of the original data.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2020-09-10
  • 最后修改日期:2020-09-28
  • 录用日期:2020-12-07
  • 在线发布日期: