Extraction of typical scenarios for power system planning based on Gaussian process regression and uncertainty coupling relationship
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    Abstract:

    The typical scene generation method has problems such as failing to fully consider the coupling relationship between uncertain factorslike wind, light and load, traditional clustering methods perform poorly on high-dimensional data sets, and the extracted typical scenes cannot well reflect the characteristics of the original data. To solve these problems,combining with Bayesian formula and multivariate Gaussian distribution, this paperfirstly uses Gaussian process regression (GPR) to model the coupling relationship of various uncertain factors in the power systemand generate the simulation operation data on the basis of improving the kernel function. Secondly,using the time series segmented typical scene extraction method, the total scheduling interval is divided into several sub-intervals. The center points are clustered respectively. Sub-interval weighted typical scenes are obtained and connected by Cartesian productto generate typical scene set of the full scheduling interval. Then, a method based on land movement distance (EMD) is applied to evaluate the extraction effect of typical scenes.Finally, it is verified that the extracted typical scenes can better retain the probability distribution characteristics of the original basic scene setand fully reflect the coupling relationship between uncertain factors in the original data set. The resultsshow that the typical scenes extracted by the method can be better reflect the characteristics of the original data.

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李峰,高效海,郑鹏飞,刘帅,高洁.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[J].电力科学与技术学报英文版,2022,37(1):64-73. LI Feng, GAO Xiaohai, ZHENG Pengfei, LIU Shuai, GAO Jie. Extraction of typical scenarios for power system planning based on Gaussian process regression and uncertainty coupling relationship[J]. Journal of Electric Power Science and Technology,2022,37(1):64-73.

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  • Online: April 01,2022
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