Information Gap Decision Theory-Spectrum Clustering Typical Scenario Generation Considering Source Load Uncertainty

doi:10.16183/j.cnki.jsjtu.2023.580

Abstract

Abstract:

The high proportion of new energy and dynamic load brings significant bidirectional uncertainty of source and load to power system. The strong uncertainty makes scheduling planning face high dimensional decision space and increases planning risk. Therefore, an information gap decision theory (IGDT)-spectral clustering typical scenario generation method considering the uncertainty of source load is proposed to provide a more accurate planning scenario for the determination of the operation mode of multi-source joint systems. First, the source load uncertainty can be quantified effectively by the IGDT theory without considering the uncertain quantitative probability distribution. The source load fluctuation range is described using the IGDT robust model and the IGDT chance model, and the original scenario representing each uncertainty situation is generated using the Latin hypercube sampling method to ensure the adequacy and accuracy of sample space. Then, to address the huge scale of the original scenario caused by the uncertainty of the source load, a spectral clustering method considering the adjustment ability of the system is introduced to mine the feature vectors of different source load fluctuations which have an important impact on the scheduling decision to reduce the complex original scenario set to a representative typical scenario of the source load. Finally, the simulation analysis of the actual system and operation data of a provincial power grid shows that compared with the traditional spectral clustering method, the proposed method generates four more typical scenarios after considering the bidirectional uncertainty of source load, the comprehensive average Pearson correlation coefficient is increased by 8.76%, the comprehensive average Euclidian distance is reduced by 43.48%, and the clustering scenario is more similar to the actual scenario.

Key words: source load uncertainty, information gap decision theory (IGDT), spectral clustering, typical scenario generation, dispatch planning

CLC Number:

TM732

ZHANG Jiamin, CAI Ye, TANG Xiafei, TAN Yudong, CAO Yijia, LI Junxiong. Information Gap Decision Theory-Spectrum Clustering Typical Scenario Generation Considering Source Load Uncertainty[J]. Journal of Shanghai Jiao Tong University, 2025, 59(11): 1625-1636.

Figures/Tables 10

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机组类型	机组容量/MW	机组数量/台	总容量/MW
火电机组	660	6	3960
	600	13	7800
	300	20	6000
抽水蓄能机组	300	4	1200
风电机组			6690
光伏机组			3906
水电机组			15806

方案	平均皮尔逊相关系数
方案	风	光	负荷	综合
1	0.5643	0.9751	0.9672	0.8355
2	0.7746	0.9838	0.9678	0.9087

方案	平均欧氏距离标幺值
方案	风	光	负荷	综合
1	0.6698	0.6680	0.1144	0.4841
2	0.4561	0.2402	0.1245	0.2736

方法	CH指标	DB指标	SIL
K均值	333.63	3.18	0.6788
谱聚类	480.42	3.03	0.6866
IGDT-谱聚类	2597.45	2.46	0.6876