Abstract: The high proportion of new energy and dynamic load bring significant bidirectional uncertainty of source and load to power system. 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. Firstly, the source load uncertainty can be quantified effectively by using IGDT theory without considering the advantage of uncertain quantitative probability distribution. The source load fluctuation range is described by IGDT robust model and IGDT chance model, and the original scenario representing each uncertainty situation is generated by Latin hypercube sampling method, so as to ensure the adequacy and accuracy of sample space. Secondly, in view of 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 that have an important impact on the scheduling decision, so as to reduce the complex original scenario set to a representative typical scenario of the source load. Finally, through the simulation analysis of the actual system and operation data of a provincial power grid, it is shown that compared with the traditional spectral clustering method, the proposed method generates 4 more typical scenarios after considering the bidirectional uncertainty of source load, and 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