With the increasing penetration of renewable energy represented by wind power and photovoltaic, the large-scale time sequence scenario caused by the uncertainty of their output brings high computational complexity to the optimization analysis of a power system. One of the effective methods to solve the above problems is to accurately describe the regional wind power and photovoltaic output characteristics with scene reduction. In this paper, a scenario reduction method based on clustering and optimization algorithms is proposed. First, the data are preprocessed e.g. by cleaning and noise reduction. Secondly, the elbow method and silhouette coefficient are used to determine the number of wind power and photovoltaic categories and clusters. Then, the typical output curves of wind power and photovoltaic are extracted by particle swarm optimization and genetic algorithm respectively, and the results of the two algorithms are compared to generate typical scenarios. Taking the wind power and photovoltaic output data from January 1, 2015 to December 31, 2019 provided by European transmission system operator Amprion as the research object, the output curve obtained by the proposed method can effectively reflect the typical scenario of wind power and photovoltaic output in the region, and provide data support for subsequent power system planning and operation optimization. This work is supported by the National Natural Science Foundation of China (No. 51777126).