An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of source-load-temperature scenarios is proposed to improve the multi-energy complementation and the reliability of energy supply in extreme scenarios. First

based on the historical meteorological data

the typical meteorological clusters and extreme temperature types are obtained. Then

to reflect the uncertainty of energy consumption and renewable energy output in different weather types

a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module. At the same time

to cover the potential high energy consumption scenarios with extreme temperatures

the extreme scenarios with fewer data samples are expanded. Then

the scenarios are reduced by clustering analysis. The normal days of different typical scenarios and extreme temperature scenarios are determined

and the cooling and heating loads are determined by temperature. Finally

the optimal configuration of a multi-energy microgrid system is carried out. Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system. The proposed method can accurately capture the complementary potential of energy sources. And the economy of the system configuration is improved by 14.56%.