Optimal configuration planning of multi-energy microgird based on deep joint generation of source-load-temperature scenarios

Abstract

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. Firstly, 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 of radiation-electric load-temperature scenario based on 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. After that, the scenarios are reduced by clustering analysis. The typical 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%.