An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of e...An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy 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%.展开更多
基金supported by National Key Research and Development Program of China(2019YFB1505400)Jilin Science and Technology Development Program(20160411003XH)Jilin Industrial Technology Research and Development Program(2019C058-8).
文摘An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy 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%.
