数据驱动的综合能源系统运行优化研究OA

In recent years,the rapid development of digital technologies such as the Internet of Things,big data,and artificial intelligence has provided new methods for the operation optimization of integrated energy systems(IES).A data-driven operation optimization method for IES was proposed.For an industrial park with a self-contained energy station in northern China,a deep learning long short-term memory neural network model was used for multi-load joint forecasting and photovoltaic power forecasting,providing accurate support for the operation optimization of the energy station.The main energy supply equipment was modeled under full operating conditions through data-driven machine learning algorithms.Taking energy efficiency,economic,and comprehensive benefit indicators as optimization objectives,the particle swarm optimization algorithm was applied to obtain typical daily operation optimization results.Under the condition of optimal energy efficiency indicator,the comprehensive energy utilization rate of the system reached 83.0%,and the operating cost was 64 802 yuan.Under the condition of optimal economic indicator,the system operating cost was as low as 64 590 yuan,and the comprehensive energy utilization rate reached 79.3%.Under the condition of optimal comprehensive benefit indicator,compared to the actual operation of the energy station,the comprehensive energy utilization rate increased by 7.5%,and the operating cost was reduced by 6 444 yuan.The results indicate that this operation optimization method has practical significance for guiding the operation optimization of IES.