全球能源互联网（英文）2024，Vol.7Issue(3)：347-361,15.DOI:10.1016/j.gloei.2024.06.007
基于BiGRU自注意力机制和LQPSO的多能源微电网预测与调度
Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO
摘要
Abstract
To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.关键词
微电网/双向门控循环单元/自注意力/莱维量子粒子群优化/多目标优化Key words
Microgrid/Bidirectional gated recurrent unit/Self-attention/Lévy-quantum particle swarm optimization/Multi-objective optimization引用本文复制引用
段宇宸,李鹏,夏静..基于BiGRU自注意力机制和LQPSO的多能源微电网预测与调度[J].全球能源互联网（英文）,2024,7(3):347-361,15.基金项目
This work was supported by the National Natural Science Foundation of China under Grant 51977004,and the Beijing Natural Science Foundation under Grant 4212042. （）
