Abstract
To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean decomposition–long short-term memory (BPNN–LMD–LSTM) load prediction, the design is based on a fixed-time consistency algorithm with random delay to predict the economic dispatch of microgrids. Firstly, the initial power load prediction sequence is obtained by continuous training of the back propagation neural network (BPNN); the residual sequence with other influencing factors is decomposed by local mean decomposition (LMD); and the long short-term memory neural network (LSTM) is used to predict the output prediction residual sequence, and the final short-term power load prediction is obtained. Based on predicting load, the fixed-time consistency algorithm with random delay is used to add supply and demand balance constraints to optimize the power distribution of the power generation units of the distributed microgrid and reduce the power generation cost of the microgrid. The results show that the prediction model has better prediction accuracy, and the scheduling algorithm based on the prediction model has a faster convergence rate to reach the lowest power generation cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.