Joint application of multi-object beetle antennae search algorithm and BAS-BP fuel cost forecast network on optimal active power dispatch problems
作者：
Highlights：
• Novel multi-object beetle antennae search algorithm (NMBAS) is proposed and applied to solve MOAPD problems.
• Seven MOAPD experiments verify NMBAS algorithm effectively reduces the fuel cost, power loss and emission of power systems.
• BAS-BP fuel cost prediction network is proposed to seek the higher-quality power flow schemes with smaller time cost.
• NMBAS and BAS-BP achieve zero constraint-violation & evenly-distributed PF & preferable dispatch schemes.
摘要
•Novel multi-object beetle antennae search algorithm (NMBAS) is proposed and applied to solve MOAPD problems.•Seven MOAPD experiments verify NMBAS algorithm effectively reduces the fuel cost, power loss and emission of power systems.•BAS-BP fuel cost prediction network is proposed to seek the higher-quality power flow schemes with smaller time cost.•NMBAS and BAS-BP achieve zero constraint-violation & evenly-distributed PF & preferable dispatch schemes.
论文关键词：Fuel cost forecast,Beetle antennae search algorithm,Optimal active power dispatch,BP network,Multi-object optimization
论文评审过程：Received 22 November 2020, Revised 23 January 2021, Accepted 12 May 2021, Available online 14 May 2021, Version of Record 18 May 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107149
