An enhanced adaptive bat algorithm for microgrid energy scheduling

Microgrid (MG) systems have been growing rapidly with increasing electric power generation through small distributed generators (DGs) including renewable systems. Optimal energy scheduling is one of the most important and challenging issues in field MG. In this paper, an enhanced adaptive bat algorithm (EABA) proposed for optimal MG system. original many its variants, information sharing between bats lacking speed each previous used equally, which may decrease their search performance. To overcome problem, EABA introduces mechanism assigns weight to generation. Moreover, different mechanisms are applied early late stages further improve The performance first demonstrated on some benchmark optimization problems. Then employed schedule DGs containing three wind plants, two photovoltaic a combined heat plant grid-off Simulation results confirm superior over other eleven algorithms considered