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