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 a...

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Vydáno v:Energy (Oxford) Ročník 232; s. 121014
Hlavní autoři: Yang, Qiangda, Dong, Ning, Zhang, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.10.2021
Elsevier BV
Témata:
Adaptive algorithms
Algorithms
Bat algorithm
Chiroptera
Cogeneration
Distributed generation
Electric power
Electric power distribution
Electric power generation
Energy
Energy scheduling
heat
Information sharing
Microgrid
Optimization
Photovoltaics
power generation
Power plants
Scheduling
Searching
solar energy
Wind power
Energy scheduling
Microgrid
Bat algorithm
ISSN:0360-5442, 1873-6785
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Shrnutí: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. •An enhanced adaptive bat algorithm (EABA) is proposed for microgrid scheduling.•EABA contains an information sharing mechanism with adaptive weights.•Renewable energy generation uncertainty and equipment malfunctions are considered.•EABA gives the best scheduling performance when compared with 11 other algorithms.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.121014