期刊
IEEE ACCESS
卷 9, 期 -, 页码 12267-12281出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3051175
关键词
Fires; Optimization methods; Maintenance engineering; Economics; Genetic algorithms; Power system stability; Moon; Binary alternative moth-flame optimization; binary moth-flame optimization; economic load dispatch; lambda-iteration method; operational scheduling; priority list; repair strategy; unit commitment
This study proposes a novel binary optimization method BAMFO, which discovers better solutions in the search space through four alternative characteristics and balances exploration and exploitation performance through random parameters. Experimental verification of BAMFO showed superior performance compared to other methods.
This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.
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