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A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment

doi: 10.3390/app9091776
The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem.
- Zhengzhou University China (People's Republic of)
- Shenzhen Institutes of Advanced Technology China (People's Republic of)
- Zhengzhou University China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
Technology, unit commitment (UC), QH301-705.5, T, Physics, QC1-999, Engineering (General). Civil engineering (General), Chemistry, binary optimization, competitive swarm optimizer, TA1-2040, Biology (General), QD1-999
Technology, unit commitment (UC), QH301-705.5, T, Physics, QC1-999, Engineering (General). Civil engineering (General), Chemistry, binary optimization, competitive swarm optimizer, TA1-2040, Biology (General), QD1-999
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
