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A new binary moth-flame optimization algorithm (BMFOA) - development and application to solve unit commitment problem

A new binary moth-flame optimization algorithm (BMFOA) - development and application to solve unit commitment problem

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A binary variant of moth-flame optimizer, namely the binary moth-flame optimizer algorithm (BMFOA) is developed in this chapter and is applied to solve unit commitment (UC) problem in power system operation. The moth-flame algorithm is a bio-inspired optimization algorithm that mimics the traverse navigation mechanism of moth around flames. The navigation mechanism is modelled as a spirally converging approach of moth towards flame. However, the direct application of real-valued moth-flame optimization algorithm (MFOA) to binary matured problems such as UC problem is not possible considering the binary search space attributes. Thus, a binary variant BMFOA is developed via modified sigmoidal transformation of real-valued MFOA. The efficacy of proposed BMFOA is demonstrated through numerical experiments using test systems of different sizes ranging from small-to-medium and large scale. The simulation results are presented, discussed and compared to various existing approaches to solve UC problems. In addition, the statistical significance of BMFOA with respect to other existing approaches is established by performing standard statistical tests such as Friedman, Friedman aligned ranks test, Wilcoxon pairwise test and Quade test. The comparison of statistical test results confirms the statistical significance of proposed BMFOA for solving UC problem of different scales.

Chapter Contents:

  • Abstract
  • 10.1 Introduction
  • 10.2 Problem formulation
  • 10.2.1 Nomenclature
  • 10.2.2 Objective
  • 10.2.3 Constraints
  • 10.3 Binary moth–flame optimization algorithm
  • 10.3.1 Overview of MFOA
  • 10.3.2 Continuous valued MFOA
  • 10.3.3 Binary moth–flame optimization algorithm
  • 10.3.3.1 Modified sigmoid transformation for BMFOA
  • 10.4 BMFOA implementation to UC problem
  • 10.4.1 Constraint repair
  • 10.4.1.1 Minimum up/down constraints
  • 10.4.1.2 Spinning reserve and load satisfaction repair
  • 10.4.1.3 Decommitment algorithm under excessive spinning reserve
  • 10.5 Numerical results and discussion
  • 10.6 Statistical analysis
  • 10.6.1 Friedman test
  • 10.6.2 Friedman aligned ranks test
  • 10.6.3 Wilcoxon pairwise test
  • 10.6.4 Quade test
  • 10.7 Conclusion
  • References

Inspec keywords: search problems; navigation; statistical testing; power generation scheduling; power generation dispatch; optimisation

Other keywords: unit commitment problem; bioinspired optimization algorithm; binary moth-flame optimization algorithm; Wilcoxon pairwise testing; modified sigmoidal transformation; Friedman aligned rank testing; statistical testing; traverse navigation mechanism; binary matured problems; UC problem; Quade testing; power system operation; binary search space; binary variant BMFOA

Subjects: Power system management, operation and economics; Combinatorial mathematics; Generating stations and plants; Other topics in statistics; Optimisation techniques

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