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A novel hybrid DE–random search approach for unit commitment problem

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Abstract

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

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Acknowledgments

The authors wish to thank I.K. Gujral Punjab Technical University, Jalandhar, for providing advanced research facilities during research work.

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Authors and Affiliations

  1. Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Kapurthala, India

    Vikram Kumar Kamboj

  2. Electrical Engineering, Giani Zail Singh Campus College of Engineering & Technology, Bathinda, Punjab, India

    S. K. Bath

  3. Department of Electrical and Instrumentation Engineering, SLIET, Longowal, Punjab, India

    J. S. Dhillon

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  1. Vikram Kumar Kamboj
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  2. S. K. Bath
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  3. J. S. Dhillon
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Correspondence to Vikram Kumar Kamboj.

Appendix

Appendix

See Tables 18, 19, 20 and 21 and Figs. 5 and 6.

Table 18 Test data for 4-unit system [76]
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Table 19 Load demand for 4-unit test system
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Table 20 Test data for 10-unit system [76]
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Table 21 Load demand pattern for 24 h for 10-unit system
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Fig. 5
figure 5

PSEUDO code for DE and random search algorithm

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Fig. 6
figure 6

Load demand pattern for 10-, 20- and 40-unit test system

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Kamboj, V.K., Bath, S.K. & Dhillon, J.S. A novel hybrid DE–random search approach for unit commitment problem. Neural Comput & Applic 28, 1559–1581 (2017). https://doi.org/10.1007/s00521-015-2124-4

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