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Multiobjective multiarea unit commitment using hybrid differential evolution algorithm considering import/export and tie-line constraints

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Abstract

Recent power system networks are characterized by large proportions, high interconnections, and high nonlinearities. Challenge of supplying the nation with high-quality, reliable electrical energy at a reasonable cost converted government policy into deregulation and restructuring environment. To achieve significant cost-savings, multiarea unit commitment strategies are employed, whose intention is to establish the optimal commitment stratagem for power generating units situated in numerous areas which are interconnected through tie-lines, and combined operation of generation resources can result in considerable operational cost-savings. Differential evolution is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it gorgeous for optimization. Differential evolution uses somewhat greedy and less stochastic approach for optimization problem solution. Although global exploration ability of differential evolution (DE) algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low, and it suffers from the problem of untimely 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 differential evolution algorithm combined with random search algorithm is presented in the proposed research to solve multiobjective and multiarea unit commitment problem of electric power system. The performance of the proposed hybrid algorithm is tested with benchmark of three-area interconnected system, which consist of IEEE-30 Bus system. Experimental results show that proposed technique has the prospective for the solution of multiobjective and multiarea unit commitment problem and power generation scheduling in deregulated electricity market with import and export constraints.

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Authors and Affiliations

  1. Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Jalandhar-Kapurthala Highway, Kapurthala, 144601, Punjab, India

    Vikram Kumar Kamboj

  2. Department of Electrical Engineering, Giani Zail Singh Campus College of Engineering and Technology, Maharaja Ranjit Singh State Technical University, Bathinda, 151001, Punjab, India

    S. K. Bath

  3. Department of Electrical and Instrumentation Engg., Sant Longowal Institute of Engineering and Technology, (Deemed-to-be-University), Longowal, Sangrur, 148106, Punjab, India

    J. S. Dhillon

Authors
  1. Vikram Kumar Kamboj
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  2. S. K. Bath
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  3. J. S. Dhillon
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Corresponding author

Correspondence to Vikram Kumar Kamboj.

Appendix

Appendix

See Tables 6, 7, 8, and 9.

Table 6 Test data for IEEE-30 Bus system
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Table 7 Emission and fuel cost coefficients for IEEE-30 bus system
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Table 8 PSEUDO code to turn-on additional generating units (when generated power < load demand + spinning reserve)
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Table 9 PSEUDO code to turn-off additional generating units (when generated power ≥ load demand + spinning reserve)
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Kamboj, V.K., Bath, S.K. & Dhillon, J.S. Multiobjective multiarea unit commitment using hybrid differential evolution algorithm considering import/export and tie-line constraints. Neural Comput & Applic 28, 3521–3536 (2017). https://doi.org/10.1007/s00521-016-2240-9

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  • DOI: https://doi.org/10.1007/s00521-016-2240-9

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