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Crisscross Team Game Algorithm for Economic-Emission Power Dispatch Problem with Multiple Fuel Options

Author

Listed:
  • P. S. Bhullar

    (Sant Longowal Institute of Engineering and Technology, Longowal)

  • J. S. Dhillon

    (Sant Longowal Institute of Engineering and Technology, Longowal)

  • R. K. Garg

    (Sant Longowal Institute of Engineering and Technology, Longowal)

Abstract

This paper proposes a crisscross team game algorithm (CTGA) to solve single and multi-objective optimization problems. CTGA integrates dual crisscross mechanisms orthogonally with operators of the team game algorithm (TGA) to balance exploration and exploitation. The proposed amalgamation enhances the search capabilities and convergence behaviour of TGA. The economic-emission power dispatch (EEPD) problem of thermal units with multiple fuel options and the crucial operational limitations of an electric power system is successfully solved using the proposed algorithm. The objectives, operating cost, and emission of pollutants are combined by the non-interactive technique exploiting the price penalty method. On the basis of the replacement technique and proportional power sharing of the unmet load demand, feasible solutions are discovered heuristically. The applicability of the proposed algorithm is verified on unconstrained (viz. unimodal and multimodal) standard benchmark optimization problems, along with five electric power test problems having real-world constraints, including restricted operation zones and ramp-rate limits. CTGA’s superior performance over TGA in experimental evaluations and graphical representations explicitly demonstrates the necessity of the proposed amalgamation. The Wilcoxon signed-rank test and Friedman test illustrate CTGA’s eminence over other competing algorithms. The suggested algorithm has fewer sensitive parameters to tune.

Suggested Citation

  • P. S. Bhullar & J. S. Dhillon & R. K. Garg, 2024. "Crisscross Team Game Algorithm for Economic-Emission Power Dispatch Problem with Multiple Fuel Options," SN Operations Research Forum, Springer, vol. 5(2), pages 1-60, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00305-z
    DOI: 10.1007/s43069-024-00305-z
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    References listed on IDEAS

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    1. Shen, Xin & Zou, Dexuan & Duan, Na & Zhang, Qiang, 2019. "An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch," Energy, Elsevier, vol. 186(C).
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    3. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    4. Hossein Nourianfar & Hamdi Abdi, 2022. "Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique," Sustainability, MDPI, vol. 14(6), pages 1-26, March.
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