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Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

Author

Listed:
  • Shahbaz Hussain

    (Department of Electrical Engineering, Qatar University, P.O. Box 2713 Doha, Qatar)

  • Mohammed Al-Hitmi

    (Department of Electrical Engineering, Qatar University, P.O. Box 2713 Doha, Qatar)

  • Salman Khaliq

    (Intelligent Mechatronics Research Center, Korea Electronics Technology Institute (KETI), Gyeonggi-do 13509, Korea)

  • Asif Hussain

    (Department of Electrical Engineering, University of Management and Technology, Lahore 54792, Pakistan)

  • Muhammad Asghar Saqib

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

Abstract

This paper presents the optimization of fuel cost, emission of NO X , CO X, and SO X gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

Suggested Citation

  • Shahbaz Hussain & Mohammed Al-Hitmi & Salman Khaliq & Asif Hussain & Muhammad Asghar Saqib, 2019. "Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant," Energies, MDPI, vol. 12(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2037-:d:234835
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    References listed on IDEAS

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    1. Mahor, Amita & Prasad, Vishnu & Rangnekar, Saroj, 2009. "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2134-2141, October.
    2. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    3. Mahdi, Fahad Parvez & Vasant, Pandian & Kallimani, Vish & Watada, Junzo & Fai, Patrick Yeoh Siew & Abdullah-Al-Wadud, M., 2018. "A holistic review on optimization strategies for combined economic emission dispatch problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3006-3020.
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    Cited by:

    1. Yanhong Luo & Zhenxing Yin & Dongsheng Yang & Bowen Zhou, 2019. "A New Wind Power Accommodation Strategy for Combined Heat and Power System Based on Bi-Directional Conversion," Energies, MDPI, vol. 12(13), pages 1-16, June.
    2. Yunhai Zhou & Pinchao Zhao & Fei Xu & Dai Cui & Weichun Ge & Xiaodong Chen & Bo Gu, 2020. "Optimal Dispatch Strategy for a Flexible Integrated Energy Storage System for Wind Power Accommodation," Energies, MDPI, vol. 13(5), pages 1-18, March.
    3. Yu, Xiaobing & Duan, Yuchen & Luo, Wenguan, 2022. "A knee-guided algorithm to solve multi-objective economic emission dispatch problem," Energy, Elsevier, vol. 259(C).
    4. Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.
    5. Ho-Sung Ryu & Mun-Kyeom Kim, 2020. "Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm," Energies, MDPI, vol. 13(23), pages 1-20, December.

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