IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i11p2037-d234835.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/11/2037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/11/2037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lai, Wenhao & Zheng, Xiaoliang & Song, Qi & Hu, Feng & Tao, Qiong & Chen, Hualiang, 2022. "Multi-objective membrane search algorithm: A new solution for economic emission dispatch," Applied Energy, Elsevier, vol. 326(C).
    2. Sharma, Abhimanyu & Padhy, Narayana Prasad, 2024. "Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems," Energy, Elsevier, vol. 294(C).
    3. Kheshti, Mostafa & Ding, Lei & Ma, Shicong & Zhao, Bing, 2018. "Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems," Renewable Energy, Elsevier, vol. 125(C), pages 1021-1037.
    4. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
    5. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.
    6. Carlos Cárdenas-Bravo & Rodrigo Barraza & Antonio Sánchez-Squella & Patricio Valdivia-Lefort & Federico Castillo-Burns, 2021. "Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries," Energies, MDPI, vol. 14(13), pages 1-24, June.
    7. Wang, K.Y. & Luo, X.J. & Wu, L. & Liu, X.C., 2013. "Optimal coordination of wind-hydro-thermal based on water complementing wind," Renewable Energy, Elsevier, vol. 60(C), pages 169-178.
    8. Mbuli, N. & Ngaha, W.S., 2022. "A survey of big bang big crunch optimisation in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    9. Qingbin Yu & Yuliang Dong & Yanjun Du & Jiahai Yuan & Fang Fang, 2022. "Optimizing Operation Strategy in a Simulated High-Proportion Wind Power Wind–Coal Combined Base Load Power Generation System under Multiple Scenes," Energies, MDPI, vol. 15(21), pages 1-21, October.
    10. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    11. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    12. Urazel, Burak & Keskin, Kemal, 2023. "A new solution approach for non-convex combined heat and power economic dispatch problem considering power loss," Energy, Elsevier, vol. 278(PB).
    13. Clarke, Daniel P. & Al-Abdeli, Yasir M. & Kothapalli, Ganesh, 2015. "Multi-objective optimisation of renewable hybrid energy systems with desalination," Energy, Elsevier, vol. 88(C), pages 457-468.
    14. Xue, Lin & Wang, Jianxue & Zhang, Yao & Yong, Weizhen & Qi, Jie & Li, Haotian, 2023. "Model-data-event based community integrated energy system low-carbon economic scheduling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    15. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response," Energy, Elsevier, vol. 239(PA).
    16. Mellouk, Lamyae & Ghazi, M. & Aaroud, A. & Boulmalf, M. & Benhaddou, D. & Zine-Dine, K., 2019. "Design and energy management optimization for hybrid renewable energy system- case study: Laayoune region," Renewable Energy, Elsevier, vol. 139(C), pages 621-634.
    17. Shehab Al-Sakkaf & Mahmoud Kassas & Muhammad Khalid & Mohammad A. Abido, 2019. "An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch," Energies, MDPI, vol. 12(8), pages 1-25, April.
    18. Fu, Yiwei & Lu, Zongxiang & Hu, Wei & Wu, Shuang & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Research on joint optimal dispatching method for hybrid power system considering system security," Applied Energy, Elsevier, vol. 238(C), pages 147-163.
    19. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Lin, Zhongwei & Fang, Fang & Chen, Qun, 2021. "Optimal operation of integrated electricity and heat system: A review of modeling and solution methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Ahmed Ginidi & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ragab El-Sehiemy, 2021. "An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids," Mathematics, MDPI, vol. 9(17), pages 1-25, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2037-:d:234835. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.