IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v5y2024i2d10.1007_s43069-024-00305-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-024-00305-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43069-024-00305-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    4. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    Full references (including those not matched with items on IDEAS)

    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. Chen, Xu, 2020. "Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects," Energy, Elsevier, vol. 203(C).
    2. El-Sayed, Wael T. & El-Saadany, Ehab F. & Zeineldin, Hatem H. & Al-Sumaiti, Ameena S., 2020. "Fast initialization methods for the nonconvex economic dispatch problem," Energy, Elsevier, vol. 201(C).
    3. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    4. Ghulam Abbas & Irfan Ahmad Khan & Naveed Ashraf & Muhammad Taskeen Raza & Muhammad Rashad & Raheel Muzzammel, 2023. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    5. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    6. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    7. Rajakumar, R. & Sekaran, Kaushik & Hsu, Ching-Hsien & Kadry, Seifedine, 2021. "Accelerated grey wolf optimization for global optimization problems," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    8. Mengqi Zhao & Xiaoling Wang & Jia Yu & Lei Bi & Yao Xiao & Jun Zhang, 2020. "Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm," Energies, MDPI, vol. 13(1), pages 1-17, January.
    9. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    10. Chen, Yue & Wei, Wei & Liu, Feng & Shafie-khah, Miadreza & Mei, Shengwei & Catalão, João P.S., 2018. "Optimal contracts of energy mix in a retail market under asymmetric information," Energy, Elsevier, vol. 165(PB), pages 634-650.
    11. Al-Bahrani, Loau Tawfak & Horan, Ben & Seyedmahmoudian, Mehdi & Stojcevski, Alex, 2020. "Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment," Energy, Elsevier, vol. 195(C).
    12. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    13. Araby Mahdy & Abdullah Shaheen & Ragab El-Sehiemy & Ahmed Ginidi & Saad F. Al-Gahtani, 2023. "Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor," Energies, MDPI, vol. 16(5), pages 1-27, March.
    14. Elsakaan, Asmaa A. & El-Sehiemy, Ragab A. & Kaddah, Sahar S. & Elsaid, Mohammed I., 2018. "An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions," Energy, Elsevier, vol. 157(C), pages 1063-1078.
    15. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).
    16. Wenqiang Yang & Yihang Zhang & Xinxin Zhu & Kunyan Li & Zhile Yang, 2024. "Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm," Energies, MDPI, vol. 17(6), pages 1-29, March.
    17. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & El-Fergany, Attia & Elsayed, Abdallah M., 2023. "Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer," Energy, Elsevier, vol. 283(C).
    18. Arunachalam Sundaram & Nasser S. Alkhaldi, 2024. "Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model," Energies, MDPI, vol. 17(4), pages 1-26, February.
    19. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    20. Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.

    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:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00305-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.