IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i5d10.1007_s13198-022-01635-z.html
   My bibliography  Save this article

Combined economic emission dispatch on dynamic systems using hybrid CSA-JAYA Algorithm

Author

Listed:
  • Sourav Basak

    (Indian Institute of Technology (Indian School of Mines))

  • Biplab Bhattacharyya

    (Indian Institute of Technology (Indian School of Mines))

  • Bishwajit Dey

    (Indian Institute of Technology (Indian School of Mines)
    Gandhi Institute of Engineering and Technology)

Abstract

Utilities are no longer only concerned with distributing power at a low cost. Rather, utilities are focusing on lowering the hazardous chemicals discharged into the environment as a result of greater usage of traditional fossil-fuelled generators to meet rising electrical energy demand. This may be done by increasing the use of renewable energy sources (RES) to provide clean power, hence preventing fossil fuel depletion. This article evaluates the dynamic economic emission dispatch (DEED) approach for two large test systems. Two DEED approaches, the price-penalty factor (PPF) method and fractional programming (FP) method, were studied for each of the test systems, and a comparative analysis was conducted based on the balanced trade-off solution between the least amount of hazardous and toxic gases released into the environment and fuel cost. The current article provides a unique hybrid CSA-JAYA method as an optimization tool. According to numerical information acquired, FP emits fewer dangerous and poisonous compounds into the environment than the PPF technique. Because of the valve point loading effect, fitness functions are non-convex and non-linear, necessitating the use of a metaheuristic over traditional optimization methods. The proposed CSA-JAYA algorithm consistently beat a long list of algorithms in producing high-quality results.

Suggested Citation

  • Sourav Basak & Biplab Bhattacharyya & Bishwajit Dey, 2022. "Combined economic emission dispatch on dynamic systems using hybrid CSA-JAYA Algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2269-2290, October.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01635-z
    DOI: 10.1007/s13198-022-01635-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01635-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/s13198-022-01635-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. Bishwajit Dey & Fausto Pedro García Márquez & Sourav Kr. Basak, 2020. "Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm," Energies, MDPI, vol. 13(13), pages 1-23, July.
    2. Ma, Haiping & Yang, Zhile & You, Pengcheng & Fei, Minrui, 2017. "Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging," Energy, Elsevier, vol. 135(C), pages 101-111.
    3. Dai, Wei & Yang, Zhifang & Yu, Juan & Cui, Wei & Li, Wenyuan & Li, Jinghua & Liu, Hui, 2021. "Economic dispatch of interconnected networks considering hidden flexibility," Energy, Elsevier, vol. 223(C).
    4. Roy, Sanjoy, 2018. "The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability," Energy, Elsevier, vol. 147(C), pages 915-923.
    5. Yuan, Xiaohui & Su, Anjun & Yuan, Yanbin & Nie, Hao & Wang, Liang, 2009. "An improved PSO for dynamic load dispatch of generators with valve-point effects," Energy, Elsevier, vol. 34(1), pages 67-74.
    6. Manjulata Badi & Sheila Mahapatra & Bishwajit Dey & Saurav Raj, 2022. "A Hybrid GWO-PSO Technique for the Solution of Reactive Power Planning Problem," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 13(1), pages 1-30, January.
    7. Wu, Kunming & Li, Qiang & Chen, Ziyu & Lin, Jiayang & Yi, Yongli & Chen, Minyou, 2021. "Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems," Energy, Elsevier, vol. 222(C).
    8. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    9. Toopshekan, Ashkan & Yousefi, Hossein & Astaraei, Fatemeh Razi, 2020. "Technical, economic, and performance analysis of a hybrid energy system using a novel dispatch strategy," Energy, Elsevier, vol. 213(C).
    10. 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)

    Citations

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


    Cited by:

    1. Swarupa Pinninti & Srinivasa Rao Sura, 2023. "Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 353-373, March.
    2. Mousumi Banerjee & Vanita Garg & Kusum Deep, 2023. "Solving structural and reliability optimization problems using efficient mutation strategies embedded in sine cosine algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 307-327, March.
    3. Shoyab Ali & Annapurna Bhargava & Akash Saxena & Pavan Kumar, 2023. "A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter," Mathematics, MDPI, vol. 11(3), pages 1-25, January.

    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. Swarupa Pinninti & Srinivasa Rao Sura, 2023. "Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 353-373, March.
    2. Sourav Basak & Bishwajit Dey & Biplab Bhattacharyya, 2023. "Uncertainty-based dynamic economic dispatch for diverse load and wind profiles using a novel hybrid algorithm," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4723-4763, May.
    3. Srikant Misra & P. K. Panigrahi & Bishwajit Dey, 2023. "An efficient way to schedule dispersed generators for a microgrid system's economical operation under various power market conditions and grid involvement," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1799-1809, October.
    4. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    5. Le Chi Kien & Thanh Long Duong & Van-Duc Phan & Thang Trung Nguyen, 2020. "Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization," Sustainability, MDPI, vol. 12(3), pages 1-35, February.
    6. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    7. 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.
    8. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    9. Mousavi, Seyed Ali & Toopshekan, Ashkan & Mehrpooya, Mehdi & Delpisheh, Mostafa, 2023. "Comprehensive exergetic performance assessment and techno-financial optimization of off-grid hybrid renewable configurations with various dispatch strategies and solar tracking systems," Renewable Energy, Elsevier, vol. 210(C), pages 40-63.
    10. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    11. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    12. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    13. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    14. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    15. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.
    16. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    17. Liu, Zhi-Feng & Zhao, Shi-Xiang & Zhang, Xi-Jia & Tang, Yu & You, Guo-Dong & Li, Ji-Xiang & Zhao, Shuang-Le & Hou, Xiao-Xin, 2023. "Renewable energy utilizing and fluctuation stabilizing using optimal dynamic grid connection factor strategy and artificial intelligence-based solution method," Renewable Energy, Elsevier, vol. 219(P1).
    18. 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).
    19. 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.
    20. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).

    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:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01635-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.