IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0261709.html
   My bibliography  Save this article

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

Author

Listed:
  • Ijaz Ahmed
  • Um-E-Habiba Alvi
  • Abdul Basit
  • Tayyaba Khursheed
  • Alwena Alvi
  • Keum-Shik Hong
  • Muhammad Rehan

Abstract

The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

Suggested Citation

  • Ijaz Ahmed & Um-E-Habiba Alvi & Abdul Basit & Tayyaba Khursheed & Alwena Alvi & Keum-Shik Hong & Muhammad Rehan, 2022. "A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-32, January.
  • Handle: RePEc:plo:pone00:0261709
    DOI: 10.1371/journal.pone.0261709
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261709
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0261709&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0261709?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
    ---><---

    References listed on IDEAS

    as
    1. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    2. Abdullah Khan & Hashim Hizam & Noor Izzri bin Abdul Wahab & Mohammad Lutfi Othman, 2020. "Optimal power flow using hybrid firefly and particle swarm optimization algorithm," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    3. Tai-Yu Ma, 2021. "Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-27, May.
    4. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings," Applied Energy, Elsevier, vol. 213(C), pages 11-21.
    5. Filip Johnsson & Jan Kjärstad & Johan Rootzén, 2019. "The threat to climate change mitigation posed by the abundance of fossil fuels," Climate Policy, Taylor & Francis Journals, vol. 19(2), pages 258-274, February.
    6. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
    7. Adel Saad Assiri, 2021. "On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-27, January.
    8. Zhou, Ping & Hu, Xikui & Zhu, Zhigang & Ma, Jun, 2021. "What is the most suitable Lyapunov function?," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    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. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    2. Xia, Wanjun & Murshed, Muntasir & Khan, Zeeshan & Chen, Zhenling & Ferraz, Diogo, 2022. "Exploring the nexus between fiscal decentralization and energy poverty for China: Does country risk matter for energy poverty reduction?," Energy, Elsevier, vol. 255(C).
    3. Olanrewaju Lasabi & Andrew Swanson & Leigh Jarvis & Anuoluwapo Aluko & Arman Goudarzi, 2024. "Coordinated Hybrid Approach Based on Firefly Algorithm and Particle Swarm Optimization for Distributed Secondary Control and Stability Analysis of Direct Current Microgrids," Sustainability, MDPI, vol. 16(3), pages 1-28, January.
    4. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    5. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.
    6. Luigi Maffei & Antonio Ciervo & Achille Perrotta & Massimiliano Masullo & Antonio Rosato, 2023. "Innovative Energy-Efficient Prefabricated Movable Buildings for Smart/Co-Working: Performance Assessment upon Varying Building Configurations," Sustainability, MDPI, vol. 15(12), pages 1-37, June.
    7. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    8. Qiu, Hao & Wang, Kai & Yu, Peifeng & Ni, Mingjiang & Xiao, Gang, 2021. "A third-order numerical model and transient characterization of a β-type Stirling engine," Energy, Elsevier, vol. 222(C).
    9. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    10. Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    11. Munawar, Muhammad Assad & Khoja, Asif Hussain & Naqvi, Salman Raza & Mehran, Muhammad Taqi & Hassan, Muhammad & Liaquat, Rabia & Dawood, Usama Fida, 2021. "Challenges and opportunities in biomass ash management and its utilization in novel applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    12. Xie, Ying & Zhou, Ping & Yao, Zhao & Ma, Jun, 2022. "Response mechanism in a functional neuron under multiple stimuli," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    13. Liu, Jia & Chen, Xi & Yang, Hongxing & Li, Yutong, 2020. "Energy storage and management system design optimization for a photovoltaic integrated low-energy building," Energy, Elsevier, vol. 190(C).
    14. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    15. Klamka, Jonas & Wolf, André & Ehrlich, Lars G., 2020. "Photovoltaic self-consumption after the support period: Will it pay off in a cross-sector perspective?," Renewable Energy, Elsevier, vol. 147(P1), pages 2374-2386.
    16. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    17. Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
    18. 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.
    19. Suroso Isnandar & Jonathan F. Simorangkir & Kevin M. Banjar-Nahor & Hendry Timotiyas Paradongan & Nanang Hariyanto, 2024. "A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition," Energies, MDPI, vol. 17(15), pages 1-28, August.
    20. Qiu, Xin & Jin, Jianjun & He, Rui & Mao, Jiansu, 2022. "The deviation between the willingness and behavior of farmers to adopt electricity-saving tricycles and its influencing factors in Dazu District of China," Energy Policy, Elsevier, vol. 167(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0261709. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.