IDEAS home Printed from https://ideas.repec.org/a/hin/complx/7289674.html
   My bibliography  Save this article

An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems

Author

Listed:
  • Xu Chen
  • Bin Xu
  • Wenli Du

Abstract

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeography-based learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

Suggested Citation

  • Xu Chen & Bin Xu & Wenli Du, 2018. "An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    • Handle: RePEc:hin:complx:7289674
    DOI: 10.1155/2018/7289674
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/7289674.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/7289674.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/7289674?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. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
    2. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    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. 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. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    3. Chun-Yao Lee & Maickel Tuegeh, 2020. "An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem," Energies, MDPI, vol. 13(14), pages 1-16, July.

    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. 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).
    2. 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.
    3. Ali S. Alghamdi, 2022. "Greedy Sine-Cosine Non-Hierarchical Grey Wolf Optimizer for Solving Non-Convex Economic Load Dispatch Problems," Energies, MDPI, vol. 15(11), pages 1-19, May.
    4. Meng, Anbo & Li, Jinbei & Yin, Hao, 2016. "An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects," Energy, Elsevier, vol. 113(C), pages 1147-1161.
    5. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    6. Kheshti, Mostafa & Kang, Xiaoning & Bie, Zhaohong & Jiao, Zaibin & Wang, Xiuli, 2017. "An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units," Energy, Elsevier, vol. 129(C), pages 1-15.
    7. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    8. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    9. Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
    10. 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.
    11. 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.
    12. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    13. Mario Šipoš & Zvonimir Klaić & Emmanuel Karlo Nyarko & Krešimir Fekete, 2021. "Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm," Energies, MDPI, vol. 14(1), pages 1-13, January.
    14. 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.
    15. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    16. Jorge Pérez-Aracil & Carlos Camacho-Gómez & Eugenio Lorente-Ramos & Cosmin M. Marina & Laura M. Cornejo-Bueno & Sancho Salcedo-Sanz, 2023. "New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL," Mathematics, MDPI, vol. 11(7), pages 1-22, March.
    17. 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.
    18. Azizipanah-Abarghooee, Rasoul & Golestaneh, Faranak & Gooi, Hoay Beng & Lin, Jeremy & Bavafa, Farhad & Terzija, Vladimir, 2016. "Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power," Applied Energy, Elsevier, vol. 182(C), pages 634-651.
    19. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    20. Faisal Tariq & Salem Alelyani & Ghulam Abbas & Ayman Qahmash & Mohammad Rashid Hussain, 2020. "Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm," Energies, MDPI, vol. 13(23), pages 1-36, November.

    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:hin:complx:7289674. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.