IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v77y2017icp1206-1220.html
   My bibliography  Save this article

Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: A review

Author

Listed:
  • Jebaraj, Luke
  • Venkatesan, Chakkaravarthy
  • Soubache, Irisappane
  • Rajan, Charles Christober Asir

Abstract

Economic Load Dispatch (ELD) is an imperative assignment in contemporary aggressive power demand market. Dearth of power generation in all dimensions of energy resources will result escalating in generation cost wants the optimal power dispatch at minimum fuel cost. Owing to the confined optimum convergence, the predictable optimization methods are not proficient to crack such problems. Evolutionary optimization techniques are proved to be superior to the conventional techniques to solve ELD problems. Differential Evolution Algorithm (DEA) is one of the foremost and recent evolutionary techniques in modern optimization state of affairs. The application of DEA in multi directional ELD problem has been technologically summarized in this paper.

Suggested Citation

  • Jebaraj, Luke & Venkatesan, Chakkaravarthy & Soubache, Irisappane & Rajan, Charles Christober Asir, 2017. "Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1206-1220.
    • Handle: RePEc:eee:rensus:v:77:y:2017:i:c:p:1206-1220
    DOI: 10.1016/j.rser.2017.03.097
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117304331
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2017.03.097?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. Coelho, Leandro dos Santos & Souza, Rodrigo Clemente Thom & Mariani, Viviana Cocco, 2009. "Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3136-3147.
    2. 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.
    3. Archana Gupta & Shashwati Ray, 2011. "Interval-based differential evolution approach for combined economic emission load dispatch," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 5(3/4), pages 270-284.
    4. Yuan, Xiaohui & Yuan, Yanbin & Zhang, Yongchuan, 2002. "A hybrid chaotic genetic algorithm for short-term hydro system scheduling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(4), pages 319-327.
    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. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    2. Yu, Xiaobing & Duan, Yuchen & Luo, Wenguan, 2022. "A knee-guided algorithm to solve multi-objective economic emission dispatch problem," Energy, Elsevier, vol. 259(C).
    3. Karar Mahmoud & Mohamed Abdel-Nasser & Eman Mustafa & Ziad M. Ali, 2020. "Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems," Sustainability, MDPI, vol. 12(2), pages 1-21, January.
    4. Gerardo J. Osório & Miadreza Shafie-khah & Juan M. Lujano-Rojas & João P. S. Catalão, 2018. "Scheduling Model for Renewable Energy Sources Integration in an Insular Power System," Energies, MDPI, vol. 11(1), pages 1-16, January.
    5. Lai, Wenhao & Song, Qi & Zheng, Xiaoliang & Tao, Qiong & Chen, Hualiang, 2023. "A new version of membrane search algorithm for hybrid renewable energy systems dynamic scheduling," Renewable Energy, Elsevier, vol. 209(C), pages 262-276.
    6. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    7. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Raheela Jamal & Baohui Men & Noor Habib Khan & Muhammad Asif Zahoor Raja, 2019. "Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind," Energies, MDPI, vol. 12(13), pages 1-23, July.
    9. Fatima Zahra Harmouch & Ahmed F. Ebrahim & Mohammad Mahmoudian Esfahani & Nissrine Krami & Nabil Hmina & Osama A. Mohammed, 2019. "An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm," Energies, MDPI, vol. 12(15), pages 1-23, August.

    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. Zhang, Huifeng & Yue, Dong & Xie, Xiangpeng & Dou, Chunxia & Sun, Feng, 2017. "Gradient decent based multi-objective cultural differential evolution for short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic power," Energy, Elsevier, vol. 122(C), pages 748-766.
    2. 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.
    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.
    4. Fang-Fang Li & Jia-Hua Wei & Xu-Dong Fu & Xin-Yu Wan, 2012. "An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4073-4090, November.
    5. 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.
    6. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    7. 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.
    8. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    9. Alizadeh, Somayeh & Ghazanfari, Mehdi, 2009. "Learning FCM by chaotic simulated annealing," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1182-1190.
    10. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    11. R. Arunkumar & V. Jothiprakash, 2013. "Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5207-5222, December.
    12. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Jalili, Mahdi, 2023. "Optimization of integrated load dispatch in multi-fueled renewable rich power systems using fractal firefly algorithm," Energy, Elsevier, vol. 278(PA).
    13. Rajakumar Ramalingam & Dinesh Karunanidy & Sultan S. Alshamrani & Mamoon Rashid & Swamidoss Mathumohan & Ankur Dumka, 2022. "Oppositional Pigeon-Inspired Optimizer for Solving the Non-Convex Economic Load Dispatch Problem in Power Systems," Mathematics, MDPI, vol. 10(18), pages 1-24, September.
    14. Coelho, Leandro dos Santos & Souza, Rodrigo Clemente Thom & Mariani, Viviana Cocco, 2009. "Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3136-3147.
    15. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2018. "Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling," Energy, Elsevier, vol. 147(C), pages 59-80.
    16. Mahmoudabadi, Abbas & Seyedhosseini, Seyed Mohammad, 2014. "Solving Hazmat Routing Problem in chaotic damage severity network under emergency environment," Transport Policy, Elsevier, vol. 36(C), pages 34-45.
    17. Ramon Abritta & Frederico Panoeiro & Leonardo Honório & Ivo Silva Junior & André Marcato & Anapaula Guimarães, 2020. "Hydroelectric Operation Optimization and Unexpected Spillage Indications," Energies, MDPI, vol. 13(20), pages 1-20, October.
    18. 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).
    19. Thang Trung Nguyen & Dieu Ngoc Vo & Hai Van Tran & Le Van Dai, 2019. "Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm," Complexity, Hindawi, vol. 2019, pages 1-28, May.
    20. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.

    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:eee:rensus:v:77:y:2017:i:c:p:1206-1220. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.