IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4554-d1165270.html
   My bibliography  Save this article

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

Author

Listed:
  • Abdulaziz Almalaq

    (Department of Electrical Engineering, College of Engineering, University of Hail, Hail 2240, Saudi Arabia)

  • Tawfik Guesmi

    (Department of Electrical Engineering, College of Engineering, University of Hail, Hail 2240, Saudi Arabia)

  • Saleh Albadran

    (Department of Electrical Engineering, College of Engineering, University of Hail, Hail 2240, Saudi Arabia)

Abstract

The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

Suggested Citation

  • Abdulaziz Almalaq & Tawfik Guesmi & Saleh Albadran, 2023. "A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem," Energies, MDPI, vol. 16(12), pages 1-34, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4554-:d:1165270
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4554/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4554/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.
    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. Ghasemi, Mojtaba & Aghaei, Jamshid & Akbari, Ebrahim & Ghavidel, Sahand & Li, Li, 2016. "A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems," Energy, Elsevier, vol. 107(C), pages 182-195.
    4. 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.
    5. Mohammed A. El-Shorbagy & Islam M. Eldesoky & Mohamady M. Basyouni & Islam Nassar & Adel M. El-Refaey, 2022. "Chaotic Search-Based Salp Swarm Algorithm for Dealing with System of Nonlinear Equations and Power System Applications," Mathematics, MDPI, vol. 10(9), pages 1-30, April.
    6. Xinlin Xu & Zhongbo Hu & Qinghua Su & Zenggang Xiong, 2018. "Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem," Complexity, Hindawi, vol. 2018, pages 1-20, November.
    7. Yuanyuan Wang & Zexu Yu & Zhenhai Dou & Mengmeng Qiao & Ye Zhao & Ruishuo Xie & Lianxin Liu, 2022. "Decentralized Coordination Dispatch Model Based on Chaotic Mutation Harris Hawks Optimization Algorithm," Energies, MDPI, vol. 15(10), pages 1-26, May.
    8. Qasim M. Zainel & Saad M. Darwish & Murad B. Khorsheed, 2022. "Employing Quantum Fruit Fly Optimization Algorithm for Solving Three-Dimensional Chaotic Equations," Mathematics, MDPI, vol. 10(21), pages 1-21, November.
    9. Ali S. Alghamdi, 2022. "A New Self-Adaptive Teaching–Learning-Based Optimization with Different Distributions for Optimal Reactive Power Control in Power Networks," Energies, MDPI, vol. 15(8), pages 1-24, April.
    10. Hongyue Li & Xihuai Wang & Jianmei Xiao, 2018. "Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems," Energies, MDPI, vol. 11(7), pages 1-19, June.
    11. Gherbi, Yamina Ahlem & Bouzeboudja, Hamid & Gherbi, Fatima Zohra, 2016. "The combined economic environmental dispatch using new hybrid metaheuristic," Energy, Elsevier, vol. 115(P1), pages 468-477.
    12. Cheng, Yu-Shan & Chuang, Man-Tsai & Liu, Yi-Hua & Wang, Shun-Chung & Yang, Zong-Zhen, 2016. "A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint," Renewable Energy, Elsevier, vol. 88(C), pages 58-72.
    13. Audet, Charles & Bigeon, Jean & Cartier, Dominique & Le Digabel, Sébastien & Salomon, Ludovic, 2021. "Performance indicators in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 292(2), pages 397-422.
    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. Sile Hu & Yuan Gao & Yuan Wang & Yuan Yu & Yue Bi & Linfeng Cao & Muhammad Farhan Khan & Jiaqiang Yang, 2024. "Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm," Energies, MDPI, vol. 17(5), pages 1-14, February.
    2. Xinhua Gao & Song Liu & Shan Jiang & Dennis Yu & Yong Peng & Xianting Ma & Wenting Lin, 2024. "Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable," Mathematics, MDPI, vol. 12(7), pages 1-27, March.

    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. Iman Ahmadianfar & Saeed Noshadian & Nadir Ahmed Elagib & Meysam Salarijazi, 2021. "Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3513-3538, September.
    2. Zhang, Xian & Wang, Huaizhi & Peng, Jian-chun & Liu, Yitao & Wang, Guibin & Jiang, Hui, 2018. "GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration," Energy, Elsevier, vol. 144(C), pages 404-419.
    3. Lin, Jian & Wang, Zhou-Jing, 2019. "Multi-area economic dispatch using an improved stochastic fractal search algorithm," Energy, Elsevier, vol. 166(C), pages 47-58.
    4. Zekai Xu & Jinghan He & Zhao Liu & Zhiyi Zhao, 2023. "Collaborative Optimization of Transmission and Distribution Considering Energy Storage Systems on Both Sides of Transmission and Distribution," Energies, MDPI, vol. 16(13), pages 1-23, July.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Jean Bigeon & Sébastien Le Digabel & Ludovic Salomon, 2021. "DMulti-MADS: mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization," Computational Optimization and Applications, Springer, vol. 79(2), pages 301-338, June.
    10. 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.
    11. Xin-gang, Zhao & Ze-qi, Zhang & Yi-min, Xie & Jin, Meng, 2020. "Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization," Energy, Elsevier, vol. 195(C).
    12. Hossein Lotfi, 2022. "A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    13. Ana Fernández-Guillamón & Antonio Vigueras-Rodríguez & Emilio Gómez-Lázaro & Ángel Molina-García, 2018. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    14. Gholamreza Shojatalab & Seyed Hadi Nasseri & Iraj Mahdavi, 2023. "New multi-objective optimization model for tourism systems with fuzzy data and new approach developed epsilon constraint method," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1360-1385, September.
    15. Chen, Xu & Tang, Guowei, 2022. "Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm," Energy, Elsevier, vol. 238(PC).
    16. Sharifian, Yeganeh & Abdi, Hamdi, 2023. "Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm," Energy, Elsevier, vol. 267(C).
    17. Sukumar, Shivashankar & Mokhlis, Hazlie & Mekhilef, Saad & Naidu, Kanendra & Karimi, Mazaher, 2017. "Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid," Energy, Elsevier, vol. 118(C), pages 1322-1333.
    18. Lin, Chenhao & Liang, Huijun & Pang, Aokang, 2023. "A fast data-driven optimization method of multi-area combined economic emission dispatch," Applied Energy, Elsevier, vol. 337(C).
    19. 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.
    20. Narimani, Hossein & Razavi, Seyed-Ehsan & Azizivahed, Ali & Naderi, Ehsan & Fathi, Mehdi & Ataei, Mohammad H. & Narimani, Mohammad Rasoul, 2018. "A multi-objective framework for multi-area economic emission dispatch," Energy, Elsevier, vol. 154(C), pages 126-142.

    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:gam:jeners:v:16:y:2023:i:12:p:4554-:d:1165270. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.