IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10614-d1187599.html
   My bibliography  Save this article

Presenting a Novel Evolutionary Method for Reserve Constrained Multi-Area Economic/Emission Dispatch Problem

Author

Listed:
  • Hossein Lotfi

    (Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar 96131, Iran)

  • Mohammad Hasan Nikkhah

    (Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar 96131, Iran)

Abstract

Economic dispatch (ED) attempts to find the most cost-effective combination of power generation units while meeting operational constraints. Another problem that can’t be resolved by standard economic dispatch problems is figuring out the method of generating dispatch that would be most cost-effective in meeting the local demand without exceeding the tie-line capacity. Making a trade-off between fuel costs and environmental concerns, a contentious problem in industrialized countries, seems essential. As a result, this study introduces a multi-objective approach for different ED problems, such as multi-area emission economic dispatch (MAEED) and reserve constrained multi-area emission economic dispatch (RCMAEED), when there are real-world restrictions present, like the valve point effect (VPE), prohibited operating zones (POZs), multi-fuel operation (MFO), and ramp-rate (RR) restrictions. In this study, the generation cost and emissions are taken into consideration as objective functions. Since the MAED problem in the power system is inherently nonlinear, adding the aforementioned restrictions makes the problem even more challenging. To address the complexity of the multi-objective optimization problem, the modified grasshopper optimization (MGO) algorithm, based on the chaos mechanism, is proposed in this paper. The proposed method has been tested on a four-area power system with sixteen electrical generators, and the results are contrasted with those of previous evolutionary techniques. Based on the results, it can be concluded that using the proposed MGO method to solve the MAED and RCMAED problems will result in generation costs that are around $300 and $600 less than using the MPSO and PSO methods, respectively. Also, the proposed MGO method has reduced emission levels by roughly 30% as compared to the GO method in order to solve the RCMAEED problem.

Suggested Citation

  • Hossein Lotfi & Mohammad Hasan Nikkhah, 2023. "Presenting a Novel Evolutionary Method for Reserve Constrained Multi-Area Economic/Emission Dispatch Problem," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10614-:d:1187599
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10614/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/10614/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Fesanghary, M. & Ardehali, M.M., 2009. "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem," Energy, Elsevier, vol. 34(6), pages 757-766.
    3. 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.
    4. 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.
    5. 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).
    6. Ragab El-Sehiemy & Abdullah Shaheen & Ahmed Ginidi & Mostafa Elhosseini, 2022. "A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units," Energies, MDPI, vol. 15(20), pages 1-22, October.
    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. Sharifian, Yeganeh & Abdi, Hamdi, 2024. "Multi-area economic dispatch problem: Methods, uncertainties, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    2. 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).
    3. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
    4. 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).
    5. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    6. Chen, Xu & Lu, Qi & Yuan, Ye & He, Kaixun, 2024. "A novel derivative search political optimization algorithm for multi-area economic dispatch incorporating renewable energy," Energy, Elsevier, vol. 300(C).
    7. Zhen Li & Wanmin Zhao & Miaoyao Nie, 2021. "Scale Characteristics and Optimization of Park Green Space in Megacities Based on the Fractal Measurement Model: A Case Study of Beijing, Shanghai, Guangzhou, and Shenzhen," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    8. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    9. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    10. Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    11. 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.
    12. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2009. "Multi-objective congestion management incorporating voltage and transient stabilities," Energy, Elsevier, vol. 34(9), pages 1401-1412.
    13. Huang, Yuqing & Lan, Hai & Hong, Ying-Yi & Wen, Shuli & Yin, He, 2019. "Optimal generation scheduling for a deep-water semi-submersible drilling platform with uncertain renewable power generation and loads," Energy, Elsevier, vol. 181(C), pages 897-907.
    14. Panigrahi, B.K. & Ravikumar Pandi, V. & Das, Sanjoy & Das, Swagatam, 2010. "Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem," Energy, Elsevier, vol. 35(12), pages 4761-4770.
    15. 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.
    16. Basu, M., 2023. "Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot," Energy, Elsevier, vol. 275(C).
    17. Xu Chen & Shuai Fang & Kangji Li, 2023. "Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch," Energies, MDPI, vol. 16(9), pages 1-23, April.
    18. 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.
    19. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).
    20. Zhang, Heng & Hu, Xiao & Cheng, Haozhong & Zhang, Shenxi & Hong, Shaoyun & Gu, Qingfa, 2021. "Coordinated scheduling of generators and tie lines in multi-area power systems under wind energy uncertainty," Energy, Elsevier, vol. 222(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:gam:jsusta:v:15:y:2023:i:13:p:10614-:d:1187599. 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.