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Environmental economic dispatch optimization using niching penalized chimp algorithm

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  • Zhang, Le
  • Khishe, Mohammad
  • Mohammadi, Mokhtar
  • Mohammed, Adil Hussein

Abstract

The use of traditional fossil fuels has contributed to rapid economic expansion while simultaneously having negative implications, such as increasing global warming and the devastation of the biosphere. Niching Penalized Chimp Optimization (NPChOA) is presented in this research in order to address the Environmental Economic Dispatch (EED) problem. In terms of global search capability, robustness, convergence rate, and durability, the proposed NPChOA outperforms the current ChOA. Following that, a novel constrained handling operator addresses the multi-objective optimization difficulty. The performance of NPChOA is evaluated using an IEEE 30 bus with six generators and a ten-unit system. The result of NPChOA is compared with Grey Wolf Optimizer (GWO), Space Reduction Strategy Particle Swarm Optimization (SRSPSO), Chaotic Biogeography-Based Optimizer (CBBO), Dynamic Population Artificial Bee Colony (DPABC), Modified Bacterial Foraging Algorithm (MBFA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), standard ChOA, Dynamic Levy Flight ChOA (DLF-ChOA), and Weighted ChOA (WChOA) as the most recent modified version of ChOA to confirm its efficiency. NPChOA's evaluation score and convergence rate are outstanding compared to other benchmarks for single and multi-objective optimizations. The NPChOA's efficacy and robustness in dealing with environmental economic dispatch challenges have been demonstrated by discovering a good compromise value.

Suggested Citation

  • Zhang, Le & Khishe, Mohammad & Mohammadi, Mokhtar & Mohammed, Adil Hussein, 2022. "Environmental economic dispatch optimization using niching penalized chimp algorithm," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021454
    DOI: 10.1016/j.energy.2022.125259
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    References listed on IDEAS

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    1. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    2. Elattar, Ehab E., 2019. "Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm," Energy, Elsevier, vol. 171(C), pages 256-269.
    3. 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.
    4. Cui, Da & Yin, Helin & Liu, Yupeng & Li, Ji & Pan, Shuo & Wang, Qing, 2022. "Effect of final pyrolysis temperature on the composition and structure of shale oil: Synergistic use of multiple analysis and testing methods," Energy, Elsevier, vol. 252(C).
    5. 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.
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    Cited by:

    1. Shin, Hansol & Kim, Wook, 2023. "Comparison of the centralized and decentralized environmentally constrained economic dispatch methods of coal-fired generators: A case study for South Korea," Energy, Elsevier, vol. 275(C).
    2. SeyedGarmroudi, SeyedDavoud & Kayakutlu, Gulgun & Kayalica, M. Ozgur & Çolak, Üner, 2024. "Improved Pelican optimization algorithm for solving load dispatch problems," Energy, Elsevier, vol. 289(C).

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