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A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units

Author

Listed:
  • Ragab El-Sehiemy

    (Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Abdullah Shaheen

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ahmed Ginidi

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Mostafa Elhosseini

    (Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
    College of Computer Science and Engineering, Taibah University, Yanbu 46411, Saudi Arabia)

Abstract

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7603-:d:942679
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    References listed on IDEAS

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    1. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    2. 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.
    3. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    4. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    5. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    6. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & Elattar, Ehab E., 2021. "Optimal economic power and heat dispatch in Cogeneration Systems including wind power," Energy, Elsevier, vol. 225(C).
    7. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    8. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Ginidi, Ahmed R., 2022. "An improved heap optimization algorithm for efficient energy management based optimal power flow model," Energy, Elsevier, vol. 250(C).
    9. Lianming Li & Defeng He & Jianrong Jin & Baoyun Yu & Xiang Gao, 2021. "Multi-Objective Load Dispatch Control of Biomass Heat and Power Cogeneration Based on Economic Model Predictive Control," Energies, MDPI, vol. 14(3), pages 1-13, February.
    10. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Lucas do Carmo Yamaguti & Juan Manuel Home-Ortiz & Mahdi Pourakbari-Kasmaei & José Roberto Sanches Mantovani, 2023. "Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation," Energies, MDPI, vol. 16(12), pages 1-21, June.
    2. 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.
    3. 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.
    4. Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.
    5. Araby Mahdy & Abdullah Shaheen & Ragab El-Sehiemy & Ahmed Ginidi & Saad F. Al-Gahtani, 2023. "Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor," Energies, MDPI, vol. 16(5), pages 1-27, March.

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