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Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Author

Listed:
  • Hossein Nourianfar

    (Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran)

  • Hamdi Abdi

    (Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran)

Abstract
This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3173-:d:766623
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    References listed on IDEAS

    as
    1. 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.
    2. Mi Li & Huan Chen & Xiaodong Wang & Ning Zhong & Shengfu Lu, 2019. "An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 833-866, May.
    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. Xiaobing Yu & Xianrui Yu & Yiqun Lu & Jichuan Sheng, 2018. "Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    5. 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.
    6. Xiong, Guojiang & Shuai, Maohang & Hu, Xiao, 2022. "Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization," Energy, Elsevier, vol. 244(PB).
    7. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    8. 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.
    9. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2020. "An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    10. Yang, Qiangda & Liu, Peng & Zhang, Jie & Dong, Ning, 2022. "Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation," Applied Energy, Elsevier, vol. 307(C).
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    Cited by:

    1. 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.
    2. P. S. Bhullar & J. S. Dhillon & R. K. Garg, 2024. "Crisscross Team Game Algorithm for Economic-Emission Power Dispatch Problem with Multiple Fuel Options," SN Operations Research Forum, Springer, vol. 5(2), pages 1-60, June.

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