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Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm

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  • Elattar, Ehab E.

Abstract

In this paper, a new version of shuffle frog leaping algorithm (SFLA) which called modified SFLA (MSFLA) is proposed. To drive the proposed method both local and global search mechanisms in the original SFLA are modified. The local search mechanism is modified by introducing the movement inertia equation of particle swarm optimization (PSO). Furthermore, the global search mechanism is modified using the crossover and mutation operators of genetic algorithm (GA). Also, the formulation of the combined heat, emission and economic dispatch (CHEED) problem considering the availability of wind and solar power is presented where the objective functions of the CHEED problem are converted into a single objective function using price penalty function. Then, the proposed MSFLA is employed to solve the problem of CHEED with wind and solar power. To show the effectiveness of the proposed MSFLA, different test systems with different scenarios are used. In addition, the scalability of the proposed MSFLA is tested using large-scale test systems. The results of the proposed MSFLA are compared with those obtained by other heuristic and non-heuristic published methods. The comparison with other methods proves the efficiency and the superiority of the proposed MSFLA over other methods in all scenarios.

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  • 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.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:256-269
    DOI: 10.1016/j.energy.2019.01.010
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    References listed on IDEAS

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    Cited by:

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    3. Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
    4. 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.
    5. Ahmed I. Omar & Ziad M. Ali & Mostafa Al-Gabalawy & Shady H. E. Abdel Aleem & Mujahed Al-Dhaifallah, 2020. "Multi-Objective Environmental Economic Dispatch of an Electricity System Considering Integrated Natural Gas Units and Variable Renewable Energy Sources," Mathematics, MDPI, vol. 8(7), pages 1-37, July.
    6. Zhang, Le & Khishe, Mohammad & Mohammadi, Mokhtar & Mohammed, Adil Hussein, 2022. "Environmental economic dispatch optimization using niching penalized chimp algorithm," Energy, Elsevier, vol. 261(PA).
    7. 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.
    8. 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).
    9. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
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    11. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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