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Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power

Author

Listed:
  • Dinu Calin Secui

    (Department of Energy Engineering, Faculty of Energy Engineering and Industrial Management, University of Oradea, 410058 Oradea, Romania)

  • Cristina Hora

    (Department of Energy Engineering, Faculty of Energy Engineering and Industrial Management, University of Oradea, 410058 Oradea, Romania)

  • Codruta Bendea

    (Department of Energy Engineering, Faculty of Energy Engineering and Industrial Management, University of Oradea, 410058 Oradea, Romania)

  • Monica Liana Secui

    (Department of Psychology, Faculty of Social-Humanistic Sciences, University of Oradea, 410058 Oradea, Romania)

  • Gabriel Bendea

    (Department of Energy Engineering, Faculty of Energy Engineering and Industrial Management, University of Oradea, 410058 Oradea, Romania)

  • Florin Ciprian Dan

    (Department of Energy Engineering, Faculty of Energy Engineering and Industrial Management, University of Oradea, 410058 Oradea, Romania)

Abstract

Economic dispatch, emission dispatch, or their combination (EcD, EmD, EED) are essential issues in power systems optimization that focus on optimizing the efficient and sustainable use of energy resources to meet power demand. A new algorithm is proposed in this article to solve the dispatch problems with/without considering wind units. It is based on the Social Group Optimization (SGO) algorithm, but some features related to the selection and update of heuristics used to generate new solutions are changed. By applying the highly disruptive polynomial operator (HDP) and by generating sequences of random and chaotic numbers, the perturbation of the vectors composing the heuristics is achieved in our Modified Social Group Optimization (MSGO). Its effectiveness was investigated in 10-unit and 40-unit power systems, considering valve-point effects, transmission line losses, and inclusion of wind-based sources, implemented in four case studies. The results obtained for the 10-unit system indicate a very good MSGO performance, in terms of cost and emissions. The average cost reduction of MSGO compared to SGO is 368.1 $/h, 416.7 $/h, and 525.0 $/h for the 40-unit systems. The inclusion of wind units leads to 10% reduction in cost and 45% in emissions. Our modifications to MSGO lead to better convergence and higher-quality solutions than SGO or other competing algorithms.

Suggested Citation

  • Dinu Calin Secui & Cristina Hora & Codruta Bendea & Monica Liana Secui & Gabriel Bendea & Florin Ciprian Dan, 2024. "Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power," Sustainability, MDPI, vol. 16(1), pages 1-35, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:1:p:397-:d:1312044
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    References listed on IDEAS

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    1. Ahmed Ginidi & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ragab El-Sehiemy, 2021. "An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids," Mathematics, MDPI, vol. 9(17), pages 1-25, August.
    2. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    3. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).
    4. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
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