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A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems

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  • El Sehiemy, Ragab A.
  • Selim, F.
  • Bentouati, Bachir
  • Abido, M.A.

Abstract

Optimal Power Flow (OPF) problem is one of the most widely nonlinear optimization problems in power systems. This paper proposes a novel hybrid optimization algorithm that combines the merits of salp swarm optimization (SSO) algorithm with particle swarm optimization (PSO) algorithm for solving the OPF problem. The proposed hybrid method is considered to accomplish economic, environmental and technical benefits. The proposed method is applied to single and multi-objective optimization problems with different objective functions such as generation cost minimization, emission reduction, transmission power loss minimization, voltage profile improvement, and voltage stability enhancement. To prove the capability of the proposed hybrid optimization algorithm, 18 case studies are employed and tested on three standard test systems. The proposed PSO–SSO algorithm achieves significantly the effectiveness and robustness of the OPF results for the cases considered. The simulation results demonstrate that the proposed method leads to superior levels of techno-economic-environmental benefits compared with those reported in the literature. In addition, the sensitivity analysis study confirms that the proposed hybrid method produces robust results against parameter variations.

Suggested Citation

  • El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219325125
    DOI: 10.1016/j.energy.2019.116817
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    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. Shahenda Sarhan & Ragab El-Sehiemy & Amlak Abaza & Mona Gafar, 2022. "Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    7. Ali S. Alghamdi, 2022. "Optimal Power Flow in Wind–Photovoltaic Energy Regulation Systems Using a Modified Turbulent Water Flow-Based Optimization," Sustainability, MDPI, vol. 14(24), pages 1-27, December.
    8. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    9. Yin, Linfei & Zhao, Lulin, 2021. "Rejectable deep differential dynamic programming for real-time integrated generation dispatch and control of micro-grids," Energy, Elsevier, vol. 225(C).
    10. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    11. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    12. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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