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Three powerful nature-inspired algorithms to optimize power flow in Algeria's Adrar power system

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  • Makhloufi, Saida
  • Mekhaldi, Abdelouahab
  • Teguar, Madjid

Abstract

This paper is intended to solve the optimal power flow (OPF) dispatch in the presence of wind power generation (WPG) in the Adrar power system. Towards this aim, the performances of three powerful meta-heuristic algorithms-namely, the cuckoo search algorithm (CSA), firefly algorithm (FFA), and flower pollination algorithm (FPA) are investigated. The proposed algorithms are applied to best capture the active power produced with the minimum value of a multi-objective function. This latter includes: the fuel cost, the NOx emissions, and the imbalance cost of the WPGs. Furthermore, considering the uncertainties governing wind resources, the maximum wind power output is estimated using the wind speed carrying maximum energy. It was found that all algorithms perform well in providing accurate solutions. Interestingly, the convergence is reached in the first 135 iterations. A remarkable outcome of the present work is that CSA outperforms FPA and FFA. CSA has proved itself to be a great tool to optimize Adrar's power flow system in term of iterations and computational time.

Suggested Citation

  • Makhloufi, Saida & Mekhaldi, Abdelouahab & Teguar, Madjid, 2016. "Three powerful nature-inspired algorithms to optimize power flow in Algeria's Adrar power system," Energy, Elsevier, vol. 116(P1), pages 1117-1130.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:1117-1130
    DOI: 10.1016/j.energy.2016.10.064
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    References listed on IDEAS

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    1. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    2. Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
    3. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    4. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    5. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    6. Wen, Jiang & Zheng, Yan & Donghan, Feng, 2009. "A review on reliability assessment for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2485-2494, December.
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    Citations

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    Cited by:

    1. Dongxiao Niu & Weibo Zhao & Si Li & Rongjun Chen, 2018. "Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
    2. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    3. Makhloufi, Saida & Khennas, Smail & Bouchaib, Sami & Arab, Amar Hadj, 2022. "Multi-objective cuckoo search algorithm for optimized pathways for 75 % renewable electricity mix by 2050 in Algeria," Renewable Energy, Elsevier, vol. 185(C), pages 1410-1424.
    4. Abubaker Younis & Fatima Belabbes & Petru Adrian Cotfas & Daniel Tudor Cotfas, 2024. "Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model," Forecasting, MDPI, vol. 6(2), pages 1-21, May.
    5. Daaou Nedjari, H. & Haddouche, S. Kheder & Balehouane, A. & Guerri, O., 2018. "Optimal windy sites in Algeria: Potential and perspectives," Energy, Elsevier, vol. 147(C), pages 1240-1255.

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