Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks
Bakry, O., Dardeer, M., Senjyu, T., Alkhalaf, S. (2021). Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks. EKB Journal Management System, 1(2), 34-69. doi: 10.21608/aujst.2021.226489
Omima Bakry; Mostafa Dardeer; Tomonobu Senjyu; Salem Alkhalaf. "Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks". EKB Journal Management System, 1, 2, 2021, 34-69. doi: 10.21608/aujst.2021.226489
Bakry, O., Dardeer, M., Senjyu, T., Alkhalaf, S. (2021). 'Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks', EKB Journal Management System, 1(2), pp. 34-69. doi: 10.21608/aujst.2021.226489
Bakry, O., Dardeer, M., Senjyu, T., Alkhalaf, S. Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks. EKB Journal Management System, 2021; 1(2): 34-69. doi: 10.21608/aujst.2021.226489

Multi-Objective Hybrid Genetic Algorithms and Equilibrium Optimizer GAEO to Integrate Renewable Energy Sources with Distribution Networks

Aswan University Journal of Sciences and Technology
Volume 1, Issue 2, December 2021, Page 34-69  XML PDF (3.64 MB)
Document Type: Original papers
DOI: 10.21608/aujst.2021.226489
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Authors
Omima Bakry email 1; Mostafa Dardeer2; Tomonobu Senjyu3; Salem Alkhalaf4
1Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt
2Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt
3Electrical Engineering Department, Faculty of Engineering, Ryukyus University, Japan
4Computer Sciences Department, Faculty of Arts and Sciences, Qassim University, Saudi Arabia
Abstract
This paper aims to implement the hybrid Genetic Algorithm Equilibrium Optimizer (GAEO) to enhance the overall performance of radial networks using renewable energy resources (RER) based multi-objective optimization. The GAEO is applied to determine the appropriate location, and capacity of RER unit to reduce the line losses, improve the voltage profile, fuel cost and reduce the pollution emission considering inequality constraints. The suggested hybrid GAEO is tested in three different networks with small, medium and large size. The test systems are IEEE-33 bus, IEEE-69 bus and IEEE-118 bus. A comparative study is performed to judge the accuracy of the proposed hybrid GAEO over GA and or EO in terms of fast conversions, and low RER unit capacity. The suggested RER systems are photovoltaic, fuel cell, and wind energy.
Keywords
distribution networks; Genetic Algorithms; Equilibrium optimizer; Renewable Energy Sources; Power loss minimization; Voltage profile; fuel cost minimization; pollutant emissions
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