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

Bakry, Omima; Dardeer, Mostafa; Senjyu, Tomonobu; Alkhalaf, Salem

doi:10.21608/aujst.2021.226489

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

Document Type : Original papers

Authors

¹ Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

² Electrical Engineering Department, Faculty of Engineering, Ryukyus University, Japan

³ Computer Sciences Department, Faculty of Arts and Sciences, Qassim University, Saudi Arabia

10.21608/aujst.2021.226489

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