Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement
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Line loss
Voltage profile

How to Cite

Adebisi, O. I., Ogundare, A. B., Olajuwon, B. J., & Adebeshin, A. (2024). Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 20(1), 133-150. Retrieved from


Power supply-demand imbalance is one of the critical challenges bedeviling the electricity grid operation of a third world nation such as Nigeria. This has consequently led to the system’s poor voltage profile with its associated high-power losses. Many potent solutions have been proposed to address these problems; however, technological advancements favour the adoption and widespread use of a class of economic and fast acting solid-state controllers called flexible alternating current transmission systems (FACTS). This study, therefore, assessed the potential of an optimally placed static var compensator (SVC) via genetic algorithm (GA) in enhancing the performance of the Nigerian 330 kV, 28-bus electricity grid. The static power flow model of the system with and without SVC was analyzed using Newton-Raphson method and simulated in MATLAB R2020a environment. The system performance with SVC placed optimally using GA was compared with the first principle approach. The simulation results revealed that before compensation, the test grid had five buses namely Ayede, New-Haven, Gombe, Kano and Makurdi with voltage magnitudes of 0.926, 1.058, 0.906, 0.859 and 0.944 p.u., respectively, violating the acceptable voltage tolerance limit of 0.95≤Vi≤1.05p.u. The first principle-based compensation improved the voltage magnitudes of the critical buses on the test grid to 1.015, 1.008, 0.958, 0.995 and 0.975 p.u., respectively, while compensation via GA enhanced the voltage magnitudes to 1.05, 0.985, 1.038, 1.016 and 0.996 p.u. respectively. The grid’s total active and reactive line losses reduced from 181.479 to 149.786 MW and 145.323 to 125.161 MVAr, respectively with compensation from the first principle approach, whereas the values were minimized to 115.675 MW and 109.336 MVAr, respectively with the GA based compensation. The GA based optimal placement of SVC exhibited better enhancement characteristics on the considered Nigerian electricity grid than the first principle method.

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