Reduction of Power Losses and Voltage Profile Improvement in a Smart Grid Incorporated with Electric Vehicles

Mlungisi Ntombela () and Musasa Kabeya
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Mlungisi Ntombela: Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4000, South Africa
Musasa Kabeya: Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4000, South Africa

Sustainability, 2023, vol. 15, issue 13, 1-13

Abstract: Governments worldwide have adopted energy-saving policies out of concern for the planet. System efficiency and renewable energy are needed to reduce greenhouse gas emissions, which cause climate change. Electricity generation is the biggest polluter, followed by transportation. Electric vehicles would strain electricity infrastructure without technical solutions. This study uses a hybrid genetic algorithm particle sworn optimization (HGAPSO) to find the optimal switching and feeder reconfiguration approach. Meet transmission constraints while reducing real power losses and improving system bus voltage. In the context of power system change, what are the benefits of employing the HGAPSO approach as opposed to the GA method and the PSO method, respectively. HGAPSO increases network power losses and voltage dispersion. Improved algorithms can help solve this crucial issue. It uses many heuristic optimization techniques to reconfigure transmission network connectivity and determine the best configuration. Limit bus voltage changes while maintaining the system’s radial structure and lowering power usage. MATLAB’s IEEE 33-bus communication network evaluated procedure reliability and performance. The results show that the proposed method reduces power waste during standalone runs and speeds up processing.

Keywords: electric vehicles; reduction of power loss; voltage profile improvement; optimization algorithms; power network optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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