What question did this study set out to answer?

This research aims to enhance the placement of electric vehicle fast charging stations and distributed generation units using an improved algorithm.

April 23, 2026

Random opposition flow direction algorithm with Lévy distribution for optimum placement of EV fast charging stations and distributed generation

Key Points

This research aims to enhance the placement of electric vehicle fast charging stations and distributed generation units using an improved algorithm.
Developed the ROFDA algorithm integrating Lévy distribution and random opposition-based learning.
Tested the algorithm on 23 benchmark functions and applied it to 33- and 69-bus distribution systems.
Compared performance with traditional algorithms such as FDA, OFDA, and MFDA.
ROFDA significantly outperformed traditional FDA methods.
Achieved over 70% reduction in power losses in systems with multiple EV fast charging stations of varying capacities.
Maintained optimal voltage profiles during optimization.

Abstract

This article presents an improved flow direction algorithm (FDA) called ROFDA, which integrates Lévy distribution and random opposition-based learning (ROBL) to enhance performance. The Lévy distribution refines the flow velocity vector, enabling more accurate movement toward optimal solutions through flexible and random velocity prediction. ROBL further strengthens the decision-making process by enhancing exploration and convergence. ROFDA is tested on 23 standard benchmark functions and applied to optimise the placement of electric vehicle fast charging stations (EVFCSs) and distributed generation (DG) units within 33- and 69-bus distribution systems (DS). Results show that ROFDA outperforms traditional FDA, OFDA, and MFDA methods. In systems with multiple EVFCSs of varying capacities and locations, it achieves over 70% reduction in power losses in some cases, while maintaining optimal voltage profiles. These improvements confirm ROFDA's effectiveness for ensuring reliable and efficient power delivery, making it a promising tool for EVFCS and DG planning.

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