A Modified Frilled Lizard Optimizer for simultaneous allocation of DG, capacitor, and reconfiguration in the presence of EV stations in distribution systems

The increasing penetration of Distributed Generation (DG), Capacitor Banks (CBs), and Electric Vehicles (EVs) introduces significant operational challenges in Distribution Systems (DSs) due to changing power flows, voltage variations, and uncertain charging demand. This paper proposes a Modified Frilled Lizard Optimizer (MFLO) for the coordinated optimization of DG allocation, CB placement, and distribution system reconfiguration (DSR) while considering EV charging demand. MFLO enhances the original Frilled Lizard Optimizer (FLO) through a defensive strategy phase and an adaptive local search phase, improving population diversity, exploration–exploitation balance, and local refinement. EV charging uncertainty is modeled to capture the stochastic characteristics of EV demand, while the coordinated DG–CB–DSR planning is designed considering the worst-case EV loading condition to ensure adequate network support under high-demand scenarios. The proposed framework is evaluated on IEEE 69-bus and IEEE 141-bus distribution systems under different operating conditions. In addition to the main optimization cases, EV penetration levels from 0 to 100% and load-growth levels from 2.5% to 15% are investigated to assess robustness under changing demand conditions. The results demonstrate substantial power-loss reduction while maintaining acceptable voltage profiles and show that the optimal DG, CB, and DSR configurations adapt to variations in EV demand and load growth. Furthermore, MFLO is comprehensively benchmarked against ten established and recent metaheuristic algorithms using several independent runs. MFLO achieves the lowest mean loss and coefficient of variation, with statistical tests confirming significant superiority over seven competing algorithms. The proposed framework therefore provides an effective and robust approach for coordinated planning and operation of modern distribution networks with increasing EV penetration.

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Publication Details

Journal
Scientific Reports
Published
2026-10-06
DOI
https://doi.org/10.1038/s41598-026-71239-1
Primary Topic
Optimal Power Flow Distribution
Type
article
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article

A Modified Frilled Lizard Optimizer for simultaneous allocation of DG, capacitor, and reconfiguration in the presence of EV stations in distribution systems

Ali S. Aljumah, Mohammed H. Alqahtani, Ahmed R. S. Ginidi, Abdullah M. Shaheen
Scientific Reports
Optimal Power Flow Distribution
article

A Modified Frilled Lizard Optimizer for simultaneous allocation of DG, capacitor, and reconfiguration in the presence of EV stations in distribution systems

Ali S. Aljumah, Mohammed H. Alqahtani, Ahmed R. S. Ginidi, Abdullah M. Shaheen
article en

Abstract

The increasing penetration of Distributed Generation (DG), Capacitor Banks (CBs), and Electric Vehicles (EVs) introduces significant operational challenges in Distribution Systems (DSs) due to changing power flows, voltage variations, and uncertain charging demand. This paper proposes a Modified Frilled Lizard Optimizer (MFLO) for the coordinated optimization of DG allocation, CB placement, and distribution system reconfiguration (DSR) while considering EV charging demand. MFLO enhances the original Frilled Lizard Optimizer (FLO) through a defensive strategy phase and an adaptive local search phase, improving population diversity, exploration–exploitation balance, and local refinement. EV charging uncertainty is modeled to capture the stochastic characteristics of EV demand, while the coordinated DG–CB–DSR planning is designed considering the worst-case EV loading condition to ensure adequate network support under high-demand scenarios. The proposed framework is evaluated on IEEE 69-bus and IEEE 141-bus distribution systems under different operating conditions. In addition to the main optimization cases, EV penetration levels from 0 to 100% and load-growth levels from 2.5% to 15% are investigated to assess robustness under changing demand conditions. The results demonstrate substantial power-loss reduction while maintaining acceptable voltage profiles and show that the optimal DG, CB, and DSR configurations adapt to variations in EV demand and load growth. Furthermore, MFLO is comprehensively benchmarked against ten established and recent metaheuristic algorithms using several independent runs. MFLO achieves the lowest mean loss and coefficient of variation, with statistical tests confirming significant superiority over seven competing algorithms. The proposed framework therefore provides an effective and robust approach for coordinated planning and operation of modern distribution networks with increasing EV penetration.

Scientific ReportsVol. 16(1)
Suez University (EG), Prince Sattam Bin Abdulaziz University (SA)
Openalex Percentile: Top 22%
Optimal Power Flow Distribution
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