# Optimal electric vehicle charging stations and distributed generation placement by partitioning the distribution network using the modified newman fast algorithm

## TL;DR

This paper presents a new framework for optimally placing electric vehicle charging stations and distributed generation units in power networks to improve efficiency and stability.

## Contribution

A novel framework using a modified Newman Fast Algorithm with Electrical Coupling Strength to partition networks into Virtual Microgrids for optimized resource allocation.

## Key findings

- The framework reduced active power losses by 82% in the 33-bus network and improved voltage magnitudes and stability indices.
- PO outperformed other algorithms in convergence speed, making it suitable for large-scale optimization tasks.
- The approach showed significant improvements in voltage profiles and stability indices for both 33-bus and 118-bus networks.

## Abstract

This article introduces a novel and numerically validated framework for the well-optimized placement and capacity selection of Distributed Generation (DG) units and Electric Vehicle Charging Stations (EV-CSs) in power distribution networks (PDNs). The methodology employs a Modified Newman Fast Algorithm (NFA) enhanced with Electrical Coupling Strength (ECS) to partition the network into electrically cohesive Virtual Microgrids (VMs). Within each VM, resources are optimally allocated using two recent metaheuristic techniques: the Starfish Optimization (SFO) and the Puma Optimization (PO) methods and compared against the conventional Particle Swarm Optimization (PSO) approach. Each approach is executed for 500 iterations with 30 search agents. The discussed framework is tested on the IEEE 33-bus and IEEE 118-bus PDNs. For the 33-bus PDN, the approach minimized active power losses by approximately 82%, improved the lowest bus voltage magnitude from 0.8361 p.u to 0.979 p.u, and increased the Stability Index (SI) from 0.6256 p.u to 0.927 p.u. For the 118-bus network, real-power losses were decreased by 68–69%, with notable enhancements in both voltage profile and SI. Additionally, PO demonstrated the fastest convergence speed among the tested algorithms, confirming its suitability for large-scale optimization. The study results demonstrate the effectiveness of the presented VM-based co-allocation strategy in enhancing power system performance and scalability, with future work focusing on cost-aware multi-objective optimization and real-world deployment in Egyptian PDNs.