# Network reconfiguration and DG based compensation of Wolaita Sodo distribution system by using particle swarm optimisation

## TL;DR

This paper uses particle swarm optimization to improve voltage and reduce losses in a real Ethiopian distribution system by reconfiguring the network and placing distributed generators.

## Contribution

The study applies simultaneous network reconfiguration and DG placement using PSO to a real, unbalanced Ethiopian distribution system.

## Key findings

- Active power losses are reduced by 72.064% using the proposed method.
- Voltage deviation decreases from 24.63% to 4.5% with improved minimum bus voltage.
- Annual financial savings of 16.3956 million ETB are achieved with a six-year payback period.

## Abstract

Improving voltage profiles and reducing power losses are critical challenges in modern distribution systems, especially in real, unbalanced and weakly interconnected grids. This study presents a novel application of simultaneous network reconfiguration and distributed generation (DG) placement using Particle Swarm Optimization (PSO) to the Wolaita Sodo distribution network in Ethiopia. Unlike most prior works conducted on IEEE standard feeders, this research uses actual network topology, load data, conductor parameters and operational constraints from a real Ethiopian distribution system. The optimization problem considers radiality constraints, DG size limits and voltage and current limits, with objectives of minimizing active/reactive power losses, improving voltage profiles and maximizing economic benefits. A backward-forward sweep load flow method evaluates network performance under four scenarios: base case, reconfiguration only, DG placement only and simultaneous implementation. Results show that the proposed method reduces active power losses by 72.064%, decreases voltage deviation from 24.63% to 4.5% and improves the minimum bus voltage from 0.7537 pu to 0.9550 pu. The annual financial savings of 16.3956 million ETB yield a payback period of six years. This grouping of real-network data, simultaneous optimization, and integrated techno-economic analysis offers a practical and replicable decision-support tool for utilities in emerging power systems.