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Related Experiment Video

Updated: Apr 30, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

New classification-based global optimization approach for sustainable active power distribution networks.

Rasha Elazab¹, Abdelazim Salem²

¹Faculty of Engineering, Capital University, (Formerly: Helwan University), Cairo, Egypt. r_m_elazab@h-eng.helwan.edu.eg.

Scientific Reports

|April 28, 2026

Summary

This study introduces a Classification-based Global Optimization (CGO) method to reduce power loss and voltage drops in active distribution networks by optimizing distributed generation (DG) and capacitor banks (CBs). The approach significantly cuts energy losses and enhances grid stability.

Keywords:

Active Power Distribution Networks Capacitor Bank (CB)Distributed Generation (DG)Power Loss Minimization Radial Distribution Systems Sustainable Development Goals (SDG)Sustainable Power Systems Voltage Stability

Related Experiment Videos

Last Updated: Apr 30, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

Electrical Engineering
Optimization Theory
Power Systems Analysis

Background:

Active power distribution networks face challenges with voltage drops and power losses due to radial topology and unidirectional power flow.
Integration of distributed energy resources (DERs) and advanced optimization is crucial for grid flexibility and efficiency.
Conventional metaheuristic methods often lack interpretability and structured optimization frameworks.

Purpose of the Study:

To propose a novel Classification-based Global Optimization (CGO) approach for optimal placement and sizing of distributed generation (DG) and capacitor banks (CBs) in active distribution networks.
To enhance voltage stability and reduce active power losses in radial distribution systems.
To provide a computationally efficient and interpretable optimization framework.

Main Methods:

The Classification-based Global Optimization (CGO) approach classifies distribution buses based on voltage sensitivity and power flow characteristics.
A deterministic global optimization function is applied for optimal DG and CB placement and sizing.
The methodology is validated on IEEE 33-bus and IEEE 69-bus test systems.

Main Results:

Achieved a 94.75% reduction in active power losses for the IEEE 33-bus system and 98.061% for the IEEE 69-bus system with simultaneous DG and CB integration.
Significantly improved voltage stability, with the voltage stability index (VSI) reaching 0.9740 (33-bus) and 0.9773 (69-bus).
Demonstrated superior computational efficiency with average simulation times of 18.62 s (33-bus) and 21.45 s (69-bus).

Conclusions:

The proposed CGO approach offers a scalable and interpretable solution for optimizing active distribution networks.
The method effectively reduces energy losses and enhances voltage stability, supporting renewable energy integration.
CGO provides a structured alternative to conventional metaheuristic methods, improving computational efficiency and network performance.