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Related Experiment Video

Updated: Jun 10, 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

Coordinated voltage control in renewable energy integrated power systems using ant colony optimization.

K Durgadevi¹, A Murugesan², Viharika Chaudhari³

¹ECE Department, SRM Valliammai Engineering College, Chennai, India. durgadevik.ece@srmvalliammai.ac.in.

Scientific Reports

|June 8, 2026

Summary

This study introduces a hybrid Ant Colony Optimization (ACO) and Deep Q-Network (DQN) controller for coordinated voltage control in renewable energy grids. The novel framework enhances voltage stability and reduces losses in modern power systems.

Keywords:

Ant colony optimization Deep Q-networks Grid optimization and grid resilience Renewable energy integration Sustainable energy Voltage stability

Related Experiment Videos

Last Updated: Jun 10, 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
Power Systems Engineering
Artificial Intelligence in Energy

Background:

Modern power systems face challenges integrating high shares of renewable energy sources (RES).
Effective voltage control is crucial for maintaining grid stability and operational efficiency with RES integration.
Existing control strategies may struggle with the dynamic and intermittent nature of renewable generation.

Purpose of the Study:

To develop a novel Coordinated Voltage Control (CVC) framework for RES-integrated grids.
To combine Ant Colony Optimization (ACO) for voltage profile optimization with a Deep Q-Network (DQN) controller for real-time reactive power adjustment.
To enhance voltage stability, reduce system losses, and improve the computational efficiency of voltage regulation.

Main Methods:

A hybrid approach integrating ACO and DQN for coordinated voltage control.
ACO optimizes steady-state voltage profiles.
DQN provides real-time reactive power adjustments based on historical and simulated grid data.

Main Results:

Significant improvements in voltage stability across various renewable generation and load scenarios.
Demonstrated reduction in overall system power losses.
The ACO-DQN framework achieved computational efficiency, converging in approximately 45-50 iterations.
The controller showed effective prediction of optimal reactive power actions for scalable voltage regulation.

Conclusions:

The proposed ACO-DQN framework offers a practical and flexible solution for voltage control in renewable-rich power systems.
This hybrid approach enhances grid stability and operational efficiency.
The method provides a reliable and scalable approach to voltage regulation in the face of renewable energy integration challenges.