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

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.