Optimal Control Method for Microgrid Distributed Generation Based on Multi-Agent Adaptive Decision-Making

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Microgrids face challenges with fluctuating power generation, variable loads, and dynamic topology.
• Existing distributed control methods struggle with complex operational scenarios.

Purpose of the Study:

• To propose an optimal control methodology for distributed generation in microgrids.
• To enhance microgrid dynamic adaptability, operational safety, and economic performance.

Main Methods:

• Developed a multi-agent adaptive decision-making framework for distributed generation control.
• Utilized a topology correlation matrix for online learning and optimization.
• Implemented a safety optimization model based on barrier Lyapunov functions for action verification.
• Employed an enhanced execution control module with adaptive ramp rate limitations.

Main Results:

• Maintained voltage fluctuations within 1.6 V during load fluctuations and topology reconfiguration.
• Achieved frequency recovery within 0.01 seconds.
• Demonstrated rapid power sharing response with minimal steady-state error.
• Improved system operational economy by approximately 8.2% compared to conventional methods.

Conclusions:

• The proposed multi-agent adaptive control method effectively addresses microgrid operational challenges.
• The methodology significantly enhances dynamic adaptability, safety, and economic efficiency.
• Validated through simulation and experimental studies in complex microgrid scenarios.