Dynamic Leader Election and Model-Free Reinforcement Learning for Coordinated Voltage and Reactive Power Containment Control in Offshore Island AC Microgrids

Island microgrids are essential for the exploitation and utilization of offshore renewable energy resources. However, voltage regulation and accurate reactive power sharing remain significant technical challenges that need to be addressed. To tackle these issues, this paper proposes an algorithm that integrates a dynamic leader election (DLE) mechanism and model-free reinforcement learning (RL). The algorithm aims to address the issue of fixed leaders restricting reactive power flow between buses during heavy load variations in island microgrids, while also overcoming the challenge of obtaining model parameters such as resistance and inductance in practical microgrids. First, we establish a voltage containment control and reactive power error model for island alternating current (AC) microgrids and construct a corresponding value function based on this error model. Second, a dynamic leader election algorithm is designed to address the issue of fixed leaders restricting reactive power flow between buses due to preset voltage limits under unknown or heavy load conditions. The algorithm adaptively selects leaders based on bus load, allowing the voltage limits to adjust accordingly and regulating reactive power flow. Then, to address the difficulty of accurately acquiring parameters such as resistance and inductance in microgrid lines, a model-free reinforcement learning method is introduced. This method relies on real-time measurements of voltage and reactive power data, without requiring specific model parameters. Ultimately, simulation experiments on offshore island microgrids are conducted to validate the effectiveness of the proposed algorithm.