Voltage Fluctuation Control Strategy Based on Reinforcement Learning Digital Twin Model for Wind-Solar-Water-Storage Smart Microgrid Energy Storage Converter Side

Abstract

With the increasing penetration of distributed renewable energy (DRE), the stable operation of smart microgrids integrating wind, solar, hydro, and storage faces significant challenges. The core issue lies in the strong intermittency and randomness of distributed generation outputs, which can easily trigger voltage fluctuations, particularly at the grid connection points of Power Conversion Systems (PCS), seriously threatening power quality and supply reliability. This study innovatively integrates Deep Reinforcement Learning and Digital Twin (DRL+DT) technologies to establish a novel control framework, which constructs a digital twin of the microgrid. Based on the Proximal Policy Optimization (PPO) algorithm, a DRL controller is designed, with clearly defined state spaces, action spaces, and a composite reward function incorporating multi-objective constraints. This modeling approach transforms the voltage control problem into a sequential decision-making task solvable through data-driven methods. The trained policy neural network serves as an intelligent controller for performance comparison in subsequent sections. To validate the proposed approach, simulation tests verify that the proposed method suppresses voltage fluctuations more rapidly and smoothly compared to conventional PI control and single DRL approaches. The voltage deviation is reduced by 62.4%, while the State of Charge (SOC) of the energy storage system is effectively maintained within a healthy range. The experimental results not only verify the technical advantages of the combined DRL+DT framework in addressing complex energy control challenges in microgrids but also demonstrate its capability to support the development of highly resilient and self-healing smart microgrid control systems, highlighting both advanced functionality and practical utility.