An AI-Based Power Reserve Control Strategy for Photovoltaic Power Generation Systems Participating in Frequency Regulation of Microgrids

Abstract

In this paper, a novel AI-based power reserve control strategy is proposed for photovoltaic (PV) power generation systems participating in the frequency regulation (FR) of microgrids. The proposed strategy uses a frequency response module to determine the target power reserve ratio of the PV system based on microgrid frequency deviation, as well as a power reserve control module to obtain the target duty cycle, which is input to the BOOST converter. The use of artificial neural networks (ANN) in the power reserve control module enables the PV system to work at a specified power reserve ratio, producing appropriate power and mitigating frequency fluctuations in the microgrid. Additionally, a deep reinforcement learning (DRL) algorithm is employed as the decision maker for variable step-size control and initial power reserve ratio determination. Simulations were performed to validate the effectiveness of the proposed method, demonstrating a significant reduction in average frequency deviation by 72.36% when subjected to random variations in irradiance intensity and load conditions. Overall, the proposed AI-based power reserve control strategy has good potential for practical applications in real-world microgrids, promoting the absorption of new energy led by PV and reducing the phenomenon of light abandonment.