Cooperative Frequency Control of Microgrids Based on Quantum Reinforcement Learning

Due to the lack of support from the main grid, isolated microgrids composed of multiple parallel inverters may experience significant frequency deviations when subjected to load changes or power source failures, and the frequency recovery time may not meet expectations. To address this issue, this study proposes a quantum reinforcement learning (QRL)-based cooperative secondary frequency control method for microgrids. This method takes the frequency deviation generated by the primary control as the input state for the secondary control and utilizes quantum reinforcement learning to accumulate maximum rewards, thereby generating a frequency compensation loop. The effectiveness of the proposed QRL-based secondary frequency control method for microgrids in rapidly adapting to load changes and restoring frequency during power source outages is verified through Matlab/Simulink simulations.