A Fuzzy Q-Learning Algorithm for Storage Optimization in Islanding Microgrid

Abstract

In islanding microgrids, energy storage plays a key role in obtaining flexible power control and operation. The energy storage solves the effects of randomness, intermittency and uncertainty of renewable energy through its peak regulation and frequency modulation. In order to better to improve the economics of the microgrid, this paper proposes a Q-learning algorithm based on fuzzy control. It is a model-free algorithm, without complicated modelling, the new algorithm can handle the state space explosion problem of the Q learning algorithm. This method improves the system's renewable resource utilization rate by controlling the energy flow of the energy storage and adjusting the system's energy scheduling. The results show that compared with the Q-learning, the fuzzy Q-learning algorithm reduced the use of fossil fuels by 33.7%, increases the utilization rate of renewable resources from 85.7 to 93.5%.