Optical materials have a wide range of applications in various fields，and accurate identification of optical material parameters is crucial for material design and performance optimization. As an emerging renewable energy power supply technology，the accurate identification of system parameters in photovoltaic microgrid systems is of great significance for system design and operation. This article proposes a parameter identification method for photovoltaic microgrid optical materials based on an improved grey wolf algorithm. Firstly，the traditional Grey Wolf optimization algorithm was improved by combining orthogonal learning methods，using a local exploration method to identify uncertain parameters of optical materials in photovoltaic cell models，and modifies vector parameters to promote a reliable balance between the two stages of the algorithm. Secondly，combined with improved algorithms，the parameters in the photovoltaic microgrid system are gradually iteratively optimized to further improve the accuracy of parameter identification. Finally，the improved Grey Wolf optimization algorithm proposed in this article was used to estimate the optical material uncertainty parameters of photovoltaic module models based on single diode model（SDM）and double diode model（DDM）. The experimental results show that this method can effectively identify key parameters of optical materials in photovoltaic microgrid systems，and has good performance in recognition accuracy and convergence speed.