This paper investigates the frequency performance problem of microgrids (MGs) integrated with renewable employing an energy storage system (ESS) equipped with virtual inertial control (VIC) support. To tackle the uncertainties related to the system operation, a two-layer multiple model predictive control (TLMMPC) method, consisting of nominal and ancillary MMPCs, is proposed to submit effective control signals to the ESS for improving system frequency performance. The ancillary MMPC generates the control commands for the VIC-based ESS utilizing the signals provided by the nominal MMPC and the frequency deviation signal of the actual system considering uncertainties and operating constraints. The control commands are generated to attain the minimum value of frequency response error with the least control endeavor while considering various operational and physical limitations. The TLMMPC method has the capability to work with different state of charge (SoC) levels to obtain the desired SoC and highest efficiency from the ESS and preserve the ESS's longevity. The dynamic performance of the proposed TLMMPC technique is investigated on an islanded MG and compared to model predictive control (MPC), fractional-order MPC, and tilt-integral-derivative controllers under different scenarios. The results validate that the proposed TLMMPC technique significantly improves the system frequency response from viewpoints of settling time, peak overshoot, and undershoot and obtains the most efficient ESS compared to the other methods.