The research develops a multi-stage stochastic Mixed-Integer Linear Programming (MILP) model for managing dispatch schedules in microgrids with significant renewable energy integration. The primary objective is to optimize the integration of renewable energy sources with energy storage systems and grid power, concurrently aiming to reduce operational costs and address uncertainties associated with renewable energy resources. The model effectively captures the variability inherent in renewable sources through the use of scenarios and implements a multi-stage MILP formulation that incorporates storage and load constraints. The methodology employs stochastic optimization techniques to regulate fluctuations in renewable generation by analyzing diverse energy availability scenarios. The optimization process is designed to minimize grid power consumption while maximizing the utilization of renewable energy via storage and load constraints that guarantee a balanced energy supply. The model achieves optimal operational costs by producing results that amount to 46,600 USD while successfully controlling renewable energy variability. The research demonstrates two main achievements by integrating high renewable penetration levels and providing valuable insights into how energy storage systems and grid independence lower costs.