The study introduces a multi-objective dispatch system that operates effectively under extreme uncertainties affecting solar and wind power production in hybrid renewable energy microgrids. The system uses diesel power generation together with battery energy storage and demand response systems to minimize operational costs and carbon dioxide emissions, manage supply risk, and provide dependable service to customers. The testing procedure examined uncertainty levels ranging from zero percent to thirty percent using a Python/CBC MILP implementation and compared the results with those from stochastic optimization. The simulation results show that the robust dispatch system maintains a high load reliability of 87.5 percent while supporting system resilience across renewable energy penetration levels ranging from 49.99 percent to 34.99 percent. The maximum diesel power output increases from 15 MW to 21 MW, while the total diesel power output ranges from 415.57 MW to 540.20 MW depending on the uncertainty scenario. The robust model decreases the probability of major fluctuations, helping stabilize power production and increasing the use of renewable energy sources compared with stochastic methods. The research demonstrates that robust multi-objective optimization successfully achieves cost control, emission reduction, and maintenance of system reliability during periods of extreme renewable energy fluctuations. The methodology provides microgrid operators with a practical solution for sustainable energy management that combines efficient operation with resilient performance.

Keywords

Robust Optimization, Hybrid Renewable Energy Microgrid, Solar and Wind Uncertainty, Demand Response Integration