The research establishes a comprehensive optimal dispatch system for a renewable energy microgrid operating in South Africa's North West Province, handling extreme uncertainties in solar and wind energy production. The system combines diesel power generation with battery energy storage and demand response to maintain operational dependability during worst-case periods of renewable energy production. The system creates hourly dispatch schedules through a Mixed-Integer Linear Programming model, which the Python/CBC solver uses to meet power balance requirements, generator and storage restrictions, and demand response specifications. The analysis of different renewable uncertainty scenarios shows that diesel power generation peaks at 21 MW, total diesel power generation increases to 372 MW, and renewable energy production drops to 63.8%. The system shows strong load reliability at 87.5%, but its resilience index remains at 0%, indicating the system needs better reinforcement. The framework enables organizations to measure their operational efficiency, helping them understand their reliability and resilience capabilities, as well as their cost estimation results. The upcoming research will investigate adaptive control methods and real-time resilience solutions to improve microgrid operations during extreme, unpredictable events.

Keywords

Robust optimization, Hybrid microgrids, Renewable energy uncertainty, Demand response, Resilient dispatch.