Efficient Energy Scheduling for Microgrids under Uncertainty

This study aims to develop a strategy for scheduling energy in a community microgrid that considers electricity costs, loads, and climate uncertainties. These microgrids are a localized, self-sufficient energy network that can generate, store, and supply energy to a local community and can be managed by individual control systems or in alignment with the country's utility grid. The system proposed is interconnected with a utility grid, a solar photovoltaic (PV) plant, a wind generation system, a micro-turbine, an energy storage system (ESS), and a load. A flexible math-ematical problem is formulated and solved using an efficient meta-optimization method called the marine predator algorithm (MPA). The strategy considered uses intelligent management software to meet load demands efficiently while minimizing costs by enhancing the utilization of the resources.