Optimal Source-Load-Storage Day-Ahead Scheduling Considering Source and Load Uncertainties

As global electrical energy demand continues to rise, the integration of distributed generation, particularly from renewable sources like wind and solar, presents both opportunities and challenges for power systems. The inherent variability of these energy sources complicates grid stability and operational efficiency. This study introduces a day-ahead optimal scheduling model that leverages Gaussian mixture model (GMM) to accurately represent the uncertainties associated with renewable energy forecasts. By employing probabilistic distribution stochastic optimization, we transform uncertainty constraints into manageable deterministic forms. The model aims to minimize overall operational costs, encompassing thermal, wind, photovoltaic, and energy storage expenses, while satisfying essential system constraints. Additionally, it features a structured two-charge and two-discharge cycle for energy storage, optimizing economic performance during peak load periods. Through scenario-based evaluations, we illustrate the model's effectiveness in enhancing system reliability and reducing costs in the face of uncertainty.