Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty

This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability.