Day-Ahead Optimization Scheduling of Hybrid AC/DC Distribution Networks Considering Uncertainties

In recent years, the optimization and scheduling of alternating current/direct current (AC/DC) distribution networks have become a hot topic of research, focusing on system stability, optimization performance, and the handling of uncertainties in distributed energy resources. This study addressed the issue of computational efficiency when dealing with uncertainties. We propose a new day-ahead optimization scheduling strategy that accounts for uncertainties in power system operations and use scenario analysis to quantitatively analyze these uncertainties. Initial scenarios were generated using the Latin Hypercube Sampling method, and scenario reduction techniques were employed to optimize scenario selection. The objective is to minimize the operational costs during day-ahead scheduling. This study used MATLAB for case study simulations and validated the proposed method through an enhanced IEEE 33-node test case.