Worse-Case Guarantees for DNN Based DCOPF Under High RES Penetration

Addressing DC Optimal Power Flow (DCOPF) challenges in power systems with a high penetration of renewable energy sources (RES) necessitates innovative solutions due to the limitations of data accessibility, dynamic network adaptability, and worst-case scenario planning, including load shedding and RES curtailment. Traditional datasets for neural network training often fall short in quality and relevance, impeding the accurate modeling of machine learning solutions for energy distribution. This study introduces a robust deep neural network (DNN) model tailored to effectively manage high levels of RES integration by solving the DCOPF problem. Utilizing a dataset derived from the DCOPF configurations of a multi-bus power grid, the model's efficacy is demonstrated through comprehensive evaluations of its predictions against real-world data, showcasing superior performance over conventional neural network approaches. This research not only enhances the adaptability and scalability of DCOPF solutions across multiple bus systems, including Garver's 6, IEEE 9, 14, and 30 bus systems but also set a precedent for future advancements in the field.