A Deep-Learning-Based Optimal Energy Flow Method for Reliability Assessment of Integrated Energy Systems

The energy interactions and uncertain factors of integrated systems (IES) have brought risks to the reliable supply. A large number states need be analyzed obtain a stable reliability value. However, different operating characteristics complicate optimal flow (OEF) model, which brings tremendous computational cost. To address that, deep-learning-based approach is proposed as an alternative way solve OEF problems. This constructs mapping between system state allocation directly load curtailment. Thereafter, assessment framework for IES improve efficiency. Additionally, Gaussian noise data-processing strategies are involved achieve higher accuracy. Compared model-based approach, method increases efficiency by 6 orders time. With accuracy over 95%, it outperforms other autoencoder random forest methods. Method has remained above 90% in various scenarios.