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

Abstract

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

Keywords

• Integrated energy system
• deep learning
• optimal energy flow
• stacked denoising auto-encoder (SDAE)
• reliability assessment