Data-Driven Frequency Regulation Reserve Prediction Based on Deep Learning Approach

Day-ahead frequency regulation reserves can be procured to compensate power imbalance capacity for the purpose of stabilizing the power system. Due to the intermittency and uncertainty characteristics of renewable generations in a general power system, the dynamic nature of multi-scale system features cannot be fully captured through the existing approaches. This further causes inaccurate prediction and ineffective system operation. To tackle this issue, we propose, in this paper, a deep learning approach to accurately predict the amount of frequency regulation reserves of a general power system through the consideration of network information and power reserves. First, we use the power flow model to generate the net active power imbalance, frequency regulation reserves, and power matrix of a general power system. Second, we combine multiple dynamic system features into a complete input dataset and perform data pre-processing before model training and testing. Third, the proposed deep long short-term memory (DLSTM) model is developed to accurately predict the net active power imbalance in the system, as well as predicting the frequency regulation reserves. Our simulation results show that, when considering the entire power network information, our proposed deep learning approach outperforms the four baseline techniques on predicting the frequency regulation reserves in a general power system. These promising results contribute to large economical benefits in power system operations.