Optimal frequency regulation based on characterizing the air conditioning cluster by online deep learning

Abstract

The air conditioning cluster (ACC) is a potential candidate to provide frequency regulation reserves. However, the effective assessment of the ACC willing reserve capacity is often the obstacle for the existing demand response (DR) programs, influenced by incentive prices, temperatures, etc. In this paper, the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as the demand response characteristic (DRC). To learn the DRC along with real-time frequency regulation, an online deep learning-based DRC (ODL-DRC) modeling methodology is designed to retrain the deep neural network-based model continuously. The ODL-DRC model trained by incoming new data does not require massive historical training data any more, which is more time-efficient. Then, the coordinate operation between ODL-DRC modeling and optimal frequency regulation (OFR) is put forward. A robust decentralized sliding mode controller (DSMC) is designed to manage the ACC response power in primary frequency regulation against the ACC response uncertainty. An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration. So that the ODL-DRC model can be applied to minimize the total operation cost while maintaining frequency stability, without waiting for a well-trained model. The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning, which can capture the real DRC and optimize the regulation performance simultaneously with strong robustness against the ACC response uncertainty and the learning error.