Optimal Frequency Regulation Based on Characterizing the Air Conditioning Cluster by Online Deep Learning

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 an obstacle for 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 a demand response characteristic (DRC). To learn about DRC along with real-time frequency regulation, an online deep learning-based DRC (ODL-DR C) modeling methodology is designed to continuously retrain the deep neural network-based model. The ODL-DRC model trained by incoming new data does not require massive historical training data, which makes it more time-efficient. Then, the coordinate operation between ODL-DRC modeling and optimal frequency regulation (OFR) is presented. A robust decentralized sliding mode controller (DSMC) is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty. An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration. Thereby, the ODL-DRC model can be applied to minimize the total operational 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 simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.