DOI:10.35833/MPCE.2020.000573 Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation Page view: 0        Net amount: 145 Author: Xinyi Chen1,2,Qinran Hu1,2,Qingxin Shi3,Xiangjun Quan1,2,Zaijun Wu1,2,Fangxing Li3 Author Affiliation: 1.School of Electrical Engineering, Southeast University, Nanjing, China;2.Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing, China;3.School of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA Foundation: This work was supported by the National Natural Science Foundation of China (No. 51907026), Natural Science Foundation of Jiangsu (No. BK20190361), and Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, and Global Energy Interconnection Research Institute (No. SGGR0000WLJS1900107). Abstract: As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multi-armed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users. Keywords: Heating, ventilation, and air conditioning (HVAC) ; load control ; multi-armed bandit ; online learning ; secondary frequency regulation Received:August 01, 2020               Online Time:2020/12/03 View Full Text       Download reader