Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

doi:10.35833/MPCE.2020.000573

Home > Archive>Volume 8, Issue 6, 2020 >1160-1167. DOI:10.35833/MPCE.2020.000573

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Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

DOI:

10.35833/MPCE.2020.000573

Author:

Xinyi Chen ^{^1,2}
Xinyi Chen
School of Electrical Engineering, Southeast University, Nanjing, China;Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing, China
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Qinran Hu ^{^1,2}
Qinran Hu
School of Electrical Engineering, Southeast University, Nanjing, China;Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing, China
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Qingxin Shi ^³
Qingxin Shi
School of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA
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Xiangjun Quan ^{^1,2}
Xiangjun Quan
School of Electrical Engineering, Southeast University, Nanjing, China;Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing, China
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Zaijun Wu ^{^1,2}
Zaijun Wu
School of Electrical Engineering, Southeast University, Nanjing, China;Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Nanjing, China
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Fangxing Li ^³
Fangxing Li
School of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA
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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

Fund Project:

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).

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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.

Key words:Heating, ventilation, and air conditioning (HVAC) ; load control ; multi-armed bandit ; online learning ; secondary frequency regulation

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Received:August 01,2020
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Online: December 03,2020
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