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.

中文翻译：

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基于规避风险的多臂Bandit学习的住宅HVAC聚合用于二次频率调节

随着可再生能源渗透率的不断提高，随机和间歇性发电资源逐渐取代了常规发电机，在稳定电力系统频率方面带来了巨大挑战。因此，聚集需求侧资源进行频率调节吸引了学术界和工业界的关注。然而，在实践中，常规的聚合方法遭受诸如选择退出控制信号的用户的随机和不确定行为。采用规避风险的多武装匪徒学习方法来学习用户的行为，并为住宅采暖，通风和空调（HVAC）开发了一种新颖的汇总策略，以提供可靠的辅助频率调节。与传统方法相比，仿真结果表明，规避风险的多臂强盗学习方法在次级频率调节中表现更好，选择的用户较少，选择退出控制。此外，所提出的方法对于用户的随机和变化的行为更加健壮。