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 f...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 multiarmed 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.展开更多
基金supported by the National Natural Science Foundation of China(No.51907026)Natural Science Foundation of Jiangsu(No.BK20190361)+1 种基金Jiangsu Provincial Key Laboratory of Smart Grid Technology and EquipmentGlobal Energy Interconnection Research Institute(No.SGGR0000WLJS1900107)
文摘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 multiarmed 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.
