Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited applica...Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".展开更多
针对电池储能等常规储能不能快速响应能源枢纽风电出力短期扰动问题,文中将混合储能引入能源枢纽当中,考虑混合储能在枢纽运行中的损耗,提出了能源枢纽日运行成本最优时的储能容量规划模型。然后基于信息间隙决策理论(Information Gap D...针对电池储能等常规储能不能快速响应能源枢纽风电出力短期扰动问题,文中将混合储能引入能源枢纽当中,考虑混合储能在枢纽运行中的损耗,提出了能源枢纽日运行成本最优时的储能容量规划模型。然后基于信息间隙决策理论(Information Gap Decision Theory,IGDT)在风险规避策略与机会寻求策略下,建立考虑风电出力不确定性的鲁棒与机会模型,为决策者提供能源枢纽容量规划方案。最后通过算例分析,证明使用混合储能的能源枢纽对风电出力存储转化效率更高,日运行成本更少。同时基于IGDT混合储能容量规划模型,能够为决策者提供在满足预期运行目标时风电出力最大/最小波动范围,分析风电出力不确定性与混合储能容量之间的关系,通过定量分析为不同风险偏好策略提供容量规划依据。展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.42377170).
文摘Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".
文摘针对电池储能等常规储能不能快速响应能源枢纽风电出力短期扰动问题,文中将混合储能引入能源枢纽当中,考虑混合储能在枢纽运行中的损耗,提出了能源枢纽日运行成本最优时的储能容量规划模型。然后基于信息间隙决策理论(Information Gap Decision Theory,IGDT)在风险规避策略与机会寻求策略下,建立考虑风电出力不确定性的鲁棒与机会模型,为决策者提供能源枢纽容量规划方案。最后通过算例分析,证明使用混合储能的能源枢纽对风电出力存储转化效率更高,日运行成本更少。同时基于IGDT混合储能容量规划模型,能够为决策者提供在满足预期运行目标时风电出力最大/最小波动范围,分析风电出力不确定性与混合储能容量之间的关系,通过定量分析为不同风险偏好策略提供容量规划依据。
