城市电网涵盖多个电压等级,调度操作复杂,为降低城市电网N-1后的运行风险,同时解决N-1-1发生时导致大面积停电的问题,提出一种转供区域识别方法,并结合研究场景对其进行分区处理,在保证求解准确度的前提下提高问题求解效率。提出降低城...城市电网涵盖多个电压等级,调度操作复杂,为降低城市电网N-1后的运行风险,同时解决N-1-1发生时导致大面积停电的问题,提出一种转供区域识别方法,并结合研究场景对其进行分区处理,在保证求解准确度的前提下提高问题求解效率。提出降低城市电网N-1-1运行风险拓扑重构双层优化模型,综合考虑了N-1-1时的负荷损失量和N-1后节点电压偏移量与设备负载率均衡度,使用二进制蝙蝠算法(Binary Bat Algorithm,BBA)对模型进行求解。最后通过某地区实际城市电网算例分析,验证了所提模型的有效性。展开更多
Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relat...Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.展开更多
文摘城市电网涵盖多个电压等级,调度操作复杂,为降低城市电网N-1后的运行风险,同时解决N-1-1发生时导致大面积停电的问题,提出一种转供区域识别方法,并结合研究场景对其进行分区处理,在保证求解准确度的前提下提高问题求解效率。提出降低城市电网N-1-1运行风险拓扑重构双层优化模型,综合考虑了N-1-1时的负荷损失量和N-1后节点电压偏移量与设备负载率均衡度,使用二进制蝙蝠算法(Binary Bat Algorithm,BBA)对模型进行求解。最后通过某地区实际城市电网算例分析,验证了所提模型的有效性。
基金supported by the Key Discipline Construction Project in Hunan Province(2008001)the National Natural Science Foundation of China and the Scientific Research Fund of Hunan Provincial Education Department(13A051)
文摘Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.
