This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)f...This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.展开更多
The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefor...The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.展开更多
为降低微电网发电侧运行成本,同时优化微电网需求侧用户的用电体验,建立了将需求侧管理(DSM:Demand Side Management)纳入微电网经济优化的模型。该模型包括一个光伏发电装置,两个柴油发电机和一个蓄电池,需求侧用户根据动态定价机制作...为降低微电网发电侧运行成本,同时优化微电网需求侧用户的用电体验,建立了将需求侧管理(DSM:Demand Side Management)纳入微电网经济优化的模型。该模型包括一个光伏发电装置,两个柴油发电机和一个蓄电池,需求侧用户根据动态定价机制作出响应并对负荷进行转移,以节省电费。将用户感到的不便与负荷转移时间建立联系,运用遗传算法同时优化发电侧经济性和需求侧用户体验。仿真结果表明,实施DSM后,发电成本显著降低,用户获得了一定的经济效益。展开更多
文摘This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.
基金supported by the Ministry of Science,Research and Arts(MWK)of Baden-Württemberg,Germany,as part of a Ph.D.scholarship
文摘The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.
文摘为降低微电网发电侧运行成本,同时优化微电网需求侧用户的用电体验,建立了将需求侧管理(DSM:Demand Side Management)纳入微电网经济优化的模型。该模型包括一个光伏发电装置,两个柴油发电机和一个蓄电池,需求侧用户根据动态定价机制作出响应并对负荷进行转移,以节省电费。将用户感到的不便与负荷转移时间建立联系,运用遗传算法同时优化发电侧经济性和需求侧用户体验。仿真结果表明,实施DSM后,发电成本显著降低,用户获得了一定的经济效益。
