Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configur...Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configuration of a China’s national renewable generation demonstration project combining a large-scale BESS with wind farm and photovoltaic(PV)power station,all coupled to a power transmission system,is introduced,and the key technologies including optimal control and management as well as operational status of this BESS are presented.Additionally,the technical benefits of such a large-scale BESS in dealing with power fluctuation and intermittence issues resulting from grid connection of large-scale renewable generation,and for improvement of operation characteristics of transmission grid,are discussed with relevant case studies.展开更多
A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.展开更多
In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the futu...In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method.展开更多
基金supported by National Natural Science Foundation of China(No.51107126 and No.512111046)the Key Projects in National Science and Technology Pillar Program(No.2011BAA07B07)+1 种基金the Beiing Nova Program(No.Z141101001814094)the Science and Technology Foundation of State Grid Corporation of China(No.DG71-14-032)
文摘Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configuration of a China’s national renewable generation demonstration project combining a large-scale BESS with wind farm and photovoltaic(PV)power station,all coupled to a power transmission system,is introduced,and the key technologies including optimal control and management as well as operational status of this BESS are presented.Additionally,the technical benefits of such a large-scale BESS in dealing with power fluctuation and intermittence issues resulting from grid connection of large-scale renewable generation,and for improvement of operation characteristics of transmission grid,are discussed with relevant case studies.
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.
文摘In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method.
