Utilizing the Community Atmosphere Model,version 4,the influence of Arctic sea-ice concentration(SIC)on the extended-range prediction of three simulated cold events(CEs)in East Asia is investigated.Numerical results s...Utilizing the Community Atmosphere Model,version 4,the influence of Arctic sea-ice concentration(SIC)on the extended-range prediction of three simulated cold events(CEs)in East Asia is investigated.Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia.The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale.It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad,as compared with random SIC perturbations under the same constraint.Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process,and then influence the remote temperature by horizontal advection and vertical convection terms.Consequently,the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains,leading to the largest prediction uncertainty of the CEs in the fourth pentad.These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.展开更多
随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,...随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。展开更多
Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful ...Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.展开更多
基金the National Natural Science Foundation of China(Grant Nos.42288101,41790475,42175051,and 42005046)the State Key Laboratory of Tropical Oceanography(South China Sea Institute of Oceanology,Chinese Academy of Sciences+1 种基金Grant No.LTO2109)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515011868).
文摘Utilizing the Community Atmosphere Model,version 4,the influence of Arctic sea-ice concentration(SIC)on the extended-range prediction of three simulated cold events(CEs)in East Asia is investigated.Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia.The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale.It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad,as compared with random SIC perturbations under the same constraint.Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process,and then influence the remote temperature by horizontal advection and vertical convection terms.Consequently,the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains,leading to the largest prediction uncertainty of the CEs in the fourth pentad.These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.
文摘随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。
文摘Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.