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
为了解决地质建模软件Earth Volumetric Studio (EVS)建立的地质模型与实际地形地貌、地层倾向不符的问题,以贵州金象煤矿为研究对象,提出一种能真实反映地形地貌与地层倾向的建模方法(EVS耦合地形–优化钻孔数据的建模法)。通过工程实...为了解决地质建模软件Earth Volumetric Studio (EVS)建立的地质模型与实际地形地貌、地层倾向不符的问题,以贵州金象煤矿为研究对象,提出一种能真实反映地形地貌与地层倾向的建模方法(EVS耦合地形–优化钻孔数据的建模法)。通过工程实例将该方法与地层建模进行对比分析,结果表明采用耦合地形–优化钻孔数据的建模方法建立的模型比地层建模方法建立的模型更能反映实际的地质情况,与勘探方法探测的地质情况也更相近,且模型误差更小。展开更多
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.
文摘为了解决地质建模软件Earth Volumetric Studio (EVS)建立的地质模型与实际地形地貌、地层倾向不符的问题,以贵州金象煤矿为研究对象,提出一种能真实反映地形地貌与地层倾向的建模方法(EVS耦合地形–优化钻孔数据的建模法)。通过工程实例将该方法与地层建模进行对比分析,结果表明采用耦合地形–优化钻孔数据的建模方法建立的模型比地层建模方法建立的模型更能反映实际的地质情况,与勘探方法探测的地质情况也更相近,且模型误差更小。
