Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appea...Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.展开更多
Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way.Three-dimension(3D)spatially resolved transcriptomic atlases constructed from multi-view and h...Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way.Three-dimension(3D)spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples,revolutionizing the understanding of gene regulatory interactions and cell niches.However,limited accessible tools for data visualization impede the potential impact and application of this technology.Here we introduce VT3D,a visualization toolbox that allows users to explore 3D transcriptomic data,enabling gene expression projection to any 2D plane of interest,2D virtual slice creation and visualization,and interactive 3D data browsing with surface model plots.In addition,it can either work on personal devices in standalone mode or be hosted as a web-based server.We apply VT3D to multiple datasets produced by the most popular techniques,including both sequencing-based approaches(Stereo-seq,spatial transcriptomics,and Slide-seq)and imaging-based approaches(MERFISH and STARMap),and successfully build a 3D atlas database that allows interactive data browsing.We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics,thus accelerating related studies such as embryogenesis and organogenesis processes.The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D,and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.展开更多
基金supported by the National Science Foundation for Distinguished Young Scholars of China under Grant(52125702).
文摘Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.
基金supported by the General Program(Key Program,Major Research Plan)of National Natural Science Foundation of China(No.32170439).
文摘Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way.Three-dimension(3D)spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples,revolutionizing the understanding of gene regulatory interactions and cell niches.However,limited accessible tools for data visualization impede the potential impact and application of this technology.Here we introduce VT3D,a visualization toolbox that allows users to explore 3D transcriptomic data,enabling gene expression projection to any 2D plane of interest,2D virtual slice creation and visualization,and interactive 3D data browsing with surface model plots.In addition,it can either work on personal devices in standalone mode or be hosted as a web-based server.We apply VT3D to multiple datasets produced by the most popular techniques,including both sequencing-based approaches(Stereo-seq,spatial transcriptomics,and Slide-seq)and imaging-based approaches(MERFISH and STARMap),and successfully build a 3D atlas database that allows interactive data browsing.We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics,thus accelerating related studies such as embryogenesis and organogenesis processes.The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D,and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.