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
Kriging models are widely employed due to their simplicity and flexibility in a variety of fields.To gain more distributional information about the unknown parameters,we focus on constructing the fiducial distribution...Kriging models are widely employed due to their simplicity and flexibility in a variety of fields.To gain more distributional information about the unknown parameters,we focus on constructing the fiducial distribution of Kriging model parameters.To solve the challenge of constructing the fiducial marginal distribution for the spatially related parameter,we substitute the Bayesian posterior distribution for the fiducial distribution of this spatially related parameter and present a quasi-fiducial distribution for Kriging model parameters.A Gibbs sampling algorithm is given to get the samples of the quasi-fiducial distribution.Then a model selection criterion based on the quasi-fiducial distribution is proposed.Numerical studies demonstrate that the proposed method is superior to the Lasso and Elastic Net.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 National Social Science Found of China[Grant number 23BTJ064].
文摘Kriging models are widely employed due to their simplicity and flexibility in a variety of fields.To gain more distributional information about the unknown parameters,we focus on constructing the fiducial distribution of Kriging model parameters.To solve the challenge of constructing the fiducial marginal distribution for the spatially related parameter,we substitute the Bayesian posterior distribution for the fiducial distribution of this spatially related parameter and present a quasi-fiducial distribution for Kriging model parameters.A Gibbs sampling algorithm is given to get the samples of the quasi-fiducial distribution.Then a model selection criterion based on the quasi-fiducial distribution is proposed.Numerical studies demonstrate that the proposed method is superior to the Lasso and Elastic Net.
