A Monte Carlo study on the crossover from 2-dimensional to 3-dimensional aggregations of clusters is presented. Based on the traditional cluster-cluster aggregation （CCA） simulation, a modified growth model is propo...A Monte Carlo study on the crossover from 2-dimensional to 3-dimensional aggregations of clusters is presented. Based on the traditional cluster-cluster aggregation （CCA） simulation, a modified growth model is proposed. The clusters （including single particles and their aggregates） diffuse with diffusion step length 1 （1≤l≤7） and aggregate on a square lattice substrate. If the number of particles contained in a cluster is larger than a critical size Sc, the particles at the edge of the cluster have a possibility to jump onto the upper layer, which results in the crossover from 2-dimensional to 3-dimensional aggregations. Our simulation results are in good agreement with the experimental findings.展开更多
A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including ...A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including diffusion, and rotation) of clusters is related to its mass, which is given by D-m = D-0s(-gamma D) and theta(m) = theta(0)s (-gamma theta,) respectively. We concentrate on revealing the details of the influence of deposition flux F, cluster diffusion factor gamma(D) and cluster rotation factor gamma(B) on the dynamics of fractal aggregation on liquid surfaces. It is shown that the morphologies of clusters and values of cluster density and fractal dimension depend dramatically on the deposition flux and migration factors of clusters.展开更多
Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ens...Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.展开更多
We have studied the aggregation of particles on a hetero-substrate consisting of two different substrates A and B with finite surface barriers EAB and EBA between the AB and BA boundaries, respectively. With the diffu...We have studied the aggregation of particles on a hetero-substrate consisting of two different substrates A and B with finite surface barriers EAB and EBA between the AB and BA boundaries, respectively. With the diffusion energy limited aggregation （DELA） model, we find that the number of clusters and the mean radius of gyration of the clusters are dependent on the surface barriers EAB and EBA. For the case with a constant of EBA, a series of minima are summarized as EAB ： （E0 - kBAEBA）/kAB with kAB and kBA being two integers, for main minima （kBA = kAB = 1） and two local minima （kBA = kAB and kBA = kAB ＋ 1） between two neighbouring main minima.展开更多
针对车网互动(vehicle-to-grid,V2G)场景下大规模电动汽车(electric vehicle, EV)接入电网时处理速度慢、精度低等问题,提出一种基于自适应密度空间聚类(density-based spatial clustering of applications with noise, DBSCAN)算法的E...针对车网互动(vehicle-to-grid,V2G)场景下大规模电动汽车(electric vehicle, EV)接入电网时处理速度慢、精度低等问题,提出一种基于自适应密度空间聚类(density-based spatial clustering of applications with noise, DBSCAN)算法的EV动态分类和多步马尔科夫链聚合方法。在分类阶段,利用k-dist曲线和差分k-dist曲线对DBSCAN算法进行改进,并引入增量式聚类的概念,对EV数据进行动态分类,得到不同荷电状态(state of charge,SOC)、剩余在网时长及可调控容量的多维特征EV集群。在聚合阶段,提出考虑多步状态转移的马尔科夫链理论,利用该理论对每一EV集群在线建立聚合模型,并考虑多步状态转移的情况,弥补了传统马尔科夫链无法处理多特征EV动态聚合的缺陷,从而得到更准确的聚合功率。仿真结果表明,所提出的分类方法能够快速准确地将接入电网的大规模EV划分为不同集群,并且EV集群经过聚合后其功率准确度得到显著提高,能够有效解决大规模EV入网时存在的问题。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.11374082 and 11074215)the Science Foundation of Zhejiang Province Department of Education,China(Grant No.Y201018280)+1 种基金the Fundamental Research Funds for Central Universities,China(Grant No.2012QNA3010)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20100101110005)
文摘A Monte Carlo study on the crossover from 2-dimensional to 3-dimensional aggregations of clusters is presented. Based on the traditional cluster-cluster aggregation （CCA） simulation, a modified growth model is proposed. The clusters （including single particles and their aggregates） diffuse with diffusion step length 1 （1≤l≤7） and aggregate on a square lattice substrate. If the number of particles contained in a cluster is larger than a critical size Sc, the particles at the edge of the cluster have a possibility to jump onto the upper layer, which results in the crossover from 2-dimensional to 3-dimensional aggregations. Our simulation results are in good agreement with the experimental findings.
文摘A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including diffusion, and rotation) of clusters is related to its mass, which is given by D-m = D-0s(-gamma D) and theta(m) = theta(0)s (-gamma theta,) respectively. We concentrate on revealing the details of the influence of deposition flux F, cluster diffusion factor gamma(D) and cluster rotation factor gamma(B) on the dynamics of fractal aggregation on liquid surfaces. It is shown that the morphologies of clusters and values of cluster density and fractal dimension depend dramatically on the deposition flux and migration factors of clusters.
基金supported by the National Key R&D Program of China(2017YFB0902200)Science and Technology Project of State Grid Corporation of China(4000-202255057A-1-1-ZN,5228001700CW).
文摘Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.
基金supported by the Natural Science Foundation of Zhejiang province,China(Grant No Y607142)by the National Natural Science Foundation of China(Grant No 20771092)
文摘We have studied the aggregation of particles on a hetero-substrate consisting of two different substrates A and B with finite surface barriers EAB and EBA between the AB and BA boundaries, respectively. With the diffusion energy limited aggregation （DELA） model, we find that the number of clusters and the mean radius of gyration of the clusters are dependent on the surface barriers EAB and EBA. For the case with a constant of EBA, a series of minima are summarized as EAB ： （E0 - kBAEBA）/kAB with kAB and kBA being two integers, for main minima （kBA = kAB = 1） and two local minima （kBA = kAB and kBA = kAB ＋ 1） between two neighbouring main minima.
文摘针对车网互动(vehicle-to-grid,V2G)场景下大规模电动汽车(electric vehicle, EV)接入电网时处理速度慢、精度低等问题,提出一种基于自适应密度空间聚类(density-based spatial clustering of applications with noise, DBSCAN)算法的EV动态分类和多步马尔科夫链聚合方法。在分类阶段,利用k-dist曲线和差分k-dist曲线对DBSCAN算法进行改进,并引入增量式聚类的概念,对EV数据进行动态分类,得到不同荷电状态(state of charge,SOC)、剩余在网时长及可调控容量的多维特征EV集群。在聚合阶段,提出考虑多步状态转移的马尔科夫链理论,利用该理论对每一EV集群在线建立聚合模型,并考虑多步状态转移的情况,弥补了传统马尔科夫链无法处理多特征EV动态聚合的缺陷,从而得到更准确的聚合功率。仿真结果表明,所提出的分类方法能够快速准确地将接入电网的大规模EV划分为不同集群,并且EV集群经过聚合后其功率准确度得到显著提高,能够有效解决大规模EV入网时存在的问题。
