二维网格布局方式，项目属性和容器属性以及案例，全面掌握新式二维布局方式，。。。。。。。。。。。。。。。。内部资料。。。

grid网格容器属性和项目属性，适用有一定的html，css基础，flex布局更好，前端学习，疑难解惑ppt教学资源，内部资料！！！！！！！！！！！！！！！！！！！！！grid网格容器属性和项目属性，适用有一定的html，css基础，flex布局更好，前端学习，疑难解惑ppt教学资源，内部资料。。。。。

Oracle Grid Infrastructure Installation Guide 11g Release 2 (11.2) for Win x64

Oracle Database 19c Grid Infrastructure (19.3)& RAC for Windows Server 2019 (64 -bit) --详细讲解windows server 2019系统下oracle19c rac安装

docker-compose-selenium-grid，包含hub，node（firefox chrome edge），版本4.1.2-20220217

udocker是一个基本的用户工具，可以在用户空间中执行简单的docker容器而无需root特权。 启用不可用dockerLinux系统中非特权用户下载和执行docker容器。 它可用于在由其他实体（例如网格基础结构或外部管理的批处理或交互式系统）管理Linux批处理系统和交互式集群中拉入和执行docker容器。 udocker不需要任何类型的特权，也不需要系统管理员部署服务。 最终用户可以完全下载和执行它。 udocker是一些工具的包装，这些工具可模仿docker功能的子集，包括拉取图像和运行功能最少的容器。 它是如何工作的 udocker是一个用Python编写的简单工具，它具有最少的依赖项集，因此可以在各种Linux系统中执行。 udocker不使用docker，也不需要它的存在。 udocker通过在提取的容器上简单地提供类似于chroot的环境来“执行”容器。 当前的实现支持模拟chroot的不同方法，从而可以在类似chroot的环境下执行容器而无需特权。 udocker透明地支持使用工具和库执行容器的几种方法，例如： 根 Fakechroot 运行 奇点 优点

Grid++Report 5.6 锐浪报表 破解版 仅供研究使用 请支持国产正版 谢谢

Surfer Grid，导入/导出。 Matlab <-> Golden Software Surfer 和 Grapher 该包包含两个简单的例程：grd_write.m和grd_read.m。 他们通过 GRD 文件格式（ASCII 版本）将 Matlab 与 Golden Software Surfer 通信。 grd_write（矩阵，xmin，xmax，ymin，ymax，namefile） 输入： 矩阵 = 要导出的矩阵xmin,xmax,ymin,ymax = 网格限制namefile = 要写入的文件的名称（包括“.grd”扩展名） 输出： 当前目录下的grd文件 [矩阵 xmin xmax ymin ymax]=grd_read（文件名） 输入： nomarch = 要读取的文件的名称，包括“.grd”扩展名输出： 矩阵 = 读取数据的矩阵xmin xmax ym

As the main building block of the smart grid systems, microgrid (MG) integrates a number of local distributed generation units, energy storage systems, and local loads to form a small-scale, low- and medium-voltage level power system. In gen- eral, an MG can operate in two modes, i.e., the grid-connected and islanded mode. Recently, in order to standardize its operation and functionality, hierarchical con- trol for islanded MG systems has been proposed. It divides the control structure into three layers, namely, primary, secondary, and tertiary control. The primary control is based on each local distributed generation (DG) controller and is realized in a de- centralized way. In the secondary layer, the frequency and voltage restoration control as well as the power quality enhancement is usually carried out. In the tertiary con- trol, economic dispatch and power flow optimization issues are considered. However, conventionally both the secondary and tertiary control are realized in a centralized way. There are certain drawbacks to such centralized control, such as high compu- tation and communication cost, poor fault tolerance ability, lack of plug-and-play properties, and so on. In order to overcome the above drawbacks, distributed control is proposed in the secondary control and tertiary optimization in this book. In the secondary control, restorations for both voltage and frequency in the droop- controlled inverter-based islanded MG are addressed. A distributed finite-time con- trol approach is used in the voltage restoration which enables the voltages at all the DGs to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed fre- quency control is proposed for frequency restoration, subject to certain control input constraints. The proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. Then the distributed control strategy is also employed in the secondary voltage unbalance compensation to replace the conventional centralized controller. The con- cept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each xvii xviii Distributed Control and Optimization Technologies in Smart Grid Systems local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. In the tertiary layer, a distributed economic dispatch (ED) strategy based on pro- jected gradient and finite-time average consensus algorithms is proposed. By de- composing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution for the optimization problem in a distributed manner with limited communication among neighbors. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Besides, two distributed multi-cluster optimization meth- ods are proposed for a large-scale multi-area power system. We first propose to divide all the generator agents into clusters (groups) and each cluster has a leader to com- municate with the leaders of its neighboring clusters. Then two different schemes are proposed for each agent to iteratively estimate a solution of the optimization prob- lem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. In addition, a novel hierarchical decentralized optimization architecture is proposed to solve the ED problem. Similar to distributed algorithms, each local generator only solves its own problem based on its own cost function and generation constraint. An extra co- ordinator agent is employed to coordinate all the local generator agents. Besides, it also takes the responsibility for handling the global demand supply constraint. In this way, different from existing distributed algorithms, the global demand supply con- straint and local generation constraints are handled separately, which would greatly reduce the computational complexity. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. A distributed optimal energy scheduling strategy is also proposed in the tertiary layer, which is based on a newly proposed pricing strategy named PD pricing. Con- ventional real-time pricing strategies only depend on the current total energy con- sumption. In contrast to this, our proposed pricing strategy also takes the incremen- tal energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. Two different dis- tributed optimization algorithms with different communication strategies are pro- posed to solve the problem.

Python中numpy库中，X,Y = np.meshgrid(x,y)最详细理解（附理解代码） 一. 导入numpy库 import numpy as np 二. 生成X,Y = np.meshgrid(x,y)并详解 N = 3 M=7 #生成两个一维矩阵 x = np.linspace(-2, 2, N) #[-2 0 2] y = np.linspace(-3, 3,M)#[-3 -2 1 0 1 2 3 ] X,Y = np.meshgrid(x,y) #成为两个二维矩阵 话不多说，我们直接看输出结果： 从X二维矩阵可以看出来：7行3列（M行N列） 每一行显示[-2 0 2]