Kazuaki Iwamura, Y. Nakanishi, U. Lewlomphaisarl, N. Estoperez, A. Lomi
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The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. 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引用次数: 0
摘要
本文介绍了一种名为GGOD (Grid of Grids Optimal Designer)的设施规划优化平台的体系结构和应用,并将其应用于可扩展集群型微电网的安装和运行。可扩展集群型微电网是由双向输电网络连接的一组微电网。此外,电源也联网。特别是,通过电源之间的连接,确保了需求地区社会活动所需的电力。所建议的体系结构基于面向服务的体系结构,这意味着优化功能作为服务执行。为了提高灵活性,这些服务由基于可扩展标记语言文本的请求执行。可用的优化是用元数据编写的,最终用户可以从称为clearinghouse的元数据数据库系统访问元数据。元数据有两种类型,一种用于单个优化,另一种用于组合优化。GGOD中的流程由解释元数据中的描述的管理功能执行。在元数据中,编写了优化函数的名称和激活顺序。基本执行遵循顺序、分支或循环流流程,这些流程执行组合优化,比较两种以上的优化流程,并分别执行迭代模拟。作为该架构的应用,在地理空间综合资源规划场景下对发电站点和输电网络进行了优化。在此应用中,举例说明了GGOD中组件功能组合的结构和方法。此外,GGOD还建议通过有效组合基本优化函数来提升许多应用程序。
Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations
This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions.