Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations: Planning optimization micro-grid installations and operations - Iwamura, Kazuaki; Nakanishi, Yosuke; Lewlomphaisarl, Udom; Estoperez, Noel; Lomi, Abraham (Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 2021)
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.