Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations

Planning optimization micro-grid installations and operations

Authors

  • Kazuaki Iwamura Waseda University, Graduate School of Energy and Environment, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
  • Yosuke Nakanishi Waseda University, Graduate School of Energy and Environment, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
  • Udom Lewlomphaisarl National Advanced Automation and Electronics Research Unit, 112 Phahonyothin Road, Khlong Nueng, Khlong Luang District, Pathumthani 12120, Thailand
  • Noel Estoperez Mindanao State University, Illigan Institute of Technology, Illigan City, Andres Bonifacio Ave, Iligan City, 9200 Lanao del Norte, Philippines
  • Abraham Lomi Renewable Energy Research Center, National Institute of Technology Malang, Jl. Raya Karanglo, Km. 2, Malang 65143, Indonesia

DOI:

https://doi.org/10.53560/PPASA(58-sp1)742

Keywords:

Clearinghouse, Grid of Grids Optimal Designer, Power Generation Sites, Service-Oriented Architecture, Transmission Networks

Abstract

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.

References

C. Schwaegerl, and L. Tao. The microgrids concept. In: Microgrids: Architecture and control. N. Hatziargyriou (Ed). Wiley, New Jersey, USA. p. 4–7 (2014). DOI: 10.1002/9781118720677.ch01

B. Novianto., K. Abdullah., A.S. Uyun., E. Yandri., N.S. Muhammad., H. Susanto., Z. Vincēviča-Gaile, R.H. Setyobudi, and Y. Nurdiansyah. Smart micro- grid performance using renewable energy. E3S Web Conference 188(00005): 1–11 (2020). DOI: 10.1051/e3sconf/202018800005

K. Iwamura., Y. Nakanishi., H. Takamori., U. Lewlomphaisarl., N. Estoperez, and A. Lomi. Optimal design suite for expandable micro-grid clusters. 7th International Conference on Renewable Energy Research and Applications, Paris, France, 2018, p. 354–359 (2018). DOI: 10.1109/ ICRERA.2018.8566707

S. Ahmad., M.H. Zafar., M. Ashraf., I. Khan, and F.Q. Khan. Energy-Efficient TDMA based Clustering Scheme for WSN. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 55 (3): 53–65 (2018)

Homer. HOMER page website. [Online] from https://www.homerenergy.com, (2019) [Accessed on July 27th, 2019].

WASP. WASP page website. [Online] from https:// www.energyplan.eu. (2019) [Accessed on July 27th, 2019].

International Renewable Energy Agency. Planning for the renewable future: Long-term modelling and tools to expand variable renewable power in emerging economies. [Online] from https://www. irena.org/publications/2017/Jan/Planning-for- the-renewable-future-Long-term-modelling-and- tools-to-expand-variable-renewable-power. (2017) [Accessed on July 27th, 2019].

S.D. Stewart., C.D. Piros, J.C. Heisler. Portfolio Management: Theory and Practice. John Wiley & Sons, US (2019).

K. Nishiyama., K. Iwamura, and Y. Nakanishi. Optimized site selection for new wind farm installations based on portfolio theory and geographical information. 8th IEEE PES Innovative Smart Grid Technologies Conference North America (Washington, USA, 2019). DOI: 10.1109/ ISGT.2019.8791636

B. Korte, and J. Vygen. Combinatorial Optimization, Theory and Algorithms. Springer Nature, Switzerland AG. (2018).

B.R. Phillips, and R.S. Middleton, SimWIND: A geospatial infrastructure model for optimizing wind power generation and transmission. Energy Policy 43:291–302(2012). DOI: 10.1016/j. enpol.2012.01.006

K. Iwamura., R. Kobayashi., K. Nishiyama, and Y. Nakanishi. A combined geospatial approach to extension planning of wind farms and transmission networks. 8th IEEE PES Innovative Smart Grid Technologies Conference Europe (Sarajevo, Bosnia- Herzegovina, 2018). p. 1–6 (2018). DOI: 10.1109/ ISGTEurope.2018.8571437

Downloads

Published

2021-10-12

How to Cite

Iwamura, K. ., Nakanishi, Y. ., Lewlomphaisarl, U. ., Estoperez, N. ., & Lomi, A. . (2021). Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations: Planning optimization micro-grid installations and operations. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 58(S), 101–107. https://doi.org/10.53560/PPASA(58-sp1)742

Similar Articles

You may also start an advanced similarity search for this article.