Wind Power Forecasting using an Artificial Neural Network for ASPCS

Kazuma Hanada, Takataro Hamajima, Makoto Tsuda, Daisuke Miyagi, Takakazu Shintomi, Tomoaki Takao, Yasuhiro Makida, Masataka Kajiwara

Advanced Research Institute for the Sciences and Humanities, Nihon University, Tokyo, Japan.
Department of Electrical and Electronics Systems, Hachinohe Institute of Technology, Aomori, Japan.
Department of Electrical Engineering, Tohoku University, Miyagi, Japan.
Department of Engineering and Applied Sciences, Sophia University, Tokyo, Japan.
High Energy Accelerator Research Organization, Ibaraki, Japan.
Iwatani Corporation, Tokyo, Japan.

DOI: 10.4236/epe.2013.54B080                

Abstract

In order to use effectively renewable energy sources, we propose a new system, called Advanced Superconducting Power Conditioning System (ASPCS) that is composed of Superconducting Magnetic Energy Storage (SMES), Fuel Cell-Electrolyzer (FC-EL), hydrogen storage and DC/DC and DC/AC converters in connection with a liquid hydrogen station for fuel cell vehicles. The ASPCS compensates the fluctuating electric power of renewable energy sources such as wind and photovoltaic power generations by means of the SMES having characteristics of quick response and large Input-Output power, and hydrogen energy with FC-EL having characteristics of moderate response and large storage capacity. The moderate fluctuated power of the renewable energy is compensated by a trend forecasting method with the Artificial Neural Network. In case of excess of the power generation by the renewable energy to demand it is converted to hydrogen with EL. In contrast, shortage of the electric power is made up with FC. The faster fluctuation power that cannot be compensated by the forecasting method is effectively compensated by SMES. In the ASPCS, the SMES coil with an MgB₂ conductor is operated at 20 K by using liquid hydrogen supplied from a liquid hydrogen tank of the fuel cell vehicle station. The necessary storage capacity of SMES is estimated as 50 MJ to 100 MJ depending on the forecasting time for compensating fluctuation power of the rated wind power generation of 5.0 MW. As a safety case, a thermosiphon cooling system is used to cool indirectly the MgB₂ SMES coil by thermal conduction. In this paper, a trend forecasting result of output power of a wind power generation and the estimated storage capacity of SMES are reported.

Keywords

Renewable Energy; Artificial Neural Network; Forecasting; SMES; Liquid Hydrogen; Fuel Cell and Electrolyzer

Conflicts of Interest

The authors declare no conflicts of interest.

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