Back
 EPE  Vol.5 No.4 B , July 2013
Wind Power Forecasting using an Artificial Neural Network for ASPCS
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.
Cite this paper: K. Hanada, T. Hamajima, M. Tsuda, D. Miyagi, T. Shintomi, T. Takao, Y. Makida and M. Kajiwara, "Wind Power Forecasting using an Artificial Neural Network for ASPCS," Energy and Power Engineering, Vol. 5 No. 4, 2013, pp. 414-417. doi: 10.4236/epe.2013.54B080.
References

[1]   T. Hamajima, et al., “Application of SMES and Fuel Cell System Combined with Liquid Hydrogen Vehicle Station to Renewable Energy Control,” IEEE Transactions on Applied Superconductivity, Vol. 22, No. 3, 2011, p. 5701704. doi:10.1109/TASC.2011.2175687

[2]   T. Shintomi, et al., “Design Study of SMES System Cooled by Thermo-siphon with Liquid Hydrogen for Effective Use of Renewable Energy,” IEEE Transaction Supercond., Vol. 22, No. 3, 2011, p. 5701604. doi:10.1109/TASC.2011.2178575

[3]   T. Nakayama, et al., “Optimization of SMES Compensation Capacity for Stochastic Power Using a Kalman Filter,” TEION KOGAKU, Vol. 45, No. 3, 2010, pp. 99-106. doi:10.2221/jcsj.45.99

[4]   D. E. Rumelhart, et al., “Parallel Distributed Processing,” MIT Press, 1986.

[5]   T. J. Sejnowski, et al., “Parallel Networks that Learn to Pronounce English Text,” Complex System, Vol. 1,1987, pp. 145-168.

[6]   J. B. MacQueen, “Some Methods of Classification and Analysis of Multivariate Observations,” Proceedings of 5th Berkeley Symposium on Math, Stat. and Prob., 1967, pp. 281-297.

[7]   E. W. Forgy, “Cluster Analysis of Multivariate data: efficiency vs. Interpretability of Classifications,” Biometrics, Vol. 21, 1965, pp. 768-769.

 
 
Top