JACIII Vol.18 No.6 pp. 1034-1043
doi: 10.20965/jaciii.2014.p1034


Effective Method for Wind and Solar Power Grid Systems Based on Recurrent Neural Networks

Keisuke Kimura, Takayuki Kimura, Takefumi Hiraguri,
and Kenya Jin’no

Nippon Institute of Technology, 4-1-1 Gakuendai, Miyashiro, Minami-Saitama, Saitama 345-8501, Japan

October 14, 2013
June 18, 2014
November 20, 2014
complex networks, linear programming problems, recurrent neural networks, renewable energysystems
In this paper, the control method based on recurrent neural networks is proposed for optimizing large-scale wind and solar power generation systems. Recently, an optimal control method based on recurrent neural networks was proposed for wind and solar power generation systems. In this method, optimization problems are regarded as linear programming problems, which are solved by recurrent neural networks. Results suggest that this control method based on recurrent neural networks could be implemented in realworld systems. However, only small power generation systems were used to evaluate this control method in previous studies. Then, the method for power generation systems is evaluated by more realistic conditions. The results of our numerical experiments show that this control method delivers high performance with large-scale power generation systems. Furthermore, if the power generation systems has specific topologies, almost 20%of the supplying capacity is improved.
Cite this article as:
K. Kimura, T. Kimura, T. Hiraguri, and K. Jin’no, “Effective Method for Wind and Solar Power Grid Systems Based on Recurrent Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.6, pp. 1034-1043, 2014.
Data files:
  1. [1] F. P. Sioshansi, “Smart grid: Integrating renewable, distributed & efficient energy,” Academic Press, 2011.
  2. [2] T. Hikihara, K. Tashiro, and Y. Kitamori, “Power packetization and routing for smart management of electricity,” Proc. of the Int. Energy Conversion Engineering Conference, Vol.2012-3732, pp. 1-6, 2012.
  3. [3] M. Sharafi and T. Y. ELMekkawy, “Multi-objective optimal design of hybrid renewable energy systems using pso-simulation based approach,” Renewable Energy, Vol.68, pp. 67-71, 2014.
  4. [4] J. Zeng, M. Li, J. F. Liu, J. Wu, and H. W. Ngan, “Operational optimization of a stand-alone hybrid renewable energy generation system based on an improved genetic algorithm,” IEEE Trans. on Power and Energy Society General Meeting, pp. 1-6, 2010.
  5. [5] M. E. Gamez, E. N. Sanchez, and L. J. Ricalde, “Optimal operation via a recurrent neural network of a wind- solar energy system,” Proc. of the Int. Joint Conf. on Neural Networks, Vol.69, pp. 2222-2228, 2011.
  6. [6] D. J. Watts and S. H. Strogatz, “Collective dynamics of smallworld networks,” Nature, Vol.393, pp. 440-442, 1998.
  7. [7] G. A. Pagani and M. Aiello, “Power grid complex network evolutions for the smart grid,” Physica A, Vol.396, pp. 248-266, 2014.
  8. [8] G. B. Dantzig, “Linear programming and extensions,” Princeton Univ. Press, 1998.
  9. [9] W. Li, “A new neural network approach of linear programming,” Proc. of the 7th Int. Conf. on Machine Learning, pp. 723-727, 2008.
  10. [10] N. Karmaekar, “A new polynomial-time algorithm for linear programming,” Combinatorica, Vol.4, pp. 373-395, 1984.
  11. [11] J. Wang, “Analysis and design of a recurrent neural network for linear programming,” IEEE Trans. on Circuits and Systems, Vol.40, pp 613-618, 1993.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 18, 2024