Optimal Power Control of Smart Grid Based on Neural Network Optimization

Li Yan Zhang; Shu Hai Quan

doi:10.4028/www.scientific.net/KEM.474-476.626

Paper Titles

Ploidy Determination of Parthenogenetic Artemia by Flow Cytometry
p.605

Fatigue Damage Accumulation Model and Fatigue Life Predication of Composite Laminate T300/ Epoxy-Resin
p.609

Design and Implementation of a Manufacturing Business Collaboration Platform Based ESB Technology
p.615

New Feature Subset Selection Algorithm Using Class Association Rules Mining
p.622

Optimal Power Control of Smart Grid Based on Neural Network Optimization
p.626

Machinability of ZTC4 Titanium Alloy Analysis Using Finite Element Simulation and Milling Experiment
p.633

Bearing Fault Detection Based on Order Tracking and Complex Morlet Wavelet Transform
p.639

The Covered Operation of Absolutely Grey Number and Grey Power Function
p.645

The Method to Construct Elementary Dependent Function on Single Interval
p.651

HomeKey Engineering MaterialsKey Engineering Materials Vols. 474-476Optimal Power Control of Smart Grid Based on...

Optimal Power Control of Smart Grid Based on Neural Network Optimization

Abstract:

This paper presents a neural network based optimal control approach for the power allocation problem of smart grid. Firstly the optimal control framework which consists of center control layer, area control layer and generator control layer is presented. In order to reduce computation time of optimal control, the neural network optimization method and schematic diagram is proposed. Simulation is implemented in a modified IEEE30-bus power system comprising 6 conventional generators and a wind plant. Simulation results demonstrate that the proposed neural network optimization approach is satisfied the demand of computation time and the optimal power controller can allocate the power into loads reasonably and cope with the fluctuation of the load and renewable energy source’s output power.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Pages:

626-632

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.474-476.626

Citation:

Cite this paper

Online since:

April 2011

Authors:

Li Yan Zhang, Shu Hai Quan

Keywords:

Neural Network Optimization, Optimal Power Control, Renewable Energy Source, Smart Grid (SG)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Apt: Journal of Power Sources, vol. 169, no. 2 (2007), pp.369-374.

Google Scholar

[2] V. S. Pappala, I. Erlich, K. Rohrig and J. Dobschinski: IEEE Transactions on Power Systems, vol. 24, no. 2 (2009), pp.940-950.

DOI: 10.1109/tpwrs.2009.2016504

Google Scholar

[3] M.P. Kennedy, L.O. Chua, Neural networks for nonlinear programming, IEEE Transactions on Circuits and Systems, vol. 35, no. 5, pp.554-562, May, (1988).

DOI: 10.1109/31.1783

Google Scholar

[4] Y.S. Xia, J. Wang: IEEE Transactions on Systems, Man and Cybernetics—Part B vol. 31, no. 1 (2001), p.147–154.

Google Scholar

[5] D.W. Tank, J.J. Hopfield: IEEE Trans. Circuits Syst. vol. CAS-33, no. 5 (1986), p.533–541.

Google Scholar

[6] I. N. da Silva, W. Cd Amaral, L. V. de Arruda: Applied Mathematical Modelling, vol. 31, no. 1 (2007), pp.78-92.

Google Scholar

[7] Y.S. Xia and J. Wang: IEEE Transactions on Circuits and Systems, vol. 51, no. 7 (2004), pp.1385-1394.

Google Scholar

[8] B. Fardanesh: IEEE Computer Applications in Power, vol. 15, no. 3 (2002), pp.24-31.

Google Scholar

[9] G. Bumiller, L. Lampe, H. Hrasnica: IEEE Communications Magazine, vol. 48, no. 4 (2010), pp.106-113.

Google Scholar

[10] T. Y. Lee: IEEE Transactions on Power Systems, vol. 11, no. 4 (2007), pp.1612-1621.

Google Scholar