A Dispatch Algorithm for Smart Grid with Wind Generation

Ming Sheng Gao; Hui Jiang

doi:10.4028/www.scientific.net/AMM.704.186

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 704A Dispatch Algorithm for Smart Grid with Wind...

A Dispatch Algorithm for Smart Grid with Wind Generation

Abstract:

Due to the volatility and intermittence of wind generation, it is challenging to develop an economic dispatch algorithm for smart grids that not only meets the dynamic demand of electric energy, but also maximizes the benefits of energy suppliers using such renewable resource. In this paper, we account for smart grids with two categories of energy users, namely traditional energy users and opportunistic energy users, and address pricing and dispatch at one period of time (e.g., in hours). We model this problem as a stochastic programming problem that can be solved to determine the optimal day-ahead retail price, real-time price and the procurement of electricity energy.

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Periodical:

Applied Mechanics and Materials (Volume 704)

Pages:

186-189

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.704.186

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Online since:

December 2014

Authors:

Ming Sheng Gao*, Hui Jiang

Keywords:

Dispatch, Smart Grid (SG), Wind Generation

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* - Corresponding Author

References

[1] I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, IEEE Trans. Energy Convers., vol. 19, no. 2, pp.352-361, Jun. (2004).

DOI: 10.1109/tec.2003.821865

Google Scholar

[2] B. G. Brown, R.W. Katz, and A. H. Murphy, Time series models to simulate and forecast wind speed and wind power,J. Clim. Appl. Meterol., vol. 23, pp.1184-1195, Aug. (1984).

DOI: 10.1175/1520-0450(1984)023<1184:tsmtsa>2.0.co;2

Google Scholar

[3] S. Li, D. C. Wunsch, E. A. O'Hair, and M. G. Giesselmann, Using neural networks to estimate wind turbine power generation, IEEE Trans. Energy Convers., vol. 16, no. 3, pp.276-282, Sep. (2001).

DOI: 10.1109/60.937208

Google Scholar

[4] M. He, S. Murugesan, and J. Zhang, Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration, in Proc. IEEE Infocom, June (2011).

DOI: 10.1109/infcom.2011.5935204

Google Scholar

[5] S. -E. Fleten and E. Pettersen, Constructing bidding curves for a pricetaking retailer in the norwegian electricity market, IEEE Trans. Power Syst., vol. 20, pp.701-708, May (2005).

DOI: 10.1109/tpwrs.2005.846082

Google Scholar