Assessing the Impact of Load and Renewable Energies’ Uncertainty on a Hybrid System
International Journal of Energy and Power Engineering
Volume 5, Issue 2-1, March 2016, Pages: 1-8
Received: Mar. 30, 2015; Accepted: Mar. 31, 2015; Published: Dec. 17, 2015
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Author
Amin Shokri Gazafroudi, EE Department, Imam Khomeini International University, Qazvin, Iran
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Abstract
As increasing of fossil fuels and the trend of expiring, using of alternative fuels has been on the agenda of most countries particularly in the past two decades. In the meantime, the using of wind energy and solar radiation are extremely popular as sources of green energy and high-efficiency. Hence, the prediction of wind and solar power is important. The power output of these power plants depends on wind speed, temperature and radiation. In this paper, the uncertainty of wind and solar power generation, and load forecasting are considered based on correlation analysis on wind power, solar radiation, and ambient temperature time series. Predicted values are given to the hybrid system (wind–fuel cell–photovoltaic) to provide electrical load for the 24-hours. Finally, the proposed model is applied to demonstrate its effectiveness based on actual examples information of load, wind, radiation, temperature of wind farm and solar power plants.
Keywords
Renewable Energies, Neural Network, Correlation Analysis, Forecasting Method, Uncertainty
To cite this article
Amin Shokri Gazafroudi, Assessing the Impact of Load and Renewable Energies’ Uncertainty on a Hybrid System, International Journal of Energy and Power Engineering. Special Issue: Electricity Market. Vol. 5, No. 2-1, 2016, pp. 1-8. doi: 10.11648/j.ijepe.s.2016050202.11
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