Nowadays, increasing the renewable energy applications in power system, especially wind power, has caused higher imbalance probability between generation and demand. Therefore, an accurate estimation of wind farm reserve requirements and the reserve cost reduction in power systems with high wind power penetration is very important. In this paper, the reserve requirements of a wind farm are estimated by using a probabilistic approach. Reserve requirements of wind farm are divided into two categories provided by fast-responsive and slow-responsive resources. Indeed the purpose of this division is decreasing the cost of reserve provision by reducing the use of fast-responsive resources that is more expensive in comparison with slow-responsive resources. Wind speed prediction has been done by the ANN (Artificial Neural Network) and ARIMA (Autoregressive Integrated Moving Average), with using real measured data on a wind farm in the state of Pennsylvania. In this study, the Reserve requirements of wind farms and the cost of provision of reserve requirements will be reduced by using artificial neural networks that is a method based on artificial intelligence and is more accurate than statistical and traditional method ARIMA.
Khosravi, S., Emarati, M. R., fadaeinedjad, R., & keynia, F. (2023). Economic Estimation of Reserve Requirements of Wind Farms with using Neural Networks to Predict Wind Speed. Energy Engineering and Management, 7(1), 2-13.
MLA
Sam Khosravi; Mohammad Reza Emarati; Roohollah fadaeinedjad; Farshid keynia. "Economic Estimation of Reserve Requirements of Wind Farms with using Neural Networks to Predict Wind Speed", Energy Engineering and Management, 7, 1, 2023, 2-13.
HARVARD
Khosravi, S., Emarati, M. R., fadaeinedjad, R., keynia, F. (2023). 'Economic Estimation of Reserve Requirements of Wind Farms with using Neural Networks to Predict Wind Speed', Energy Engineering and Management, 7(1), pp. 2-13.
VANCOUVER
Khosravi, S., Emarati, M. R., fadaeinedjad, R., keynia, F. Economic Estimation of Reserve Requirements of Wind Farms with using Neural Networks to Predict Wind Speed. Energy Engineering and Management, 2023; 7(1): 2-13.
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