Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois

2013 ◽  
Vol 4 (1) ◽  
pp. 250-261 ◽  
Author(s):  
Audun Botterud ◽  
Zhi Zhou ◽  
Jianhui Wang ◽  
Jean Sumaili ◽  
Hrvoje Keko ◽  
...  
Keyword(s):  
Wind Power ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

2021 ◽  
Vol 13 (24) ◽  
pp. 13609
Author(s):  
Diaa Salman ◽  
Mehmet Kusaf
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Wind Power ◽  
Unit Commitment ◽  
Optimization Technique ◽  
Test System ◽  
Optimization Techniques ◽  
Support Vector ◽  
Wind Power Forecasting ◽  
Power Forecasting

Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.

Wind power forecasting uncertainty and unit commitment

2011 ◽  
Vol 88 (11) ◽  
pp. 4014-4023 ◽  
Author(s):  
J. Wang ◽  
A. Botterud ◽  
R. Bessa ◽  
H. Keko ◽  
L. Carvalho ◽  
...  
Keyword(s):  
Wind Power ◽  
Unit Commitment ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3764 ◽  
Author(s):  
Shahram Hanifi ◽  
Xiaolei Liu ◽  
Zi Lin ◽  
Saeid Lotfian
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Unit Commitment ◽  
Hybrid Methods ◽  
Cost Effective ◽  
Predictive Modelling ◽  
Computational Time ◽  
Wind Power Forecasting ◽  
Effective Operation ◽  
Power Forecasting

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

scholarly journals Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Hao Chen ◽  
Jianzhong Zhang ◽  
Yubo Tao ◽  
Fenglei Tan
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Asymmetric Effect ◽  
Wind Power Forecasting ◽  
Asymmetric Volatility ◽  
Forecasting Performance ◽  
Characteristics Analysis ◽  
Asymmetric Garch ◽  
Power Forecasting

AbstractWind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.

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