Unit Commitment and Economic Dispatch for Operations Planning of Power Systems with Significant Installed Wind Power Capacity

2017 ◽  
pp. 327-364
Author(s):  
Barry G. Rawn ◽  
Madeleine Gibescu ◽  
Bart C. Ummels ◽  
Engbert Pelgram ◽  
Wil L. Kling
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
Power Capacity ◽  
Operations Planning

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.

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 Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power

Energy ◽  
2020 ◽  
Vol 193 ◽  
pp. 116826 ◽  
Author(s):  
Zhenjia Lin ◽  
Haoyong Chen ◽  
Qiuwei Wu ◽  
Weiwei Li ◽  
Mengshi Li ◽  
...  
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Economic Dispatch ◽  
Model Based ◽  
High Penetration ◽  
Tracking Model

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.

Power System Adequacy Exploration Based on UC Simulation Model

2015 ◽  
Vol 734 ◽  
pp. 744-747
Author(s):  
Zhen Bin Li ◽  
Xiao Lei Zhai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Simulation Model ◽  
Wind Power ◽  
Large Scale ◽  
Unit Commitment ◽  
System Operation ◽  
Short Run ◽  
Adequacy Assessment ◽  
Random Fluctuations

This paper takes Unit Commitment with Wind Power Electric Systems for instance and studies the application of relevant indicators in the short run adequacy decisions. It first conducted running adequacy assessment of the RBTS system, and then given unit combination solutions in the ample index constraints. The results show that the relevant indicators can be more accurately to depict the influence of random factors on system operation adequacy, and Unit Commitment decisions based adequacy index is able to give a unit commitment program at different adequacy levels. The research results provide a set of assessment indicators and assessment methods for short running adequacy problems of random fluctuations power accessing to large-scale power systems..

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