DocumentCode :
2897797
Title :
Research on the influence of forecasting precision to the hydro-wind generation coordinated operation
Author :
Ma, Cheng Fei ; Zhao, Cai Hong ; Li, Zhen ; Wang, Qian Qian
Author_Institution :
Electr. & Autom. Eng. Coll., Nanjing Normal Univ., Nanjing, China
fYear :
2011
fDate :
6-9 July 2011
Firstpage :
1803
Lastpage :
1807
Abstract :
The research on wind generation and hydro generation is deeply and widely at home and abroad. Hydro generation has become the best technological way to eliminate the impact of fluctuations of wind generation. With the development of wind generation in our country, there are many researching on the hydro-wind generation coordinated operations. But researching on the influence of forecasting precision to the hydro-wind generation coordinated operation is not reported in our country. This paper makes research and discussion in this field. Firstly, the three different wind prediction models will be studied in this paper, which are persistence forecasting model, ARIMA forecasting model and improved BP neural network forecasting model. According to the specific example, improved BP network forecasting model is better than ARIMA forecasting model and ARIMA forecasting model is better than persistence forecasting model in the same situation. Secondly, building different models of the hydro-wind generation coordinated operation is based on the above three different wind forecasting models. In the model, daily load is supplied by both wind generation and hydro generation, but wind generation must be used up and then the other is supplied by hydro generation. Because wind generation cannot be stored, the target function can only choose the variables related the hydro generation when the model of the hydro-wind generation coordinated operation is established. The present study shows that the more accurate the Wind generation prediction is, the less its influences over the water assumption in hydro generation stations are.
Keywords :
autoregressive moving average processes; forecasting theory; hydroelectric power stations; power generation planning; power system management; wind power plants; ARIMA forecasting model; back propagation neural network forecasting model; daily load; forecasting precision; hydro generation; hydro-wind generation coordinated operation; wind prediction model; Forecasting; Load modeling; Power systems; Predictive models; Wind forecasting; Wind power generation; Wind speed; Hydro generation; Wind generation; coordinated operation; forecasting precision;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on
Conference_Location :
Weihai, Shandong
Print_ISBN :
978-1-4577-0364-5
Type :
conf
DOI :
10.1109/DRPT.2011.5994191
Filename :
5994191
Link To Document :
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