Ultra-short term probabilistic transmission congestion forecasting considering wind power integration

2009 ◽  
Author(s):  
Guoqiang Zhang ◽  
Boming Zhang ◽  
Hongbin Sun ◽  
Wenchuan Wu
Keyword(s):  
Wind Power ◽  
Transmission Congestion ◽  
Short Term ◽  
Wind Power Integration

Improving wind power integration by a novel short-term dispatch model based on free heat storage and exhaust heat recycling

Energy ◽  
2018 ◽  
Vol 160 ◽  
pp. 940-953 ◽  
Author(s):  
Jinda Wang ◽  
Zhigang Zhou ◽  
Jianing Zhao ◽  
Jinfu Zheng
Keyword(s):  
Wind Power ◽  
Heat Storage ◽  
Short Term ◽  
Wind Power Integration ◽  
Model Based ◽  
Exhaust Heat

scholarly journals Short-term wind power prediction based on GPR-BSO model

2021 ◽  
Vol 256 ◽  
pp. 02035
Author(s):  
Tao Chen ◽  
Xinjian Li ◽  
Zhemeng Zhang ◽  
Tongguang Yang ◽  
Shengtao He ◽  
...  
Keyword(s):  
Wind Power ◽  
Gaussian Process Regression ◽  
Optimization Method ◽  
Stable Operation ◽  
Local Optimum ◽  
Power Prediction ◽  
Short Term ◽  
Additional Information ◽  
Wind Power Integration ◽  
Wind Power Prediction

Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parameters intelligent optimization method is proposed in this paper. Firstly, the hyper-parameters solution of GPR is formulated as a nonlinear optimization with constraints. Then, an intelligent algorithm named Brain-storming optimization (BSO) is adopted to obtain the optimal hyper-parameters of GPR. Furthermore, the performance is examined on short-term wind power data. Most importantly, the GPR incorporating BSO can avoid the hyper-parameters at local optimum.

Study on Short-Term Wind Power Prediction Model Based on ARMA Theory

2013 ◽  
Vol 448-453 ◽  
pp. 1875-1878 ◽  
Author(s):  
Wei Li ◽  
Hong Tu Zhang ◽  
Ting Ting An
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Arma Model ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Integration ◽  
Proposed Model ◽  
Wind Power Prediction ◽  
High Prediction ◽  
New Research

At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows the science and operability of the proposed model. It provides a new research method for the wind power prediction.

Wind Power Integration: Connection and system operational aspects

2007 ◽  
Author(s):  
Fox ◽  
Jenkins ◽  
O'Malley ◽  
Bryans ◽  
Anaya-Lara ◽  
...  
Keyword(s):  
Wind Power ◽  
Wind Power Integration ◽  
Operational Aspects
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