The operating risk evaluation of power system with a large scale of wind farms considering uncertainty of wind power prediction and extreme weather

2013 ◽  
Author(s):  
Yinli Cao ◽  
Wei Wang ◽  
Zongxiang Lu ◽  
Haibo Li
Keyword(s):  
Power System ◽  
Wind Power ◽  
Risk Evaluation ◽  
Large Scale ◽  
Wind Farms ◽  
Extreme Weather ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Operating Risk

scholarly journals M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction

2020 ◽  
Vol 10 (21) ◽  
pp. 7915
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Kunjin Chen ◽  
Xinyang Chen
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Task Graph ◽  
Probabilistic Prediction ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

scholarly journals Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Bingbing Xia ◽  
Qiyue Huang ◽  
Hao Wang ◽  
Liheng Ying
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Wind Power ◽  
Large Scale ◽  
Weather Prediction ◽  
Measurement Data ◽  
Stable Operation ◽  
Power Prediction ◽  
Actual Measurement ◽  
Wind Power Prediction

Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

scholarly journals Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models

2005 ◽  
Vol 29 (6) ◽  
pp. 475-489 ◽  
Author(s):  
Henrik Madsen ◽  
Pierre Pinson ◽  
George Kariniotakis ◽  
Henrik Aa. Nielsen ◽  
Torben S. Nielsen
Keyword(s):  
Wind Power ◽  
Electricity Markets ◽  
Large Scale ◽  
Prediction Models ◽  
Wind Farms ◽  
Offshore Wind ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Prediction Systems

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

Research on Wind Power Fluctuation and its Impacts on Power System Frequency

2013 ◽  
Vol 291-294 ◽  
pp. 407-414 ◽  
Author(s):  
Guo Peng Zhou ◽  
Fu Feng Miao ◽  
Xi Sheng Tang ◽  
Tao Wu ◽  
Shan Ying Li ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farms ◽  
Frequency Stability ◽  
Frequency Deviation ◽  
Intrinsic Mode Functions ◽  
Power Fluctuation ◽  
System Frequency ◽  
The Impact

The output power of wind farms has significant randomness and variability, which results in adverse impacts on power system frequency stability. This paper extracts wind power fluctuation feature with the HHT (Hilbert-Huang Transform) method. Firstly, the original wind power data was decomposed into several IMFs (Intrinsic Mode Functions) and a tendency component by using the EMD (Empirical Mode Decomposition) method. Secondly, the instantaneous frequency of each IMF was calculated. On this basis, taking a WSCC 9-bus power system as benchmark, the impact on power system frequency caused by wind power fluctuation was simulated in a real-time simulation platform, and the key component which results in the frequency deviation was found. The simulation results validate the wind power fluctuation impacts on frequency deviation, underlying the following study on power system frequency stability under the situation of large-scale intermittent generation access into the grid.

Research of Aggregated Wind Power Prediction Method for a Region

2013 ◽  
Vol 860-863 ◽  
pp. 262-266
Author(s):  
Jin Yao Zhu ◽  
Jing Ru Yan ◽  
Xue Shen ◽  
Ran Li
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Power Characteristics ◽  
Wind Power Prediction ◽  
Grey Correlation Theory

Wind power is intermittent and volatility. Some new problems would arise to power system operation when Large-scale wind farm is connected with power systems. One of the most important effect is the influence on the grid dispatch. An aggregated wind power prediction method for a region is presented. By means of analyzing power characteristics and correlation, then the greater correlation is selected as model input. Based on grey correlation theory, a least squares support vector machine prediction model is established. Finally, this method is executed on a real case and integrated wind power prediction method can effectively improve the prediction accuracy and simplify the prediction step are proved.

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