Marginal Bottleneck Identification in Power
System Considering Correlated Wind
Power Prediction Errors

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.

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Assessment of wind resources and short-term power prediction in Lithuania

Energetika ◽

10.6001/energetika.v62i1-2.3309 ◽

2016 ◽

Vol 62 (1-2) ◽

Author(s):

Giedrius Gecevičius ◽

Mantas Marčiukaitis ◽

Antanas Markevičius ◽

Vladislovas Katinas

Keyword(s):

Wind Energy ◽

Wind Power ◽

Wind Farms ◽

Prediction Errors ◽

Power Prediction ◽

Energy Potential ◽

Wind Power Prediction ◽

Wind Energy Potential ◽

Wind Measurements ◽

Diurnal Wind

The installed wind power in Lithuania reached 422 MW in 2015, and it was one of the most developing renewable energy sectors in the country. For this reason, it is important to estimate wind energy potential and the tendencies of wind power prediction accuracy. In this work, the results of statistical analysis of wind measurements in a number of locations in Lithuania are presented, which makes the basis for wind energy potential estimation. Wind power prediction errors of different time scales have been analysed, and the influence of seasonal and diurnal wind power variation is pointed out. Also, the possibilities of connection of new wind farms to the grid are analysed in the paper. Investigation shows that northern and middle regions of Lithuania are the most favourable for further wind power development with the goal of reaching the total installed power of 840 MW till 2030.

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Error Analysis of Ultra Short Term Wind Power Prediction Model and Effect on the Power System Frequency

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) ◽

10.1109/appeec45492.2019.8994671 ◽

2019 ◽

Author(s):

Xiaofan Zhu ◽

Rui Chen ◽

Kai Ding ◽

Deqiang Gong ◽

Wei Xi ◽

...

Keyword(s):

Error Analysis ◽

Power System ◽

Prediction Model ◽

Wind Power ◽

Power Prediction ◽

Short Term ◽

Wind Power Prediction ◽

System Frequency

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The investigation of factors determining wind power prediction accuracy: case study of western Lithuania

Energetika ◽

10.6001/energetika.v65i1.3978 ◽

2019 ◽

Vol 65 (1) ◽

Author(s):

Giedrius Gecevičius ◽

Mantas Marčiukaitis ◽

Marijona Tamašauskienė

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Statistical Methods ◽

Hybrid Methods ◽

Prediction Errors ◽

Power Prediction ◽

Local Conditions ◽

Minimal Impact ◽

Wind Power Prediction

In order to mitigate climate change, more attention every year is being given to wind energy. However, despite minimal impact of wind turbines on the environment, there is a negative side as well. Wind speed variations are a stochastic process, and it is difficult to predict wind power accurately. Therefore, unpredictable power can disbalance the power grid; besides, huge power reserves are necessary. Wind energy can be forecasted based on statistical, physical or hybrid methods and models. However, all methods and models generate power prediction errors during different time horizons. The paper presents an analysis of wind power prediction errors determining factors based on statistical, physical and hybrid approaches. Investigation revealed that combination of statistical methods – nonlinear regression, model output statistics, the most suitable power curve and wind speed correction methods – reduced wind power prediction errors up to 1.5%. A detailed evaluation of relief variations and surface roughness increased wind power accuracy by 2%. Considering the local conditions of the western part of Lithuania, the best suitable tool for a short-term wind power prediction is a hybrid model including a detailed description of topographical conditions and the most precise statistical methods.

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