Multi-Stage Stochastic Planning of Wind Generation Considering Decision-Dependent Uncertainty in Wind Power Curve

2019 ◽  
Author(s):  
Wenqian Yin ◽  
Yan Xue ◽  
Shunbo Lei ◽  
Yunhe Hou
Keyword(s):  
Wind Power ◽  
Wind Generation ◽  
Power Curve ◽  
Multi Stage ◽  
Stochastic Planning ◽  
Decision Dependent Uncertainty

Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve

2020 ◽  
Vol 11 (2) ◽  
pp. 938-946 ◽  
Author(s):  
Huan Long ◽  
Linwei Sang ◽  
Zaijun Wu ◽  
Wei Gu
Keyword(s):  
Wind Power ◽  
Data Detection ◽  
Power Curve

Beneficial role of diurnal smoothing for grid integration of wind power

2021 ◽  
Author(s):  
Anasuya Gangopadhyay ◽  
Ashwin K Seshadri ◽  
Ralf Toumi
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Diurnal Cycle ◽  
Power Plants ◽  
Large Scale ◽  
Positive Impact ◽  
Forecast Error ◽  
Wind Generation ◽  
Grid Integration ◽  
Wind Maximum

<p>Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about  0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.</p>

scholarly journals A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2442 ◽  
Author(s):  
Jussi Ekström ◽  
Matti Koivisto ◽  
Ilkka Mellin ◽  
Robert Millar ◽  
Matti Lehtonen
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Statistical Modeling ◽  
Renewable Energy Sources ◽  
Measurement Data ◽  
Wind Power Generation ◽  
System Level ◽  
Wind Generation ◽  
The Impact

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

Wind Power Curve Modeling With Asymmetric Error Distribution

2020 ◽  
Vol 11 (3) ◽  
pp. 1199-1209 ◽  
Author(s):  
Yun Wang ◽  
Qinghua Hu ◽  
Shenglei Pei
Keyword(s):  
Wind Power ◽  
Error Distribution ◽  
Power Curve ◽  
Curve Modeling

Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

Probabilistic Interval Prediction of Wind Power

2015 ◽  
Vol 740 ◽  
pp. 429-432
Author(s):  
Mao Yang ◽  
Gang Du ◽  
Li Sun
Keyword(s):  
Wind Power ◽  
Prediction Error ◽  
Rapid Development ◽  
Wind Generation ◽  
Power Prediction ◽  
System Operation ◽  
Probabilistic Distribution ◽  
Wind Power Prediction ◽  
Predicted Values ◽  
Point Forecast

As wind power generation rapid development in china, wind power prediction is the key to the system operate safely. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. this paper based on the point forecast, calculate wind power prediction error, formulate the distribution of prediction error, you can get the historical probabilistic distribution of prediction error, use the distribution of error to build the risk assessment of wind power after prediction, give the fluctuate range of predicted values. Probabilistic interval forecasting can obtain the probably of power system operation safely and reliability assessment criterion.

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