Improving short-term wind power forecasts during ramp events by including nacelle wind speed observations

2021 ◽  
Author(s):  
Moritz Lochmann ◽  
Heike Kalesse-Los ◽  
Michael Schäfer ◽  
Ingrid Heinrich ◽  
Ronny Leinweber
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Deviation ◽  
Short Term ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
Ramp Events

<p>Wind energy is and will be one of the key technologies for a transition to green electricity. However, the smooth integration of the generated wind energy into the electrical grid depends on reliable power forecasts. Rapid changes in power generation, so-called ramps, are not always reflected properly in NWP data and pose a challenge for power predictions and, therefore, grid operation. While contributions to the topic of ramp forecasting increased in the recent years, this work approaches the mitigation of deviations from the forecast more directly.</p> <p>The power forecast tool used here is based on an artificial neural network, trained and evaluated on multiple years of data. It is applied in comparison to power generation data for a 44 MW wind farm in Brandenburg. For short-term wind power forecasts, NWP wind speeds in this power forecast tool are replaced with recent Doppler Lidar wind profiles and nacelle wind speed observations from ultra-sonic anemometers, aiming to provide an easy-to-implement way to reduce negative impacts of ramps. Compared to NWP input data, this persistence approach with observational data aims to improve the forecast quality especially during the time of wind ramps.</p> <p>Different ramp definitions and forecast horizons are explored. In general, the number of ramps detected increases dramatically when using wind speed observations instead of the (too smooth) NWP model data. In addition, the mean deviation between power forecast and actual power generation around ramp events decreases, indicating a reduced need for balancing efforts.</p>

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

scholarly journals Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3901 ◽  
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Observation Data ◽  
Short Term ◽  
Term Memory ◽  
Current Observation ◽  
Long Short Term Memory

Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

scholarly journals Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

scholarly journals A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Yongsheng Wang ◽  
Jing Gao ◽  
Zhiwei Xu ◽  
Jidong Luo ◽  
Leixiao Li
Keyword(s):  
Wind Energy ◽  
Prediction Model ◽  
Wind Power ◽  
Output Power ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term Memory ◽  
Evaluation Metrics ◽  
Power Prediction ◽  
Short Term

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

scholarly journals Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 650 ◽  
Author(s):  
Jianguo Zhou ◽  
Xiaolei Xu ◽  
Xuejing Huo ◽  
Yushuo Li
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Meteorological Data ◽  
Frequency Component ◽  
High Frequency Component ◽  
Power Prediction ◽  
Short Term ◽  
Combined Model ◽  
Wind Power Prediction

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

The Application of Wind Speed Numerical Simulation in Wind Power Generation

2013 ◽  
Vol 380-384 ◽  
pp. 3370-3373 ◽  
Author(s):  
Li Yang Liu ◽  
Jun Ji Wu ◽  
Shao Liang Meng
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Stable Operation ◽  
Wind Gust ◽  
Gradual Change ◽  
Wake Effect

With the massive development and application of wind energy, wind power is having an increasing proportion in power grid. The changes of the wind speed in a wind farm will lead to fluctuations in the power output which would affect the stable operation of the power grid. Therefore the research of the characteristics of wind speed has become a hot topic in the field of wind energy. In the paper, the wind speed at the wind farm was simulated in a combination of wind speeds by which wind speed was decomposed of four components including basic wind, gust wind, stochastic wind and gradient wind which denote the regularity, the mutability, the gradual change and the randomness of a natural wind respectively. The model is able to reflect the characteristics of a real wind, easy for engineering simulation and can also estimate the wind energy of a wind farm through the wind speed and wake effect model. This paper has directive significance in the estimation of wind resource and the layout of wind turbines in wind farms.

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