scholarly journals Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 712 ◽  
Author(s):  
Ines Würth ◽  
Laura Valldecabres ◽  
Elliot Simon ◽  
Corinna Möhrlen ◽  
Bahri Uzunoğlu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Measurement Data ◽  
Physical Models ◽  
Energy Applications ◽  
Short Term Forecasting ◽  
Grid Operators

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

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.

scholarly journals Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

2019 ◽  
Vol 11 (7) ◽  
pp. 781 ◽  
Author(s):  
Charlotte Hasager ◽  
Mikael Sjöholm
Keyword(s):  
Remote Sensing ◽  
Wind Energy ◽  
Wind Farm ◽  
Doppler Radar ◽  
Doppler Lidar ◽  
Atmospheric Conditions ◽  
Special Issue ◽  
Energy Applications ◽  
Wind Turbine Control ◽  
Wind Lidar

This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented.

Distribution Optimizing Model of Benefits of Wind Power, Thermal Power Baling Delivery

2013 ◽  
Vol 389 ◽  
pp. 102-107
Author(s):  
Tao Lei ◽  
Zhong Fu Tan ◽  
Li Qiong Lin ◽  
Li Wei Ju
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Thermal Power ◽  
Clean Energy ◽  
Energy Development ◽  
Renewable Energy Development ◽  
Power Peak ◽  
Optimizing Model

Wind energy curtailment to reduce wind power and solving wind power grid connected problem is the key element to increase wind power grid-connected and promote renewable energy development. Firstly, from the perspective of clean energy development ,utilization and increasing in wind farm investment income, this paper has analyzed the reason of wind energy curtailment, and summarized effective policies for the management of wind energy curtailment from all over the world. On this basis, this paper considered the loss of interest of thermal units during wind power peak regulation, in order to enhance the enthusiasm of thermal units to participate wind power and thermal power bale delivery, this paper has established wind power, thermal power bale delivery model of calculation of interest. From the numerical example, we conclude that after wind power and thermal power generation rights trading, the wind turbine will get higher revenue due to higher pool purchase price,while thermal power units are exposed to a loss because the tradeoff price is lower than the stake electrovalence.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

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.

Analysis of the Use of Remote Sensing Measurements for Developing Wind Power Projects

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Gustavo S. Böhme ◽  
Eliane A. Fadigas ◽  
Julio R. Martinez ◽  
Carlos E. M. Tassinari
Keyword(s):  
Remote Sensing ◽  
Wind Power ◽  
Power Plants ◽  
Measurement Data ◽  
Flow Modeling ◽  
Short Term ◽  
Wind Power Plants ◽  
Reduced Data ◽  
Average Uncertainty

Micrositing wind flow modeling presents one of the most relevant uncertainties in the project of wind power plants. Studies in the area indicate that the average uncertainty related to this item varies between 2.4% and 8% of the annual energy production (AEP). The most efficient form to mitigate this uncertainty is to obtain additional measurements from the site. This can be achieved by installing met masts and by applying short-term remote sensing campaigns (LIDAR and SODAR). Ideally, measurement campaigns should have at least one complete year of data to capture seasonal changes in the local wind behavior and to increase the long-term representation of the sample. However, remote sensing is frequently performed in reduced periods of measurement, coming down to months or even weeks of campaign. The main contribution of this paper is to analyze whether short-term remote sensing measurements contribute to the development of wind power projects, given the associated uncertainties due to low representativeness of the reduced data sample. This study was performed using over 60 years of wind measurement data. Its main findings indicate that the contribution of short-term remote sensing campaigns vary depending on the complexity of the local terrain, and the respective uncertainty related to horizontal and vertical extrapolation of micrositing models. The results showed that in only 30% of the cases, a 3 month measurement campaign reduced the projects overall uncertainty. This number increases to 50% for a 6 month campaign and 90% for a 10 month campaign.

Study of Influence on Power System Voltage Stability of Wind Farm Power Output Fluctuation

2013 ◽  
Vol 694-697 ◽  
pp. 846-849
Author(s):  
Jian Yuan Xu ◽  
Wei Fu Qi ◽  
Yun Teng
Keyword(s):  
Power System ◽  
Wind Power ◽  
Power Output ◽  
Voltage Stability ◽  
Wind Farm ◽  
Reactive Power ◽  
Simulation Analysis ◽  
Power Grid ◽  
Reactive Power Compensation ◽  
Grid Integration

This paper mainly studies wind power fluctuations how to affect voltage stability after the wind power grid integration, and reactive power compensation equipment on improving effect. In certain parts of the wind farm, for example, firstly, analyzing the wind farm reactive power problems. Then introduce the reactive power compensation equipment that used in the wind farm. Finally, with PSCAD software, making a simulation analysis about the influence on the power grid voltage according to adopting the different reactive power compensation devices or not.

Operation and Simulation of Hybrid Wind and Gas Turbine Power System Employing Wind Power Forecasting

2012 ◽  
Author(s):  
Junrong Xia ◽  
Pan Zhao ◽  
Yiping Dai
Keyword(s):  
Power System ◽  
Gas Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Large Wind ◽  
Operation Scheduling

Due to the intermittence and fluctuation of wind resource, the integration of large wind farms in a power grid introduces an additional stochastic component to power system scheduling. This always brings challenges to maintain the stability of power system. Integrating gas turbine units with wind farms can compensate their output fluctuation. In this paper, a methodology for the operation scheduling of a hybrid power system that consists of a large wind farm and gas turbine units is presented. A statistical model based on numerical weather prediction is used to forecast power output of the wind farm for the next 24 hours at quarter-hour intervals. Forecasts of wind power are used for optimizing the operation scheduling. In order to study the dynamic performance of the proposed hybrid power system, dynamic modeling of this hybrid power system is addressed. Wind farm and gas turbine units are integrated through an AC bus, and then connected to a power grid. An aggregated model of the wind farm and detailed models of gas turbine units are developed, and are implemented using MATLAB/Simulink. Simulation studies are carried out to evaluate the system performance using real weather data. The simulation results show that the proposed hybrid power system can compensate fluctuating wind power effectively and make wind power more reliable.

Grid Tripping and Re-connection: Full-scale Experimental Validation of a Dynamic Wind Turbine Model

2003 ◽  
Vol 27 (3) ◽  
pp. 205-213 ◽  
Author(s):  
Niels Raben ◽  
Martin Heyman Donovan ◽  
Erik Jørgensen ◽  
Jan Thisted ◽  
Vladislav Akhmatov
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
System Stability ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Shaft System ◽  
The Impact ◽  
Turbine Model

An experiment with tripping and re-connecting a MW wind turbine generator was carried out at the Nøjsomheds Odde wind farm in Denmark. The experimental results are used primarily to validate the shaft system representation of a dynamic wind turbine model. The dynamic wind turbine model is applied in investigations of power system stability with relation to incorporation of large amounts of wind power into the Danish power grid. The simulations and the measurements are found to agree. The experiment was part of a large R&D program started in Denmark to investigate the impact of the increasing capacity of wind power fed into the Danish power grid.

Effects of global warming on wind energy potential

2015 ◽  
Vol 12 (4) ◽  
pp. 369-374 ◽  
Author(s):  
Afsin Gungor
Keyword(s):  
Global Warming ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Data ◽  
Energy Potential ◽  
Time Intervals ◽  
Wind Energy Generation ◽  
Wind Energy Potential ◽  
Yearly Data

A recent study conducted to determine the potential of wind power in Nigde which used 35 year wind data, has shown that global warming may also affect the potential of wind power negatively. The wind data were collected on 10 min time intervals at 10 m mast height. The missing data were 3.9%. When the results are closely examined it is observed that the potential of wind power has decreased dramatically throughout the years. The 35 yearly data has shown a decrease of wind power density from 48.14 W/m2 to 13.25 W/m2. These results are of extreme importance because of various reasons given below. The first problem we may see is that it is possible to observe an area which was once regarded as a highly suitable region for wind energy generation is now not as sustainable as it was assumed to be. Thus it may stand as a hidden but great risk for certain wind farm investments. Therefore, the calculation of wind power potential is a really serious matter to deal with. Moreover, if the loss of the wind power potential is observed consistently and continually as a result of global warming, the only reasonable solution to this problem may be the relocation of the whole power-plant.

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