Short-Term Power Fluctuations of Large Wind Power Plants

2002 ◽  
Author(s):  
Yih-Huei Wan ◽  
Demy Bucaneg
Keyword(s):  
Wind Power ◽  
Power Output ◽  
Power Plants ◽  
Spatial Diversity ◽  
Ancillary Services ◽  
Short Term ◽  
Output Data ◽  
Wind Power Plants ◽  
Power Fluctuations ◽  
Large Wind

With electric utilities and other power providers showing increased interest in wind power and with growing penetration of wind capacity into the market, questions about how wind power fluctuations affect power system operations and about wind power’s ancillary services requirements are receiving lots of attention. To evaluate short-term wind power fluctuations and the range of ancillary service of wind power plants, the National Renewable Energy Laboratory (NREL), in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. The project’s purpose is to acquire actual, long-term wind power output data for analyzing wind power fluctuations, frequency distribution of the changes, the effects of spatial diversity, and wind power ancillary services. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. Although the efforts to monitor wind power plants are ongoing, we can already conclude from the available data that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant.

Short-Term Power Fluctuations of Large Wind Power Plants*

2002 ◽  
Vol 124 (4) ◽  
pp. 427-431 ◽  
Author(s):  
Yih-huei Wan ◽  
Demy Bucaneg,
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Power Level ◽  
Short Term ◽  
Wind Power Plants ◽  
Weather Events ◽  
Power Fluctuations ◽  
Large Wind

To evaluate short-term wind power fluctuations and their impact on electric power systems, the National Renewable Energy Laboratory, in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. From the available data, we can already conclude that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant. The data also offer encouraging evidence that accurate wind power forecasting is feasible. To the utility system, large wind power plants are not really random burdens. The narrow range of power level step changes provides a lot of information with which system operators can make short-term predictions of wind power. Large swings of wind power do occur, but those infrequent large changes (caused by wind speed changes) are always related to well-defined weather events, most of which can be accurately predicted in advance.

Output Power Correlation Between Nearby Wind Power Plants

2003 ◽  
Author(s):  
Yih-Huei Wan ◽  
Michael Milligan ◽  
Brian Parsons
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Power Output ◽  
High Frequency ◽  
Power Plants ◽  
Meteorological Data ◽  
Low Frequency ◽  
Spatial Separation ◽  
Wind Power Plant ◽  
Wind Power Plants

The National Renewable Energy Laboratory (NREL) started a project in 2000 to record long-term, high-frequency (1-Hz) wind power output data from large commercial wind power plants. Outputs from about 330 MW of wind generating capacity from wind power plants in Buffalo Ridge, Minnesota, and Storm Lake, Iowa, are being recorded. Analysis of the collected data shows that although very short-term wind power fluctuations are stochastic, the persistent nature of wind and the large number of turbines in a wind power plant tend to limit the magnitudes and rates of changes in the levels of wind power. Analyses of power data confirm that spatial separation greatly reduces variations in the combined wind power output relative to output from a single wind power plant. Data show that high frequency variations of wind power from two wind power plants 200 km apart are independent of each other, but low frequency power changes can be highly correlated. This fact suggests that time-synchronized power data and meteorological data can aid in the development of statistical models for wind power forecasting.

scholarly journals Frequency Support Markets and Wind Power Integration

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7450
Author(s):  
Anzhelika Ivanova ◽  
José Luis Domínguez-García ◽  
Cristina Corchero
Keyword(s):  
Wind Power ◽  
Electricity Markets ◽  
Power Plants ◽  
Economic Benefits ◽  
Ancillary Services ◽  
Renewable Sources ◽  
Wind Generators ◽  
Wind Power Plants ◽  
Market Practices ◽  
System Benefit

Europe’s initiative to reduce the emissions of harmful gases has significantly increased the integration of renewable sources into power networks, particularly wind power. Variable renewable sources pose challenges to sustain the balance between generation and demand. Thus, the need for ancillary services to cope with this problem has increased. In this regard, the integration of larger shares of wind generation would have a clear system benefit when wind generators are able to provide these ancillary services. This would also have implications for electricity markets, enabling these services from wind power plants. This article gives an overview of several European markets for frequency support (FS) services, also referred to as FS markets. It identifies the changes in national regulations of 10 European countries to standardize these services based on the ENTSO-E guidelines. However, most of the countries still use their national service definitions, which presents a problem for researchers to understand the national regulations in relation to the ENTSO-E guidelines. This article provides a classification of the national FS services under the definitions of the ENTSO-E guidelines to facilitate research on this topic. Furthermore, it highlights the main requirements for the market practices that would encourage the participation of wind power generation in the provision of these services. An estimation of the economic benefits for wind producers from the provision of FS services is provided as well to show a possible outcome if changes are not made in national policies.

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.

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