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Documents presented here are not the product of nor are they necessarily endorsed by National Wind Watch. These resource documents are provided to assist anyone wishing to research the issue of industrial wind power and the impacts of its development. The information should be evaluated by each reader to come to their own conclusions about the many areas of debate.


Date added:  January 11, 2017
North Dakota, TechnologyPrint storyE-mail story

Construction of Xcel Energy’s Courtenay Wind Farm, North Dakota

Author:  Xcel Energy


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Date added:  January 3, 2017
TechnologyPrint storyE-mail story

Grid-scale fluctuations and forecast error in wind power

Author:  Bel, G.; Connaughton, C.P.; Toots, M.; and Bandi, M.M.

Abstract:
Wind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid. We find that not only are grid-scale fluctuations temporally correlated up to a day, but they possess a self-similar structure—a signature of long-range correlations in atmospheric turbulence affecting wind power. Using the statistical structure of temporal correlations in fluctuations for generated and forecast power time series, we quantify two types of forecast error: a timescale error (eτ) that quantifies deviations between the high frequency components of the forecast and generated time series, and a scaling error (eζ) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations for generated power. With no a priori knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error (eτ) and the scaling error (eζ).

G Bel
Department of Solar Energy and Environmental Physics, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Israel

C P Connaughton
Centre for Complexity Science, University of Warwick, Coventry, U.K.

M Toots and M M Bandi
Collective Interactions Unit, Okinawa Institute of Science and Technology Onna, Okinawa, Japan

Published 1 February 2016 • New Journal of Physics, Volume 18, February 2016
doi: 10.1088/1367-2630/18/2/023015

Download original document: “Grid-scale fluctuations and forecast error in wind power”

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Date added:  January 2, 2017
TechnologyPrint storyE-mail story

New insights into fluctuations of wind energy, with implications for engineering and policy

Author:  Okinawa Institute of Science and Technology Graduate University

Summary:
The amount of energy generated by renewables fluctuates depending on the natural variability of resources at any given time. The sun isn’t always shining, nor is the wind always blowing, so traditional power plants must be kept running, ready to fill the energy gap at a moment’s notice. Because the grid has no storage, and unlike coal or nuclear, there is no control over the fluctuating production of renewable energy, the energy they produce has to be consumed straight away, or risk collapsing the electrical grid. On particularly windy days, for example, surges in power generated by wind turbines have been known to overwhelm the electrical grid, causing power outages. To avoid this, operators of large power plants sometimes resort to paying consumers to use electricity on particularly sunny and windy days when there is too much excess power in the system, in order to balance the supply and demand of energy at the grid.

FULL STORY

The amount of energy generated by renewables fluctuates depending on the natural variability of resources at any given time. The sun isn’t always shining, nor is the wind always blowing, so traditional power plants must be kept running, ready to fill the energy gap at a moment’s notice. Because the grid has no storage, and unlike coal or nuclear, there is no control over the fluctuating production of renewable energy, the energy they produce has to be consumed straight away, or risk collapsing the electrical grid. On particularly windy days, for example, surges in power generated by wind turbines have been known to overwhelm the electrical grid, causing power outages. To avoid this, operators of large power plants sometimes resort to paying consumers to use electricity on particularly sunny and windy days when there is too much excess power in the system, in order to balance the supply and demand of energy at the grid.

Dealing with the peaks and troughs of intermittent renewable energy will become increasingly challenging as governments try to phase out of more stable coal-powered energy sources in the coming decades. In order to mitigate or manage these fluctuations in renewable energy, we need to understand the nature of these fluctuations better. Professor Mahesh Bandi, head of the Collective Interactions Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) has used turbulence theory combined with experimental wind plant data to explain the statistical nature of wind power fluctuations in a single-author paper published in Physical Review Letters.

Wind speed patterns can be depicted as a wind speed spectrum on a graph. In 1941, Russian physicist Andrei Kolmogorov worked out the spectrum of wind speed fluctuations. Subsequently, it was shown that the spectrum for wind power follows the exact same pattern. However, until now, it was simply assumed that these spectra were identical due to the relationship between power and speed, where power equals wind speed cubed. But this proved to be a red herring. Professor Bandi has shown for the first time that the spectrum of wind power fluctuations follows the same pattern as wind speed fluctuations for a different reason.

Kolmogorov’s 1941 result applies to measurements of wind speed made at several distributed points in space at the same time. But wind power fluctuations at a turbine are measured at a fixed location over an extended time period. The two measurements are fundamentally different, and by carefully accounting for this difference, Professor Bandi was able to explain the spectrum of wind power fluctuations for an individual turbine.

We can think of turbulence as a ball of air, or an ‘eddy’, of fluctuating wind speed. Long time-scale, low frequency eddies can span hundreds of kilometres. Inside these large eddies are shorter time-scale, high frequency eddies that might span a few kilometres. Therefore, if all of the turbines in the same wind plant fall within the same short and long time-scale eddies, the energy they produce fluctuates as if the entire plant were one giant turbine. This is exactly what Professor Bandi found when he looked at the wind power fluctuations of all of the turbines in a wind plant in Texas.

In fact, even geographically dispersed wind plants can exhibit correlated fluctuations in power if they fall within the same short and long time-scale eddies. However, as the distance between wind plants increases, their power fluctuations start to decouple from each other. Two geographically dispersed wind plants might encounter the same long time-scale wind speed fluctuations whilst encountering completely distinct shorter time-scale wind speed fluctuations.

In the past, some scientists have underestimated the problem of turbulence, arguing that the power produced by geographically dispersed wind turbines in windy and calm locations at any one point in time will average out when they reach a centralised grid. However, Professor Bandi’s findings show for the first time, that this phenomenon, known as ‘geographic smoothing’, only works to a certain extent.

The power generated by geographically dispersed turbine plants averages at high frequencies, because while one plant might fall within the short time-scale eddy, the other might not. In other words, the surge in power output at one plant is averaged out by a trough in power output from another, far-away plant at high frequencies. But because the plants still fall within the same long time-scale eddy, the power they produce will have correlated fluctuations at low frequencies, which generate the most power. A surge in power at one wind turbine plant will coincide with the surge at a far-away plant within the same long time-scale eddy, meaning that the power they feed to the grid cannot be averaged out. This means that there is a natural limit to how much one can average fluctuations in wind power; a limit beyond which fluctuations can continue to wreak havoc on the grid. Using data from 20 wind plants in Texas and 224 wind farms in Ireland Professor Bandi showed that this limit exists in reality.

“Understanding the nature of fluctuations in wind turbine power has immediate implications for economic and political decision making,” says Professor Bandi.

Due to the variability of renewables, coal-fired power plants providing back-up energy are kept running in case of sudden power outages, meaning that more energy is produced than needed. This means that ‘green’ energy is still contributing to carbon emissions, and there is an associated cost of maintaining reserve energy, that will only increase as the proportion of renewables increases in the years to come. The discovery of a limit in geographical smoothing, articulated by Professor Bandi, will enable better estimates of the operative amount of reserves that needs to be maintained.

This discovery will also impact environmental policy. By considering the limit for averaging fluctuations of power, combined with the availability of different renewable resources such as sun, wind and waves in a particular area, policy-makers will be better equipped to work out optimal combinations of different energy sources for specific regions

“Understanding the nature of fluctuations for wind turbines could also open up other avenues of research in other fluctuating systems,” says Professor Bandi.

Science Daily, December 31, 2016, https://www.sciencedaily.com/releases/2016/12/161231184935.htm

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Date added:  December 21, 2016
Europe, WildlifePrint storyE-mail story

Patterns of migrating soaring migrants indicate attraction to marine wind farms

Author:  Skov, Henrik; et al.

Abstract:
Monitoring of bird migration at marine wind farms has a short history, and unsurprisingly most studies have focused on the potential for collisions. Risk for population impacts may exist to soaring migrants such as raptors with K-strategic life-history characteristics. Soaring migrants display strong dependence on thermals and updrafts and an affinity to land areas and islands during their migration, a behaviour that creates corridors where raptors move across narrow straits and sounds and are attracted to islands. Several migration corridors for soaring birds overlap with the development regions for marine wind farms in NW Europe. However, no empirical data have yet been available on avoidance or attraction rates and behavioural reactions of soaring migrants to marine wind farms. Based on a post-construction monitoring study, we show that all raptor species displayed a significant attraction behaviour towards a wind farm. The modified migratory behaviour was also significantly different from the behaviour at nearby reference sites. The attraction was inversely related to distance to the wind farm and was primarily recorded during periods of adverse wind conditions. The attraction behaviour suggests that migrating raptor species are far more at risk of colliding with wind turbines at sea than hitherto assessed.

Henrik Skov, Mark Desholm, Stefan Heinänen, Johnny A. Kahlert, Bjarke Laubek, Niels Einar Jensen, Ramūnas Žydelis, Bo Præstegaard Jensen

Published 21 December 2016.
Biology Letters, volume 12, issue 12
DOI: 10.1098/rsbl.2016.0804

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