Resource Documents — latest additions
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.
Author: Hawkins, Kent
The nature of the short-term operation of an electricity system is more like that of a machine than a market.
A paper published by Joseph Cullen in the American Economic Journal: Economic Policy (November 2013), “Measuring the Environmental Benefits of Wind-Generated Electricity”  is important in two regards. First, using Texas data, it shows that even with notable emissions savings attributed to wind, the highly subsidized cost of wind is exceeded only by high estimates of the social costs of pollution.
Secondly and perhaps more importantly, his paper provides an opportunity to illustrate where wind-performance analyses fall short. This is the subject of this two-part post today and tomorrow, and is independent of the issue of carbon dioxide social benefits versus social costs.
Professor Cullen first determines how much electricity production of other generator types is offset by the presence of wind plants in the grid using a reduced form econometric model based on “observed behavior and current market conditions.” The time frames for production are 15 minute intervals and two hour ahead forecasting by market participants. The market-oriented approach is exemplified by the following quote:
When low marginal cost wind-generated electricity enters the grid, higher marginal cost fossil fuel generators will reduce their output. [emphasis added]
This assumption that the suppliers of generation resources make these decisions is questionable because the nature of the short term operation of an electricity system is more like that of a machine than a market. The electricity system has been described as, “the largest and most complex machine ever made.” Usually the production offset by wind is that which is most easily and quickly varied by the system operator regardless of fuel source or cost. Further, depending on the generation fleet online profile at the time, this can be other emissions free generation, such as hydro or even in some cases nuclear, which admittedly is not easily varied.
This is not to say there is not a wholesale electricity market, but that critical short term electricity system operational considerations are less dependent on decisions by the market participants and more dependent on the system operator matching supply and demand in real time using available online dispatchable generation resources.
So, although the operation of the electricity system is intended to provide the lowest wholesale cost from available choices, the overall reliability of the system is the paramount consideration. Other factors impacting operational decisions include: (1) government mandates to accept wind plant production whenever it occurs; (2) the profile of generation plants in use at the time (this typically varies considerably throughout the 24 hour day, and can be a limited subset, particularly at night); and (3) as already indicated, which of the available online generation plants can most easily be varied to meet the erratic output of wind plants. The Cullen analysis does not take into account the over-riding reliability consideration, as explained at the bottom of page 111.
Finally, the econometric approach cannot comment on wind-induced reliability, or congestion issues that engineering approaches are geared to address.
Having determined the production offsets, the paper suggests that “it is straight forward to calculate emissions offsets by wind.” The emphasis is added because the “straight forward” claim is very questionable. So this review will put both sets of results to the test, and they will be found wanting.
Can opposition to wind plant implementations be encouraged by the results nonetheless? I suggest not without considerable caution for a number of reasons:
- As introduced above, the paper uses an econometric modelling technique involving regression claiming to control for other influencing factors to ultimately lead to a determination of emissions savings from wind. This is a questionable approach in the absence of supporting proof of a clear cause and effect relationship between wind electricity production and claimed emissions and electricity production offsets. An example of such proof will be provided. Other corroboration used by the author will be shown to be not useful.
- The analysis is based on questionable or incomplete data, especially emissions data, both in the approach to calculate the reported emissions savings, and in other reports used to support the “robustness” of this paper’s results.
- The treatment of wind costs is incomplete.
- As acknowledged by Cullen, the results of the sample used, in this case the Texas ERCOT electricity system, are not likely transferrable to other jurisdictions or different levels of wind penetration.
- Other acknowledged shortcomings of the analysis are too important to support a complete understanding of the effect of introducing wind plants into electricity systems.
- The use of questionable claims/opinions do not contribute to the overall dependability and hence usefulness of the study.
Part I of this series addresses the following in more detail:
- Analysis approach
- Robustness claims
A summary of Cullen’s approach is:
Utilizing information on production decisions [by market participants] in 15-minute intervals on the Texas electricity grid, I estimate the response of each generator to exogenous changes in wind power. Realizing that wind power production is not completely random, I control for factors that may drive the incentives for electricity production, which may also be correlated with wind power production. The resulting quasi-experimental residual variation is then used to identify a substitution coefficient for each generator on the grid. Importantly, I show that failing to control for impact that wind has on the dynamic process of electricity production overestimates the production offsets. These production offsets then translate directly into emission offsets using generator emission rates. [emphasis added]
This approach may work if all the factors in determining production and emissions offsets are accounted for, which I suggest is not an easy task, and has not been convincingly and completely done by anyone. Cullen does include a number of factors, but are important ones missed?
One way to test for this is to provide two finely-grained (5 minute intervals or less) time series plots for wind production versus both fossil fuel production offsets and emissions offsets claimed for wind. If these plots clearly show, in timing and relative size, that a very close relationship (or mirror image in the case of negative correlation), then there is merit to the approach. In the absence of a demonstration of this strong cause and effect relationship, the degree of mismatch is an indication of the impact of other factors not modelled.
In the case of the Kaffine, McBee and Lieskovsky study referred to by Cullen, because it employed the same approach “using different data sources”, when such time series plots were provided, there was little indication of a cause and effect relationship between wind production and the residual emissions claimed as a consequence of wind presence.
As a quick test of the possible causal links between wind and fossil fuel electricity production I looked to a chart I had previously prepared for Texas using EIA data. It includes the period analyzed in Cullen’s paper, that is 2005-2007, and is shown in Figure 1. This chart focuses on the changes in electricity production on a yearly basis for fossil fuel plants, wind plants and net imports as an indicator of plausible cause and effect.
Figure 1 shows that in five of the eight years the annual change in wind production is in the same direction (both up or down) as that for fossil fuel, which suggests that wind is not the major factor in offsetting fossil fuel generation. However, for five of these years the change in net imports is in the opposite direction as changes in fossil fuel production. Note the relative sizes as well. This suggests that imports are more likely a factor than wind in offsetting fossil fuel electricity production.
Do not be confused by the use of the term net imports, in the context of a net exporter for the time period in Cullen’s analysis. Net imports are used here because imports are an additive generation source and are thus shown as positive in the chart. When net import changes are positive, year over year, this means there was an increase in imports relative to exports.
This test may not be conclusive, but it reinforces that further verification as described above would be appropriate.
In the absence of such corroboration, the results remain suspect because the sophistication of the approach is not a guarantee of accuracy. See more comment under the Robustness Claim (below) and Interstate Trade in Electricity sections in Part II.
There is interesting information bearing on the impact of cycling fossil fuel plants, whether for just “normal” frequency regulation (load variation only) or the additional requirement of balancing wind.  On a short term basis, such as minutes (or less), both have the same type of system impact. This is referred to in a KEMA  report, “Emissions Comparison for a 20 MW Flywheel-based Frequency Regulation Power Plant” in which a reference is made to another KEMA report on the impact of such cycling on the fuel consumption and hence emissions of fossil fuel plants. Attempts to get a copy of the referenced report have not been successful, but Dr. Kees lePair, in the Netherlands, claims to have had access to it and describes its findings in his paper “Wind turbines increase fossil fuel consumption & CO2 emission” as follows:
Recently we received some information concerning a fuel flow recording of a coal fired generator during cycling. The generator running stationary for some time at 100% of its optimal capacity reduced its output to 80% and up again to 100%. The whole cycle took place in one hour. The total fuel consumption during that period was 1,2% more than it would have been had the machine continued running at 100%. It was suggested that for a CCGT this outcome should have been 1%.
Given the combined effect of: (1) the more frequent cycling of fossil fuel plants than KEMA observed, whether normal frequency regulation operations or balancing wind plants, and (2) an overall reduced electricity output, and therefore less efficient operation due to periods of part loading in the fossil fuel plants as a result, it is difficult to escape the expectation of very little emissions savings at best over a normal steady operation of the fossil fuel plants meeting the demand alone. In other words, wind presence likely increases emissions overall.
In the first paragraph in the “Robustness” section of Cullen’s paper he expresses some concern about using averages of emission rates and acknowledges emissions effects missed, but counters this with the claim that as the number of fossil fuel plants supporting this cycling requirement increases, the changed output of each would be eased thus reducing the fuel and emissions impacts. This misses the important consideration that the frequency of such cycling is at least, if not more than, as important as the extent of the changes required. Further, grid topology could also restrict wind’s impact to subsets of fossil fuel plants.
In the “Robustness” section the paper looks to the Katzenstein and Apt analysis “Air Emissions Due to Wind and Solar Power” for confirmation of results. Most readers appear to miss the many caveats that Katzenstein and Apt correctly identify. For example:
As discussed in the Supporting Information, the emission and heat rate data we obtained for the gas turbines did not cover all combinations of power and ramp rate. We therefore further constrain the model to operate only in regions of the power-ramp rate space for which we have data.” [emphasis and link added]
and caution that we have made no attempt to ensure the stability of an electrical grid. Grid dynamic response may somewhat change our results. [it is suggested that “somewhat” is an understatement]
Realistically, displaced generators will differ from the generators providing fill-in power and would produce different results.
With respect to the last quote above, there is, or will be, a trend to introduce different types of generator plants (less expensive and more flexible) to balance wind as wind penetration increases. One example would be the increased use of gas turbine (aka GT or SCGT) plants versus combined cycle gas plants (CCGT). The gas turbine plants produce about 50% more emissions per MWh than combined cycle gas plants, the latter being designed to operate in steady state base load or intermediate electricity production roles. This is a major and often overlooked factor that contributes to reducing wind emissions offsets. See additional comments on this in the Questionable Data section in Part II.
Further, the impact of low operating cost of wind plants on wholesale markets, in part due to subsidization, makes more expensive operating cost, but more reliable, generation plant investments less attractive, thus putting at risk long range capacity needs. This is a serious matter for longer term electricity system reliability.
This is not to say that all wind costs are as indicated by wholesale market bidding, which is based only on the cost at the wind plant site, because there are substantial additional electricity systems costs which are incurred solely because of wind’s presence. This is discussed further in Part II.
From the Katzenstein and Apt paper Supporting Information addendum:
Therefore, the results seen in Table 1 of the main paper, obtained from using the full time series of the 5 data sets (see Table S6), estimates only the emission reductions for the conditions that existed during the periods when the data were collected. Ideally, a significant number of high time-resolution independent power plant outputs would be used in our simulations. However we did not have access to such a data set, only to the 5 data sets described. [emphasis added]
The wind data sets were for the relatively short periods of 15, 84, 240 and 370 hours. The fifth data set was for solar PV. This speaks to the limited data impact on conclusive results.
In summary, the Katzenstein and Apt paper does not contribute to robustness for the Cullen paper, due to the limitations of both.
Part I has shown that Cullen’s approach and robustness claims are questionable. Part II will look in more detail at the reliability of the available data, the interstate trade in electricity considerations, and discuss briefly some of Cullen’s acknowledged caveats and some other claims made.
 There is an earlier version which may be accessed at http://www.u.arizona.edu/~jcullen/Documents/measuringwind.pdf , but no attempt has been made to confirm that it is an identical document.
 EIA http://www.eia.gov/electricity/data/state/ Go to first spreadsheet entitled “Net Generation by State by Type of Producer by Energy Source (EIA-906, EIA-920, and EIA-923)” for Wind data, http://www.eia.gov/electricity/state/texas/ Table 10 for Net Interstate Trade and Table 4 for fossil fuel data.
 The short term (minutes or less) variation of load and wind in combination produces a greater range of variance than either individually, with a bias towards greater instances of higher variance. An analysis of the Bonneville Power Administration (BPA) in the US northwest electricity production by fuel source at 5 minute intervals shows this. Note that this assumes an arithmetic summation of all wind production, and grid restrictions might exacerbate this experience on a more localized basis.
Where Wind Studies Go Wrong: Cullen in AEJ (Part II)
“The level of emissions savings provided by wind plants has never been conclusively determined, taking into account all the factors.”
Part I yesterday questioned the analysis and robustness of Joseph Cullen’s study, “Measuring the Environmental Benefits of Wind-Generated Electricity”.  Part II completes the commentary on this paper, covering:
- Questionable data, which seriously inhibits any analysis of wind performance
- Interstate trade in electricity, an often overlooked, but important, consideration in understanding impacts on emissions
- A summary of the acknowledged shortcomings of this paper
- Questionable opinions/claims made
The level of emissions savings provided by wind plants has never been conclusively determined, taking into account all the factors. Further, there is no published accurate, minute-by-minute, actual fuel consumption or emissions by individual plant, especially for systems with notable levels of wind present. Note the limitations in the Katzenstein and Apt paper looked to by Cullen for corroboration as discussed in Part I.
In general, government reported emissions are estimates based on calculations using assumptions and relatively simple algorithms. In some cases, actual measurements are taken but are no better than those calculated as reported by the International Energy Agency (see page 35).
Commercial instrumentation is available for monitoring CO2 concentration and flue gas volume flows. Given the limitations of such instrumentation, the accuracy of directly measured CO2 release is probably no better than that derived by indirect calculation. [emphasis added]
A report by The Sustainable Energy Authority in Ireland, “Renewable Energy in Ireland”, in Appendix 1 also refreshingly recognizes the limitations to existing reporting methods.
The assumption underpinning this approach is that the renewable plant is displacing the last plants to be dispatched to meet electricity demand, i.e. the marginal oil and gas plants. There are clear limitations in this analysis but it does provide useful indicative results.” [emphasis added for “indicative”, which is taken to mean “suggestive”]
“The limitations and caveats associated with this methodology include that it ignores any plant used to meet the associated reserve requirements of renewables. These open cycle plants will typically have lower efficiency and generate increased CO2 and NOx emissions compared with CCGT and these emissions should be incorporated into the analysis. The purpose of presenting a simplified analysis here is to provide initial insights into the amount of fossil fuels that are displaced by renewables and the amount of emissions thereby avoided. [emphasis added]
The issue raised in the last quote speaks to the comments made in the Robustness section in Part I.
The above comments point out some of the typical shortcomings of many current approaches in determining reported emissions offsets for wind. At best, such results are useful only as some indication and for rough comparison purposes, for example between jurisdictions and time periods, and not reliable for absolute levels.
The Cullen article admits that the modelling approach used, “relies only on publicly available generator output and characteristics”. Emphasis has been added to the quote because there is no adequate, publicly available information on the constant cycling required of other generation plants mirroring wind plants’ highly random output on a short-term basis, as indicated in Katzenstein and Apt’s paper in Part I.
Cullen somewhat distances his results from the EPA system CEMS on the basis that CEMS reporting includes less than two-thirds of the generators in the ERCOT system, a notable comment in itself.
As a robustness check, I estimate the same model with hourly emissions data from the EPA’s Continuous Emissions Monitoring System (CEMS) as the dependent variable. Using CEMS data may be able to account for the changes in the emissions rate due to efficiency changes, though it may exacerbate ramping effects.
The latter part of this quote needs elaboration. Cullen shows that using the CEMS data in his model results in 4% lower CO2 emissions offsets for wind. He concludes that this indicates relative robustness, but the CEMS data “may” exacerbate ramping effects. This implies that Cullen has in fact captured the ramping effects, which is very questionable as already described.
However, CEMS reporting is subject to question as indicated above. Also note that the CEMS data is hourly based, which likely masks ramping effects on much shorter time intervals in which electricity system balancing must operate to ensure system reliability. In summary the CEMS information more likely understates the ramping effect.
So questionable or incomplete data is a problem in the determination of complete and accurate results, and any corroboration claimed by Cullen is questionable.
Interstate Trade in Electricity
This is a factor that is almost always overlooked in analyses of wind performance, and is not taken fully into account in the modelling here. The general reason for this is that electricity exports to or imports from another jurisdiction are a somewhat complicated matter, and unfortunately often are taken to be relatively inconsequential, which they usually are not.
When Cullen is talking about electricity imports, it is presumed he is talking about net interstate trade, as opposed to import/export of electricity in connection with another country. For simplicity here, the terms “interstate trade” and “exports/imports” will be used to refer to interstate trade only. Further it is important to be clear whether or not any reference to this is a net number of exports/imports as is often the case in reported values.
Cullen also says that he observes the flows of electricity over connection lines in neighboring grids in 15 minute intervals and claims that less than 1 percent of daily generation is exchanged with other grids, while wind accounts for approximately 2%, which allows him to restrict his analysis to within the Texas system. However, this does not agree with EIA reports,  as summarized in Table 1.
Table 1 – Wind Production and Net Interstate Trade as Percent of Total Texas Electricity Production.
Note that total wind production is at the same level as net interstate trade for two of the three years that Cullen analyzed, but the net of the interstate trade and could conceal larger amounts of export and import levels over the same period.
Further, Cullen talks in terms of imports, but in the three years analyzed, Texas was a net exporter of electricity. Note the balancing of Supply and Disposition in the reference shown for Table 1. Some clarification of this by Cullen would have been helpful.
In summary, notwithstanding the relative isolation of the Texas electricity system, interstate trade in electricity cannot be ignored in the analysis, and exports/imports even on a net basis could just as easily account for much of the reductions in fossil fuel plant emissions as wind.
To further examine misunderstandings in connection with inter-jurisdiction trade in electricity, in footnote 30 for imports, Cullen suggests that if it was assumed that the emissions offset profile of imports to be the same as in the models, this would change the results found.
It is preferable to assume imports carry no emissions, as it would be very difficult to identify the specific source of the electricity generation profile behind the exported electricity. For the purposes of simplicity in explaining this, assume that such distinction can be made in a couple of simple cases.
The exporting jurisdiction is exporting electricity associated with emissions. For the importing jurisdiction to also be charged with these emissions would be double accounting, unless the exporter took a balancing credit. Imagine the complex negotiation associated with this arrangement.
In the case of exporting electricity from non-emissions producing generation there is no need to associate emissions with either the export or import as well. However there is an example where this is not done. Denmark reports emissions in two ways: (1) as produced, and (2) after taking credit to reduce its actual emissions based on the amount of exported wind production.
The latter view is often cited in error. See Peeling Away the Onion of Danish Wind for more details. Here as above, this works if the receiving jurisdiction takes a balancing increase in reported emissions, which is unlikely, resulting in a double accounting for emissions reductions, an undesirable outcome.
Admittedly inter-jurisdiction trade in electricity is somewhat complicated, and the above descriptions are simplifications to illustrate the need to view associated emissions as staying within the electricity generating jurisdiction. Imports/exports from and to another jurisdiction should be treated as emissions free.
Perhaps the most important matter is that in most cases, even in Texas, imports and exports of electricity must be fully taken into account when analyzing the emissions impact of wind presence.
The paper contains appropriate caveats including:
- Limitations with respect to transferring results to different wind penetrations or electricity systems.
- No consideration is given to electricity system reliability.
- It is not a comprehensive cost benefit analysis of wind power.
- It does not address the different nature of future generation plant type investments induced by wind.
- Note 32 says the paper does not “disentangle how the variability of wind power affects the emissions offset.”
These properly acknowledged limitations reduce the value of the paper in providing useful insights and reliable conclusions about the effects of deployment of wind power.
There are a number these, which further reduce the value of this paper.
The statement, “In fact, nearly all costs associated with wind power production are incurred during the construction and installation phase of a wind farm.” is not correct. Substantial additional costs are incurred by the presence of wind in (1) otherwise not needed, dispatchable capacity to balance wind’s persistently, erratic behavior within short periods of time (minutes or less) and unreliability on a longer term basis of hours and days, and (2) in substantial increases to the grid unique to wind to gather wind’s dispersed generation, transmit it to typically distant demand centers and support demand management in distribution systems. See the series on “Wind Consequences” for more information.
A modern 1 MW (not 1 MWh) wind turbine does not require only “roughly $1 million” to install. Overnight implementation costs are over twice this amount according to the EIA.
Cullen assumes a Wind turbine life of 20 years and that any change in operating efficiency over its lifetime is negligible. This is contrary to experience that shows substantial reductions in performance measured by load factor (aka capacity factor). Figure 2 shows experience in Denmark and the UK. 
Figure 2 – Performance Degradation of Wind Turbines in Denmark and the UK
The values shown here are weighted by capacity, because this better captures the effect of the most numerous wind turbine sizes, and produces more typical results than simple averaging. The years with no values are because of little or no experience. Outages due to mechanical failure are included in these statistics.
Most of today’s wind turbine installations have occurred since 2000. These are typically 1-3 MW and much larger than their predecessors. These larger wind turbines have a 100 ton  blade assembly and nacelle enclosing the generator on top of a 200 foot, or more, tower. They are reported to be the largest rotating structures in the world. A possible explanation of Denmark’s slower degradation experience may be because most were installed before 2000 and the majority are of a much smaller size.
There are many considerations behind these numbers, but a strong case is made for concern about considerably shorter than generally expected life times for wind turbines, as indicated by the quote from this paper:
With such low levels of performance it seems very unlikely that large wind farms will continue in operation beyond 10 years of age, with a strong likelihood of re-powering at that point. The consequence is that large scale reliance upon wind power seems likely to involve a regular – and costly – commitment to upgrading major components of the wind turbines.
There are other reports on frequent, major component failures requiring substantial costs in the order those for the initial implementation. 
On page 112, Cullen refers to a paper by Holland and Mansur, which describes how real time retail pricing would shape demand by reducing peaks and shifting load to low cost periods at night. Holland and Mansur explain that this can have an impact on emissions, but whether the impact is positive or negative depends on the generation plant profile of the jurisdiction. Cullen claims that wind plants likewise reshape demand, which is not obvious at all. Apart from the fact that there is no mention by Cullen of the necessary attendant real-time retail pricing, wind usually has higher production at night, and it thus tends to shape low cost production base load generation (not demand) in this period. Further he states that this re-shaping of demand necessarily leads to emissions offsets, which is different from Holland and Mansur’s conclusions which allows for the possibility of increased emissions as well.
As described above in the Analysis Approach section, a major effect of wind is its impact on short term residual demand variances, a totally different type of demand shaping. This shapes the residual demand in a non-beneficial way and arguably increases emissions overall.
Cullen states, “However, for both economic and environmental reasons, hydro facilities are unlikely to spill water over dams without generating electricity.” This is clearly an inappropriate assumption and demonstrates his emphasis on market participants determining electricity system operations. The Bonneville Power Administration (BPA) in the Pacific Northwest provides an instructive case study of system operational issues and associated contortions required here and here.
Another related comment by Cullen is, “Both nuclear and aggregate hydropower production will be largely unaffected by the roll out of wind farms.” The use of “largely” is acknowledged, but the statement can be read to suggest non-problematic impacts. The BPA experience above speaks to the hydro aspects. Admittedly nuclear is an unlikely form of electricity generation to be offset by wind, but it does happen as discussed here.
A final very questionable claim is, “First, technology advancements in wind turbines have reduced the cost of wind power by 80 percent over the past 30 years (Wiser and Bolinger 2007).” I could not find confirmation of this claim in the referenced paper. A chart on page 21 does show project costs per kW reducing by about 65% between 1982 to 2000 and then increasing thereafter. This has a related comment in footnote 35, ” Limited sample size early on – particularly in the 1980s – makes it difficult to pin down this number with a high degree of confidence.”
Further to claim technology advancements have been the cause is questionable. The cost per kW of a wind turbine has been largely influenced by simple mechanical considerations, primarily the size of the blades and height of the tower, both of which have increased dramatically over this period.
Given the very large portion of national wealth (multiples of $trillions) necessary for the contemplated extensive deployment of wind plants, and the associated longer term risks to electricity system viability and reliability, we simply have to do better at complete and conclusive analysis of the impact of wind plants with much improved public availability of operational data on generation plants, including real time fuel consumption and accurate emissions. Fuel consumption may be the only realistic way to assess emissions.
It is questionable that sophisticated econometric modelling is appropriate for this task.
 As indicated in Part I, there is an earlier version which may be accessed at http://www.u.arizona.edu/~jcullen/Documents/measuringwind.pdf , but no attempt has been made to confirm that it is an identical document.
 Hughes, Gordon (2012). “The Performance of Wind Farms in the United Kingdom and Denmark” http://www.ref.org.uk/attachments/article/280/ref.hughes.19.12.12.pdf Chart is based on calculations from Figure 2.
 This is not widely reported, and some links to sites previously dealing with this topic appear to have removed the documents in question. Here are some currently available reports relating to this matter.
Author: Larsson, Conny; and Öhlund, Olof
Wind turbine (WT) sound annoys some people even though the sound levels are relatively low. This could be because of the amplitude modulated “swishing” characteristic of the turbine sound, which is not taken into account by standard procedures for measuring average sound levels. Studies of sound immission from WTs were conducted continually between 19 August 2011 and 19 August 2012 at two sites in Sweden. A method for quantifying the degree and strength of amplitude modulation (AM) is introduced here. The method reveals that AM at the immission points occur under specific meteorological conditions. For WT sound immission, the wind direction and sound speed gradient are crucial for the occurrence of AM. Interference between two or more WTs could probably enhance AM. The mechanisms by which WT sound is amplitude modulated are not fully understood.
Studying AM is very complex due the many factors that govern sound propagation from WTs. For an ideal analysis of how AM is produced and transmitted, emitted sound power, wind direction, temperature gradients, wind gradients, and turbulence would need to be known three dimensionally in small time steps. This is impossible to measure in the field, so simplifications must be made. Furthermore, the interaction of sound from several WTs complicate the analysis.
Higher prevalence of AM is detected when the sun is close to or under the horizon, which corresponds well with when temperature inversions occurs on clear nights. A temperature inversion near the ground changes the angle of incidence of the sound waves and affects the ground attenuation. The reflected sound waves are normally less damped if the sound comes more from the zenith than parallel to the ground. At the Dragaliden site when AM was present, a typical pattern was approximately 15 s of distinct AM followed by a minute of steadier sound levels.
Analyzing approximately 30h of AM measurements recorded simultaneously at both an emission and an immission point shows that enhanced AM at an immission point could not be explained by enhanced AM at the emission point. It is instead an effect of interference between sound from several WTs or of different ray paths of the sound from one turbine. However, this last possibility requires further testing.
The AM detection method works well and does not react to passing cars, birds, or airplanes. During strong masking, the WT signal is lost using the detection method; the sound will of course not be experienced as amplitude modulated, but the signal may still be present in the background noise. We could conclude from our measurements that amplitude modulated sound from WTs is more common under certain meteorological conditions and is observable approximately 20%–30% of the operational time, depending on the distance from the turbines. In future studies, it would be interesting to investigate WT sound annoyance coupled to conditions with and without AM present.
Journal of the Acoustical Society of America 135(1):67–73, January 2014
Conny Larsson and Olof Öhlund
Department of Earth Sciences, Uppsala University
Author: Alves-Pereira, Mariana; and Castelo Branco, Nuno
My group has been studying low-frequency noise for 30 years. Initially we began with people working in low-frequency noise [environments].
Low-frequency noise is a little bit like light. You know that there are x-rays that you do not see, but it is light. You know that there is ultraviolet that is bad for your skin and you do not see it. If you use dark glasses for x-rays or ultraviolet, it will not protect you. This is similar to noise.
Over the years, we, the scientific community, have decided that noise only affects your ears. That is why we have the dB(A) unit to measure noise. Because we are interested in only measuring the noise that will causing hearing damage. Low-frequency noise does not cause hearing damage. It will not make you deaf. It is not measured when you use a dB(A) unit.
Compare the light with the noise. On top you have noise, on bottom you have light. Look what we do with the light. We separate UV, infrared, x-ray. We do not do this with acoustics. So how do I know which part is affecting your heart? Or you lungs? It’s all in one big bag. This is the problem with measuring the agent of disease.
On top you see what is a noise wave. I am here and the noise that is coming is going to hit my body. What you see on the right is your cells. So when the cell gets the impact of the wave, it will move. Like you see underneath.
Our case with wind turbines began in 2007. As you can see on the left, you have the house and the 4 wind turbines that were put around this house in 2006. This is what it looks like.
I am a scientist, I can not put up information about the noise that was measured without giving you the technical specifications of how it was measured.
So, the red is the noise in the house without wind turbines working. The black and the grey is with the wind turbines working day and night, night and day. This is measured inside the bedroom – not outside. Do you sleep outside? So if anyone comes to measure the noise because of the wind turbines, and they do not go into your house, you will laugh – yes? So as you can see at least in this house, these wind turbines were responsible for a great increase in the low-frequency noise in the bedroom.
So what happened to this family? The wind turbines began operation in November 2006. Immediately the family recognised that they were waking up tired. The dogs that used to be jumping and always wanting attention were now sleeping. You had to step over them. The horses were lying down in their stable. And ants disappeared. In March 2007 the parents received a letter from the teacher of the boy, of the son, asking why this child was so tired now. He was losing all interest in school, he had no energy for physical education and a very good student was suddenly going down, down, down.
And this is the point [when] that the family contacted us. Unlike the Massachusetts report, and others, we did not give questionnaires to find out how the people’s health was developing. It is not through questionnaires. No questionnaires. If you have people with cancer, would you accept a doctor who would attempt to cure the cancer with a questionnaire? Tell me what disease is evaluated just with questionnaires?
We gave this family the tests, the medical (not subjective) tests, that we would give to the workers in low-frequency noise. What you are seeing now is the development of symptoms and signs related to the disease developed by exposure to low-frequency noise. Signs and symptoms associated with the disease that you develop if you are in low-frequency noise. As you see, it is over years that it develops. If you have low-frequency noise in your home on day 1, day 2 it’s okay, day 3, day 4 – in a year! In 6 months, a year, 2 years, then it’s a problem. Then you realise. Then it is 6 months that you are tired and can not sleep, not a wink.
This is the test that we give to people to see if they have problems with low-frequency noise disease. On the right the test measures the time your brain takes to respond to a stimulus. Normal is 300 milliseconds. In the 12-year-old child in the wind turbine house, he was having in June 2007 352 milliseconds – this is a huge difference. When the child left the house for vacation, in September after being away from the house for 2 months the measurement came closer to normal.
On this side we have a breathing test. This breathing test measures how well your brain is controlling your breathing. Usually, if you have lesions in the brain, you will have problem in the breathing – and this is what this machine measures – not questionnaires. Normal for that examination is above 60%, and as you see the husband and the wife were already showing lower values. The child was not tested.
What is happening with the legal [case]? This man, this family put the wind turbine developer in court. In 2007 as a precautionary measure, the court said, take out wind turbine 2 – as you can see the white arrow, there used to be a wind turbine there. The court said that one has to go. And the other three must be turned off at night. And while this court case was ongoing, the developer continued putting wind turbines all along (red arrows).
Today, the Supreme Court of Portugal decided that all four wind turbines must be removed. Of course it is only those four because the other ones were not covered by this case. The wife and children no longer live in this house since 2007. Only the father is in the house to take care of their horses. Today his health has visibly deteriorated.
This is basically my information to you about what we know about wind turbines. Of course, 30 years of research into low-frequency noise – you can not speak in 10 minutes all the information.
Presented 20 November 2013 at Nieuw Buinen, The Netherlands, by courtesy of Stichting Platform Storm
Transcript and video captures by courtesy of Stop These Things.
Author: Morris, Mary
Dear Planning Minister Rau;
I was a participant in the Waterloo wind farm noise study and you would be aware from all my previous correspondence with you over more than two and a half years, that I am personally acquainted with the large number of decent and genuine people in the Waterloo district community who ARE suffering adverse sleep impacts and other health effects from this wind farm.
I feel compelled to write to you and point out that your Ministerial decisions to approve Keyneton and Ceres wind farms, superficially justified by EPA Waterloo Noise study 2013 study are ill-founded and reprehensible.
In October 2012 you in confirming the Interim wind farm DPA, you ignored key recommendations from the DPAC concerning greater setback distances, precautionary approach, cumulative impacts, safety concerns and many others.
You clearly rushed the Keyneton decision through with indecent haste, a mere 10 days after the EPA Waterloo report was released. Insufficient time for through comprehension of the complex and lengthy document and without the benefit of peer reviews by other acoustic experts.
Why did you not wait until thorough peer reviews of the EPA study and reports were forthcoming?
Why did you not wait until the results of independent researchers monitoring side by side with the EPA (Emeritus Professor Colin Hansen and Steven Cooper) were published?
Now it appears that you have repeated the rushed performance with the CERES Project on the Yorke Peninsula, just before your government goes into caretaker mode.
Can you please justify taking the conclusions of the EPA at face value without bothering to wait for independent verification?
The first PUBLICLY AVAILABLE peer review of the document has only just come to light!
My requests for a copy of the NSW EPA peer review cited by the SA EPA have been denied by the SA EPA and the NSW EPA, and seems that this is a confidential document and not available for scrutiny – Why not? What does it say about the EPA study?
And how can you justify ignoring the recently released opinion of acoustic engineers (Cooper and Hansen) with arguably the most experience of monitoring wind farm noise in the local environment at Waterloo?
Last week, after many weeks of examining the EPA report, expert acoustic engineer Emeritus Professor Colin Hansen made public his comments about the study.
Professor Hansen commented:
The EPA found “no evidence linking the noise from the wind farm to adverse impacts on residents” and there are several reasons why this conclusion may have been reached erroneously. These include certain limitations of the current guidelines as well as aspects of the study that could have been improved. In some cases, interpretation of the data has led to generalisations that are not well backed up by the supporting figures.
I do not believe that the EPA study has shown that the noise impact on residents from the Waterloo wind farm is insignificant. More detailed analysis of the data and analysis of the appropriateness of the existing EPA guidelines would in my opinion indicate a significant impact of the wind farm noise on local residents.
As a layman, I too, have spent a significant amount of time in the last couple of months scrutinising the SA EPA’s 2013 Waterloo Wind Farm Environmental Noise Report.
Being familiar with the homes and residents where EPA monitoring took place and having been given copies of the diaries submitted to the EPA, I have identified significant issues with methodology, collation of diary entries, microphone placement, inappropriate timing of shutdowns to secure meaningful results, equipment failure, missing data, failure to consider “awakenings” and sleep disturbance as an indicator of noise level, errors in tables, errors in shutdown dates (pdf version lists the last 4 shut downs as occurring in May, when they were in June). The document is large and cumbersome to follow. I am still compiling my complete list of flaws in the document, but just some of them are attached to this email.
It is obvious that you applied no scrutiny to this EPA document, because a mere 10 days after the EPA report was released, with indecent haste, you approved Keyneton wind farm on Friday 6 December 2013.
Your words from your press release and media conference included:
There is no detectable concern based on noise emanating from these proposed installations and for that reason the approval has been given.
No evidence was found linking noise from the wind farm to adverse impacts on residents.
However, the SA EPA online report contains the following disclaimer (which apparently was edited from the pdf version of the report):
The conclusions of the study may not be valid for other wind farms, and may only be valid for the Waterloo Wind Farm under the specific conditions (eg weather, wind farm operating conditions, etc) under which the study was undertaken. It also may not necessarily be valid for all residences potentially affected by noise emission from the Waterloo Wind Farm.
The SA EPA stated from the beginning of the study and repeatedly throughout, that their study was not a health study.
The Waterloo study does not and can not support claims that there are no adverse health impacts from the Waterloo wind farm as it was not a health study.
17 of the 28 households involved in the study have provided me with a copy of their noise diary as sent to the EPA over the course of the study, with a view to wider scrutiny of the SA EPA’s analysis and conclusions. This process is still a work in progress.
The SA EPA have only considered diary entries relating to audible noise in their report, and neglected other impacts – most notably – sleep disturbance and awakenings.
The consequence of this selectivity is that a large number of adverse impacts and events recorded by the residents specifying time as and dates of disturbance which could be have been compared to noise data have been ignored in the analysis and report.
For example, the following entry has been disregarded; [it] clearly indicates adverse impacts from sleep disturbance and yet the EPA has illogically come to the conclusion that there are no adverse impacts and the guidelines do not need to be reviewed.
8th–10th May Loud whining noise constantly. Had son who works as diesel mechanic 7:30am–5pm daily. Yelling out at night as the windmill noise was keeping him awake 3 nights in a row. Went to work tired
At the time of the EPA study, this family lived 7 km from the turbines, but have since (November 2013) moved to another home further away where their sleep is not disturbed.
The EPA have summarized just over 800 diary entries from the 28 households in a series of tables in the report.
This equates to an average of 80 complaints per week over the 10 week period.
Only diary entries relating to audible noise, vibration and pulsing have been summarized.
In just the 17 noise diaries I have access to, there are well over 900 combined entries (audible noise + symptoms) which pinpoint times and dates of awakenings and other events – ALL of which could have been investigated, but a significant number have not because they were “not audible”.
Consequently much useful information which relates to sleep disturbance and health effects seems to have been disregarded by the EPA.
The Waterloo and Districts community reject the EPA’s conclusion that the current guidelines are sufficient to protect the residents from adverse impacts and do not need reviewing. Standards that result in residents abandoning their homes full time or partially clearly do not afford the community adequate protection from adverse impacts, are inadequate and must be amended.
WHO Guidelines on Community Noise 1995 are clear that night time noise inside homes exceeding 30dB(A) causes sleep disturbance and extensively documented associated adverse health effects.
Yet SA wind farm noise guidelines do not include a requirement to measure inside houses in the night time. The EPA only requires measurements outside homes and all measurements over a 2 week period are effectively averaged.
It is peak pulsing noise levels that matter – not averages – especially at night time when people are trying to sleep.
The current SA noise guidelines seem to be crafted to ensure that peak levels are hidden in averages and therefore compliance is practically assured. They lag behind international best practice and do not reflect recent independent scientific both locally and in the US.
It seems that the SA wind farm PLANNING, NOISE REGULATION and COMPLIANCE processes are a farce driven by political rather than scientific impetuses.
Justice demands a thorough audit and inquiry.
I await your response with interest