Resource Documents: Economics (147 items)
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.
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Author: May, Murray
A response to “Wind farms – to be or not to be”, Nature and Society Feb-March 2013, pp. 6-7
When I joined the public service in Canberra in 1973 as a Graduate Clerk, I was fortunate to work first with the National Estate Committee of Inquiry, a pioneering environmental inquiry of the Whitlam government that set the scene for later significant environmental policy. Not a boring public service department, but straight into the deep end with the likes of Judith Wright, Len Webb, Milo Dunphy, and David Yencken.
By the late 1980s there was a surge of public and political interest in the urgency and environmental significance of climate change. Other than reference to four, five, or six degrees, a Greenhouse Alert! broadsheet produced for Australian schools for World Environment Day 1989 could have been written yesterday. Environment Ministers set up branches, then divisions, and then whole departments to deal with the issue.
Move forward another two decades and where are we? The ‘growth forever’ model is still well entrenched. Governments have facilitated the expansion of the emissions-heavy aviation industry. Mega coal mines have been opened to export yet more Australian coal. Emissions keep rising. Yet now we have planning and environment departments, even Prime Ministers, pushing a new saviour, most often seen in the classic environmental icon of the industrial wind turbine. Convinced? I’m not. On so many fronts, including adverse health effects, divided communities in conflict, questionable reduction in greenhouse gas emissions, and disrupted landscapes, industrial wind scores badly.
Alby Schultz MP gave a speech on wind power in the House of Representatives on 13 February 2013 . His electorate of Hume adjacent to the ACT takes in towns such as Goulburn, Yass, and Boorowa, where considerable wind farm activity is underway or planned. He summarises the situation well when he said that “communities are at war with each other, adjacent landholders face serious land value losses and health issues continue to emerge.”
With respect to the widely discussed health issue, Simon Chapman at the University of Sydney continues to promote his psychogenic theory, suggesting that any problems linked to adverse health effects from wind farms are psychologically created. Much was also made of this so-called ‘nocebo’ effect at a Senate hearing in 2012 to discuss a bill to control excessive noise from wind farms. The nocebo effect has been used by wind energy proponents such as Chapman and various wind energy associations as a way of invalidating claims about adverse health effects.
What is dangerous about Chapman’s use of psychogenic theory is that all manner of technology (e.g. industrial wind turbines, mobile phone towers, Wi-Fi) can be declared benign, when more detailed knowledge of the areas in question suggests the opposite.
For example, Chapman casts current concerns about electromagnetic fields as being merely a form of ‘technophobic’ anxiety about modern technology (Chapman, 2012). Although his background training is as a sociologist, he nevertheless gives mobile phones and mobile phone towers a clean bill of health. On the other hand, neurosurgeon Vini Khurana et al. (2010) reviewed epidemiological evidence of health risks, citing studies reporting increased prevalence of adverse neurobehavioural symptoms or cancer in populations living less than 500 metres from mobile base stations.
The shallowness and inaccuracy of Chapman’s assertions are highlighted by a major report — BioInitiative 2012 (www.bioinitiative.org) — which provides a rationale for biologically-based exposure standards for low-intensity electromagnetic radiation. With expertise in the biophysical and medical sciences, the contributing authors discuss the implications of 1,800 new studies since the 2007 BioInitiative report.
There is now reinforced scientific evidence of risk from chronic exposure to low-intensity electromagnetic fields and to wireless technologies. The report argues that the status quo is no longer acceptable in light of the evidence for harm, particularly given the large number of people exposed worldwide.
Chapman’s use of a one size fits all sociogenic theory is thus overworked, shallow, and simplistic. With respect to wind turbines, he ignores and is not interested in the direct biological effects of low frequency noise for example. Using the ‘nocebo’ concept as an explanation for the chronic sleep disorders from nighttime arousals related to noise is simply irresponsible.
There continues to be corporate and institutional denial of adverse health effects, in spite of the fact that there is strong evidence that wind turbines cause serious health problems in nearby residents at a nontrivial rate. The bulk of the evidence takes the form of thousands of adverse event reports. These reports provide compelling evidence of the seriousness of the problems. Nonetheless, proponents of turbines have sought to deny these problems by making contradictory claims such as the evidence does not ‘count’, the outcomes are not ‘real’ diseases and/or are the victims’ own fault, and that acoustical models cannot explain why there are health problems, and so the problems must not exist (Phillips, 2011).
There is some systematic peer reviewed research, such as a study in Maine USA, which demonstrated disturbed sleep, daytime sleepiness, and impaired mental health in residents living within 1.4 km of two wind turbine installations (Nissenbaum, Aramini, & Hanning, 2012). A study in New Zealand likewise found lower overall physical and environmental quality of life measures, including significantly lower sleep quality, in residents living within 2 km of a turbine installation (Shepherd, McBride, Welch, Dirks, & Hill, 2011). There is clearly a need for further systematic research as recommended by an Australian Senate inquiry on wind farms in 2011 (Senate Community Affairs References Committee, 2011). However, institutional inertia has been evident in implementing such recommendations to date, and in Canada, strong reservations have been expressed about the independence of proposed research by Health Canada (“Prominent physician and surgeon Dr. Robert McMurtry calls for wind turbine moratorium,” 2012).
Significantly, a legal hearing in 2011 in Ontario, Canada, heard evidence from teams of experts arguing for and against claims of adverse health effects from wind turbines. The Environmental Review Tribunal (2011) concluded: “This case has successfully shown that the debate should not be simplified to one about whether wind turbines can cause harm to humans. The evidence presented to the tribunal demonstrates that they can, if facilities are placed too close to residents.”
The current standards for assessing noise from wind farms in Australia are inadequate, particularly as they do not address the low frequency sound and infrasound strongly implicated in adverse health effects. Turbine noise has a character that makes it far more annoying and stressful than other sources of noise at the same sound level. This is in part because of an up and down amplitude modulation from the blade passage past the tower. In addition, a ‘pulsing’ infrasound and low frequency pattern is transmitted for long distances, and can readily penetrate walls and resonate inside rooms.
There is further a critique of the economics of wind power, with large subsidies being required to support wind. In Alby Schultz’s electorate of Hume alone, the subsidy for new wind turbines, excluding existing turbines, is set to reach $500 million to $1,000 million per year, or up to $10 billion over 10 years. Wind turbines are uneconomic unless they receive these very large subsidies. Moreover, wind requires backup when the wind is not blowing. Coal continues to be burnt while it is in standby mode (at least at 90% capacity), and coal consumption at power stations, according to industry figures, has not decreased.
Some argue that the costs of wind generation are coming down, but this is occurring by increasing the size of the turbines, creating more community angst, as the larger turbines are a significant imposition on the landscape and have a greater low frequency noise component. The costs of other renewables, particularly solar, are expected to come down much faster. Dieter Helm (Professor of Energy Policy at Oxford University) considers that there has been much ‘hype’ about wind power and its ability to curb carbon emissions (Helm, 2012). It is no good trying to pick winners for the task of reducing carbon emissions successfully. Rather, market reforms that emphasise price are required, in order to get carbon emissions down in the cheapest way first, not the most expensive.
The mainstream ‘green’ position on wind turbines generally assumes that wind power reduces human production of greenhouse gases, and that some people may suffer some discomfort. It argues that wind power, while not perfect is of net benefit, and there is no way of reducing greenhouse gas emissions without human cost. I consider that this summation to be flawed and illogical.
Both the Greens and Doctors for the Environment Australia invoke the precautionary principle in relation to coal seam gas, but ignore it in relation to wind turbines. Ironic indeed when companies like AGL are involved in both wind farms and coal seam gas, the latter mining activity producing low frequency noise emissions too. If the precautionary principle were used, setbacks from houses of at least 10 km would be justified, given the lack of a systematic research base to support the safety of wind turbines. The health problems are often severe, forcing people out of their homes. Who can say what effect it is having on other species. The pernicious nature of the sound is considerably worse than other noise sources at the same decibel level.
Arguing in favour of a flawed approach by comparison with coal is a little like saying that execution by lethal injection is better than by hanging. Ideology has primarily driven the green argument, whereas there is scant access to and awareness of knowledge on the noise and health fronts for example. This underlines the critical importance of a holistic health/social cohesion/technology/economic/climate change assessment, not in silos by people coming at it from different perspectives. When a holistic assessment is undertaken, solar PV and solar thermal are way ahead when compared with industrial wind in my view – to say nothing of energy conservation measures.
Chapman, S. (2012). The sickening truth about wind farm syndrome New Scientist. Retrieved from http://www.newscientist.com/article/mg21628850.200-the- sickening-truth-about-wind-farm-syndrome.html
Environmental Review Tribunal. (2011, 18 July). Erickson v. Director, Ministry of the Environment. Retrieved 13 March, 2012, from http://www.ert.gov.on.ca/files/201107/ 00000300-AKT5757C7CO026-BGI54ED19RO026.pdf
Helm, D. (2012, 6 February). Dieter Helm: Forget the Huhne hype about wind power. The Times. Retrieved from http://www.dieterhelm.co.uk/media
House of Representatives proof Federation Chamber Bills Second Reading Speech 13 February. (2013). Speech: Alby Schultz MP (pp. 147-149).
Khurana, V. G., Hardell, L., Everaert, J., Bortkiewicz, A., Carlberg, M., & Ahonen, M. (2010). Epidemiological evidence for a health risk from mobile phone base stations. International Journal of Occupational and Environmental Health, 16(3), 263-267.
Nissenbaum, M. A., Aramini, J. J., & Hanning, C. D. (2012). Effects of industrial wind turbine noise on sleep and health. Noise & Health, 14(September-October), 237-243.
Phillips, C. V. (2011). Properly interpreting the epidemiologic evidence about the health effects of industrial wind turbines on nearby residents. Bulletin of Science, Technology & Society, 31(4), 303-315.
Prominent physician and surgeon Dr. Robert McMurtry calls for wind turbine moratorium. (2012, 19 July). Retrieved 14 March, 2013, from http://www.canadafreepress.com/ index.php/article/48174
Senate Community Affairs References Committee. (2011). The social and economic impact of rural wind farms. Canberra: Commonwealth of Australia.
Shepherd, D., McBride, D., Welch, D., Dirks, K. N., & Hill, E. M. (2011). Evaluating the impact of wind turbine noise on health-related quality of life. Noise & Health, 13(September-October), 333-339.
Author: Wagman, David
Recent reports by the National Renewable Energy Laboratory and others suggest that the emissions-reducing benefits of renewable energy sources such as wind and solar may have been overstated and the cost of cycling fossil-fueled plants underestimated. These findings may change how utilities and policymakers weigh the costs and benefits of wind and solar energy.
The American Wind Energy Association (AWEA) said in early January that 1,833 MW of wind power capacity had been installed during the third quarter of 2012. Those additions brought total installed wind capacity for the first three quarters of the year to 4,728 MW and pushed the total installed wind capacity in the U.S. to 51,630 MW, from more than 40,000 turbines. AWEA also reported that as of September 2012, more than 8,400 MW of capacity were under construction in 29 states and Puerto Rico. What’s more, the wind industry has added more than 35% of all new U.S. generating capacity during the past five years, second only to natural gas.
All of that new wind capacity is aimed, at least in part, at displacing fossil-fueled generating sources and reducing atmospheric and greenhouse gas emissions such as nitrous oxide (NOx), sulfur dioxide (SO₂), and the still-unregulated carbon dioxide (CO₂). Wind generation has inherent benefits: The turbines produce no emissions during their operating lifetimes and have no fuel cost. But some industry observers contend that adding intermittent resources such as wind and solar energy to the system actually increases rather than decreases greenhouse gas emissions.
Those observers point out that many power generators add fast-start gas-fired generating units (generally aeroderivative gas turbine and gas-fired engines) to back up renewable resources and generate power during the times when the sun doesn’t shine or the wind doesn’t blow. Those fossil-fueled resources are variously available as spinning reserves or as fast-start machines that can rapidly ramp to respond to changing output from renewable resources. Observers also contend that cycling or turndown operations at baseload coal and natural gas–fired plants to accommodate wind and solar also may increase air emissions because those fossil-fueled plants end up operating at less-than-optimal levels.
A fact sheet published by AWEA said that, on average, adding 3 MW of wind energy to the U.S. electric grid reduces emissions from fossil power plants by 1,200 pounds of CO₂ per hour. It said adding this amount of wind would “at most require anywhere from 0 to 0.01 MW of additional spinning reserves, and 0 to 0.07 MW of non-spinning reserves.” AWEA said it is likely that those reserves would be provided by zero-emission hydroelectric resources, but even under a worst-case scenario in which a fossil fuel plant with an efficiency penalty of 1.5% must be used for reserves and all of the non-spinning reserves would be activated, the increase in emissions would “still be less than 1 pound of CO₂.” Given that hydropower is always dispatched first and seldom cycled, and coal still provides around 40% of the electricity nationwide and is being cycled, this is a narrow and highly unlikely scenario (see below).
The Facts About Wind Energy’s Pollution Reductions
Editor: The American Wind Energy Association (AWEA) recently contacted POWER to request an opportunity to respond to the editorial “Under Siege” published in the December 2012 issue. The following is AWEA’s response to that editorial.
As wind energy’s growth has continued, spurred by improving technology and declining costs, wind energy’s role in reducing harmful pollution has become even clearer. Empirical data for the United States and Europe clearly indicates not only that wind energy results in the expected pollution reductions by directly offsetting the use of fossil fuels at power plants, but that by displacing the most expensive and therefore least efficient power plants first, wind energy results in even larger pollution savings than expected.
There is no dispute that every MWh of wind energy added to the power grid displaces a MWh that would have been produced by the most expensive power plant currently operating, which is typically the least efficient fossil-fired power plant. However, some have attempted to claim, without support, that adding wind energy to the power system can negatively affect the efficiency of other power plants, reducing the emissions savings produced by wind energy.
Fortunately, a large body of real-world data is now available to assess how wind energy affects the efficiency of other power plants, allowing one to approach the question from multiple angles. To start with, the U.S. Department of Energy collects detailed data on the amount of fossil fuels consumed at power plants, as well as the amount of electricity produced by those power plants. By comparing how the efficiency of power plants has changed in states that have added significant amounts of wind energy against how it has changed in states that have not, one can test the unsupported hypothesis that wind energy has a negative impact on the efficiency of fossil-fired power plants.
The data clearly shows that there is no such relationship, and in fact, states that use more wind energy have seen the efficiency of their fossil-fired power plants fare slightly better than states that use less wind energy. Specifically, coal plants in the 20 states that obtain the most electricity from wind saw their average efficiency decline by only 1.00% between 2005 and 2010, versus 2.65% in the other 30 states. Increases in the efficiency at natural gas power plants were virtually identical in the top 20 wind states and the other states, at 1.89% and 2.03% improvement respectively. The efficiency of fossil-fired power plants fared comparably well in the top 10 wind states (which obtain between 5% and 16% of their electricity from wind), with coal plant efficiency increasing by 0.51% in the top 10 wind-using states and declining by 2.65% in the other 40 states, while gas plant efficiency improved by 0.78% in the top 10 wind states and 2.17% in the other 40 states.
Similar results can be found in International Energy Agency data for Europe, which shows that the top 5 wind countries (which obtain between 7% and 23% of their electricity from wind) saw the average efficiency of their natural gas power plants increase by 11% as they ramped up their use of wind energy from 1999-2010, larger than the 7% increase in efficiency seen across all of OECD Europe. Over that time period, coal plant efficiency fell by 1% in the top 5 wind countries and remained unchanged across all OECD Europe countries.
Another method to assess whether wind energy is producing the expected emissions savings is to calculate whether increases in the use of wind energy are correlated with decreases in the amount of carbon dioxide emitted per MWh produced. A correlation coefficient of 0 would indicate that there is no statistical relationship between wind energy output and emissions intensity, a coefficient of −1 would indicate that wind output increases always coincided with increases in emissions, and the observed coefficients of nearly +1 indicate that increases in wind output nearly always coincided with major decreases in emissions. The correlation between increasing wind energy output and declining emissions intensity in the leading wind energy countries over the period 1999 to 2010 was extremely strong, with a correlation coefficient of 0.77 for Denmark, 0.82 for Germany, 0.86 for Portugal, 0.90 for Spain, and a whopping 0.96 for Ireland.
These correlation coefficients were far higher than for any other possible explanatory factors for the observed decreases in emissions intensity, such as increased use of hydroelectric or nuclear energy, increased use of natural gas instead of coal, changes in the efficiency of fossil-fired power plants, or changes in electricity imports or exports. If wind energy were causing large declines in the efficiency of fossil-fired power plants, zero or negative correlations would have been found, instead of correlations approaching 1.
These findings are further confirmed by the preliminary results of a new report from the National Renewable Energy Laboratory that uses empirical data from another source, EPA’s network of power plant continuous emissions monitors, to evaluate the impact of wind energy on the efficiency of all fossil-fired power plants in the Western U.S. The in-depth, multi-year, and peer-reviewed analysis found that even in a scenario with wind providing 25% of all electricity in the Western U.S., wind’s total impact on the efficiency of fossil-fired power plants would be “negligible,” accounting for less than 0.2% of the emissions savings produced by wind energy. As a result, carbon dioxide emissions declined by 29–34% in the 25% renewable energy case. Moreover, the analysis found that adding wind energy to the grid actually slightly increases the average efficiency of coal and natural gas combined cycle power plants by offsetting the least efficient plants.
No matter how one approaches the question, the data is clear that wind energy greatly reduces fossil fuel use and pollution. Moreover, the results discussed above are in addition to a large body of independent grid operator, utility, and government analyses and data that have already examined how wind energy interacts with the power system and unanimously found that wind energy produces pollution savings that are as large or larger than expected.
— Michael Goggin is the manager of transmission policy at the American Wind Energy Association.
AWEA said that although the wind may suddenly slow down at one location and cause the output from a single turbine to decrease, regions with high penetrations of wind energy may have hundreds or even thousands of turbines spread over hundreds of miles. As a result, it typically takes minutes or even hours for a region’s total wind energy output to change significantly. Yet when the resource does unexpectedly drop, the amount of that reduction must be added immediately to the grid, first with spinning reserve capacity or with fast-start assets.
The unpredictability of the resource explains the large number of gas-fired assets built over the past several years. The trade group said that gas-fired units make it “relatively easy for utility system operators to accommodate these changes without relying on reserves.” It said the task of accommodating variations in output can be made easier by using forecasting, which allows system operators to “predict changes in wind output hours or even days in advance with a high degree of accuracy.”
Despite the AWEA fact sheet, industry observers have found room to question the claimed environmental benefits of wind energy. For example, two researchers, Warren Katzenstein and Jay Apt of Carnegie Mellon University, wrote in 2009 that life-cycle assessments of renewable energy projects often failed to account for emissions from backup and cycling fossil-fired generation sources. The pair found that CO₂ emission reductions from a wind or solar photovoltaic (PV) system coupled with a natural gas system are likely to be 75% to 80% of those assumed by policymakers. Even for the best system they analyzed, NOx reductions with 20% wind or solar PV penetration were 30% to 50% of those expected.
To estimate emissions from fossil-fueled generators that are called on to compensate for variable wind and solar power, the Carnegie Mellon authors modeled a combination of variable renewable power with a fast-ramping natural gas–fired turbine. They used a regression analysis of measured emissions and heat rate data taken at 1-minute resolution from two types of gas turbines to model emissions and heat rate as a function of power and ramp rate. They next determined the required gas turbine power and ramp rate to fill in the variations in 1-minute data from four wind farms and one large solar PV plant, and, finally, computed the emissions from the regression model.
The research team obtained 1-minute resolution emissions data for seven General Electric LM6000 natural gas combustion turbines (CTs) and two Siemens-Westinghouse 501FD natural gas combined cycle (NGCC) turbines. The LM6000 CTs had a nameplate power limit of 45 MW and utilized steam injection to mitigate NOx emissions. A total of 145 days of LM6000 emissions data was used in the regression analysis. The Siemens-Westinghouse 501FD NGCC turbines had a nameplate power limit of 200 MW with GE’s dry low-NOx (DLN) system and an ammonia selective catalytic reduction (SCR) system for NOx control. Emissions data for 11 days was obtained for the 501FD combined cycle machine. The renewables data included 1-second, 10-second, and 1-minute resolution and was from four wind farms and one large solar PV facility in the Eastern Mid-Atlantic, Southern Great Plains, Central Great Plains, Northern Great Plains, and Southwest regions of the U.S.
Based on their analysis, the authors concluded that the conventional method used to calculate displaced emissions was inaccurate, particularly for NOx emissions. They said that if system operators recognize the potential for ancillary emissions from gas generators used to fill in for variable renewable power, they can take steps to produce a greater displacement of emissions. They said that “by limiting generators with GE’s DLN system to power levels of 50% or greater, ancillary emissions can be minimized.” Operation of DLN controls with existing firing modes that reduce emissions when ramping may be practical. They also said that on a time scale compatible with renewable portfolio standard implementation, design and market introduction of generators that are more appropriate from an emissions viewpoint may be feasible to pair with variable renewable power plants.
Utilities that have relatively high and growing amounts of intermittent renewable resources on their systems also have analyzed renewable integration costs, paying particular attention to the cost of wear and tear on equipment and increased maintenance at existing conventional facilities.
For example, Public Service Company of Colorado (PSCo), a unit of Xcel Energy, prepared a report for state regulators in August 2011 that said the utility would add around 700 MW of wind power to its system by 2015, in line with its 2007 Colorado Resource Plan. That additional wind capacity meant PSCo would have around 1,934 MW of nameplate wind generation capacity on its system. One shortcoming of its planning process, however, was its failure to consider wind-induced cycling costs. With growing amounts of wind on its system, the utility said the cost impacts both of unit cycling and wind curtailments will increase, making it important to consider those costs as part of its future planning decisions. The importance of such calculations was highlighted for a single hour last spring when wind energy supplied 57% of the Colorado system’s electricity.
“With an ever-larger wind portfolio, the depth and frequency of cyclical operation of baseload units will increase and affect more and more generators,” the PSCo report said. “Coal-fired units that have historically been base loaded will be required to turn-down to their minimum capacity, or possibly turn off entirely. These cycling evolutions will be occurring more rapidly and more frequently with greater levels of wind generation.”
The study said that any plant cycling causes component wear-and-tear costs. In particular, when a thermal generator is turned off and on, the boiler, steam lines, turbine, and auxiliary components endure large thermal and pressure stresses. Eventually, those stresses can cause component failures and drive up maintenance costs. During low-load operation, pressures and temperatures fluctuate in pipes and tubes, causing fatigue and, ultimately, early failure. Fatigue further erodes the designed stress tolerances of full-output operation, or creep tolerance. PSCo identified this creep-fatigue interaction as “one of the most important phenomena” contributing to component failure.
Wind-induced cycling costs among PSCo’s coal-fired fleet pose an additional “hidden” cost of integrating wind generation onto the system, the report said. “It is appropriate to determine this additional wind integration cost and appropriately burden incremental wind power with this cost in future resource planning efforts.” A sample of the cost findings is shown in Table 1.
The study evaluated two coal plant cycling protocols. The first (referred to as “curtail”) involved cycling coal plants down to their economic minimum generation levels to accommodate wind and curtailing wind in excess of the level needed to meet system load. The second protocol (referred to as “deep cycle”) involved cycling coal plants down to their lower emergency minimum levels to accommodate wind and curtailing wind in excess of the level needed to meet system load.
Although the analysis identified no significant difference in the cost of each protocol, the deep-cycle protocol was found to maximize wind output while minimizing coal burn and associated CO₂ emissions. PSCo said this protocol may result in reduced system reliability as a result of routinely operating baseload coal units down to their emergency minimum loading levels. It said such a condition would increase the wear and tear on these units and possibly lead to more coal unit outages. In contrast, the curtail protocol would result in slightly less wind generation than the deep-cycle protocol but would avoid deep cycling the coal units and the potential downside of reduced system reliability under a deep-cycle protocol.
PSCo chose deep cycling as the preferred operational protocol for its system in the near term, given that there was no distinct cost advantage to either protocol. However, it stopped short of considering some additional factors that it said could influence total costs. In particular, changes in SO₂ and NOx emissions that may occur to accommodate wind due to reduced coal burn or coal units operating at suboptimal generating levels were not considered.
The Carnegie Mellon and PSCo studies, among others, urge a systemwide approach to understanding wind and solar energy’s effects on emissions. These studies helped lead researchers at the National Renewable Energy Laboratory (NREL) to acknowledge in 2012 that many efforts to assess the emissions benefits of wind had failed to account for ancillary emissions from generating units that cycle or ramp to compensate for the renewable resources’ intermittent generation.
In a paper given at the IEEE Power and Energy Society General Meeting in San Diego last July, NREL researchers, along with analysts from Intertek-APTECH (IA), said that regional integration studies have shown that wind and solar may cause fossil-fueled generators to cycle on and off and ramp more frequently. They identified increased cycling, deeper load following, and rapid ramping as leading to potential wear and tear on fossil-fueled generators. They said this additional wear and tear can lead to higher capital and maintenance costs, higher equivalent forced outage rates, and degraded performance over time. What’s more, they said that heat rates and emissions from fossil-fueled generators may be higher during cycling and ramping than during steady-state operation.
The conference paper concluded that “the impacts of generator cycling and part-loading can be significant; however, these impacts are modest compared with the overall benefits of replacing fossil-fueled generation with variable renewable generation.”
The NREL/IA team along with GE Energy built on this initial work with a second, more comprehensive study using continuous emissions monitoring (CEM) data obtained from the Environmental Protection Agency to model ramping and cycling effects across the Western Interconnection based on a variety of scenarios of solar and wind penetration. The impacts of solar- and wind-induced cycling on emissions proved to be mixed, one of the report’s authors told POWER in an interview.
“My conclusion regarding SO₂ is there is hardly any increase at all, since SO₂ is controlled by scrubbers,” said Steve Lefton, director of power plant projects at IA. He said plant operators can control SO₂ emissions during ramping and cycling events by bringing more scrubber modules online sooner. Lefton said that analysis of hundreds of coal-fired units showed that SO₂ limits were exceeded only a few times and only for brief periods of time during startup or ramping.
NOx emissions, by contrast, are a function of temperature, meaning their production likely will be higher until temperatures at the SCR inlet reach around 500F. He characterized the resulting increase in NOx emissions as “minor” and said that it takes time to raise SCR inlet temperatures high enough to support efficient catalytic reduction.
Dr. Greg Brinkman, an NREL mechanical engineer and analyst, and report coauthor with Lefton, said that NOx emission rates (in pounds per megawatt-hour) from a typical coal-fired unit would be 14% less when operated at part load compared to operating the unit at full load. For gas units, NOx emissions are roughly 10% to 20% higher during part-load operation compared to full-load operation. NREL modeled the response of the electric power system to renewable penetration, considering part-load, startup, and ramping emission penalties. “Most emission rates at fossil-fueled generators changed by less than 2%,” he said.
“CO₂ emissions rates from the average coal plant don’t change; SO₂, and NOx emissions rates from average coal, gas combined cycle, and gas combustion turbine plants increase or decrease by up to 2%, depending on plant type and the mix of wind and solar. SO₂ emissions rates from coal plants increased or decreased depending on the mix of wind and solar. Viewed from the perspective of avoided emissions, CO₂, SO₂, and NOx benefits from wind and solar were all within 5% of what we expected based on the typical emission rates of the displaced generators,” Brinkman said.
Effects on Maintenance Costs
Although any change in emissions appears to be relatively minor, the same cannot be said for maintenance costs due to ramping and cycling.
“From all reports, I’d say we’ve either been spot on or under-projecting cycling-related damage” that results from fossil-fueled units following intermittent renewable sources, said Lefton. “Yes, wind is a great thing, but it’s not free.”
Turbine blade damage and generator failures were linked to ramping. These findings came after Lefton and his team analyzed some 400 data sets that included long-term operating and maintenance costs and cycling data. The findings showed that even combustion turbines and reciprocating engines designed for quick starts, ramping, and cycling showed higher maintenance costs, elevated numbers of forced outages, and increasing numbers of generator failures.
“Generator failures used to be rare, but now they rank third in insurance claims filed for combined cycle machines,” Lefton said. He noted higher incidences of heat recovery steam generator tube failures as well as more frequent turbine overhauls. Other maintenance issues linked to cycling include thermal barrier coatings that spall off, leaving the base metal exposed and vulnerable to cracking.
Dr. Debra Lew, an analyst with NREL and coauthor of the report, said while coal units cost the most to start up, gas-fired combustion turbines appear to be the most susceptible to higher maintenance costs as a result of ramping and cycling caused by wind and solar penetration because these units are started the most often. She said that wear and tear as a result of cycling to follow renewable energy may increase operations and maintenance costs for all types of fossil generation by $35 million to $157 million a year across the Western Interconnection, as shown in Table 2, for wind and solar penetrations up to 33%.
Last November, Lefton and several of his colleagues at IA presented a paper, “The Increased Cost of Cycling Operations at Combined Cycle Power Plants,” at the International Conference on Cyclic Operation of Power Plants & CCGT. The paper reported that higher penetration of renewables on the North American grid is increasing the number of on-off and load-cycling operations, which the authors said will increase the need for spinning reserve megawatts, their costs, and the startup charges for putting combined cycle plants online.
The desire for faster online times increases the severity of damage during gas turbine starts and is increasing thermal transients with more rapid gas turbine acceleration and higher mass gas flows at higher exhaust temperatures that reach heat recovery steam generators (HRSGs). The paper said these factors affect the gas turbine and the HRSGs, as well as the balance of plant and water chemistry, ultimately reducing overall plant reliability. The average starts on these gas turbines/combined cycle units are increasing, and run times are generally decreasing. Though capacity factors may be decreasing, production costs will likely rise significantly due to cycling operations. The paper suggested that cost estimates made by industry often underestimate by a large margin the actual costs that cycling operations can incur, as shown in Table 3.
Wind Farm Life Expectancy
Wind farm life expectancy also may reduce calculated environmental benefits and increase the total investment needed to achieve environmental goals, particularly in the UK and Europe. A December 2012 report, published by the UK-based Renewable Energy Foundation and written by Gordon Hughes of the University of Edinburgh, scrutinized wind farm lifecycle emission benefits. The foundation in the past has criticized the UK government’s Renewables Obligation policy, saying the subsidy distorts markets as well as the generation mix.
The Hughes study examined wind farm performance in the UK and Denmark and concluded that, after allowing for variations in wind speed and site characteristics, the average load factor of wind farms declines as they age, probably due to wear and tear. By 10 years of age, the contribution of an average UK wind farm to meeting electricity demand was said to have fallen by as much as one-third.
The report said this performance decline means that it is “rarely economic to operate wind farms for more than 12 to 15 years.” Investors who expect a return on their investment over 20 to 25 years will be “disappointed,” the report said. What’s more, policymakers who expected wind farms built before 2010 to contribute toward CO₂ targets in 2020 or later should allow for the possibility that the total investment required to meet those targets will be much larger than previous forecasts suggested.
The study based its findings on data reflecting the monthly output of wind farms in the UK and Denmark. Normalized age-performance curves were estimated using statistical techniques that allowed for differences between sites and over time in wind resources, and other factors. The normalized load factor for UK onshore wind farms was found to decline from a peak of about 24% at age one to 15% at age 10 and 11% at age 15. The decline in the normalized load factor for Danish onshore wind farms showed a fall from a peak of 22% to 18% at age 15. For offshore Danish wind farms, the normalized load factor was shown to fall from 39% at the start of commercial operation to 15% at age 10.
Hughes said that the reasons for the observed declines in normalized load factors could not be fully assessed using the data available, but he speculated that “outages due to mechanical breakdowns” appeared to be a contributing factor.
Hughes said that analysis of site-specific performance showed that the average normalized load factor of new UK onshore wind farms at age one “declined significantly” between 2000 and 2011. In addition, he found that larger wind farms had worse performance than smaller wind farms. Adjusted for age and wind availability, the overall performance of wind farms in the UK has “deteriorated markedly” since the beginning of the century, he found.
According to Hughes, these findings have implications for policy toward wind generation in the UK. First, they suggest that the current government subsidy is “extremely generous” if investment in new wind farms remains profitable despite the decline in performance due to age and over time. Second, meeting the UK government’s targets for wind generation will require a much higher level of wind capacity and capital investment than current projections imply. Third, the structure of contracts offered to wind generators may require modifications, because few wind farms will operate for more than 12 to 15 years.
In releasing the report, the Renewable Energy Foundation said that policymakers who were expecting wind farms built before 2010 to contribute toward CO₂ targets in 2020 or later “must allow for the likelihood that the total investment required to meet these targets will be much larger” than previous forecasts suggested.
— David Wagman is executive editor of POWER.
Author: Renewable Energy Foundation
1. Onshore wind turbines represent a relatively mature technology, which ought to have achieved a satisfactory level of reliability in operation as plants age. Unfortunately, detailed analysis of the relationship between age and performance gives a rather different picture for both the United Kingdom and Denmark with a significant decline in the average load factor of onshore wind farms adjusted for wind availability as they get older. An even more dramatic decline is observed for offshore wind farms in Denmark, but this may be a reflection of the immaturity of the technology.
2. The study has used data on the monthly output of wind farms in the UK and Denmark reported under regulatory arrangements and schemes for subsidising renewable energy. Normalised age-performance curves have been estimated using standard statistical techniques which allow for differences between sites and over time in wind resources and other factors.
3. The normalised load factor for UK onshore wind farms declines from a peak of about 24% at age 1 to 15% at age 10 and 11% at age 15. The decline in the normalised load factor for Danish onshore wind farms is slower but still significant with a fall from a peak of 22% to 18% at age 15. On the other hand for offshore wind farms in Denmark the normalised load factor falls from 39% at age 0 to 15% at age 10. The reasons for the observed declines in normalised load factors can not be fully assessed using the data available but outages due to mechanical breakdowns appear to be a contributory factor.
4. Analysis of site-specific performance reveals that the average normalised load factor of new UK onshore wind farms at age 1 (the peak year of operation) declined significantly from 2000 to 2011. In addition, larger wind farms have systematically worse performance than smaller wind farms. Adjusted for age and wind availability the overall performance of wind farms in the UK has deteriorated markedly since the beginning of the century.
5. These findings have important implications for policy towards wind generation in the UK. First, they suggest that the subsidy regime is extremely generous if investment in new wind farms is profitable despite the decline in performance due to age and over time. Second, meeting the UK Government’s targets for wind generation will require a much higher level of wind capacity – and, thus, capital investment – than current projections imply. Third, the structure of contracts offered to wind generators under the proposed reform of the electricity market should be modified since few wind farms will operate for more than 12–15 years.
Author: Kaffine, Daniel; McBee, Brannin; and Lieskovsky, Jozef
Production of electricity from wind energy has risen rapidly in the last decade, with installed capacity roughly doubling every three years in the United States. As of the end of 2010, installed wind capacity in the United States exceeded 40 gigawatts (GW) and accounted for 2% of total electricity generation. Technological advances in wind turbine design, control and siting have led to falling costs per megawatt-hour (MWh) and increased the penetration of wind energy into the power sector. In addition, government subsidies and policies have also played an important role in encouraging wind power production. For example, a majority of states have implemented Renewable Portfolio Standards mandating that a percentage of total state electricity generation be derived from renewable sources, and the federal government provides a Production Tax Credit of $22 dollars per MWh to wind power producers.
Government support for wind power development is primarily predicated on the environmental benefits of avoided emissions, such as sulphur dioxide (SO₂), nitrogen oxides (NOx), and carbon dioxide (CO₂). It is these avoided emissions that form the focus of our research efforts. In particular we ask, what is the emissions savings rate for SO₂, NOx and CO₂ per MWh of wind power produced, and how does that savings rate vary across regions with different existing generation mixes? To answer these questions, we consider more than 50,000 hourly observations of wind generation and emissions from the territories of the Electric Reliability Council of Texas (ERCOT), California Independent System Operator (CAISO) and the Midwest Independent System Operator (MISO).
Electricity generation in the United States relies heavily on fossil fuel sources. As of 2010, coal accounts for 44% of total generation while natural gas accounts for 25% of total generation, compared to 18% for nuclear, 8% for hydropower, 2% for wind power, and <1% each for solar, geothermal and biomass. However, research suggests that calculating the emissions savings from wind by replacing an average unit of generation and using average emission rates is an incorrect methodology. First, rather than displacing an average unit of power generation, wind is likely to displace generation from higher marginal cost sources that can easily accommodate wind power on the grid – most likely natural gas. Second, average plant emission rates may not appropriately reflect the actual emissions savings from wind generation, as rapid ramping of fossil fuel plants (known as cycling) to accommodate wind is emissions-intensive.
These concerns have even led some to claim that wind power produces little or no emissions savings. Given the widely varying assumptions and findings in previous research, there is clearly a need for a careful analysis of actual changes in emissions associated with wind generation. Such an analysis must capture both the marginal unit of generation displaced by wind as well as the marginal emissions from that displaced generation. This study helps to fill this crucial gap in the literature, and provides emission savings estimates based on large sample empirical data that will be of use to policymakers and future researchers. One advantage of our approach over much of the prior literature is that we are able to look at how emissions actually responded to changes in wind generation, as opposed to relying on dispatch and emission rate assumptions and modeling.
We estimate the emissions savings from wind generation across several Independent System Operator (ISO) territories in the United States. We exploit exogenous variation in hourly wind generation levels to identify the effect of wind generation on total hourly emissions of SO₂, NOx, and CO₂. Thus, our reduced-form estimation implicitly captures both the marginal unit of generation displaced by wind, as well as the marginal emissions reduction from that unit. In total, our rich data set contains over 50,000 hourly measurements of wind generation and emissions across Texas, California, and the Upper Midwest. We focus on ERCOT 2007-2009 (Texas), CAISO 2009 (California), and MISO 2008-2009 (Upper Midwest) for two reasons: first, they contain a significant portion (roughly 60%) of total wind capacity and generation in the United States, and second, these territories vary substantially in terms of their existing fossil fuel generation mix. MISO’s generation is dominated by coal, CAISO’s generation is dominated by gas, and ERCOT’s generation is roughly an even mix of both. This variation in existing generation will prove crucial in determining the emissions savings from wind generation in each territory.
We find that emissions savings across territories are less than the hypothetical savings based on average emission rate analysis. Nonetheless we do find that emissions savings from wind generation are statistically different than zero for most pollutants and vary substantially across territories. In coal dominated MISO, we find emissions savings of 4.9 lbs/MWh for SO₂, 2 lbs/MWh for NOx, and 1 ton/MWh for CO₂. By contrast in CAISO, where wind typically offsets gas generation, we find emissions savings of 0.0 lbs/MWh for SO₂, 0.05 lbs/MWh for NOx, and 0.3 tons/MWh for CO₂. Generation in ERCOT is roughly evenly balanced between coal and gas, and we find that emission savings in ERCOT fall in between MISO and CAISO, with emissions savings of 1.2 lbs/MWh for SO₂, 0.7 lbs/MWh for NOx, and 0.5 tons/MWh for CO₂. These results suggest that emissions savings are strongly driven by differences in existing generation mix – coal-intensive territories experience larger reductions in emissions due to wind generation.
Our dataset consists of over 50,000 hourly observations of total wind generation in MWh and total emissions in pounds of SO₂ and NOx and tons of CO₂ in ERCOT (2007-2009), MISO (2008-2009), and CAISO (2009).
Hourly emissions data is sourced from the Environmental Protection Agency’s (EPA) Continuous Emission Monitoring Systems (CEMS) program, which requires coal and gas power units with over 25 MW of capacity to submit hourly data on SO₂, NOx and CO₂ emissions. These emission reports are required by the EPA to monitor compliance with emission regulations, and strict quality assurance standards are in place to guarantee the accuracy of emission measurements. However, emissions per territory are not explicitly reported under CEMS. To determine which units operated in a given area, each unit is spatially referenced using latitude/longitude against the spatial footprint of each operating territory, obtained through the operating territory’s website. Units that fall under the spatial footprint of the territory are assumed to provide generation to the corresponding territory and the emissions from that plant are included in the territory’s total emissions. Thus, an observation consists of the total hourly emissions of each pollutant by territory, representing the sum of emissions from all units.
The hourly wind generation data is acquired from each operating territory (ERCOT, MISO, CAISO) and represents total electricity generation from wind turbines operating in the territory. This publicly available data, directly reported by the operating territory, is posted on the operators’ websites. It should be noted that the availability of hourly wind generation data is the primary limiting factor of our analysis, both in terms of the time period and territories over which data is available. Wind generation data is available for ERCOT from 2007, for MISO from 2008, and for CAISO from 2009. We collected this data for each of these three territories through December 31st, 2009. The 50,000 hourly observations of wind generation in our dataset thus provide a detailed look at actual wind generation levels across the three territories. Furthermore, the three territories we study account for over 60% of total wind capacity and generation in the United States.
Temperature is a key determinant of electricity demand and thus emissions. Temperature data for all territories is taken from the National Oceanic and Atmospheric Administration’s (NOAA) hourly temperature database, which is available through subscription to NOAA’s hourly surface data. A population-weighted average is created for each operating territory utilizing the major population centers within the territory’s footprint. These average hourly temperatures are used throughout the analysis.
During the 2007-2009 period, average yearly total generation in ERCOT was 306.3 million MWh, with wind power representing 4.7% of total generation. Coal accounted for 37% of total generation and gas accounted for 43% of generation. ERCOT average emission rates across all forms of generation was 2.63 lbs/MWh for SO₂, 0.72 lbs/MWh for NOx, and 0.64 tons/MWh for CO₂. Figure 1 displays the ERCOT generation mix from November 5th through November 12th in 2008. This figure reveals substantial variation in wind power produced at any given point in time. During high load periods (middle of the day), substantial gas generation is online, and variation in wind power is accommodated by gas cycling. By contrast, during low load periods (overnight), limited gas generation is available, and variation in wind power is accommodated by coal cycling (as evidenced by the drop in coal generation relative to the base level output during periods of large overnight wind generation).
During the 2008-2009 period, average annual total generation in MISO was 566.2 million MWh, with wind power representing 2% of total generation. MISO relies primarily on coal generation with 80% of total generation coming from coal and only 2.7% from gas. In coal dominated MISO, average emission rates are substantially higher than in ERCOT, at 5.74 lbs/MWh for SO₂, 2.15 lbs/MWh for NOx, and 0.86 tons/MWh for CO₂.
In 2009, total generation in CAISO was 178.6 million MWh, with wind power accounting for 3.2% of total generation. CAISO has no coal plants in their territory, while 35% of total generation came from gas. Due to the lack of coal plants, average emission rates in CAISO were much cleaner than ERCOT or MISO, at 0.00 lbs/MWh for SO₂, 0.37 lbs/MWh for NOx, and 0.16 tons/MWh for CO₂. The heterogeneity in emission rates and generation sources across these three territories will prove important in understanding the emission savings from wind emissions.
Our identification strategy hinges on exploiting the exogenous and stochastic variation in hourly wind power generation. The reduced-form model presented below captures the systematic response of conventional generation (and thus emissions) to hourly fluctuations in wind generation. Total emissions Eirt of pollutant i in territory r at hour t are separately regressed by territory against total hourly wind generation in each territory Wrt (in MWh), average hourly temperature Trt and its square Trt² in each territory, and a vector of other control variables Xt:
The coefficient of interest is βir, which represents the marginal change in emissions in each territory due to a change in wind generation. Thus, for every MWh of wind generation produced in hour t in territory r, this coefficient represents the reduction in lbs/lbs/tons of SO₂/NOx/CO₂. The remaining covariates control for trends in wind generation and emissions that may be correlated, leading to erroneous interpretations of βir. Due to heating and cooling needs, temperature is a strong driver of electricity demand and emissions and thus is explicitly included as a covariate along with the square of temperature, to account for non-linearities. The remaining covariates in the vector Xt are fixed effects to account for other sources of variation in emissions. Hourly fixed effects are included to account for diurnal wind variation over the course of the day, which can be correlated with changes in the electricity demand profile. On average, winds are strongest in the early morning hours when electricity demand and emissions are at their nadir, and therefore failing to control for this hourly variation would lead to an overestimate of the emissions reductions from wind.
Over the sample period, wind capacity steadily increased, which may be correlated with changes in demand and emissions driven by macroeconomic effects unrelated to wind generation. To account for these longer-run trends, month-year fixed effects are included, leading to identification of the effect of wind generation on emissions through within-month variation. Finally, though wind generation is not correlated with the day of the week, day-of-week fixed effects are included to capture within-week variation (primarily between weekdays and weekends) in electricity demand and emissions.
Emissions savings estimates
The estimates of the emission savings from wind generation in ERCOT, MISO, and CAISO are presented in Table 1. The reported coefficients in the first row can be interpreted as the lbs/lbs/tons of SO₂/NOx/CO₂ emissions reduced per MWh of wind generation. The first panel represents the emissions savings by pollutant due to wind power in ERCOT from 2007-2009. Each MWh of wind generation in ERCOT on average reduced SO₂ by 1.235 lbs, NOx by 0.739 lbs, and CO₂ by 0.484 tons. All coefficients are very statistically significant. It should be noted that our estimates for ERCOT are similar to those in Novan (2010) who employs an estimation strategy comparable to that presented here.
The next panel represents emission savings in coal dominated MISO from 2008-2009, where each MWh of wind generation in MISO reduced SO₂ by 4.890 lbs, NOx by 1.995 lbs, and CO₂ by 1.025 tons. Again, all coefficients are statistically significant and are larger than the estimated emissions savings in ERCOT. By contrast, in the final panel for gas dominated CAISO, we find emissions savings in 2009 of 0.008 lbs/MWh for SO₂, 0.054 lbs/MWh for NOx, and 0.299 tons/MWh for CO₂, with significant coefficient estimates for NOx and CO₂.
The estimated emission savings in ERCOT using average plant emission rates found in Cullen (2010) provide a useful reference point. Cullen calculates that 3.15 lbs of SO₂, 1.05 lbs of NOx, and 0.79 tons of CO₂ were avoided per MWh of wind power in ERCOT from 2005-2007. By contrast, our estimates for ERCOT (2007-2009) above find substantially smaller emission savings rates of 1.235 lbs/MWh for SO₂, 0.739 lbs/MWh for NOx, and 0.484 tons/MWh for CO₂. This difference is likely driven by emissions associated with cycling – relying on average emission rates for emissions savings calculations will likely overestimate emissions offset per MWh of wind power.
The importance of the generation mix can be seen by comparing estimates of emission savings across territories. Figure 2 displays emission savings per MWh against the percentage share of coal generation in each territory (fit with a quadratic polynomial). Each pollutant exhibits an upward trend with respect to coal share, with emissions savings from SO₂ displaying the steepest increase. The stronger dependence of SO₂ emission savings on coal share is driven by the fact that coal is the only source of SO₂, while NOx and CO₂ are also produced by gas. Each pollutant also exhibits a convex response to coal share. Territories with low to moderate coal share typically have a substantial number of gas plants, and it is these gas plants that are used to accommodate wind on the grid, and thereby relatively smaller emission savings are generated. As coal share increases and gas share decreases, the ability of gas to accommodate wind is also diminished, which in turn implies that base load coal is cycled more frequently to accommodate wind, increasing emission savings.
In the preceding sections, we provided estimates of emissions savings from wind power in Texas, California and the Upper Midwest. Our reduced form approach leverages the exogenous variation in hourly wind production to identify the impact of wind power on system-wide emissions. Looking to the future, accommodation of wind onto the grid will become an increasingly important issue, as wind was the second largest new source of installed capacity in the U.S. in 2008 and 2009. We show that the emissions savings corresponding to this growth in wind power will vary substantially depending on the fuel source displaced by wind. In particular, the share of coal in the existing generation mix strongly influences emissions savings from wind. This suggests that there may be benefits to adjusting the existing Production Tax Credits to reflect the regional emission savings (or a proxy thereof) from a MWh of wind power.
Based on current trends, several competing forces will influence emissions savings from wind power in the future. First, gas is the leading source of new generation capacity in the U.S. This would tend to increase the gas offset by wind power and reduce the emission savings associated with wind (although of course electricity generation from gas itself is less emissions-intensive than coal). Second, as wind capacity grows, the ability of existing gas generation to accommodate wind power will diminish, leading to increased cycling of coal plants, potentially increasing emissions savings. Finally, increasing wind capacity will likely require an increase in ramping of thermal generation, as the magnitude of shifts in wind speed is amplified into larger swings in aggregate wind generation. This increased cycling of thermal generation (in magnitude and potentially frequency) may erode the emissions savings per MWh of wind power as thermal generation is utilized less efficiently to accommodate wind. While it is unclear which of these effects will win out, it is clear that the resulting emission savings of wind power will depend critically on the factors highlighted in this study. As such, this research provides a transparent empirical framework for updating and refining emission savings estimates as data on wind generation in more territories and across longer time periods becomes available.
Callaway, D. and M. Fowlie (2009). Greenhouse gas emissions reductions from wind energy: Location, location, location? Working paper.
Cullen, J. (2010). Measuring the environmental benefits of wind-generated electricity. Working Paper.
Kaffine, D.T., B. McBee, and J. Lieskovsky (2011). Emissions savings from wind power generation: Evidence from Texas, California and the Upper Midwest. Working Paper.
Novan, K. M. (2010). Shifting wind: The economics of moving subsidies from power produced to emissions avoided. Working paper.
Daniel T. Kaffine, Division of Economics and Business, Colorado School of Mines
Brannin J. McBee, Research and Development, Bentek Energy LLC
Jozef Lieskovsky, Research and Development, Bentek Energy LLC
For further details on the research discussed in this article, see Kaffine, McBee, and Lieskovsky (2011) “Emissions savings from wind power generation: Evidence from Texas, California and the Upper Midwest.”
 For further discussion on the limitations of existing methodologies, see Callaway and Fowlie (2009) “Greenhouse gas emissions reductions from wind energy: Location, location, location?”; Cullen (2010) “Measuring the environmental benefits of wind-generated electricity”; and Novan (2010) “Shifting wind: The economics of moving subsidies from power produced to emissions avoided.”
 It should be noted that this measure of total generation, as reported by CAISO, also includes net imports, which constitute over a quarter of the reported total generation. The total generation reported above for ERCOT and MISO also include net imports, though they are much smaller as a percentage than CAISO (1% and 5% respectively).
 Standard errors for all estimations reported below correct for heteroscedasticity and autocorrelation. Newey-West standard errors are reported with a 5-day lag for SO₂, 1-day lag for NOx, and 3-day lag for CO₂.
 Alternative specifications with month and year fixed effects or flexible polynomial time trends yielded estimates nearly identical to those presented below, as did estimations with month-hour fixed effects. In addition, estimations were run with heating-degree day and cooling-degree day specifications instead of temperature, generating coefficients and standard errors that differed only trivially from those reported below.
 See Kaffine et. al (2011) for further discussion of the potential impact of imports/exports on emissions savings, discussion of the impacts of wind volatility on emission savings, estimations based on daily aggregations of wind power and emissions, estimations including load measure and other robustness checks, back-of-the-envelope calculations of national emissions savings based on generation mix and wind generation by state, and calculations of marginal benefits of avoided emissions by territory.