Gone with the wind – why the fast jet stream winds cannot contribute much renewable energy after all

Gone with the wind – why the fast jet stream winds cannot contribute much renewable energy after all

30 November 2011 Max Planck Institute for Biogeochemistry

The assumption that high jet steam wind speeds in the upper atmosphere correspond to high wind power has now been challenged by researchers of the Max Planck Institute for Biogeochemistry in Jena, Germany. Taking into account that the high wind speeds result from the near absence of friction and not from a strong power source, Axel Kleidon and colleagues found that the maximum extractable energy from jet streams is approximately 200 times less than reported previously. Moreover, climate model simulations show that energy extraction by wind turbines from jet streams alters their flow, and this would profoundly impact the entire climate system of the planet.

Jet streams are regions of continuous wind speeds greater than 25 m/s that occur at altitudes of 7-16 km. Their high speeds seem to suggest an almost unlimited source of renewable energy that would only need airborne wind energy technology to utilize it. Claims that this potential energy source could “continuously power all civilization” sparked large investments into exploitation of this potential energy resource. However, just like any other wind and weather system on Earth, jet streams are ultimately caused by the fact that the equatorial regions are heated more strongly by the sun than are polar regions. This difference in heating results in large differences in temperature and air pressure between the equator and the poles, which are the driving forces that set the atmosphere into motion and create wind. It is this differential heating that sets the upper limit on how much wind can be generated and how much of this could potentially be used as a renewable energy resource.

It is well known in meteorology that the high wind speeds of jet streams result from the near absence of friction. In technical terms, this fact is referred to in meteorology as “geostrophic flow”. This flow is governed by an accelerating force caused by pressure differences in the upper atmosphere, and the so-called Coriolis force arising from the Earth’s rotation. Because the geostrophic flow takes place in the upper atmosphere, far removed from the influence of the surface and at low air density, the slow-down by friction plays a very minor role. Hence, it takes only very little power to accelerate and sustain jet streams. “It is this low energy generation rate that ultimately limits the potential use of jet streams as a renewable energy resource”, says Dr. Axel Kleidon, head of the independent Max Planck Research Group ‘Biospheric Theory and Modelling’. Using this approach based on atmospheric energetics, Kleidon’s group used climate model simulations to calculate the maximum rate at which wind energy can be extracted from the global atmosphere. Their estimate of a maximum of 7.5 TW (1 TW = 10^12 W, a measure for power and energy consumption) is 200-times less than previously reported and could potentially account for merely about half of the global human energy demand of 17 TW in 2010.

Max Planck researchers also estimated the climatic consequences that would arise if jet stream wind power would be used as a renewable energy resource. As any wind turbine must add some drag to the flow to extract the energy of the wind and convert it into electricity, the balance of forces of the jet stream must also change as soon as energy is extracted. If 7.5 TW were extracted from jet streams as a renewable energy source, this would alter the natural balance of forces that shape the jet streams to such an extent that the driving atmospheric pressure gradient between the equator and the poles is depleted. “Such a disruption of jet stream flow would slow down the entire climate system. The atmosphere would generate 40 times less wind energy than what we would gain from the wind turbines”, explains Lee Miller, first author of the study. “This results in drastic changes in temperature and weather”.

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Tallest

Unnamed Tree - Redwood Mountain Grove - 311 feet (95 m)

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Tallest

The tallest wind turbine is Fuhrländer Wind Turbine Laasow. Its axis is 160 meters above ground and its rotor tips can reach a height of 205 meters. It is the only wind turbine in the world taller than 200 meters.

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Impacts of wind farms on surface air temperatures

Somnath Baidya Roy1 and
Justin J. Traiteur

+ Author Affiliations

Department of Atmospheric Sciences, University of Illinois, 105 South Gregory Street, Urbana, IL 61820

Edited* by Stephen H. Schneider, Stanford University, Stanford, CA, and approved August 13, 2010 (received for review January 15, 2010)

Abstract

Utility-scale large wind farms are rapidly growing in size and numbers all over the world. Data from a meteorological field campaign show that such wind farms can significantly affect near-surface air temperatures. These effects result from enhanced vertical mixing due to turbulence generated by wind turbine rotors. The impacts of wind farms on local weather can be minimized by changing rotor design or by siting wind farms in regions with high natural turbulence. Using a 25-y-long climate dataset, we identified such regions in the world. Many of these regions, such as the Midwest and Great Plains in the United States, are also rich in wind resources, making them ideal candidates for low-impact wind farms.

Wind power is one of the fastest growing energy sources in the world. Most of this growth is in the industrial sector based on large utility-scale wind farms (1). Recent studies have investigated the possible impacts of such wind farms on global and local weather and climate. Although debates exist regarding the global-scale effects of wind farms (2–5), modeling studies agree that wind farms can significantly affect local-scale meteorology (6, 7). However, these studies are based only on model simulations and are not validated against observational evidence. In this paper, we used field data and numerical experiments with a regional climate model to answer the following critical questions arising from the prior studies:

1. Does observational evidence show that wind farms affect near-surface air temperatures?


2. Can atmospheric models replicate the observed patterns of near-surface air temperatures within wind farms?


3. How can these impacts be minimized to ensure long-term sustainability of wind power?

Observed Impacts of Wind Farms

Although observed data on wind speed and turbulence in and around operational wind farms are readily available, information on other meteorological variables do not exist in the public domain. The only available information is temperature data from a wind farm at San Gorgonio, California, collected during June 18–August 9, 1989 (Fig. 1). To the best of our knowledge, this is the only meteorological field campaign conducted in an operational wind farm. The wind farm consisted of 23-m-tall turbines with 8.5-m-long rotor blades arranged in 41 rows that were spaced 120 m apart.

Data from the field campaign show that near-surface air temperatures downwind of the wind farm are higher than upwind regions during night and early morning hours, whereas the reverse holds true for the rest of the day (Fig. 2A). Thus, this wind farm has a warming effect during the night and a cooling effect during the day. The observed temperature signal is statistically significant for most of the day according to the results of a Mann–Whitney Rank Sum Test (Table 1).

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Atmos. Chem. Phys., 10, 769–775, 2010
www.atmos-chem-phys.net/10/769/2010/
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Weather response to a large wind turbine array

D. B. Barrie and D. B. Kirk-Davidoff
University of Maryland Department of Atmospheric and Oceanic Science, College Park, MD, USA

Received: 8 December 2008 – Published in Atmos. Chem. Phys. Discuss.: 29 January 2009
Revised: 8 January 2010 – Accepted: 15 January 2010 – Published: 26 January 2010

Abstract. Electrical generation by wind turbines is increasing rapidly, and has been projected to satisfy 15% of world electric demand by 2030. The extensive installation of wind farms would alter surface roughness and significantly impact the atmospheric circulation due to the additional surface roughness forcing. This forcing could be changed deliberately by adjusting the attitude of the turbine blades with respect to the wind, which would enable the “management” of a large array of wind turbines. Using a General Circulation Model (GCM), we represent a continent-scale wind farm as a distributed array of surface roughness elements. Here we show that initial disturbances caused by a step change in roughness grow within four and a half days such that the flow is altered at synoptic scales. The growth rate of the induced perturbations is largest in regions of high atmospheric instability. For a roughness change imposed over North America, the induced perturbations involve substantial changes in the track and development of cyclones over the North Atlantic, and the magnitude of the perturbations rises above the level of forecast uncertainty.

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2.2 Size of the wind farm

The wind farm simulated in this study occupies 23% of the North American land area and is positioned in the central United States and south central Canada. Figure 1 shows the extent of the wind farm, as indicated by the rectangular box.

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In early tests of the modeling studies described in this paper, it was found that a substantially smaller wind farm, with an area one quarter the size of the wind farm described in this paper, did not cause a large downstream impact. The perturbation induced by the smaller wind farm’s drag had a much weaker impact on upper level winds, leading to a lack of noticeable downstream effects. On the other hand, the scale of the wind farm described throughout this paper is larger than the area of surface damping that elicited a maximum downstream response in the shallow water model used by Kirk-Davidoff and Keith (2008). We expect that a modest change in the size of the wind farm studied here would have little effect on the magnitude of the downstream response. Ongoing work will characterize scale dependence in more detail.

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4 Conclusions

Wind farms as large as those studied in the paper do not yet exist, and as such, we view this work as a theoretical problem with the potential for real world applications in the coming decades. The study presented here depicts a strong downstream impact caused by a large surface roughness perturbation in a GCM. We have assumed that the active control of turbine orientation could produce a time-dependent change in surface roughness. Atmospheric anomalies initially develop at the wind farm site due to a slowing of the obstructed wind. The anomalies propagate downstream as a variety of baroclinic and barotropic modes, and grow quickly when they reach the North Atlantic. These responses occur within a short forecast timeframe, which suggests that predictable influences on weather may be possible. This study utilized an array of highly variable initial conditions to initialize the model. Ongoing work will catalog the initial meteorological conditions necessary to generate predictable and controlled downstream effects caused by wind farms. We performed an ensemble study of one particular case with randomized initial conditions chosen for both the wind farm and the wind farm absent cases that showed that the atmospheric perturbation persists across the ensemble members. We will continue to study the wind farm effects in an ensemble context to determine the conditions necessary for induced perturbations to project strongly onto the fastest modes of error growth. This will illustrate the statistical significance and regularity of downstream changes in the atmosphere. There are open questions regarding the importance of the size of the wind farm, the sensitivity of the impacts to the value of surface roughness, the amount of time that the wind farm would have to be turned off, and the location of the wind farm with respect to overlying atmospheric structures such as the jet stream. We are continuing this work by studying these issues in greater detail.

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Energies 2009, 2, 816-838; doi:10.3390/en20400816
Investigating the Effect of Large Wind Farms on Energy in the Atmosphere
Magdalena R.V. Sta. Maria * and Mark Z. Jacobson
Abstract: This study presents a parameterization of the interaction between wind turbines and the atmosphere and estimates the global and regional atmospheric energy losses due to such interactions. The parameterization is based on the Blade Element Momentum theory, which calculates forces on turbine blades. Should wind supply the world’s energy needs, this parameterization estimates energy loss in the lowest 1 km of the atmosphere to be ~0.007%. This is an order of magnitude smaller than atmospheric energy loss from aerosol pollution and urbanization, and orders of magnitude less than the energy added to the atmosphere from doubling CO2. Also, the net heat added to the environment due to wind dissipation is much less than that added by thermal plants that the turbines displace.

Two scenarios needing large installations of wind farms are examined—scenario A, which assumes that wind energy displaces the CO2 from all fossil fuel energy sources (both electric and non-electric), and scenario B, which assumes that all onroad vehicles are replaced by wind-powered battery electric vehicles. For scenario A, two cases were analyzed. The first case assumes that wind displaces all fossil fuel energy worldwide that produces carbon. The second case assumes that wind displaces all fossil fuel carbon in the United States. Scenario B also examines two cases: replacing onroad vehicles throughout the whole U.S. and replacing onroad vehicles in California. California is of interest because it is the U.S. state with the largest vehicle density. Table 2 summarizes the cases. The energy losses from all these cases are examined both as the loss from the lower 1 km of the atmosphere (heretofore referred to as L1)—over global land and oceans—and the loss from only sections of the L1 layer that are above the land area of interest, e.g., over U.S. land.

Figure 11 shows the results of this energy analysis for Scenario A1. The different bars indicate the losses given different mean wind speeds. Larger relative energy losses are found at lower mean wind speeds, where more turbines are required to generate the same energy. Relative energy losses in L1 above global land range from 0.06%–0.08%, and those above global land plus ocean range from 0.006%–0.008%. The relative energy losses for Scenario A2 are shown in Figure 12. In this scenario, L1 above the U.S. loses 0.19%–0.23% of its energy, but when seen in the context of the global (land plus ocean) L1 layer, only 0.0012%–0.0014% is lost. Figure 13 and Figure 14 present the relative energy losses from Scenario B1 and B2, respectively. In these two cases, there is even less of an effect, even in the local L1. If all U.S. onroad vehicles were powered by wind farms, the loss in L1 over U.S. land ranges from 0.04%–0.05%. If only California vehicles, the energy loss from L1 layer over California would be 0.10%–0.12%. In L1 over global land plus ocean, a relative energy loss of 0.00026%–0.00031% results from Scenario B1, and 0.000026%–0.000032% from Scenario B2.

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A possible explanation for this phenomenon can be drawn from the hypothesis proposed by Baidya Roy et al. that turbulence generated in the wake of the rotors enhance vertical mixing (6). In a stable atmosphere when the lapse rate is positive, i.e., a warm layer overlies a cool layer, enhanced vertical mixing mixes warm air down and cold air up, leading to a warming near the surface. In an unstable atmosphere with negative lapse rate, i.e., cool air lying over warmer air, turbulent wakes mix cool air down and warm air up, producing a cooling near the surface. Vertical profiles of temperatures from the Edwards Air Force Base corroborate this hypothesis. This base is the World Meteorological Organization (WMO) recognized weather station nearest to the San Gorgonio wind farm. Data show a positive lapse rate at 4 AM and negative lapse rates at 10 AM and 4 PM at the base during the field campaign (Fig. 2B). The corresponding temperature signal from the San Gorgonio wind farm (Fig. 2A) shows a warming effect at 4 AM but a cooling effect at 10 AM and 4 PM. This pattern is consistent with the proposed hypothesis.

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