assessing the impact of the texas competitive renewable ... · assessing the impact of the texas...

CRP 386; Professor Bjorn Sletto; Fall 2012

Assessing the Impact of the Texas Competitive Renewable Energy Zones Project (CREZ) on Wind Turbine Siting

A GIS Based Approach

Scott A. Robinson 12/9/2012



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Executive Summary

The State of Texas has invested large amounts of money into building high voltage transmission lines to carry electricity generated from wind farms in remote areas to urban centers in the center of the state. The Competitive Renewable Energy Transmission Zones (CREZ) project was authorized in 2005, and the estimated location of the zones (and the lines accompanying them) was published in October of 2010.

This report seeks to empirically evaluate the geographic effect on the siting of wind turbines in Texas caused by the CREZ project, by seeking evidence of spatial shifting, and evidence of impacts on conventional site suitability criteria. These two measures were accomplished through analysis of a dataset compiled from Federal Aviation Administration (FAA) applications. 7,731 turbine site proposals from 2008 to 2012 were used for this report. CREZ line locations were coded manually from final approved route maps from the 10 Transmission Service Providers responsible for the construction of the lines. Geoprocessing and spatial statistics tools in the ESRI software suite ARCGIS 10.0 were used for hypothesis testing.

The study found significant shifting of turbine siting to the Panhandle region after publication of the CREZ line locations through multiple redundant statistical methods. The mean geographic center of the Post-CREZ sample is 134 miles NNW of the mean geographic center of the Pre-CREZ sample, with a smaller standard deviation. The two samples demonstrate significantly different distances from the CREZ line locations on average (p < 0.001). Finally, the Pre-CREZ proposals in the Panhandle show no significant clustering, while Post-CREZ proposals are highly clustered in the same region. This redundancy grants a high degree of confidence to these findings.

Further, the empirical relevance of suitability criteria was found to have been altered after the CREZ publication, though no test for statistical significance was employed. After CREZ, more turbines were sited on areas conventionally thought to be unsuitable due to steepness of slope, distance from a road, land cover, geology, and microwave towers. There is also evidence that developers have been able to access higher-quality wind resources after completion of the project. These findings are reported and discussed along with limitations and areas for future study.



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Figure 1. WTG proposals by quarter. The industry has seen rapid growth since the second quarter of 2010.

Introduction

A combination of technological advances in turbine generators and attractive incentives at the state and federal levels have made wind energy the fastest growing form of renewable electricity in the United States today (Bird et al., 2005). In Texas, the success of the state Renewable Energy Portfolio Standard, combined with the National Production Tax Credit and excellent wind resources has incentivized the continuing development of wind resources in the State (Thornley, 2008). One way to track this development over time is through the siting proposals that wind developers must submit to the Federal Aviation Administration prior to construction. These proposals are approved or declined based on the exact geographic coordinates of each turbine, and thus represent an excellent indicator of final siting. Figure 1 shows the quarterly growth of wind turbine generator (WTG) proposals in Texas.

The development of wind energy resources is encouraged through policy means primarily to correct market failures in electricity markets—most notably the existence of large environmental externalities (Langniss & Wiser, 2003). The production of CO2, NOx, SOx, Hg, CO, and particulates as byproducts of the combustion of coal and natural gas in the power generation sector (Mirasgedis & Diakoulaki, 1997) is not reflected in the cost of this electricity to consumers (Söderholm & Sundqvist, 2003). To reflect the advantages that wind energy provides in this respect, government has recently provided critical support for the industry (Lewis & Wiser, 2007).



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In Texas, one of the highest-profile forms of support came from the Competitive Renewable Energy Zones (CREZ) project, which provided funding for the construction of high-voltage transmission lines to carry electricity from remote windy areas (primarily in West Texas and the Panhandle) to population centers such as Dallas/Fort Worth, Houston, Austin, and San Antonio (Lasher, 2008). In 2005, the Texas Legislature passed Senate Bill 20, mandating the Public Utility Commission (PUC) to identify areas in the state with excellent economic conditions for the development of wind power. In 2008, the PUC issued order 33672, assigning $4.93 billion to the construction of transmission projects in the areas it identified, with the goal of connecting Texas’ wind resources with the state’s population centers. The total cost of the project since has increased to roughly $7 Billion. The CREZ transmission lines will eventually support 18,500 MW of wind power (PUCT, 2010). While these areas were identified in 2008, it was not until October of 2010 that the proposed location of the new lines and stations that would carry the electricity were published (Smitherman, 2010).

It is estimated that this project could allow for wind to supply 21% of electricity to the Texas grid, with emissions reductions for criteria pollutants upward of 15% (Kwok, 2011). However, directly measuring the effects of this project is not a simple task.

Hypotheses

As other states seek ways to better integrate and make use of wind resources, Texas stands out as an example of a State that attempted to address this issue through policy means. The scale of the investment makes assessment of the project’s outcomes critical. Specifically, lawmakers in other states will want to know how well the project accomplished its goals of increasing development in previously inaccessible areas.

To this end, the report seeks to evaluate of the impact on wind turbine generator (WTG) siting in Texas through the testing of two hypotheses:

Hypothesis 1: WTG proposals after October 2010 are spatially distributed differently than those proposed before October 2010. Specifically, WTG’s are more likely to be located in the Panhandle.

Hypothesis 2: The goal of the CREZ project was to open up new areas for development. Thus, WTGs sited after October 2010 will be more likely to fall within areas that conventional suitability analysis shows as “unsuitable” due to lack of transmission capacity.

Methodology

The testing of the two hypotheses described above required separating WTG proposals into two categories: “Pre-CREZ” and “Post-CREZ” defined as before October 1, 2010, and after October 1, 2010. The exact date of the publication of the CREZ route report uncertain so the most



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conservative date was chosen (decreasing the likelihood of a false-positive result). The unweighted mean geographic center of each sample was then calculated using equation 1 (Levine, 1996):

(1)

where x and y are the geographic coordinates of the individual WTGs. The standard deviation was used as an indicator of the spread of this distribution across space, using the Standard Distance tool in ARCMap 10.0. While descriptive statistics of the spatial distribution provide a meaningful visualization of the hypothesized change, in order to reject the null hypothesis, the two distributions had to be shown to be highly unlikely to have been drawn from the same population. ESRI’s Near tool was used to generate nearest distances from the CREZ lines for Pre-CREZ and Post-CREZ samples (Pre-CREZ distance should be random, ceteris paribus, as the line locations were unknown), and the results subjected to a one-tailed t-test (assuming unequal variance).

The results of this t-test were further substantiated through cluster analysis of WTG proposals Pre-CREZ and Post-CREZ. In order to gauge where spatial clustering of WTGs exists, the Getis-Ord Gi* test statistic was calculated for both samples, using county boundaries as containing features. Before performing this test, it was critical to obtain the appropriate distance band in order to determine the most relevant scale at which the spatial processes driving WTG clustering operate. This was accomplished using a transformation of Ripley’s K function, which estimates spatial autocorrelation (Dixon, 2006), shown in equation 2:

(2)

This was calculated iteratively over 50 equal distance bands at 5 mile intervals. The difference between the expected (random) values and the observed values was used to determine the peak autocorrelation, in this case 125 miles. The Gi* statistic was calculated for both samples according to equation 3 (Getis & Ord, 2010).

(3)



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Testing of the second hypothesis required establishing a full suitability analysis of the area at a high resolution, as low resolution suitability analysis would not accurately predict siting locations at the turbine-level. Results of testing Hypothesis 1 were used to narrow the study area to the Panhandle in north Texas, allowing for the use of high-resolution raster data from the USGS. Suitability criteria were established through a review of the literature, shown in table 1, which identified six existing wind turbine siting analyses.

Harrison’s (2011) methodology was selected as the basis of this study, as it provides the most comprehensive use of criteria. Although it is often overlooked in the literature due to the difficulties in data acquisition, a realistic suitability study should be completed on a project-specific basis, using precise data (Hunter, Bregt, Heuvelink, Bruin, & Virrantaus, 2009; Wang, Shi, Yuan, & Chen, 2005). Thus, this report did not attempt to describe and identify new suitable areas to be targeted by development. Rather, it sought to use conventional suitability criteria as a measure of change. This meant targeting areas that are unsuitable. Suitability analysis was tested empirically by calculating post facto the number of existing turbines that the suitability model correctly sited. By using this calculation for Pre-CREZ and Post-CREZ samples, this report sought to estimate the effect of CREZ on suitability in the Panhandle as a proxy for whether the project has been successful.

Table 1. Summary of established suitability criteria. Modified from Harrison (2011).

Criteria/Constraint Baban & Parry Rodman & Meentemeyer

Van Haaren & Fthanakis

Tegou et al. Kamholtz Harrison

Proximity to Roads x x x x xProximity to Urban Areas x x x x x xProximity to Transmission Lines x x x x xProximity to Water Bodies x x x x xProximity to Forested Land x x x x xProximity to Historic Sites x x x xNational Parks, Forests,Monuments

partial partial x partial x

Military installations x xAirports x x xTribal Land x xWind Power Class x x x x x xSlope x x x x partial xAspect (orientation) x xCritical Habitat (avian) x xCritical Conservation Habitat partial partial x x partial xGeology x xLand Use x x x partial xWetlands x x xElectricity Demand x x x x x



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Unsuitable areas were identified in a three-phase process. The first, “Suitability 1” identified areas unsuitable because of basic political, administrative, environmental and engineering

attributes. Notably, this analysis differs from the established literature in that distance buffers around these areas were not used. Previous studies offered rationale based on safety or visual/auditory disturbance (Harrison, 2012; van Haaren & Fthenakis, 2011), but the exclusion of buffers allowed for a more conservative approach. Also, empirically, it was noted that multiple existing wind farms (Pre-CREZ) fell within the buffers suggested in the literature, calling their relevance into question.

Binary Boolean classification (0/1) was used to identify WTGs within these areas first-round areas using a spatial join. The second phase, “Suitability 2” identified areas unsuitable due to ground-features identified through raster classification or vector buffering. Binary Boolean classification (0/1) was used to identify WTGs within these areas. The treatment of land cover also differentiates this study

from others. While Harrison (2011) weighted land cover classes based on fairly arbitrary determinations of their suitability for development, due to a lack of agreement in the literature on this issue (Harrison, 2012; Janke, 2010; Rodman & Meentemeyer, 2006; Tegou, Polatidis, & Haralambopoulos, 2010), turbines within any unsuitable class were excluded (tables 2-4). As a novel contribution to the body of literature in wind turbine siting suitability, the location of microwave towers is used as an additional criteria. The National Telecommunications and Information Administration (NTIA) suggests that wind development allow for 2-5km buffers around towers to prevent interference (NTIA, 2005). The range of the buffer depends on the type of tower. This issue is discussed further in the study caveats.

Table 2. Criteria used to identify areas in Suitability 1.

Criteria Rationale Data Source ConstraintMiltary

InstallationsPolitical NTA Binary

Airports Administrative National Atlas BinaryCities Political USGS Binary

Critical Habitat EnvironmentalFWS, USGS,

BLMBinary

Wetlands Environmental USGS, FWS Binary

Body of Water Engineering Geocommunity Binary

National Park, Forest, Monument

Administrative USGS Binary

State, Local Parks Administrative TNRIS, TPWD Binary

Carbonate GeologyEngineering,

Environmental USGS Binary

Criteria Rationale Data Source Constraint

Land cover, Land use

Environmental USGSNLCD Classes 11, 12, 21-24,

90, 95

Microwave Towers Administrative FAA 2km Buffer

Slope Engineering USGS < 20%



Criteria Rationale Data Source ConstraintWind Power Class Economic NREL WPC < 3Transmission Line

ProximityEconomic BTS > 5mi

Proximity to Road Economic TXDOT > 20%



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The slope exclusion is based on the construction costs related to siting in these areas (Rodman & Meentemeyer, 2006) Previous studies have used a criteria of slope < 10% (Baban & Parry, 2001). However, recent advances in engineering have made the < 20% exclusion more reasonable (Harrison, 2011).

The third phase, “Suitability 3” is defined by distance-based criteria and the availability of the wind resource. These factors directly influence the economics of a wind development. Access to existing roads and transmission lines results in avoided costs for construction, and higher wind power classes (WPC) mean increased revenue over the life of the project. While this is discussed in the study caveats section, it should be noted that the wind data used in this analysis is of poor quality, and useful only for regional estimates. Raster data sets were generated based on Euclidean distance for roads and transmission lines. After the quality of the wind, access to transmission lines is the most important factor in determining site suitability. Due to the cost of building high-voltage transmission capacity, which is upwards of $800,000/mile (van Haaren & Fthenakis, 2011) or $3,000/MW (Mills, Wiser, & Porter, 2012) transmission was weighted equally with WPC (40%). Distance from roads was weighted at 20% in the combined suitability score as the costs are roughly half that of transmission for rural unpaved roads (DOT, 2012).

After completion of the three-phase suitability analysis, broader categories were created from Suitability 1 using geoprocessing tools, and the number of turbines located in unsuitable areas 1, 2, and 3 were calculated for Pre-CREZ and Post-CREZ WTG proposals. To fairly contrast these results, the number of WTGs within each area was taken as a percentage of the total proposals for Pre-CREZ and Post-CREZ samples. Because an estimation of impact on suitability was the desired outcome, these percentages were then normalized for area, to control for large differences in coverage between layers. The final measure used was percent of total proposals per 100,000 sq. miles. Gross totals and the difference (Post-CREZ – Pre-CREZ) were used to evaluate effects.

Data Acquisition & Preparation

The Federal Aviation Administration regulates the construction of all structures that could potential pose a danger to aircraft flight paths. As such, wind developers must submit siting plans on an individual turbine basis in order to secure a permit to build. Data on WTG proposals were downloaded from the FAA for each year as a spreadsheet, and geocoded in ARCGIS 10.0. FAA proposals were filtered to exclude FAA determinations of: extension, or M&L (marking and lighting) to reduce the potential for double-counting turbines that had been already built and resubmitted. CREZ line data was acquired for each line on an individual basis. Accurate and up-to-date data was not available to the public in electronic format at the time this project was undertaken, so print and electronic maps of approved construction routes were obtained from the ten Transmission Service Providers taking part in the CREZ project, georeferenced, coded into shapefiles, and joined to attribute data compiled from information on



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Transmission Service Provider and the PUCT websites. For maps that could not be found (three lines total—two around the Dallas/Fort Worth metro area and the Lancaster-Hamilton line rebuild in Val Verde and Crocket counties), PUCT overview maps from the CREZ projects website (http://www.texascrezprojects.com/) were used.

Suitability vector data were acquired through a number of government FTP portals, referenced in the Data Citations section. Raster datasets were provided by the US Geological Survey and downloaded at 3m resolution. Land cover data were only available at 10m resolution. Together, data used in this report comprised more than 34 GB.

Summary of Results

Figure Page Description

1 2 Growth of Wind Turbine Proposals over Time

2 9 Spatial Distribution of WTGs

3 10 Effect on WTG Clustering by County

4 11 Suitability 1

5 12 Suitability 2

6 13 Suitability 3

7 14 Unsuitable Areas in the Panhandle

8 15 Assessing Unsuitable Areas Post-CREZ

9 16 Detail of Post-CREZ WTGs in Unsuitable Areas

10 18 Distribution of Distance from CREZ Line Locations

11 20 Percentage of WTGs in Each Unsuitable Area

12 21 Change After CREZ in WTG siting

Results

This section contains layouts illustrating the findings reported in the analysis section. Additional notes and caveats pertaining to layouts can be found in the Limitations section. All maps are projected to NAD 1983 Texas Centric Mapping System Lambert (meters).



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Figure 10. Histogram showing the distribution of distance from CREZ lines in the Pre-CREZ and Post-CREZ samples. Y axis displays distance in thousands of miles. Data binned in 10,000 mile intervals. The right y-axis refers to the Pre-CREZ sample and the left y-axis refers to the Post-CREZ sample.

Analysis

22% of the 1,795 Pre-CREZ WTGs were sited in the Panhandle. In contrast, (58%) of the 5,936 Post-CREZ WTG proposals were located in the same region. The proposals sent to the FAA for approval in after the State’s CREZ Project line locations were found have a significantly different spatial distribution relative to CREZ line locations than the WTGs proposed before the

line locations were announced (p > 0.0001).

The Pre-CREZ proposals should theoretically be distributed randomly relative to the CREZ lines, controlling for resource availability and other suitability criteria, making this test a good measure of geographic shifting. The histograms of the distance calculation can be seen in figure 10, with the Post-CREZ WTGs displaying a tighter distribution than the Pre-CREZ WTGs, and the Pre-CREZ sample showing more distinct spikes in distance bands representative of West and South Texas development zones. The movement of the mean center of each sample shown



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in figure 2, 134 miles to the NNW, and the decrease in the standard distance (standard deviation x,y) indicates increasing numbers of proposals concentrated in the Panhandle. This finding is confirmed by hot-spot analysis at the county level, shown in figure 3. Before the CREZ project line locations were published, the only significant regional clustering (p < 0.01) at the county level in WTG proposals was in South and West Texas. Proposals made after CREZ show significant clustering in 41/48 counties in the Panhandle. The decrease in significant clustering in West Texas (no significant clustering was found Post-CREZ) and in South Texas (decrease from 13 to 2 counties with significant clustering) suggests that not only has development increased in the Panhandle, development has shifted to the Panhandle. This result supports Hypothesis 1.

Suitability analysis of the Panhandle provides further insight into this shift. The identification of Level 1 criteria (figure 4) shows that only a small amount of area in the Panhandle is rendered unsuitable for development due to rudimentary criteria. The combined area is less than 4,073 out of the 55,103 sq. miles in the Panhandle (7%). Most of this is carbonate substrates—limestone, karst, and dolomite.

Level 2 criteria (figure 5) are more restrictive, reducing suitable area by roughly 39%. Of this 21,879 sq. mi are due to land cover restrictions. Notably, microwave towers, though not included in reviewed WTG suitability analyses, account for 2,596 sq. miles, assuming 1.24 mi (2 km) buffers. A smaller area (710 sq. mi) is rendered unsuitable due to steep slope (>20%) most of this area is south-east of Amarillo.

Level 3 criteria (figure 6) are functions of distance, and the combined map demonstrates the relative scarcity of transmission capacity in the Panhandle. This finding is not surprising given that the CREZ project was initiated in order to address the need for additional capacity in congested or undeveloped areas. Here the importance of wind power class (WPC) is also demonstrated, although the quality of the data limits the relevance of this criteria. Finally, access to roads appears to be the least important level 3 criteria in terms of area constrained.

Suitability 1, 2, and 3 are combined in figure 7, showing the complete result of the suitability analysis for WTGs in the Texas Panhandle. Figure 8 demonstrates that the publication of CREZ line locations appears to have had a noticeable effect on the relevance of established suitability criteria (shown again in histogram form in figure 11). A detailed view of four selected sites is shown in figure 9, which demonstrates the several instances of Post-CREZ siting within unsuitable areas, particularly outside existing transmission line buffers.



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Figure 11. Histogram showing the percentage per 100,000 sq. mi. of WTGs proposed within unsuitable areas. No turbines were proposed within water features for either sample.

This finding is derived from the proportion of WTG proposals before and after the CREZ announcement that fell within the boundaries of unsuitable areas, after normalizing for differences in area. 7/10 of the broad categories used in this analysis became less relevant: a larger percentage of projects were sited in these areas after CREZ than before. Most categories show an increase in siting Post-CREZ, with the notable exemption of developed areas. Most of these turbines were sited within the city limits of Amarillo in December of 2009, and the gross number of turbines is small. The percentage is inflated due to the relatively small area taken up by the Development layer.

As a percentage of the total, fewer turbines were sited in areas with lower WPC after CREZ (from 1.07% Pre-CEZ to 0.2% Post_CREZ). These findings support Hypothesis 2, but go one step further. Not only has the CREZ project effected siting within unsuitable areas outside transmission line buffers, but it has effected other criteria as well, suggesting that increasing transmission capacity has had spillover benefits: specifically the reduced empirical constraint posed by land cover, geology, microwave towers, slope, and roads (figure 12).



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Figure 12. Bars represent the difference in percent from the Pre-CREZ sample to the Post-CREZ sample in siting within unsuitable areas as a percent of the total number of turbines for each. Color shows the difference in number of turbines in each area.

Conclusions & Future Research

The two-stage analysis reported above constitutes strong evidence that wind turbine generator siting proposals were significantly altered after October 1st, 2010. This suggests that developers have responded to the State of Texas’ investment in the Competitive Renewable Energy Zone project and have shifted their focus from costal wind resources in the south to the Panhandle region. The transmission capacity constraint seen in figure that justified funding for the CREZ project, had deterred investment in this region. While it is not surprising that this barrier has been reduced (figure 12), spillover benefits were not anticipated. This finding suggests that because of the improved economics Post-CREZ developers were able to allocate resources to overcome siting constraints such as access to roads, and construction on steep slopes. Additionally, this analysis has shown that fewer turbines were cited in less windy areas after CREZ, suggesting that the project accomplished its goals by opening up more windy areas to development.

Seen in the context of resource economics, the results obtained in this study are not surprising: developers will be quick pick the ‘low hanging fruit’ in an area. As technology improves, extraction techniques become more efficient, altering the return on investment available for a given project. For example, early developments could not justify building on 10% slope gradients, but now the threshold has moved to 20%--and figure 12 suggests that this too is changing. It is often overlooked in the literature that suitability criteria are not set in stone—rather they are a function of the political environment and the economics of construction. Areas that are unsuitable under one set of economic assumptions can become highly suitable if the economics of the proposal is altered. On one hand this underscores the need for high-resolution, accurate, and up-to-date data. On the other it is a caution against taking any broad suitability



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analysis too seriously. As the technology and economics of wind development continue to improve and build-out continues, developers will likely push back the boundaries of “suitability” in pursuit of profitable project sites.

The CREZ transmission project was an impressive demonstration of political will that has fundamentally altered the wind energy industry in Texas, through altering the economic fundamentals. While this report has demonstrated a significant impact on wind turbine build-out and siting, further research is needed in several areas. A full cost/benefit analysis is needed before judgment can be made on the profitability of the project for the State. A more detailed examination of proposals cited within “unsuitable” areas in the Post-CREZ climate would be able to show the specific hierarchy of importance of these criteria to siting, and potentially allow for more the development of predictive siting models. More research is needed to track the responsiveness of developers to specific line completion times, line congestion, and transmission reliability. Finally, to better control for the effects of technology learning curves, engineering advances, and economic conditions, rigorous timer-series forecasting could be utilized to estimate the change in suitability over time, and isolate the effects of the CREZ project within this context.

Works Cited

Baban, S.M.J., & Parry, T. (2001). Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renewable energy, 24(1), 59-71.

Bird, L., Bolinger, M., Gagliano, T., Wiser, R., Brown, M., & Parsons, B. (2005). Policies and market factors driving wind power development in the United States. Energy Policy, 33(11), 1397-1407.

Dixon, P.M. (2006). Ripley's K function. Encyclopedia of environmetrics. DOT. (2012). Generic cost per mile models (pp. 1-3): Department of Transportation. Getis, A., & Ord, J.K. (2010). The analysis of spatial association by use of distance statistics.

Perspectives on Spatial Data Analysis, 127-145. Harrison, J.D. (2012). Onshore wind power systems (ONSWPS): A GIS-based tool for preliminary site-

suitability analysis. (Masters Thesis), University of Southern California. Hunter, GaryJ, Bregt, ArnoldK, Heuvelink, GerardB M., Bruin, Sytze, & Virrantaus, Kirsi. (2009).

Spatial Data Quality: Problems and Prospects. In G. Navratil (Ed.), Research Trends in Geographic Information Science (pp. 101-121): Springer Berlin Heidelberg.

Janke, J.R. (2010). Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy, 35(10), 2228-2234.

Kamholz, J. 2008. Suitability of Wind Power for Texas Urban Areas. University of Texas at Austin. Available: http://www.soa.utexas.edu/files/gis/SuitabilityWindPowerTexas.pdf

Kwok, G. (2011). Costs and Emissions Reductions from the Competitive Renewable Energy Zones (CREZ) Wind Transmission Project in Texas. Duke University.

Langniss, O., & Wiser, R. (2003). The renewables portfolio standard in Texas: an early assessment. Energy Policy, 31(6), 527-535.



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Lasher, W.P. (2008). The development of competitive renewable energy zones in Texas. Paper presented at the Transmission and Distribution Conference and Exposition, 2008. T&D. IEEE/PES.

Levine, N. (1996). Spatial statistics and GIS: Software tools to quantify spatial patterns. Journal of the American Planning Association, 62(3), 381-391.

Lewis, J.I., & Wiser, R.H. (2007). Fostering a renewable energy technology industry: An international comparison of wind industry policy support mechanisms. Energy Policy, 35(3), 1844-1857.

Malczewski, Jacek. (2004). GIS-based land-use suitability analysis: a critical overview. Progress in Planning, 62(1), 3-65. doi: http://dx.doi.org/10.1016/j.progress.2003.09.002

Mills, Andrew, Wiser, Ryan, & Porter, Kevin. (2012). The cost of transmission for wind energy in the United States: A review of transmission planning studies. Renewable and Sustainable Energy Reviews, 16(1), 1-19. doi: http://dx.doi.org/10.1016/j.rser.2011.07.131

Mirasgedis, S., & Diakoulaki, D. (1997). Multicriteria analysis vs. externalities assessment for the comparative evaluation of electricity generation systems. European Journal of Operational Research, 102(2), 364-379.

NTIA. (2005). Telecommunications issues for wind power facilities: Natonal Telecommunications and Information Administration.

PUCT. (2010). CREZ Progress Report No. 1 (pp. 1-95). Rodman, Laura C., & Meentemeyer, Ross K. (2006). A geographic analysis of wind turbine placement in

Northern California. Energy Policy, 34(15), 2137-2149. doi: http://dx.doi.org/10.1016/j.enpol.2005.03.004

Smitherman, B. (2010). Building the Electric Grid of the Future in Texas; aka the CREZ Projects. Public Utility Commission of Texas.

Söderholm, Patrik, & Sundqvist, Thomas. (2003). Pricing environmental externalities in the power sector: ethical limits and implications for social choice. Ecological Economics, 46(3), 333-350. doi: http://dx.doi.org/10.1016/S0921-8009(03)00185-X

Tegou, Leda-Ioanna, Polatidis, Heracles, & Haralambopoulos, Dias A. (2010). Environmental management framework for wind farm siting: Methodology and case study. Journal of Environmental Management, 91(11), 2134-2147. doi: http://dx.doi.org/10.1016/j.jenvman.2010.05.010

Thornley, D. (2008). Texas wind energy (pp. 1-46). www.texaspolicy.com: Texas Public Policy Foundation.

van Haaren, Rob, & Fthenakis, Vasilis. (2011). GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and Sustainable Energy Reviews, 15(7), 3332-3340. doi: http://dx.doi.org/10.1016/j.rser.2011.04.010

Wang, Shuliang, Shi, Wenzhong, Yuan, Hanning, & Chen, Guoqing. (2005). Attribute Uncertainty in GIS Data. In L. Wang & Y. Jin (Eds.), Fuzzy Systems and Knowledge Discovery (Vol. 3614, pp. 614-623): Springer Berlin Heidelberg.

Data Citations

National Renewable Energy Laboratory [computer file]. 2012. Golden, CO: US Department of Energy EERE. Available: http://www.nrel.gov/gis/data_wind.html [October 21, 2012]

Federal Aviation Administration [computer files]. 2012. Washington, D.C.: OEAAA. Available: https://oeaaa.faa.gov/oeaaa/external/public/publicAction.jsp?action=showCaseDownloadForm [Nov. 11 2012]

http://dx.doi.org/10.1016/j.progress.2003.09.002

http://dx.doi.org/10.1016/j.rser.2011.07.131

http://dx.doi.org/10.1016/j.enpol.2005.03.004

http://dx.doi.org/10.1016/S0921-8009(03)00185-X

http://dx.doi.org/10.1016/j.jenvman.2010.05.010

http://www.texaspolicy.com/

http://dx.doi.org/10.1016/j.rser.2011.04.010

https://oeaaa.faa.gov/oeaaa/external/public/publicAction.jsp?action=showCaseDownloadForm



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Fish and Wildlife Service [computer files]. 2012. Washington, D.C.: FWS GIS Database. Available: http://www.fws.gov/gis/data/national/index.html#CRITICAL HABITAT; www.fws.gov/wetlands/data/wetlands-mapper.html [October 14, 2012]

Texas Parks Service [computer files]. 2012. Austin, TX.: TPS Land and Water. Available: http://www.tpwd.state.tx.us/landwater/land/maps/gis/ris/lpc/faq.phtml [October 14, 2012]

Texas General Land Office [computer files]. 2012. Austin, TX: Texas General Land Office Data Sets. Available: http://www.glo.state.tx.us/gisdata/gisdata.html [October 21, 2012]

Texas State Data Center [computer files]. 2012. Austin, TX: Texas State Data Center and Office of the State Demographer. Available: http://txsdc.utsa.edu/ [October 14, 2012]

Texas Natural Resources Information System [computer files]. 2012. Austin, TX: Mapping Data. Available: http://www.tnris.org/get-data?quicktabs_maps_data=1[October 21, 2012]

Federal Communications Commission [computer files]. Washington D.C.: FCC Wireless Communications Division. Available: http://wireless.fcc.gov/geographic/index.htm?job=us_boundary_features[November 28, 2012]

United States Geological Survey [computer files]. Washington, D.C.: BLM GAP Analysis. Available: http://gapanalysis.usgs.gov/padus/data/[October 21, 2012]

United States Geological Survey [computer files]. Washington, D.C.: Texas Geological Data. Available: http://tin.er.usgs.gov/geology/state/state.php?state=TX [October 18, 2012]

United States Geological Survey [computer files]. Washington, D.C.: National Map Viewer. Available: http://viewer.nationalmap.gov/viewer/ [November 10, 2012]

Bureau of Transportation Systems [computer files]. Washington, D.C.: BTS Publications. Available: http://www.bts.gov/publications/national_transportation_atlas_database/[November 10, 2012]

Public Utilities Commission of Texas [map]. Austin, TX.: Texas CREZ Projects. Available: http://www.texascrezprojects.com/overview.aspx[November 10, 2012]

Geocommunity [computer files]. Texas Electrical Grid. Available: http://download.geocomm.com/download.php?catalogid=USDCW-00925[November 5, 2012]

CREZ Line Final Routes were obtained individually by TSP service territory from:

Bandera Electric Coop www.banderaelectric.com

Brazos Electric Coop Dave McDaniel 254-750-6500 [email protected] www.brazoselectric.com



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Cross Texas Cameron Fredkin 806-669-3000 [email protected] www.crosstexas.com

Electric Transmission Texas (ETT) Barry Smith 512-391-6340 www.ettexas.com/projects/consortium.asp

Lower Colorado River Authority Transmission Service Corporation (LCRA TSC) Roger de la Garza 512-473-3273 [email protected] www.lcra.org/crez

Lone Star Amy Mullin 713-951-5304 [email protected] www.lonestar-transmission.com

Oncor [email protected] www.oncor.com/transmissionprojects

Sharyland Sherry Kunka 806-358-9070 [email protected] www.sharyland.com

South Texas Electric Coop (STEC) A.H. (Holly) Gifford, Transmission Project Coordinator 361-485-6134 [email protected] www.stec.org

Wind Energy Transmission Texas LLC (WETT) Christina Eckhoff, Regulatory Affairs Manager 512-279-7371 [email protected] www.windenergyoftexas.com



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APPENDIX A: Extended Methodology and Limitations



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Extended Methodology

Spatial Distribution of WTGs

1.) New spreadsheet FAA_Prop_Simple.xls, copy FAA determined and proposed cases, Texas

2.) Filter by structure: wind turbine 3.) Convert coordinate degrees to seconds 4.) Create new shapefile (points) 5.) Add XY Data FAA_Prop_Simple.xls 6.) Query: MW > .09 7.) Select by attributes: date >= 10/1/2012 8.) Export: FAA_PreCrez 9.) Select by attributes: date < 10/1/2012 10.) Export: FAA_PostCrez 11.) Add data: transmission_lines 12.) Add data: tx_roads 13.) Add data: Counties 14.) Add data: $TSPCrezProject*.jpg (43 total) 15.) Group all $TSPCrezProject*.jpg 16.) Georeference $TSPCrezProject*.jpg to tx_roads 17.) Editor: create line, CREZ_lines 18.) Editor: create points, CREZ_Subs 19.) Mean center: FAA_PreCrez 20.) Mean center: FAA_PostCrez 21.) Near: FAA_PreCrez to CREZ_Lines 22.) Export: Pre_Near.dbf 23.) Near: FAA_PostCrez to CREZ_Lines 24.) Export: Post_Near.dbf 25.) T-test for unequal variance, one tailed in SAS 26.) Add Data: WPC_TX.tif 27.) Reclassify WPC_TX by value 28.) Raster to polygon: WPC_TX 29.) Export: WPC 30.) Add Data: US_States 31.) Add Data: US_Bounds

Effect on WTG Clustering by County



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1.) Add Data: FAA_PreCrez 2.) Add Data: Tx_Counties 3.) Spatial Join: FAA_PreCrez to Tx_Counties 4.) Export: Pre_Crez_Counties 5.) Add Data: FAA_PostCrez 6.) Add Data: Tx_Counties 7.) Spatial Join: FAA_PostCrez to Tx_Counties 8.) Export: Post_Crez_Counties 9.) Multi-distance spatial cluster analysis: FAA_PostCrez 10.) Hot Spot analysis FAA_PostCrez 11.) Select by attributes p > .01 12.) Export: Post_Crez_Sig 13.) Multi-distance spatial cluster analysis: FAA_PreCrez 14.) Hot Spot analysis FAA_PreCrez 15.) Select by attributes p > .01 16.) Export: Pre_Crez_Sig 17.) Add Data: US_States 18.) Add Data: US_Bounds

Suitability 1

1.) Add Data: Cities_TX 2.) Add Data: Airports_TX 3.) Add Data: Military_Installations 4.) Add Data: TX_counties 5.) Select by attributes from TX_Counties: $Name*PanCounties 6.) Export: Pan_Outline 7.) Batch Clip: Cities_TX, Airports_TX, Military_Installations to Pan_Outline 8.) Union: Cities_TX, Airports_TX, Military_Installations 9.) Export: Developed 10.) Add Data: CrithabFWS 11.) Add Data: TX_Protect 12.) Add Data: TX_Parks 13.) Batch Clip: CrithabFWS, TX_Protect to Pan_Outline 14.) Union CrithabFWS, TX_Protect 15.) Export: Protected 16.) Add Data: Tx_Forest_lake_wetlands 17.) Add Data: Tx_water 18.) Union Tx_Forest_lake_wetlands, Tx_water 19.) Export: Water_Wet_Fo_Pan



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20.) Add Data: Tx_Geol 21.) Select by attributes in Tx_Geol: RockType1 = Limestone or Rocktype2 =

Limestone 22.) Export: TX_Limestone 23.) Clip TX_Limestone To Pan_outline 19.) Add Data: US_States 20.) Add Data: US_Bounds

Suitability 2

1.) Add Data: $NED*.tif 2.) Mosiac $NED*.tif 3.) Export: TX_DEM_MOS 4.) Mosiac to new raster (32 bit float, 28.328, single) 5.) Export: Pan_Elevation 6.) Add Data: Pan_outline 7.) Clip Pan_Elevation to Pan_outline 8.) Calculate slope Pan_Elevation 9.) Export: Slope_pan 10.) Int: Slope_pan 11.) Export: Slope_Int 12.) Select by attributes: Slope >20% 13.) Export: Sl_20 14.) Raster to polygon: Sl_20 15.) Export: 20_Slope_Poly 16.) Add Data: $LC*.tif 17.) Mosiac 18.) Export: LC_Mosiac 19.) Mosaic to new raster (32 bit float, 28.328, single) 20.) Export: LC_Full 21.) Select by attributes NLDClass = 11,12,21-34,90,95 22.) Raster to Polygon LC_Full 23.) Export: LC_unsuit_Poly 24.) Add data Microwave06142012 25.) Clip to Pan_outline 26.) Buffer 1.243 mi 27.) Export: Micro_Pan_Buf 28.) Add Data: US_States 29.) Add Data: US_Bounds



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Suitability 3

1.) Add Data: Tx_trans 2.) Add Data: Pan_outline 3.) Clip Tx_trans to Pan_outline 4.) Export: Trans_Pan 5.) Euclidean distance : Trans_Pan Max 5mi 6.) Export: Trans_Dist.tif 7.) Reclassify: equal intervals 10 8.) Export: Trans_re 9.) Add Data: Tx_rd 10.) Clip Tx_rd to Pan_outline 11.) Export: rd_Pan 12.) Euclidean distance : rd_Pan Max 5mi 13.) Export: rd_Dist.tif 14.) Reclassify: equal intervals 10 15.) Export: rd_re 16.) Add Data: WPC 17.) Reclassify: rd_rd*0.02+trans_re*.04+WPC*0.04 18.) Export Suitability_3

Unsuitable Areas in the Panhandle

1.) Add Data: Developed 2.) Add Data: Protected 3.) Add Data: Water_Wet_For_Pan 4.) Add Data: Limestone 5.) Merge: Developed, Protected, Water_Wet_For_Pan, Limestone 6.) Export: Unsuitable_1 7.) Add Data: 20_Slope_Poly 8.) Add Data: LC_unsuit_Poly 9.) Add Data: Micro_Pan_Buf 10.) Export: Unsuitable_2 11.) Add Data: trans_re 12.) Add Data: rd_re 13.) Add Data: WPC 14.) Select by attributes: WPC = 1,2,3,4,5,6 15.) Export WPC* 16.) Add Data: Unsuitable_3

Assessing Unsuitable Areas Post-CREZ



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1.) Add Data: Developed 2.) Add Data: Protected 3.) Add Data: Water_Wet_For_Pan 4.) Add Data: Limestone 5.) Merge: Developed, Protected, Water_Wet_For_Pan, Limestone 6.) Export: Unsuitable_1 7.) Add Data: 20_Slope_Poly 8.) Add Data: LC_unsuit_Poly 9.) Add Data: Micro_Pan_Buf 10.) Export: Unsuitable_2 11.) Add Data: trans_re 12.) Add Data: rd_re 13.) Add Data: WPC 14.) Select by attributes: WPC = 1,2,3,4,5,6 15.) Export WPC* 16.) Add Data: Unsuitable_3 17.) Add Data FAA_PreCREZ 18.) Add Data FAA_PostCREZ 19.) Select by Location: FAA_PreCREZ in Developed, Protected,

Water_Wet_For_Pan, Limestone, LC_unsuit_Poly, Micro_Pan_Buf, trans_re, rd_re, WPC1, WPC2, WPC3, WPC4, WPC5, WPC6

20.) New field: area: FAA_PreCREZ in Developed, Protected, Water_Wet_For_Pan, Limestone, LC_unsuit_Poly, Micro_Pan_Buf, trans_re, rd_re, WPC1, WPC2, WPC3, WPC4, WPC5, WPC6

21.) Export table: PreCrez* 22.) For each count i, sum, divide each by total Pre_Crez count 23.) For each percent i divide by area 24.) For each density, multiply by 100,000 25.) Select by Location: FAA_PostCREZ in Developed, Protected,

Water_Wet_For_Pan, Limestone, LC_unsuit_Poly, Micro_Pan_Buf, trans_re, rd_re, WPC1, WPC2, WPC3, WPC4, WPC5, WPC6

26.) New field: area: FAA_PostCREZ in Developed, Protected, Water_Wet_For_Pan, Limestone, LC_unsuit_Poly, Micro_Pan_Buf, trans_re, rd_re, WPC1, WPC2, WPC3, WPC4, WPC5, WPC6

27.) Export table: PostCrez* 28.) For each count i, sum, divide each by total Post_Crez count 29.) For each percent i divide by area 30.) For each density, multiply by 100,000 31.) For each count i, sum, divide each by total Post_Crez count



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32.) Add Data Crez_Lines

Detail of Post-CREZ WTGs in Unsuitable Areas

1.) Add Data: Unsuitable_1 2.) Add Data: Usuitable_2 3.) Add Data: Unsuitable_3 4.) Add Data: FAA_PostCREZ 5.) Add Data: FAA_PreCREZ 6.) Add Data Crez_Lines 7.) Create new data frames: NE, NW, SE, SW 8.) Add Data: Unsuitable_1 (NE, NW, SE, SW) 9.) Add Data: Usuitable_2 (NE, NW, SE, SW) 10.) Add Data: Unsuitable_3 (NE, NW, SE, SW) 11.) Add Data: FAA_PostCREZ (NE, NW, SE, SW) 12.) Add Data: FAA_PreCREZ (NE, NW, SE, SW) 13.) Add Data Crez_Lines (NE, NW, SE, SW) 14.) Extend Rectangles NE, NW, SE, SW 15.) Draw Lines (8x)

Limitations

Poor access to watershed and wetlands data limited the relevance of this portion of the suitability analysis. The bodies of water data used does not contain streams or small ponds, and the wetlands data used is very low resolution. As a result there are likely additional unsuitable areas not shown on this map. This granularity concern was also an issue in the forests, environmentally sensitive areas, and critical habitats. Other species not listed in state and federal programs such as bats and lesser prarie chickens, are important considerations in turbine siting. Carbonate geology was used as a proxy for bat location in that bat populations coincide with karst formations. Lesser prairie chicken crucial habitat was examined with the assistance of the Kansas Biological Survey, but non included in the final analysis due to the lack of protection for the species at the state and federal level. The use of land cover raster files allowed for some redundancy in these environmental areas, providing a greater degree of confidence in the application of suitability maps for the purposes of this report. However, assuming clean boundaries in geographic data is a serious limitation (Malczewski, 2004), and fuzzy logic techniques were not incorporated into this report.

Flight-paths between airports are relevant to WTG siting, but were not included due to data availability.



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The maps compiled here should not be used to site individual turbines, as there are other issues in turbine siting that are not covered by this suitability analysis. The most prominent of these is local wind speed and bandwidth variability. The wind speed data used in this report is extremely limited. It is 50m data, where most turbines are built nearer to to 80m. The 50m data used is low granularity. Developers pay thousands of dollars for high quality wind data for a site-specific area. This data is proprietary and extremely expensive to collect.

While this project used the most up-to-data data accessible at the time, including high-resolution raster data from the USGS, all suitability analysis should be backed by areal and on-the-ground survey.

One critical aspect of the CREZ project was the increase in transmission capacity, which is related to the voltage rating on the individual line. This means that the geographic location of existing lines may not actually demonstrate a suitable area—rather it is the location and congestion on the line. Due to data limitations, existing lines were not weighted based on capacity or congestion. The majority of the CREZ lines are 345kv.

Microwave towers require communication pathways between towers, not shown in this analysis due to data limitations. Also, some towers require up to 5km buffers. For more information see:

New wind turbines are proposed daily, and updated by the FAA. This project uses data last updated 11/01/2012.

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