Techno-Economic Renewable 

Energy Potential on 

Tribal Lands

Anelia Milbrandt, Donna Heimiller, 

and Paul Schwabe

National Renewable Energy Laboratory

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Technical Report

NREL/TP-6A20-70807 

July 2018 

Contract No. DE-AC36-08GO28308
National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Techno-Economic Renewable 

Energy Potential on 

Tribal Lands 

Anelia Milbrandt, Donna Heimiller, 

and Paul Schwabe

National Renewable Energy Laboratory

Suggested Citation 

Milbrandt, Anelia, Donna Heimiller, and Paul Schwabe. 2018. TechnoEconomic Renewable Energy Potential on Tribal Lands. Golden, CO: 

National Renewable Energy Laboratory. NREL/TP-6A20-70807. 

www.nrel.gov/docs/fy18osti/70807.pdf. 

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Technical Report

NREL/TP-6A20-70807 

July 2018 

Contract No. DE-AC36-08GO28308
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Indian Energy Policy and Programs. The views expressed in 

the article do not necessarily represent the views of the DOE or the U.S. Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications. 

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov. 

Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content. 
iii

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Acknowledgments

This work was funded by the U.S. Department of Energy Office of Indian Energy Policy and 

Programs. We especially thank Lizana Pierce, Project Officer and Deployment Supervisor, for 

her support and direction throughout this project. We thank Elizabeth Doris and Sherry Stout 

from the National Renewable Energy Laboratory (NREL) for their leadership, guidance, and 

feedback on this work. We acknowledge the geospatial support provided by Meghan Mooney, 

Galen Maclaurin, and Evan Rosenlieb from NREL. We also thank our external and internal 

reviewers: Austin Brown, Executive Director, University of California, Davis, Policy Institute 

for Energy, Environment, and the Economy; Jana Ganion, Sustainability and Government 

Affairs Director, Blue Lake Rancheria, California; and Anthony Lopez, David Hurlbut, and 

Philipp Beiter from NREL.

This study serves as a reference for the renewable energy resource potential data used in the 

Tribal Energy Atlas available at https://maps.nrel.gov/tribal-energy-atlas. The Atlas is an 

interactive geospatial application that allows users to view resources, infrastructure, 

demographic, and other information relevant to energy resources on tribal lands, as well as query 

the data and perform simple analyses. Although this report is specific to renewable energy data, 

the Atlas also includes conventional infrastructure and market information. Additional 

information about the Atlas is available in Appendix B of this report.

Disclaimer

The views and opinions of the authors expressed herein do not necessarily state or reflect those 

of the United States Government or any agency thereof. Neither the United States Government 

nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, 

or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights.
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Nomenclature and List of Acronyms

ATB Annual Technology Baseline

CSP concentrating solar power

EIA U.S. Energy Information Administration

GW gigawatt

GWh gigawatt-hour

h hour

km kilometer

kW kilowatt

kWh kilowatt-hour

LACE levelized avoided cost of energy

LCOE levelized cost of electricity

m meter

MW megawatt

MWh megawatt-hour

NREL National Renewable Energy Laboratory

OTSA Oklahoma Tribal Statistical Area

POR period of record

PV photovoltaic

SDTSA State Designated Tribal Statistical Area

TSA Tribal Statistical Area

TW terawatt

TWh terawatt-hour

ton short ton

tonne metric ton
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Executive Summary

Renewable energy technologies provide opportunities for diversification, energy independence, 

environmental sustainability, and new revenue streams for Native American tribes, Alaska 

Native villages, and Alaska Native Corporations. Many of these lands are in areas that have 

abundant renewable energy, such as wind, solar, and biomass.

This study estimates the technical and economic potential for renewable energy development on 

tribal lands. Renewable energy technical potential “represents the achievable energy generation 

of a particular technology given system performance, topographic limitations, environmental, 

and land-use constraints” (Lopez et al. 2012). The primary benefit of assessing technical 

potential is that it establishes an upper boundary estimate of development potential and is not 

meant to imply market potential. Renewable energy economic potential represents the 

economically viable renewable generation that is available in an area. Economic potential is “the 

subset of available resource technical potential where the cost required to generate the electricity 

(which determines the minimum revenue requirements for development of the resource) is below

the revenue available in terms of displaced energy and displaced capacity” (Brown et al. 2016).

This study was developed to support American Indian tribes and Alaska Natives in decisionmaking as they evaluate technologies, potential scales of development, and economic viability. 

The resources analyzed here include wind, solar photovoltaics (PV) and concentrating solar 

power (CSP), woody biomass, biogas, geothermal, and hydropower. This study provides updated 

information to a previous renewable energy technical potential analysis on tribal lands, 

Geospatial Analysis of Renewable Energy Technical Potential on Tribal Lands by Elizabeth 

Doris, Anthony Lopez, and Daniel Beckley. It includes current information, refined data, 

additional locations (for distributed generation in Alaska), and an expanded scope that includes

an economic evaluation of the renewable energy potential. 

Table ES-1 and Table ES-2 illustrate the results of the utility-scale technical potential on tribal 

lands within the noted boundaries plus an extended area of 10 miles, respectively; economic 

potential is presented separately. The utility-scale technical potential results are presented in 

terms of capacity (maximum power output measured in kilowatts [kW], megawatts [MW], etc.) 

and generation (the total amount of electricity generated by a power plant over a specific period 

of time, e.g., kilowatt-hours [kWh], megawatt-hours [MWh], etc.). The analysis shows that the 

utility-scale technical potential on tribal lands is approximately 6.5% of the total national 

technical potential. (The tribal lands compose approximately 5.8% of the land area in the 

contiguous United States.1

) The technical potential doubles when we consider tribal lands plus 

an extended area of 10 miles, which encompasses approximately 16.3% of the contiguous U.S. 

land area.

2 These estimates are for tribal lands in the contiguous 48 states. Alaska Native villages 

are included only in the distributed generation results.

 1 This analysis includes some of the state reservation and statistical areas as part of the tribal boundaries geospatial 

data set provided by the US Census Bureau. These locations were not included in the previous tribal technical 

potential analysis (Doris, Lopez, and Beckley 2013); thus, the land estimate in the previous report was lower. 2 The expanded area of tribal lands plus the adjacent 10 miles is for illustrative purposes to better understand the 

implications of land purchases.
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Table ES-1. Utility-Scale Technical Potential on Tribal Lands in the Contiguous 48 States

Technology

Tribal 

Capacity 

Potential 

(GW)

National 

Capacity 

Potential 

(GW)

National 

Capacity

(%)

Tribal 

Generation 

Potential 

(TWh)

National 

Generation 

Potential 

(TWh)

National 

Generation

(%)

Utility-scale 

PV 6,035 118,918 5% 10,689 197,087 5.4%

CSP 2,114 26,318 8% 7,701 92,994 8.3%

Wind 891 10,119 8.8% 2,394 30,781 7.8%

Geothermal 

(hydrothermal) 0.033 5.7 0.6% 0.228 39 0.6%

Biomass 

(wood) 0.542 34 1.6% 2 156 1.6%

Hydropower 21 62 34.4% 124 342 36.4%

Totala 9,063 155,457 5.8% 20,912 321,401 6.5%

a Each technology is evaluated separately; the same land area might be available for many technologies.

Table ES-2. Utility-Scale Extended (Tribal Land Base Plus Adjacent 10 Miles) Technical Potential

on Tribal Lands in the Contiguous 48 States 

Technology

Expanded 

Tribal 

Area 

Capacity 

Potential 

(GW)

National 

Capacity 

Potential 

(GW)

National 

Capacity

(%)

Expanded 

Tribal Area 

Generation 

Potential 

(TWh)

National 

Generation 

Potential 

(TWh)

National 

Generation

(%)

Utility-scale 

PV 13,281 118,918 11.2% 22,736 197,087 11.5%

CSP 4,012 26,318 15% 14,703 92,994 15.8%

Wind 1,816 10,119 18% 4,940 30,781 16%

Geothermal 

(hydrothermal) 0.508 5.7 9% 3.5 39 9%

Biomass 

(wood) 3.7 34 10.7% 16.8 156 10.7%

Hydropower 39 62 63% 225 342 65.8%

Totala 19,153 155,457 12.3% 42,625 321,401 13.3%

a Each technology is evaluated separately; the same land area might be available for many technologies.

This report also provides an economic assessment of the utility-scale renewable energy 

technologies by comparing the levelized cost of energy (LCOE) to the regional wholesale market 

price of electricity based on 2017 price estimates. Inputs to the model include renewable energy 

cost and generation attributes, regional market electricity prices, and energy-support policies,

among other factors. The results of this assessment indicate a sizeable but variable potential for 
vii

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land-based wind and utility-scale PV. Under this analysis, the economic potential for tribal landbased wind exceeds 1 GW, which could produce more than 3 TWh annually. For utility-scale 

PV, it is more than 61 GW, which could produce nearly 116 TWh of electricity annually. 

Assuming an average installed price of $1,219/kW and $1,573/kW for solar and wind 

respectively, this economic potential represents over $75 billion in project investment. The 

economic potential is highly sensitive to the input assumptions used; thus, the results of this 

analysis should be considered case-specific and illustrative only. 

Technical potential estimates for distributed generation are provided for biogas only. For the 

remaining technologies, we provide a discussion on siting considerations due to the nature of 

these technologies and data availability. Biogas potential is defined by the resource availability at 

existing facilities, but technologies such as wind and PV can be placed almost anywhere within 

the tribal area based on need. We have site-specific information for biogas, but for the other 

technologies this information is unknown. Regarding economic potential (giving the varying 

performance and LCOE that systems could have), we provide estimates of the lowest LCOE that 

could occur in tribal areas as a guidepost for further investigation. Distributed wind and PV 

potential exists for every tribal area; however, in low-resource areas the resulting LCOE is high 

and might not be competitive with grid electricity prices. Many tribal lands have good biogas 

potential from the sources examined here (animal manure, wastewater sludge, and landfill 

material); it is likely that many locations might have good biogas potential from food waste 

given the number of casinos on tribal lands, especially casinos that have large food services onsite. On a site-specific basis, distributed hydropower systems are feasible on tribal lands.

Future research may include procuring higher resolution data for solar, detailed data for 

hydropower, and complete biomass data for Alaska. A biogas potential analysis for food waste at 

casinos would provide additional understanding of this technology development potential on 

tribal lands and support tribes’ decisions regarding alternative uses of these waste materials. 

Future work in the economic potential assessment may include incorporating both in-region and 

out-of-region transmission costs; environmental benefits; policy drivers, such as renewable 

portfolio standards; and any sensitivities to tax-oriented policies. It is also important to 

understand how economic competitiveness can change when considering future modifications to 

renewable energy costs and projections for the broader market, prices of energy, and other 

related factors. This constantly changing cost profile is particularly important in determining the 

relative value of renewable energy compared to other replacement sources of energy.
viii

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Table of Contents

Introduction ................................................................................................................................................. 1

Technical Potential ..................................................................................................................................... 3

Wind (Land-Based)................................................................................................................................ 4

Data Information ........................................................................................................................... 4

Analysis Methodology .................................................................................................................. 4

Results .......................................................................................................................................... 5

Distributed-Scale Discussion and Results..................................................................................... 7

Solar ....................................................................................................................................................... 7

Photovoltaics................................................................................................................................. 7

Concentrating Solar Power.......................................................................................................... 11

Biomass................................................................................................................................................ 15

Woody Biomass .......................................................................................................................... 16

Biogas ........................................................................................................................................ 20

Food Waste Discussion ............................................................................................................... 25

Geothermal (Hydrothermal)................................................................................................................. 26

Data Information ......................................................................................................................... 27

Analysis Methodology ................................................................................................................ 27

Results ........................................................................................................................................ 27

Hydropower.......................................................................................................................................... 29

Data Information ......................................................................................................................... 29

Analysis Methodology ................................................................................................................ 30

Results ........................................................................................................................................ 30

Distributed-Scale Discussion and Results................................................................................... 33

Utility-Scale Economic Potential ............................................................................................................. 34

Data Information .................................................................................................................................. 34

Analysis Methodology ......................................................................................................................... 37

Caveats ................................................................................................................................................. 37

Results.................................................................................................................................................. 38

Distributed Generation Economic Indicators......................................................................................... 40

Conclusions............................................................................................................................................... 44

References................................................................................................................................................. 47

Appendix A: Technology-Specific Exclusions and Constraints.......................................................... 51

Appendix B: Tribal Energy Atlas ............................................................................................................. 53
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List of Figures

Figure 1. Levels of renewable energy potential............................................................................................ 1

Figure 2. Wind generation potential by reservation...................................................................................... 6

Figure 3. Wind generation potential by reservation (including extended areas of 10 miles adjacent to the 

tribal land boundaries) ............................................................................................................. 6

Figure 4. Photovoltaic generation potential by reservation .......................................................................... 9

Figure 5. Photovoltaic generation potential by reservation (including extended areas of 10 miles adjacent 

to the tribal land boundaries).................................................................................................. 10

Figure 6. Concentrating solar power generation potential by reservation .................................................. 13

Figure 7. Concentrating solar power generation potential by reservation (including extended areas of 10 

miles adjacent to the tribal land boundaries).......................................................................... 14

Figure 8. Biopower generation potential by reservation............................................................................. 18

Figure 9. Biopower generation potential by reservation (including extended areas of 10 miles adjacent to 

the tribal land boundaries)...................................................................................................... 19

Figure 10. Biogas power generation potential by reservation..................................................................... 23

Figure 11. Biogas power generation potential by reservation (including extended areas of 10 miles 

adjacent to the tribal land boundaries) ................................................................................... 24

Figure 12. Casinos with restaurants on tribal lands .................................................................................... 26

Figure 13. Geothermal generation potential by reservation........................................................................ 28

Figure 14. Geothermal generation potential by reservation (including extended areas of 10 miles adjacent 

to the tribal land boundaries).................................................................................................. 29

Figure 15. Hydropower generation potential by reservation ...................................................................... 31

Figure 16. Hydropower generation potential by reservation (including extended areas of 10 miles adjacent 

to the tribal land boundaries).................................................................................................. 32

Figure 17. Market price estimate with projected price changes from 2014–2034 and levelized to an 

effective current price ............................................................................................................ 36

Figure 18. Potential distributed wind levelized cost of energy in tribal areas............................................ 41

Figure 19. Potential distributed photovoltaic levelized cost of energy in tribal areas ................................ 42

Figure 20. Potential biogas levelized cost of energy in tribal areas............................................................ 42

Figure 21. Potential small-scale hydropower levelized cost of energy in tribal areas................................ 43

Screenshot of the interactive Tribal Energy Atlas tool. .............................................................................. 53
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List of Tables

Table ES-1. Utility-Scale Technical Potential on Tribal Lands in the Contiguous 48 States...................... vi

Table ES-2. Utility-Scale Extended (Tribal Land Base Plus Adjacent 10 Miles) Technical Potential on 

Tribal Lands in the Contiguous 48 States............................................................................... vi

Table 1. Technical Potential Exclusions for Wind........................................................................................ 4

Table 2. Wind Generation and Capacity Potential on Tribal Lands............................................................. 5

Table 3. Fifteen Tribal Lands with the Highest Technical Potential for Wind Electricity Generation......... 7

Table 4. Technical Potential Exclusions for Utility-Scale Photovoltaics ..................................................... 8

Table 5. Photovoltaic Generation and Capacity Potential on Tribal Lands.................................................. 9

Table 6. Fifteen Tribal Lands with the Highest Technical Potential for Photovoltaic Electricity Generation

................................................................................................................................................ 11

Table 7. Technical Potential Exclusions for Utility-Scale Concentrating Solar Power.............................. 12

Table 8. Concentrating Solar Power Generation and Capacity Potential on Tribal Lands......................... 13

Table 9. Fifteen Tribal Lands with the Highest Technical Potential for Concentrating Solar Power 

Electricity Generation ............................................................................................................ 15

Table 10. Biomass Resource and Land Cover Pairings.............................................................................. 17

Table 11. Woody Biomass Power Generation and Capacity Potential on Tribal Lands............................. 18

Table 12. Fifteen Tribal Lands with the Highest Technical Potential for Biopower Generation from 

Woody Biomass Sources ....................................................................................................... 20

Table 13. Biogas Generation and Capacity Potential on Tribal Lands....................................................... 23

Table 14. Fifteen Tribal Lands with the Highest Technical Potential for Biogas Power Generation......... 25

Table 15. Geothermal Generation and Capacity Potential on Tribal Lands ............................................... 27

Table 16. Tribal Lands with the Highest Technical Potential for Geothermal Electricity Generation....... 29

Table 17. Hydropower Generation and Capacity Potential on Tribal Lands.............................................. 30

Table 18. Fifteen Tribal Lands with the Highest Technical Potential for Hydropower Generation........... 33

Table 19. Overview of Renewable Energy Technology Cost Assumptions............................................... 35

Table 20. Estimated Tribal Economic Potential for the 48 Contiguous States at Utility Scale Based on Site 

Levelized Cost of Energy....................................................................................................... 39

Table 21. Overview of Renewable Energy Technology Cost Assumptions (2015 U.S. Dollars)............... 40

Table 22. Distributed Generation Range of Levelized Cost of Energya ..................................................... 41

Table 23. Utility-Scale Technical Potential on Tribal Lands in the Contiguous 48 States by Capacity and 

Generation.............................................................................................................................. 44

Table 24. Utility-Scale Extended (Tribal Land Base Plus Adjacent 10 Miles) Technical Potential on 

Tribal Lands in the Contiguous 48 States by Capacity and Generation................................. 45

Table A-1. Utility-Scale Wind (Land-Based Only).................................................................................... 51

Table A-2. Utility-Scale Photovoltaics....................................................................................................... 51

Table A-3. Concentrating Solar Power....................................................................................................... 52
1

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Introduction

Renewable energy technologies could provide an opportunity for diversification, energy 

independence, environmental sustainability, and new revenue streams for Native American 

tribes, Alaska Native villages, and Alaska Native Corporations. Many of these lands are in areas 

that have abundant renewable energy resources, such as wind, solar, and biomass.

The purpose of this study is to estimate the technical and economic potential for renewable 

energy development on tribal lands. It aims to support tribes in decision-making as they evaluate 

technologies, the potential scale of development, and economic viability. The resources analyzed 

here include wind, solar photovoltaics (PV) and concentrating solar power (CSP), woody 

biomass, biogas, geothermal, and hydropower. This study provides updated information to a 

previous renewable energy technical potential analysis on tribal lands (Doris, Lopez, and

Beckley 2013). It includes current information, refined data, additional locations (in Alaska), and 

an expanded scope to include an economic evaluation of the renewable energy potential. 

Note that there are several different levels of renewable energy potential, as illustrated in Figure 

1, including resource, technical, economic, and market potential. This study focuses on technical 

and economic potential estimates. Renewable energy technical potential “represents the 

achievable energy generation of a particular technology given system performance, topographic 

limitations, environmental, and land-use constraints” (Lopez et al. 2012). The primary benefit of 

assessing technical potential is that it establishes an upper boundary estimate of development 

potential.

Source: Doris, Lopez, and Beckley 2013

Figure 1. Levels of renewable energy potential
2

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Renewable energy economic potential represents the economically viable renewable generation 

that is available in an area. Economic potential is “the subset of available resource technical 

potential where the cost required to generate the electricity (which determines the minimum 

revenue requirements for development of the resource) is below the revenue available in terms of 

displaced energy and displaced capacity” (Brown et al. 2016).
3

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Technical Potential

Technical potential is an evaluation of the resource potential that theoretically could be 

developed by considering the siting requirements for a given technology and incorporating the 

technology-specific performance characteristics. This analysis uses a previously published 

methodology (Lopez et. al. 2012; Doris, Lopez, and Beckley et al. 2013; Saur and Milbrandt 

2014) that has been updated to reflect more recent modeled resource data sets and an improved 

representation of the tribal land boundaries. It also expands the lands under consideration to 

include areas near reservations that might be candidates for future acquisitions, for illustrative 

purposes. The areas considered are within 10 miles of the tribal boundaries. Most of the results 

discussed in this report focus on utility-scale development potential, but a summary of 

distributed generation technology considerations is also included. Alaska Native villages are 

included only in the distributed generation results and discussion.

The methodology for determining the technical potential on tribal lands is to: 

1. Identify the resource areas associated with the tribal land boundaries and within 10 miles 

of those lands.

2. Apply exclusions representing the technical feasibility limits for siting or implementing 

that technology as appropriate (e.g., slopes, incompatible land use areas, etc.).

3. Estimate the available land area remaining that may be suitable for each technology’s 

development.

4. Apply technology-specific performance characteristics to estimate the potential installed 

capacity and net generation. 

Note that the technical potential estimate should be considered a maximum potential estimate.

Many considerations must be factored into a decision to develop a site (e.g., cost of 

development, demand, alternative uses for the site). This analysis does not exclude culturally 

sensitive areas or areas already in use that would not be appropriate for development that are not 

part of national, publicly available data sets.

The boundaries for the Native American tribes, Alaska Native villages, and Alaska Native 

Corporations were obtained from the U.S. Census Bureau (2016). This is the most recent and 

comprehensive database of tribal lands, and it includes federally recognized tribes, state 

reservation lands, off-reservation trust lands, and tribal statistical areas (TSAs, e.g., Oklahoma

[OTSA] and State Designated [SDTSA] in tables of results).

3 Results are reported separately for 

the U.S. Census boundaries and the areas including 10 miles surrounding the tribal land. Where 

multiple tribal areas exist within 10 miles, the extended area was allocated to only the nearest

tribal boundary area.

 3 In January 2018, six Virginia tribes received federal recognition: the Chickahominy, Eastern Chickahominy, Upper 

Mattaponi, Rappahannock, Monacan, and Nansemond. Two of the tribes, Chickahominy and Eastern 

Chickahominy, are included in this analysis, as we have information on their boundaries. At the time of publication, 

we do not have geospatial data for the remaining four tribes; thus, they are not included in this study. 
4

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The detailed methodologies for determining the available resource, as well as the technology 

assumptions to convert the resource area to installed capacity and net generation, are described 

for each resource type as follows.

Wind (Land-Based)

Wind power harvests energy by converting the kinetic energy of wind into electricity through 

spinning wind turbine blades as the wind moves through an area. Wind is a variable generator, 

and modeled hourly wind resource data are used in conjunction with specific wind turbine power 

curves to estimate potential energy production. Utility-scale applications typically use wind 

speeds at wind turbine hub heights of 80 m above the surface, as demonstrated in the 2016 Wind 

Technologies Market Report (Wiser and Bolinger 2017).

Data Information

This analysis uses modeled wind resource data from the Wind Integration National Dataset 

Toolkit (Draxl et al. 2015). This data set was developed as a high spatial (2 km x 2 km) and high

temporal (5-min) resolution data set for wind energy applications. It differs from wind resource 

data used previously because the model’s period of record (POR) (2007–2013) is long enough to 

capture some interannual variability but not long enough to be representative of the long-term

average resource, and because the coarser spatial resolution (2 km instead of 200 m–1 km) might 

miss some wind-force features in complex terrain. Since the data are accessible free of charge to 

the public, they can be used directly by tribal analysts for more detailed investigation. 

Analysis Methodology

An annual average resource data set was created by summarizing the full POR of the data set for 

a hub height of 80 m above ground and hourly wind speed values for the full POR processed 

against a wind turbine power curve that was selected based on the annual average wind speed.

Technical Potential Exclusions

Wind power facilities comprise one or more individually placed wind turbines, often organized 

in one or more rows oriented toward the prevailing wind direction. The individual placement of 

wind turbines allows some flexibility in siting. This analysis did not use a minimum wind 

resource threshold, but used the technical exclusions summarized in Table 1, which are listed in 

detail in Appendix A. The exclusions are modified from those used in Lopez et al. (2012).

Table 1. Technical Potential Exclusions for Wind

Slopes > 20%

Incompatible land use areas (wetland, water, urban areas, non-ridgecrest forest)

Protected areas (parks, wilderness, wildlife refuges, etc.)
5

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Technology Characterization

A variety of wind turbine models are available that can be customized to optimize output from 

specific wind climates. Three primary classes of turbines represent broad wind climates based on

the modeled annual average wind speed:

• International Electrotechnical Commission Class 1: annual average wind speed > 9 m/s

• International Electrotechnical Commission Class II: annual average wind speed between 

8 m/s and 9 m/s

• International Electrotechnical Commission Class III: annual average wind speed ≤ 8 m/s.

The installation density for a wind power facility is assumed to be 3 MW/km2

. This reflects a 

very open turbine spacing in a wind power facility and can be taken as a conservative estimate of

potential capacity. The hourly wind profiles for each modeled site were processed against the 

selected wind turbine power curve using the National Renewable Energy Laboratory’s (NREL’s)

System Advisor Model, a performance and financial model designed to facilitate decisionmaking in the renewable energy industry.

Results

Table 2 illustrates the results of this analysis and summarizes the wind generation and capacity 

potential on tribal lands. The base resource potential is shown for comparative purposes. The 

base potential represents the wind resource potential if all the land within the tribal boundary 

were used for wind development. The table shows that approximately 40% of tribal land areas 

(200,762 km2 out of 497,942 km2) are excluded by the technical potential criteria. In the 

expanded tribal area (within 10 miles of a reservation), approximately 57% of land areas 

(806,329 km2 out of 1,411,749 km2) are excluded.

Table 2. Wind Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Area 

(km2)

Tribal lands (base potential) 3,991,272,885 1,493,825 497,942

Tribal lands (technical exclusions) 2,394,384,664 891,540 297,180

Expanded tribal lands (base potential) 11,202,177,751 4,235,247 1,411,749

Expanded tribal lands (technical potential) 4,940,289,138 1,816,261 605,420

Figure 2 and Figure 3 illustrate the wind generation potential by reservation and within the

extended areas, and Table 3 shows the 15 tribal lands with the highest technical potential for 

wind electricity generation. 
6

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Figure 2. Wind generation potential by reservation

Figure 3. Wind generation potential by reservation (including extended areas of 10 miles adjacent 

to the tribal land boundaries)
7

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Table 3. Fifteen Tribal Lands with the Highest Technical Potential for Wind Electricity Generation

Tribal Area State

Net 

Generation 

(MWh)

Installed 

Capacity 

(MW)

Available 

Land 

Area 

(km2)

Navajo Arizona 329,108,277 162,427 54,142

Cheyenne-Arapaho Oklahoma 

Tribal Statistical Area (OTSA) Oklahoma 192,695,076 53,715 17,905

Kiowa-Comanche-Apache-Fort 

Sill Apache OTSA Oklahoma 141,282,876 42,340 14,113

Chickasaw OTSA Oklahoma 129,052,128 40,094 13,365

Cheyenne River South Dakota 102,704,890 29,249 9,750

Cherokee OTSA Oklahoma 92,356,954 29,132 9,711

Choctaw OTSA Oklahoma 90,662,537 35,085 11,695

Pine Ridge South Dakota 87,626,718 24,273 8,091

Standing Rock North Dakota 84,382,346 23,324 7,775

Fort Peck Montana 77,546,643 21,523 7,174

Tohono O'odham Arizona 77,026,972 43,457 14,486

Creek OTSA Oklahoma 59,235,489 18,194 6,065

Blackfeet Montana 52,991,185 14,415 4,805

Uintah and Ouray Utah 52,474,520 30,159 10,053

Crow Montana 46,250,649 17,472 5,824

Distributed-Scale Discussion 

Distributed-scale wind development typically consists of a single turbine or a few turbines, and 

the turbines are smaller in size than those used for utility-scale development. Hub heights of 30

m–50 m are common, but they are also reported to range between 6 m and 100 m (Orrell et. al. 

2017). In general, the wind resource is reduced as the hub height is lowered. The wind resource 

at lower hub heights can be dramatically influenced by the surrounding topography and 

vegetation, and extra care must be taken to site the turbines appropriately. Depending on the 

wind climate, exposed topographic features, such as cleared ridge crests, could be more attractive 

for development. 

Broadly, tribal lands in Alaska, the western United States, and the Great Plains contain highquality resource potential for wind, even at lower hub heights. In the eastern and southeastern 

United States, wind opportunities are more limited.

Solar

Photovoltaics

PV technologies directly convert the energy of the sun to electricity. Utility-scale PV links 

multiple individual solar panels to a solar power facility. The solar panels can be set up simply as 
8

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fixed flat-plate systems, or they can be designed to track the movement of the sun to increase 

system performance.

Data Information

This analysis uses the latest update of the National Solar Radiation Database produced by NREL 

in 2016 (Habte, Sengupta, and Lopez 2017). This data set has a higher spatial resolution than that 

used in previous analyses (4 km instead of 10 km). Half-hourly resolution time-series data are 

publicly available for the entire POR. Hourly summarized global solar horizontal irradiance for 

the period from 2000–2014 was used to characterize system performance.

Analysis Methodology

Technical Potential Exclusions

Solar power facilities comprise multiple solar collectors that are physically connected in a 

concentrated area. This analysis did not use a minimum solar resource threshold, but used the 

technical exclusions summarized in Table 4, which are listed in detail in Appendix A. The 

exclusions are modified from those in Lopez et al. (2012).

Table 4. Technical Potential Exclusions for Utility-Scale Photovoltaics

Slopes > 5%

Minimum contiguous area of 1 km2

Incompatible land use areas (wetland, water, urban areas, forested areas)

Protected areas (parks, wilderness, wildlife refuges, etc.)

Technology Characterization

PV systems can be customized to individual solar site characteristics and project needs. For this 

analysis, a single-axis tracking system was used to represent a typical utility-scale system as 

described in the Annual Technology Baseline (ATB) developed by NREL (NREL 2017). Hourly 

solar resource data for the POR 2000–2014 for each modeled site were used with this system 

configuration to estimate long-term average system performance, with an assumption of 14% 

system losses. The installation density for this type of solar power facility is assumed to be 32 

MW/km2. 

Results

Table 5 illustrates the results of this analysis and summarizes the PV generation and capacity 

potential on tribal lands. The base resource potential is shown for comparative purposes. The 

base potential represents the utility-scale PV resource potential if all the land within the tribal 

boundary were to be used for PV development. This table shows that approximately 57% of 

tribal land areas are excluded by the technical potential criteria used in the analysis. In the 

expanded tribal area, approximately 67% of land areas are excluded.
9

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Table 5. Photovoltaic Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Area 

(km2)

Tribal lands (base potential) 24,526,365,637 14,111,101 361,823

Tribal lands (technical exclusions) 10,689,346,455 6,035,850 154,765

Expanded tribal lands (base potential) 66,712,060,544 39,862,842 1,022,124

Expanded tribal lands (technical potential) 22,736,684,931 13,281,491 340,551

Figure 4 and Figure 5 illustrate the PV generation potential by reservation and within the

extended areas, and Table 6 shows the 15 tribal lands with the highest technical potential for PV

electricity generation. 

Figure 4. Photovoltaic generation potential by reservation
10

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Figure 5. Photovoltaic generation potential by reservation (including extended areas of 10 miles

adjacent to the tribal land boundaries)
11

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Table 6. Fifteen Tribal Lands with the Highest Technical Potential for Photovoltaic Electricity 

Generation

Tribal Area State

Net 

Generation 

(MWh)

Capacity 

(MW)

Available 

Land Area 

(km2)

Navajo Arizona 1,829,621,623 902,154 18,987

Cheyenne-Arapaho OTSA Oklahoma 908,307,892 498,444 10,188

Kiowa-Comanche-Apache-Fort 

Sill Apache OTSA Oklahoma 769,386,990 421,026 10,470

Chickasaw OTSA Oklahoma 618,421,613 354,212 12,525

Tohono O'odham Arizona 587,704,601 281,190 6,205

Cherokee OTSA Oklahoma 405,493,464 246,585 5,450

Choctaw OTSA Oklahoma 387,527,056 236,164 9,570

Cheyenne River

South 

Dakota 327,094,263 209,098 4,207

Standing Rock

North 

Dakota 311,995,049 203,589 8,069

Pine Ridge

South 

Dakota 303,651,295 185,258 6,282

Fort Peck Montana 293,095,619 205,564 3,369

Hopi Arizona 267,273,652 130,149 1,541

Creek OTSA Oklahoma 248,846,869 149,093 3,505

Uintah and Ouray Utah 188,782,415 104,363 1,919

Wind River Wyoming 171,578,016 98,232 3,253

Distributed-Scale Discussion 

Distributed-scale PV development typically consists of smaller roof- or ground-mounted panels 

with a fixed tilt. Suitability for roof- and/or ground-mounted system installation might be 

impacted by site characteristics, such as roof orientation, pitch, age, and shading from 

surrounding buildings and vegetation. Battery storage systems paired with solar PV (and wind) 

are increasingly common to achieve baseload power at all times and to better control power use 

relative to peak demand charges and other energy rates. Although the increased solar resource

availability makes distributed PV more productive for tribes in the southern United States, 

technical potential exists for every tribal area in the United States.

Concentrating Solar Power

CSP technologies use trough or tower systems that concentrate the heat energy of the sun into a 

small area. Collected thermal energy is then transferred to high-temperature fluids (HTF), 

commonly molten salt, which provides thermal energy capable of transferring water to steam, 

which is applied to a turbine and produces electricity. This process enables a CSP system to store 
12

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energy in HTF for a period of time or immediately transfer the thermal energy to power, 

providing more flexibility in utilizing an intermittent solar resource.

Data Information

This analysis uses previously developed CSP technical potential data sets and the 2015 version 

of the National Solar Radiation Database. This is a direct normal irradiation data set, and it has a 

10-km spatial resolution.

Analysis Methodology

Technical Potential Exclusions

CSP facilities comprise multiple solar collectors focused on a central point, either a centrally 

located tower or the center of a parabolic trough array. This characteristic necessitates that the 

collectors are placed precisely to ensure that the collectors function correctly. The collectors 

move to track the sun, so they use direct normal irradiance fully. A minimum solar resource 

threshold was used in this analysis as an annual direct normal resource of 5 kWh/m2

/d. Table 7 

summarizes the technical exclusions, which are listed in detail in Appendix A. The exclusions 

are modified from those in Lopez et al. (2012).

Table 7. Technical Potential Exclusions for Utility-Scale Concentrating Solar Power

Slopes > 3%

Minimum contiguous area of 1 km2

Incompatible land use areas (wetland, water, urban areas)

Protected areas (parks, wilderness, wildlife refuges, etc.)

Technology Characterization

The system modeled consists of a solar power tower with a solar multiple of 2.4 and 10 h of 

storage. System performance was modeled using NREL’s Solar Advisor Model and the typical 

meteorological hourly profile data for each region. The installation density is assumed to be 19.4 

MW/km2

. 

Results

Table 8 illustrates the results of this analysis and summarizes the CSP generation and capacity 

potential on tribal lands. Figure 6 and Figure 7 illustrate the CSP generation potential by 

reservation and within the extended areas, and Table 9 shows the 15 tribal lands with the highest 

technical potential for CSP electricity generation. The resource threshold applied to the technical 

potential effectively limits the results to the western United States, with some limited viable 

areas in Florida.
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Table 8. Concentrating Solar Power Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Area 

(km2)

Tribal lands (technical exclusions) 7,701,328,141 2,114,570 108,750

Expanded tribal lands (technical potential) 1,470,311,455 4,011,578 206,310

Note: This project is leveraging an existing analysis that was already filtered, thus we are unable to 

calculate a base generation value.

Figure 6. Concentrating solar power generation potential by reservation
14

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Figure 7. Concentrating solar power generation potential by reservation (including extended areas

of 10 miles adjacent to the tribal land boundaries)
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Table 9. Fifteen Tribal Lands with the Highest Technical Potential for Concentrating Solar Power

Electricity Generation

Tribal Area State

Net 

Generation 

(MWh)

Capacity 

(MW)

Available 

Land Area 

(km2)

Navajo Arizona 1,953,959,322 462,886 23,806

Kiowa-Comanche-Apache-Fort 

Sill Apache OTSA Oklahoma 830,492,318 262,618 13,506

Cheyenne-Arapaho OTSA Oklahoma 807,778,806 255,435 13,137

Tohono O'odham Arizona 686,027,362 161,827 8,323

Chickasaw OTSA Oklahoma 559,018,460 176,773 9,091

Hopi Arizona 259,499,142 60,227 3,097

Pine Ridge South Dakota 235,508,182 74,472 3,830

Uintah and Ouray Utah 199,351,312 52,982 2,725

Navajo Off-Reservation Trust 

Land Arizona 175,386,888 42,047 2,162

Wind River Wyoming 132,807,983 41,268 2,122

Cheyenne River South Dakota 129,875,385 41,069 2,112

Rosebud South Dakota 109,666,172 34,679 1,783

Gila River Arizona 97,322,280 23,332 1,200

Caddo-Wichita-Delaware 

OTSA Oklahoma 77,964,977 24,654 1,268

Citizen Potawatomi-Absentee 

Shawnee OTSA Oklahoma 72,205,787 22,833 1,174

Biomass

Various biomass resources are available in the United States that are processed into electricity, 

heat, fuels, and chemicals. These resources include (1) lignocellulosic material (plant biomass 

that is composed of cellulose, hemicellulose, and lignin) such as woody biomass and crop 

residues; (2) lipids such as vegetable oils, waste oils, and animal fats; (3) sugar or starch-based 

crops; and (4) wet organic waste such as wastewater sludge, food waste, and animal manure.

This study focuses on estimating the power generation potential from woody biomass and biogas 

(from wet organic waste) on tribal lands. Woody biomass is the main resource used for biopower 

generation in the United States, while the use of agricultural crop residues is insignificant 

(Warner et al. 2017). Biogas potential is examined because of its rising capacity in recent years:

landfill gas supplied approximately 18% of total U.S. biopower generation in 2015, an increase 

from 9.5% in 2003 (Warner et al. 2017). 

The focus of this analysis is on dedicated, utility-scale biopower potential from woody biomass 

and distributed biogas potential. For additional analysis, future work may estimate the heat

generation potential from biomass (e.g., pellets production) because those applications are better 

suited for on-site feasibility studies in which local resources and market conditions can be 

examined in more detail. Future work may also investigate the combined heat and power 
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potential from biomass sources, the potential for alternative transportation fuels production, or 

biochar for applications such as soil amendment and remediation.

Woody Biomass

Data Information

The woody biomass material considered in this study includes the following types.

Forest Residues 

Forest residues include logging residues and other removable material left after silviculture 

operations and site conversions. Logging residue comprises unused portions of trees that were

cut or killed by logging and left in the woods. Other removable materials are the unused trees cut 

or killed during logging operations. The database used in this analysis illustrates 65% of logging 

residues and 50% of other removals, which is the portion that could be collected as biomass. The 

remaining portion is to be left on the field to maintain ecological functions. Data were gathered 

from the U.S. Department of Agriculture Forest Service Timber Product Output Database for

2012.

Primary Mill Residues 

Primary mill residues include wood materials (coarse and fine) and bark generated at 

manufacturing facilities (primary wood-using mills) when round wood products are processed 

into primary wood products, such as slabs, edgings, trimmings, sawdust, veneer clippings and 

cores, and pulp screenings. Note that most of this resource is currently used on site to offset 

facilities’ energy consumption. Data were gathered from the U.S. Department of Agriculture

Forest Service Timber Product Output Database for 2012.

Secondary Mill Residues 

Secondary mill residues include wood scraps and sawdust from woodworking shops—furniture 

factories, wood container and pallet mills, and wholesale lumberyards. Data on the number of 

businesses by county were gathered from the U.S. Census Bureau’s 2012 County Business 

Patterns and further processed to estimate the amount of secondary mill residues by county.

Urban Wood Waste

Urban wood waste includes wood material from municipal solid waste (such as wood chips and 

pallets), utility tree trimming and/or private tree companies, and construction and demolition 

sites. Data from the U.S. Census Bureau (2012 population data and County Business Patterns)

and BioCycle (2010) were used and further processed to estimate the amount of urban wood 

waste by county. 

Note that woody biomass data for Alaska are very limited. The state has vast forest resources,

and information on the total forest biomass is available (Alaska Energy Authority 2016; U.S. 

Forest Service 2011); however, limited data exist on the amount of resources that could be 

sustainably harvested, such as forest and primary mill residues, which are the focus of this study. 

Therefore, the estimates for biopower potential in Alaska villages should be considered partial. It 

is reasonable to assume that the biopower potential in those locations is larger than estimated 

here. 
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Analysis Methodology

The U.S. Geological Survey 2011 National Land Cover Database 30-meter land use and land 

cover data set was used to disaggregate the county-level woody biomass resource estimates to 

the subcounty level. The 2011 National Land Cover Database is the most recent national and 

spatially resolved land cover data set created by the Multi-Resolution Land Characteristics 

Consortium (Homer et al. 2015). Using the National Land Cover Database as the disaggregation 

unit, we identified and paired each biomass resource type with its most appropriate 

corresponding land cover value (Table 10). Biomass resource values were then proportionally 

allocated to the tribal lands based on the total percentage area of resource (land cover type) 

intersecting the tribal lands within a given county. Final tribal estimates are the summation of 

their intersecting county estimates.

Table 10. Biomass Resource and Land Cover Pairings

Land Cover 

Type

Woody Biomass Resource National Land Cover Database MultiResolution Land Characteristics 

Consortium Raster Values

Forest Forest residues

Primary mill residues

Deciduous forest

Evergreen forest

Mixed forest

Urban Urban wood waste

Secondary mill residues

Developed, low intensity

Developed, medium intensity

Developed, high intensity

The technical potential for bioenergy generation is calculated by assuming 1.1 MWh per dry 

tonne, which represents an average solid biomass system output with an industry-average 

conversion efficiency of 20% and a high heating value of 8,500 Btu/lb (Lopez et al. 2012). Also, 

this analysis assumes that all biomass resources considered were available for biopower, and it

did not evaluate competing uses such as heat applications or biofuels production.

Results

Table 11 illustrates the results of this analysis and summarizes the woody biomass power

generation and capacity potential on tribal lands. Significantly more woody biomass is available 

within the extended areas. The 10-mile radius is illustrative—in general, biomass resources could 

be collected within a 50-mile radius to keep the transportation cost reasonable; thus, the 

biopower potential under the extended scenario could be even higher. Figure 8 and Figure 9

illustrate the woody biomass power generation potential by reservation and within the extended 

areas, and Table 12 shows the 15 tribal lands with the highest technical potential for biopower

generation.

According to the Alaska Energy Authority, more than 999,800 tons of wood are burned in the 

form of cordwood, chips, and pellets annually (Alaska Energy Authority 2016). Our limited 

database for woody biomass (which would be the equivalent of chips) indicates that 

approximately 365,000 dry tons are produced in the state annually. 
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Table 11. Woody Biomass Power Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Tribal lands 2,472,201 542

Expanded tribal lands 16,786,866 3,678

Figure 8. Biopower generation potential by reservation
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Figure 9. Biopower generation potential by reservation (including extended areas of 10 miles

adjacent to the tribal land boundaries)
20

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Table 12. Fifteen Tribal Lands with the Highest Technical Potential for Biopower Generation from 

Woody Biomass Sources

Tribal Area State

Net 

Generation 

(MWh)

Capacity 

(MW)

Choctaw OTSA Oklahoma 446,235 98

Coeur d'Alene Reservation Idaho 140,250 31

Lumbee SDTSA North Carolina 121,055 27

Nez Perce Reservation Idaho 118,971 26

Cherokee OTSA Oklahoma 111,857 25

Creek OTSA Oklahoma 97,611 21

Quinault Reservation Washington 96,688 21

Yakama Nation Reservation Washington 89,925 20

Haliwa-Saponi SDTSA North Carolina 83,507 18

Menominee Reservation Wisconsin 60,221 13

Echota Cherokee SDTSA Alabama 59,539 13

Leech Lake Reservation Minnesota 56,873 12

Chickasaw OTSA Oklahoma 41,745 9

Adais Caddo SDTSA Louisiana 41,030 9

Warm Springs Reservation Oregon 38,573 8

Distributed-Scale Discussion

Distributed biomass power generation systems can range from less than 1 MW–50 MW, 

depending on the amount of available biomass resources (Patel 2012). The most common 

biomass generators at the distributed scale make use of the power plants’ waste heat to provide 

needed thermal energy, which allows projects to be economically viable (NREL 2016). In the 

United States, most combined heat and power systems are installed in large industrial facilities 

that have significant electrical and thermal loads as well as significant waste streams (such as 

lumber or paper mills) that serve as free fuels that would otherwise incur a disposal cost (NREL 

2016).

Gasification is another conversion technology well suited for small-scale biopower applications. 

It is a thermo-chemical process that converts organic material through partial oxidation into an 

energy-rich gas (syngas) that can fuel steam generators, combustion turbines, combined-cycle 

technologies, or fuel cells. There are, however, technical challenges regarding gas cleanup and 

ash problems that limit the development of this technology. More research is needed to 

overcome the technical barriers of biomass gasification for further commercialization. 

Biogas

Biogas is the gaseous product of anaerobic digestion, a biological process in which 

microorganisms break down biodegradable material in the absence of oxygen. Biogas is 

comprised of primarily methane and carbon dioxide and might have some amounts of other 
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elements, such as water, nitrogen, hydrogen, and ammonia. Biogas via anaerobic digestion is 

produced from various sources, such as organic landfill waste, animal manure, wastewater

sludge, and waste from food-processing industries. Biogas can also be produced from 

lignocellulosic material through thermochemical conversions, such as gasification, but most 

biogas in the United States and globally is produced via anaerobic digestion of organic waste.

With minor cleanup (e.g., siloxane removal), biogas is used to generate electricity and heat. To 

fuel vehicles, biogas must be processed to a higher purity standard. This process is called 

conditioning or upgrading, and it involves the removal of water, carbon dioxide, hydrogen 

sulfide, and other trace elements. The resulting gas, also called biomethane or renewable natural 

gas, has a higher content of methane than raw biogas, which makes it comparable to 

conventional natural gas and, thus, a suitable energy source in applications that require pipelinequality gas. This study focuses on evaluating the biogas potential via anaerobic digestion from 

organic waste for electricity generation. 

Data Information

This study includes the following biogas sources.

Wastewater 

Wastewater is rich in organic matter, and anaerobic digestion is often used to reduce sludge (the 

untreated solids that remain after wastewater processing) as part of sludge management at 

wastewater treatment facilities. In recent years, more treatment facilities use the produced biogas

on-site in heat and power applications. Data on the volume of wastewater flow at each treatment 

facility (approximately 15,000 across the United States) were gathered from the U.S.

Environmental Protection Agency’s 2012 Clean Watersheds Needs Survey (2017a). 

Animal Manure

Manure is an organic material containing nitrogen, phosphorus, potassium, and other nutrients. 

Land application is the most common disposal pathway for this material, but more livestock 

operations are implementing on-site power plant energy recovery via anaerobic digestion. Data 

on the number of heads and volume of manure produced at dairy, beef, and swine concentrated 

feeding operations across the United States (more than 32,000 locations) were provided by 

Timothy Seiple (Personal communication, March 2017) and Milbrandt et al. (2018).

Organic Landfill Waste

The organic portion of solid waste disposed in landfills (e.g., food and yard waste) decomposes 

to form biogas. The biogas from landfills is generally called landfill gas because of the digestion 

process taking place in the ground rather than in an anaerobic digester. Data on the landfill 

locations and amount of waste were gathered from the U.S. Environmental Protection Agency’s

Landfill Methane Outreach Program (U.S. Environmental Protection Agency 2017b). Note that 

only “candidate” landfills are included in this study, not existing landfill gas projects. The 

outreach program defines a candidate landfill as one that is accepting waste or has been closed 

for 5 years or less; has at least 1 million tons of waste; and does not have an operational, underconstruction, or planned project. Candidate landfills can also be designated based on actual 

interest in the site. Note that as of March 2017, there were 407 candidate landfills in the country; 

however, only 250 of those locations had the detailed data necessary for our analysis. Therefore, 

the landfill gas estimate should be considered partial.
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Analysis Methodology

The analysis methodology for estimating the biogas potential from wastewater and organic 

landfill waste is adopted from Saur and Milbrandt (2014), and the methodology for estimating 

biogas potential from animal manure is derived from Fulhage, Sievers, and Fischer et al. (2017)

and briefly described here. Given that methane is the usable portion of biogas, estimates are 

shown as methane potential and subsequently converted to electricity generation potential.

The biogas potential from wastewater is estimated to be approximately 1 cubic foot per 100 

gallons of wastewater, and methane is assumed to be 65% by volume. The annual methane 

potential is calculated as shown in Equation 1: Annual methane potential for wastewater:

aaaaaaaaaaaa mmmmmmhaaaa pppp pp (kkkk/yyyy)

= qq ∗ 1 ff 3 bb bb

100 ww ww ∗

0.0283 mm3 bb bb

ff 3 bb bb ∗

65% mm3 CCCC4

mm3 bb bb ∗

. 662 kkkk CCCC4

mm3 CCCC4

where q is the wastewater flow in gallons per year.

The biogas potential from organic landfill waste is estimated to be approximately 300 standard 

cubic feet per minute of landfill gas per 1 million tons of waste-in-place, and methane is assumed 

to be 50% by volume. The landfill gas generation is affected by whether the site is closed or if

new waste is being accepted—it is not a steady quantity over time. For open candidate landfills, 

the waste-in-place data are normalized to the year 2016 by taking the waste-in-place in the 

recorded year, different for each record, and adding additional years of waste based on the 

annual waste acceptance rate of the site up to the year 2016. For closed candidate landfills, the 

waste-in-place was calculated similarly to the open landfills, but additional years of waste are 

added only up to the year the site closed. Only landfills that closed within the last 5 years were 

included in this analysis. The annual methane potential is then calculated as shown in Equation 

2: Annual methane potential equation for organic landfill waste:

aaaaaaaaaaaa mmmmmmhaaaa pppp pp ( /yyyy)

= WWWWWW ∗ 300 ss LLLLLL

1 MMMM WWWWWW ∗

50% CCCC4

LLLLLL ∗

0.0423

mm�

1 ss ∗

525,600 mm

yyyy ∗ 2204.62

where WIP is waste-in-place in million tons of waste in 2016 for open landfills and for the year 

closed for closed landfills.

The biogas potential from manure is estimated for each animal type, and it assumes that methane 

is 60% by volume. The annual methane potential is calculated as shown in Equation 3: Annual 

methane potential equation for animal manure:

aaaaaaaaaaaa mmmmmmhaaaa pppp pp ( /yyyy) = NN ∗ K ∗ 365 ∗

60% ff 3 CCCC4

ff 3bb bb ∗ p ∗ 2204.62

where N = head count, K = potential biogas production in cubic feet per animal unit per day 

(dairy: 22.7, beef: 31, and swine: 4.1), and p = density of methane [0.0413 lb/ft3

].
23

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The technical potential for electricity generation from biogas sources is calculated by assuming

4.7 MWh/tonne of methane, which represents a typical gaseous biomass system output with an 

industry-average conversion efficiency of 30% and a high heating value of 24,250 Btu/lb (Lopez 

et al. 2012).

Results

Table 13 illustrates the results of this analysis and summarizes the power generation and capacity

potential from biogas on tribal lands. Figure 10 and Figure 11 illustrate the power generation 

potential from biogas by reservation and within the extended areas, and Table 14 shows the 15 

tribal lands with the highest technical potential for biogas electricity generation. Many 

reservations have good biogas potential from the sources examined here, but this potential is

significantly higher if tribes consider expanding their lands to surrounding areas. Note that 

biogas estimates are site-specific and were aggregated to reservation level for mapping purposes.

Table 13. Biogas Generation and Capacity Potential on Tribal Lands

Technical Potential

Net Generation 

(MWh)

Tribal area 391,591

Expanded tribal area 2,701,106

Figure 10. Biogas power generation potential by reservation
24

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Figure 11. Biogas power generation potential by reservation (including extended areas of 10 miles

adjacent to the tribal land boundaries)
25

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Table 14. Fifteen Tribal Lands with the Highest Technical Potential for Biogas Power Generation

Tribal Area State Net Generation (MWh)

Chickasaw OTSA Oklahoma 61,217

Kiowa-Comanche-Apache-Fort Sill Apache 

OTSA Oklahoma 43,699

Cher-O-Creek SDTSA Alabama 38,104

Lumbee SDTSA North Carolina 33,742

Cheyenne-Arapaho OTSA Oklahoma 29,608

Omaha Nebraska 23,417

MaChis Lower Creek State Designated Tribal 

Statistical Area (SDTSA) Alabama 22,997

Creek OTSA Oklahoma 17,961

Yankton South Dakota 16,872

Cherokee OTSA Oklahoma 15,157

Osage Oklahoma 14,536

Puyallup Washington 13,533

Coharie SDTSA North Carolina 11,549

Choctaw OTSA Oklahoma 6,756

Winnebago Nebraska 4,610

Food Waste Discussion

Food waste refers to food that is fit for human consumption but is not consumed because it is left 

to spoil or discarded by retailers or consumers (Food and Agriculture Organization of the United 

Nations 2017). Food waste is another resource for biogas production, usually co-digested with 

other material, such as wastewater sludge, but it could be digested separately as well. Food waste 

comes from various sources: industrial (e.g., food processors), institutional (e.g., hotels, 

hospitals), commercial (e.g., restaurants and supermarkets) and residential, as well as at point of 

production (power plants). 

The main food waste generation sources on tribal lands are casinos. There are approximately 486 

Indian gaming operations in the United States (500 Nations 2017; Indian Gaming 2017; National 

Indian Gaming Commission 2017); however, not all have restaurants on-site—our research 

indicates that approximately 394 locations have at least one restaurant on-site (Figure 12). The 

rest of the operations might have some limited food services on-site, but they are too small to be 

considered candidates for biogas generation.
26

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Figure 12. Casinos with restaurants on tribal lands

A single restaurant can produce approximately 25,000–75,000 lb (12.5–37.5 tons) of food waste 

in 1 year (Business Insider 2016). Our research indicates that there are approximately 1,077 

restaurants at tribal casinos; thus, the amount of food waste that is generated annually could be 

between 13,500 tons and 40,000 tons. This amount corresponds to approximately 756–2,300 

tonnes of methane, or 3.6–11 GWh of electricity. Because of a lack of data, we are unable to 

estimate the biogas potential from food waste at each casino. Without detailed on-site 

information, estimates would be misleading because each location varies in terms of restaurant 

size, number of seats, number of employees, hours of operation, etc. For reference, almost half of 

the casinos (48%) have one restaurant on-site (some could be small or very large), approximately

19% have more than five restaurants, and less than 3% have more than 10 restaurants. Casinos 

with a small amount of food waste can provide it to nearby larger casinos, or tribes can collect 

the food waste from all casinos on their lands to achieve optimum biogas generation potential 

and minimize waste disposal.

Geothermal (Hydrothermal)

Geothermal energy harnesses the heat content of the earth and converts it to electricity. There are 

several ways to access this heat content. This analysis considers only hydrothermal resources. 

These geothermal reservoirs of steam or hot water occur naturally where magma comes close 

enough to the surface to heat ground water trapped in fractured or porous rocks or where water 

circulates deep along faults (University of Colorado Boulder 2017). Hydrothermal resources are 

used for different energy purposes depending on their temperature and depth. Previous technical 

potential analyses for tribal lands also included estimates for enhanced geothermal systems

(engineered geothermal systems, as an alternative to natural convective hydrothermal resources,
27

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that recover geothermal heat from dry hot rocks), but this technology is not included here 

because it is still developing and unlikely to be feasible for near-term development opportunities.

Data Information

The sites used in this analysis have been characterized by the United States Geological Survey 

(Williams et al. 2008), and development of the data set included an evaluation of nearby 

potential exclusionary conditions. Additionally, the nature of geothermal energy extraction 

allows broad flexibility in siting the geothermal facility; it can be located outside of protected 

areas, and energy can be extracted laterally through the hot water reservoirs. Given these 

considerations, no additional technical exclusions were applied to the identified geothermal sites.

Analysis Methodology

The published U.S. Geological Survey data set includes characterization of the temperature 

resource, potential installed capacity, and a representative location for the identified resource 

site. This information was used in this analysis, but it was modified to account for geothermal 

capacity that has already been developed at a site. 

Results

Table 15 illustrates the results of this analysis and summarizes the hydrothermal generation and 

capacity potential on tribal lands. Figure 13 and Figure 14 illustrate the geothermal generation 

potential by reservation and within the extended areas. Only three identified geothermal sites fall

within the tribal boundaries directly (Table 16), and an additional 23 sites are identified within 

the expanded tribal area.

Table 15. Geothermal Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Tribal lands 228,251 33

Expanded tribal lands 3,558,943 508
28

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Figure 13. Geothermal generation potential by reservation
29

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Figure 14. Geothermal generation potential by reservation (including extended areas of 10 miles

adjacent to the tribal land boundaries)

Table 16. Tribal Lands with the Highest Technical Potential for Geothermal Electricity Generation

Tribal Area State

Net Generation 

(MWh)

Capacity 

(MW)

Pyramid Lake Paiute Nevada 121,729 17

Fort Bidwell California 64,053 9

Warm Springs Oregon 42,468 6

Hydropower

Hydropower energy is extracted from the kinetic energy of flowing water. In many cases, natural 

systems are enhanced or manipulated by creating impoundments (dams) to increase the amount 

of water flow or water drop height. Hydropower is one of the oldest sources of energy for 

producing mechanical and electrical energy (U.S. Energy Information Administration [EIA]

2017a). Because the source of hydroelectric power is water, hydroelectric power facilities are 

located on or near a water source (EIA 2017a).

Data Information

This analysis uses modeled hydropower resource data created by Oak Ridge National Laboratory 

for new stream-reach potential (Kao et al. 2014), and Oak Ridge National Laboratory published

an analysis of the potential that could be extracted from existing nonpowered dams (Hadjerioua, 
30

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Wei, and Kao 2012). The nonpowered dams are represented by a point location; the new streamreach data are represented as a polygonal area containing a stream segment being modeled.

Analysis Methodology

Technical Potential Exclusions

The analysis was completed separately for each hydro resource data set. No technical potential 

exclusions were applied to the nonpowered dams because these are existing hydropower 

installations to which energy extraction is being added. For the new stream-reach resource data

set, the analysis excluded areas that fell completely within environmental areas that would be 

limited to development (i.e., parks and wilderness areas). However, many of the stream-reach 

polygons that intersected excluded areas intersected only in part and would need further 

evaluation to determine if there is usable potential within the unexcluded area and if the usable 

potential falls within the tribal areas.

Technology Characterization

The technology characterization was included in the Oak Ridge National Laboratory’s published 

resource data sets. Our analysis directly used their values for potential site capacity and system 

performance.

Results

Table 17 illustrates the results of this analysis and summarizes the hydropower generation and 

capacity potential on tribal lands. Figure 15 and Figure 16 illustrate the hydropower generation 

potential by reservation and within the extended areas, and Table 18 shows the 15 tribal lands 

with the highest technical potential for hydropower electricity generation. 

Regionally there is significant hydropower potential on tribal lands throughout the country, 

except for the dry portions of the Southwest and parts of the Great Plains.

Table 17. Hydropower Generation and Capacity Potential on Tribal Lands

Scenario

Generation 

(MWh)

Capacity 

(MW)

Tribal lands 124,511,610 21,420

Expanded tribal lands 225,331,062 39,280
31

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Figure 15. Hydropower generation potential by reservation
32

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Figure 16. Hydropower generation potential by reservation (including extended areas of 10 miles

adjacent to the tribal land boundaries)
33

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Table 18. Fifteen Tribal Lands with the Highest Technical Potential for Hydropower Generation

Tribal Area State

Net Generation 

(MWh)

Capacity 

(MW)

Hualapai Arizona 8,528,986 1,357

Warm Springs OffReservation Trust Land Oregon 7,519,818 1,027

Warm Springs Oregon 7,126,821 968

Navajo Arizona 5,593,783 920

Karuk Off-Reservation Trust 

Land California 4,389,193 831

Nez Perce Idaho 4,180,255 776

Flathead Montana 3,550,559 580

Uintah and Ouray Utah 3,066,532 537

Crow Off-Reservation Trust 

Land Montana 2,827,420 471

Havasupai Arizona 2,553,209 407

Yurok California 2,356,901 461

Hoopa Valley California 2,348,371 459

Osage Oklahoma 2,115,399 409

Cherokee OTSA Oklahoma 2,057,204 492

Choctaw OTSA Oklahoma 2,013,322 415

Distributed-Scale Discussion 

Smaller hydropower systems typically do not involve any impoundments or diversions of the 

natural stream but instead rely on run-of-the-river systems to extract energy. Systems can be 

quite small, < 1 MW in size. The resource data sets used in this analysis do not resolve these 

smaller potential systems.
34

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Utility-Scale Economic Potential

As shown in the preceding analysis, there is a vast amount of renewable energy technical 

potential on tribal lands in the United States. Technical potential does not factor economic 

criteria for developing renewable energy. Most decisions regarding energy strategies, however, 

are made not only on what is technically possible but also according to what is economically 

favorable. This chapter applies a basic economic screen to analyze the subset of total technical 

potential for tribal lands that might be of actual commercial interest.

Data Information

Two principal data sets are used in this analysis: NREL’s ATB (2017) and regional electricity 

market price estimates compiled in Brown et al. (2016). These data sources are discussed below

with a brief listing of other assumptions used.

Levelized cost of energy (LCOE) is a critical metric in this analysis to represent the all-in cost of 

an energy technology.4 The LCOE for a given technology is estimated from capital, operations 

and maintenance, and financing costs reported in the ATB (NREL 2017). The mid-cost case 

values are used. Capital and operation and maintenance cost estimates for hydropower and 

geothermal, however, are derived from the U.S. Department of Energy Hydropower Vision Study

and forthcoming Geothermal Vision Study (DOE 2016; DOE forthcoming). Table 19 presents the 

assumptions for the capital costs, operation and maintenance expenditures, and financing rates.

5

The energy generation estimates required to calculate LCOE are based on the results of the 

technical potential analysis.

 4 The LCOE incorporates all the estimated costs during a project’s lifetime—including upfront costs, ongoing 

maintenance expenditures, financing charges, and fuel costs (if any)—which are then divided by the total amount of 

energy generation estimate. The resulting LCOE estimate provides a levelized cost per unit of energy and is 

typically reported in either cents per kilowatt-hour or dollars per megawatt-hour. 5 The capital cost estimates for utility-scale PV are based on a one-axis tracking system, which is assumed to be 

representative of most types of utility-scale PV applications. Similarly, for biopower the capital cost assumptions 

reflect a fuel source specifically harvested and designed for electricity generation purposes. A range of capital cost 

estimates are used for land-based wind and hydropower, however, to account for technology design variations such 

as wind speed or head height, and given the relatively small number of the geothermal projects identified on tribal 

land, the capital cost estimates are based on a site-specific estimate from the Geothermal Vision Study (DOE 

forthcoming). 
35

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Table 19. Overview of Renewable Energy Technology Cost Assumptions

Technology

Capital Costs 

($/kW)

Operation and 

Maintenance

($/kW/year) Financing Rate

Biopower $3,701 $108 9.02%

Geothermal $11,981–$16,422 $341–$461 9.02%

Hydropower $3,895–$6,567 $13 9.02%

Land-based Wind $1,573–$1,713 $50 9.02%

Utility-scale PV $1,219 $12 9.02%

Source: NREL 2017

The second critical input is the levelized avoided cost of energy (LACE) based on regional 

wholesale market electricity prices.

6 LACE is an important input variable because it represents a

project’s estimated revenue from electricity sales. Projects with higher LACE tend to draw more 

commercial interest than projects with lower LACE values.

Currently, no centralized data source captures the market price of electricity on a uniform basis 

across the United States. Brown et al. (2016) used a patchwork of sources to determine 

wholesale electricity price data across the United States. The market price estimates used to 

estimate LACE are illustrated in Figure 17. Brown et al. (2016) account for possible future 

electric market price increases through the year 2034 by applying an annual energy price 

escalator from the EIA’s 2014 Annual Energy Outlook Reference Case Price Projections and 

“levelized to an effective present price.” Brown et al. (2016) prices are converted to 2015 U.S.

dollars to be consistent with the 2017 ATB data.

This analysis also adjusts the Brown et al. (2016) market price estimate downward to reflect

lower natural gas and wholesale power prices since the 2014 data year. EIA (2017b) reports that 

natural gas prices for electricity production declined from $5.29/MMbtu in 2014 to $3.59/Mmbtu 

through August 2017. The change in natural gas prices is then converted to changes in wholesale 

electricity prices. Hurlbut et al. (2016) use a statistical analysis to compare natural gas prices to 

wholesale electricity prices and report that every dollar change in natural gas price was estimated 

to impact wholesale power prices by $6.73/MWh–$8.77/MWh in the same direction.7 Applying 

the national price decline in natural gas for power production reported by the EIA in 2017 with 

the wholesale power price conversion from Hurlbut et al. (2016) results in a reduction to the base 

wholesale power price of approximately $13/MWh. The $13/MWh reduction is applied to the 

LACE price estimates reported in Brown et al. (2016).

 6 In this analysis, LACE represents the available revenue to a project in $/MWh. The EIA developed the LACE 

metric, and Brown et al. (2016) describes it as follows: “The LACE metric captures available revenue to a 

renewable energy project at a specific location in terms of displaced energy and displaced capacity.”

7 Hurlbut et al. (2016) conducted this statistical analysis specific to the Mead Hub in the southwestern United States. 

This relationship is extended to the rest of the United States for this simplified analysis; however, each trading hub 

and market is likely to have a unique interplay between natural gas prices and wholesale power prices. Another 

simplifying assumption made here is adopting a simple average between off-peak and on-peak power prices. 
36

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Figure 17. Market price estimate with projected price changes from 2014–2034 and levelized to an 

effective current price8

Source: Brown et al. 2016

This analysis also incorporates the following other assumptions and considerations:

• A 10% investment tax credit is assumed for utility-scale PV and geothermal technologies.

Unlike other tax incentives, they currently do not have a stated expiration date and 

therefore are used to represent the long-term federal tax credit policy.

9

• Accelerated depreciation10 is assumed for all technologies that do not have currently a 

stated expiration date.

• Tribes are able to partner with a taxable entity to efficiently use and benefit from taxbased incentives.11

• Other state policies (e.g., state renewable portfolio standards), environmental

considerations (e.g., carbon dioxide emissions), and tribal preferences are not factored 

into the analysis.

 8 Brown et al. (2016) note higher than average wholesale market prices in Nevada and Texas that persist in this 

analysis. These higher than average prices might prove to be relative outliers over the long term and should be 

revisited with future analyses. 

9 This analysis was conducted prior to the passage of the Tax Cut and Jobs Act of 2017 that among other changes 

lowered the corporate tax rate. The impact of the legislation on renewable energy and tribal developed is not settled

and is thus identified as an area of future analysis refinement. 10 Note that the 5-year Modified Accelerated Cost Recovery System is assumed for accelerated depreciation. 11 This generally refers to a tribal entity that benefits from the renewable energy tax incentives, which can be 

difficult and costly for smaller projects with limited tax appetite. 
37

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Analysis Methodology

The basic framework for estimating economic potential for a given energy technology compares 

the estimated cost of renewable energy to the reported LACE prices in the regional electricity 

markets. Projects are considered as showing economic potential when the LACE (i.e., the 

revenue) is more than the LCOE (i.e., the costs). For example, in the illustrative Scenario 1 

below, the renewable energy shows economic potential because the LACE is more than the

LCOE. Conversely, in Scenario 2, tribal renewable energy for a specific technology does not 

show economic potential because the LACE is less than the LCOE.

• Scenario 1: Regional LACE > tribal LCOE = economic potential identified

• Scenario 2: Regional LACE < tribal LCOE = no economic potential identified.

The approach to estimating economic potential by comparing the LCOE of renewable energy to 

the LACE is described thoroughly in Brown et al. (2016). The methodology used herein to 

calculate the economic potential on tribal lands generally follows the methodology established in 

Brown et al. (2016), although several simplifying assumptions are made.12 Calculating the tribal 

economic potential methodology follows the following conceptual steps.

1. The technical potential for renewable energy is calculated for each reservation following 

the methodology described in this report. 

2. For each reservation, the LCOE is estimated for the identified available renewable energy 

technologies, including biopower, geothermal, hydropower, land-based wind, and solar 

PV.

3. The LACE is calculated for each reservation based on the data shown in Figure 17. 

4. The economic potential is determined for each reservation by comparing the estimated

LACE and LCOE components.

The economic potential analysis is limited to the renewable energy technologies typically 

developed at the utility scale. Typically, utility-scale generation projects sell output in regional 

electricity markets instead of using it on-site or locally. Technologies that are primarily used to 

offset local load—such as distributed PV, biogas, and certain hydroelectric facilities—are not 

included here because of data and scope limitations; however, they are discussed briefly in the 

next section.13

Caveats

It is important to qualify this analytical approach. There are many specific inputs for estimating 

the economic potential of a technology in a location and the actual project values might differ 

substantially from the assumptions used here. In the Results section, a limited scenario analysis 

 12 For more information on economic potential including how the methodology was established, a description of the 

underlying data sources and calculations and a comprehensive set of instructions and caveats are available in Brown 

et al. (2016). 13 Some data requirements for this analysis include load profiles for generalized tribal building types (administration 

building, casinos, etc.) and utility-specific rate structures including tier and seasonal factors, demand charges, and 

time-of-use schedules. These data and more would be needed to estimate energy savings as a measure of economic 

potential. Distributed solar PV, for example, typically offsets only energy-based charges, not demand-based charges; 

therefore, a basic rate structure is required to delineate between the two. 
38

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illustrates the high sensitivity of the economic potential to different input assumptions. Some 

caveats to this approach include:

• Energy projects on tribal lands very likely have different cost profiles than the national 

benchmarks used in the 2017 ATB. 

• Inherent site-specific cost factors might not be fully captured, including infrastructure 

access and suitability (roads, transmissions, etc.), local labor laws, unusual site

challenges, deal-specific lease and royalty terms, and uncertain or changing tribal, 

federal, state, and local energy policies.

• Federal tax credits targeted to renewable energy may possibly be changed, modified, or 

canceled.

• Environmental attributes and the value they might capture during the life of the project 

are not considered.

• This analysis focuses on the site LCOE and does not include estimates for intra-regional 

transmission costs.14 This assumes that tribal projects and nearby nontribal projects 

experience the same transmission access and costs. 

• The capacity value of each energy generation technology is not included here. This 

possible revenue stream is not considered for this analysis because capacity markets are 

not universally available across the United States, and because of analysis scope 

limitations. 

• Similarly, the declining value of variable renewable energies as their overall penetration 

on the grid increases is also not considered here, although it was a parameter in Brown et 

al. (2016).

15

• The actual future trajectory of renewable energy costs as well as the overall market prices 

of electricity might vary significantly from the assumptions used here.

Because of these limitations and caveats, along with many other factors, these economic 

potential estimates should be considered as case-specific rather than precise.

Results

Table 20 presents the results of the renewable energy economic potential on tribal lands in the 48

contiguous states for the set of utility-scale technologies considered here. The results illustrate

the economic potential in total capacity (GW) and annual generation (TWh). The results indicate 

a sizeable amount of tribal economic potential for land-based wind and utility-scale PV. Other 

 14 As described later, the inclusion of intra-regional transmission is a noted area for follow-on activities. Brown et al. 

(2016) note an average cost of approximately $7–$8/MWh for intra-regional transmission, but they acknowledge 

that this is likely a high-cost estimate because in many cases existing transmission might be used, shared with other 

regions, or provided by the energy purchaser. This busbar-based LCOE provides an initial economic assessment, 

and sites with economic potential would be further analyzed to evaluate the likely transmission costs. These costs 

are generally location-, technology- and project-specific. 15 For more information on why renewable energy sources might decrease in value with increased penetration levels, 

see Mills and Wiser (2012).
39

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renewable technologies did not show positive economic potential on tribal lands based on the set 

of assumptions used here.

Consistent with the technical potential findings, utility-scale PV shows the greatest economic 

potential—approximately 61 GW, which equates to approximately 116 TWh of electricity 

generation annually. The economic potential for land-based wind exceeds 1 GW and 3 TWh

annually. Most of the economic potential in this analysis is concentrated in high LACE areas,

such as Nevada (see note 10). Assuming an average installed price of $1,219/kW and $1,573/kW 

for solar and wind, respectively, this economic potential would represent more than $75 billion 

in project investment. 

Table 20. Estimated Tribal Economic Potential for the 48 Contiguous States at Utility Scale Based 

on Site Levelized Cost of Energy

Biopower Geothermal Hydropower Land-Based Wind Utility-Scale PV 

GW TWh GW TWh GW TWh GW TWh GW TWh

0 0 0 0 0 0 1 3 61 116

The economic potential is then examined under different natural gas price scenarios. The 

analysis examined 2014–2017 natural gas prices for electricity production, which ranged from a 

high price of $7.18/Mmbtu to a low price of $2.45/Mmbtu.

16 The conversion ratio to wholesale 

prices as described in the Data Information section is similarly applied here. For the high price

sensitivity, the economic potential for utility-scale PV rose to approximately 266 GW (523 TWh

annually), and land-based wind rose to more than 142 GW (506 TWh annually). For the recent 

low natural gas price sensitivity, utility-scale PV stayed nearly the same, at 61 GW (115 TWh

annually); whereas land-based wind fell to 0.2 GW (0.6 TWh annually). These variations 

demonstrate the importance of determining economic competitiveness under a range of future 

electricity cost scenarios that can also be extended to future renewable energy costs.

The results illustrate that of all the technical potentials identified, a subset of sites would likely 

hold enough economic potential to be examined in more depth for additional inputs such as 

transmission cost and interest of potential energy buyers. Conversely, projects that do not show 

economic potential initially will likely be revisited as the relative costs of renewable energy and 

market prices change.

 16 Based on a three-month average price. 
40

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Distributed Generation Economic Indicators

An evaluation of the LCOE for distributed wind, residential PV, biogas, and small-scale 

hydropower is presented here. The technology cost assumptions are presented in Table 21, and

they are taken from NREL’s 2017 ATB. No regional cost adjustments are included, which can be 

a significant component of total costs for distributed generation projects.

Distributed wind and PV potential exists for every tribal area; however, in low-resource areas,

the resulting LCOE is high and unlikely to be competitive with grid electricity prices (Figure 18

and Figure 19). Wind performance was determined for a 50 m hub height. Costs used are for a 

100-kW–1-MW wind turbine, and they are summarized to show the best LCOE for each tribal 

area. PV performance for a fixed flat-plate collector with tilt equal to the latitude was used, 

modeled with PVWatts, and LCOE results are shown for the best sites in each tribal area. The 

LCOE maps show the best LCOE results estimated for each tribal area. Actual LCOEs will be 

determined by the resource at the site being developed, and site-specific costs are not captured in 

this simplified calculation.

Not all tribal areas will have biogas or small hydropower potential. The LCOE for biogas was 

assumed to be equal to the regional natural gas price (Daniel Inman, personal communication, 

June 2017). A small hydropower resource was extracted from the utility-scale hydropower 

analysis, limited to systems with a capacity of 1–5 MW. Smaller systems are likely for 

distributed hydropower, but they are not included in this assessment. In Alaska, a hydropower 

resource data set showing previously published information by Idaho National Laboratory was 

used to assess whether potential existed near tribal areas, but LCOE was not calculated because 

the data lacked capacity factor estimates. The spatial distribution of the LCOEs for biogas and 

hydropower are shown in Figure 20 and Figure 21, and they are summarized for all technologies 

in Table 22. 

Table 21. Overview of Renewable Energy Technology Cost Assumptions (2015 U.S. Dollars)

Technology

Capital Costs 

($/kW)

Operation and 

Maintenance

($/kW/year) Financing Rate

Small-scale 

hydropower1

$5,614–$7,269 $31–$112 9.02%

Distributed wind2 $2,346–$7,645 

($3,751 in results)

$31–40 9.02%

Residential PV1 $2,800 $21 9.02%

Source: NREL 2017; NREL 2016.
41

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Table 22. Distributed Generation Range of Levelized Cost of Energya

Technology LCOE ($/kWh)

Biogasb $0.01–$0.02

Small-scale 

hydropower

$0.10–$0.20

Distributed wind $0.40–$41.70c

Residential PV $0.11–$0.33

a LCOE is based on the closest natural gas price hub.

b Biogas is assumed to be at parity with natural gas prices. c High LCOE value represents a very low wind resource site, estimated to have <1% annual capacity 

factor.

Figure 18. Potential distributed wind levelized cost of energy in tribal areas
42

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Figure 19. Potential distributed photovoltaic levelized cost of energy in tribal areas

Figure 20. Potential biogas levelized cost of energy in tribal areas
43

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Figure 21. Potential small-scale hydropower levelized cost of energy in tribal areas
44

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Conclusions

This study estimates the technical and economic potential of renewable energy technologies on 

tribal lands. Table 23 and Table 24 summarize the results of the utility-scale technical potential 

on tribal lands within their boundaries as well as within an extended area of 10 miles, 

respectively. The utility-scale technical potential results are presented in terms of capacity 

(maximum power output measured in kW, MW, etc.) and generation (the total amount of 

electricity generated by a power plant over a specific period of time, e.g. kWh, MWh, etc.). The 

analysis shows that the utility-scale technical generation potential on tribal lands is 

approximately 6.5% of the total national technical generation potential. (The tribal lands 

compose approximately 5.8% of the land area in the contiguous United States.) The potential 

doubles within the expanded area considered in our model. (The expanded area is approximately 

16.3% of the contiguous U.S. land area.) These estimates are for tribal lands in the contiguous 48 

states; Alaska Native villages are included only in the distributed generation results.

Table 23. Utility-Scale Technical Potential on Tribal Lands in the Contiguous 48 States by Capacity 

and Generation

Technology

Tribal 

Capacity 

Potential 

(GW)

National 

Capacity 

Potential 

(GW)

National 

Capacity

(%)

Tribal 

Generation 

Potential 

(TWh)

National 

Generation 

Potential 

(TWh)

National 

Generation

(%)

Utility-scale 

PV 6,035 118,918 5% 10,689 197,087 5.4%

CSP 2,114 26,318 8% 7,701 92,994 8.3%

Wind 891 10,119 8.8% 2,394 30,781 7.8%

Geothermal 

(hydrothermal) 0.033 5.7 0.6% 0.228 39 0.6%

Biomass 

(wood) 0.542 34 1.6% 2 156 1.6%

Hydropower 21 62 34.4% 124 342 36.4%

Totala 9,063 155,457 5.8% 20,912 321,401 6.5%

a Each technology is evaluated separately; the same land area might be available for many technologies.
45

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Table 24. Utility-Scale Extended (Tribal Land Base Plus Adjacent 10 Miles) Technical Potential on 

Tribal Lands in the Contiguous 48 States by Capacity and Generation

Technology

Expanded 

Tribal 

Area 

Capacity 

Potential 

(GW)

National 

Capacity 

Potential 

(GW)

National 

Capacity

(%)

Expanded 

Tribal Area 

Generation 

Potential 

(TWh)

National 

Generation 

Potential 

(TWh)

National 

Generation

(%)

Utility-scale 

PV 13,281 118,918 11.2% 22,736 197,087 11.5%

CSP 4,012 26,318 15% 14,703 92,994 15.8%

Wind 1,816 10,119 18% 4,940 30,781 16%

Geothermal 

(hydrothermal) 0.508 5.7 9% 3.5 39 9%

Biomass 

(wood) 3.7 34 10.7% 16.8 156 10.7%

Hydropower 39 62 63% 225 342 65.8%

Totala 19,153 155,457 12.3% 42,625 321,401 13.3%

a Each technology is evaluated separately; the same land area might be available for many technologies.

The results of the utility-scale economic assessment indicate a sizeable but variable potential for 

land-based wind and utility-scale PV. Under this analysis, the tribal economic potential for landbased wind exceeds 1 GW, which could produce more than 3 TWh annually. For utility-scale 

PV, it is more than 61 GW, which could produce nearly 116 TWh of electricity annually. The 

economic potential can vary based on the input assumptions used; thus, the results of this 

analysis should be considered case-specific and illustrative only. 

Distributed wind and PV potential exists for every tribal area, but in low-resource areas the 

resulting LCOE is high and might not be competitive with grid electricity prices. Many tribal 

lands have good biogas potential from the sources examined here (animal manure, wastewater 

sludge, and landfill material), and it is likely that many locations might also have high biogas 

potential from food waste given the number of casinos on tribal lands, especially those with large 

food services on-site. On a site-specific basis, distributed hydropower systems are feasible on 

tribal lands.

Several areas of focus for future research are worth noting here. Future technical potential 

analysis could benefit from higher resolution data for solar, more detailed data for hydropower, 

and complete biomass data for Alaska. A biogas potential analysis for food waste at casinos and 

other restaurant facilities would improve our understanding of this technology development 

potential on tribal lands and support tribes’ decisions regarding alternative uses of these waste 

materials. 

Future improvements to the economic potential assessment include incorporating both in-region 

and out-of-region transmission costs, environmental benefits, other policy drivers such as

renewable portfolio standards, and any sensitivities to tax-oriented policies. Future work could 

also examine how economic competitiveness can change when future modifications to renewable 
46

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

energy costs and projections for the broader market, prices of energy, and other related factors

are considered. This constantly changing cost profile is particularly important in determining the 

relative value of renewable energy compared to other replacement sources of energy.
47

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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51

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A: Technology-Specific Exclusions and 

Constraints

Table A-1. Utility-Scale Wind (Land-Based Only)

Exclusion/Constraint Data Source

Exclude 100% of land areas with slope

>20%

U.S. Geological Survey Shuttle Radar Topography 

Mission 90 m (2016)

Exclude 100% of federal lands designated 

as a park, wilderness, wilderness study 

area, national monument, national 

battlefield, recreation area, national 

conservation area, wildlife refuge, wildlife 

area, wild and scenic river, or inventoried 

roadless area

U.S. Geological Survey Federal Lands (2015)

U.S. Forest Service Inventoried Roadless Areas (2014)

U.S. Bureau of Land Management Areas of Critical 

Environmental Concern (2014)

Exclude 100% of incompatible land use 

areas: urban areas, airports, wetlands, 

and water bodies

U.S. Geological Survey National Land Cover Database 

(2011)

U.S. Department of Homeland Security, Homeland 

Security Infrastructure Program Gold—Airports (2012)

ESRI/U.S. Geological Survey USA Urban Extents (2012)

Exclude 50% of areas characterized as 

non-ridgecrest forested land

U.S. Geological Survey Shuttle Radar Topography 

Mission 90 m (2016)

U.S. Geological Survey National Land Cover Database 

(2011)

Table A-2. Utility-Scale Photovoltaics

Exclusion/Constraint Data Source

Exclude 100% of land areas with slope 

>5%

U.S. Geological Survey Shuttle Radar Topography 

Mission 90 m (2016)

Exclude 100% of federal lands designated 

as a park, wilderness, wilderness study 

area, national monument, national 

battlefield, recreation area, national 

conservation area, wildlife refuge, wildlife 

area, wild and scenic river, or inventoried 

roadless area

U.S. Geological Survey Federal Lands (2015)

U.S. Forest Service Inventoried Roadless Areas (2014)

U.S. Bureau of Land Management Areas of Critical 

Environmental Concern (2014)

Exclude 100% of incompatible land use 

areas: wetlands and water bodies

U.S. Geological Survey National Land Cover Database 

(2011)

Exclude remaining areas with <1 km2 in 

contiguous area

NREL Analysis
52

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-3. Concentrating Solar Power

Exclusion/Constraint Data Source

Exclude 100% of land areas with slope 

>3%

U.S. Geological Survey Shuttle Radar Topography 

Mission 90 m (2016)

Exclude 100% of federal lands designated 

as a park, wilderness, wilderness study 

area, national monument, national 

battlefield, recreation area, national 

conservation area, wildlife refuge, wildlife 

area, wild and scenic river, or inventoried 

roadless area

U.S. Geological Survey Federal Lands (2015)

U.S. Forest Service Inventoried Roadless Areas (2014)

U.S. Bureau of Land Management Areas of Critical 

Environmental Concern (2014)

Exclude 100% of incompatible land use 

areas: urban, wetlands, and water bodies

U.S. Geological Survey National Land Cover Database 

(2011)

ESRI/U.S. Geological Survey USA Urban Extents (2009)

Exclude remaining areas with <1 km2 in 

contiguous area

NREL Analysis
53

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix B: Tribal Energy Atlas

This study serves as a reference for the renewable energy resource potential data used in the 

Tribal Energy Atlas available at https://maps.nrel.gov/tribal-energy-atlas. The Atlas is an 

interactive geospatial application that allows users to view resource, infrastructure, demographic, 

and other relevant information, as well as query the data and perform simple analyses. 

NREL used the data collected and modeled in this study, along with other relevant information 

on infrastructure (e.g., conventional and renewable energy facilities, transmission lines, 

railroads), environment (e.g., water availability, protected areas), energy efficiency, electricity 

and natural gas prices, and more, to populate the Tribal Energy Atlas. The data is not limited to 

renewable energy, and also includes natural gas, petroleum, and other conventional energy 

sources. 

Screenshot of the interactive Tribal Energy Atlas tool.

The Atlas incorporates functionality that enables users to view resource, infrastructure, and other 

relevant data about specific tribal lands. It was designed to be highly intuitive, so users do not 

need prior geospatial experience to use it. The tool also incorporates functionality that enables 

users to query data, conduct simple analyses that provide demographic, installed capacity, and 

utility-scale renewable energy technical potential summaries, and download data.