The Joint Institute for Strategic Energy Analysis is operated by the Alliance 

for Sustainable Energy, LLC, on behalf of the U.S. Department of Energy’s 

National Renewable Energy Laboratory, the University of Colorado-Boulder, 

the Colorado School of Mines, the Colorado State University, the 

Massachusetts Institute of Technology, and Stanford University.

Contract No. DE-AC36-08GO28308

Geospatial Optimization of 

Siting Large-Scale Solar 

Projects 

Jordan Macknick and Ted Quinby

National Renewable Energy Laboratory

Emmet Caulfield and Margot Gerritsen 

Stanford University

Jay Diffendorfer and Seth Haines 

U.S. Geological Survey

Technical Report

NREL/TP-6A50-61375

March 2014
The Joint Institute for Strategic Energy Analysis is operated by the Alliance 

for Sustainable Energy, LLC, on behalf of the U.S. Department of Energy’s 

National Renewable Energy Laboratory, the University of Colorado-Boulder, 

the Colorado School of Mines, the Colorado State University, the 

Massachusetts Institute of Technology, and Stanford University.

JISEA® and all JISEA-based marks are trademarks or registered trademarks of the Alliance for 

Sustainable Energy, LLC.

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Strategic Energy Analysis

15013 Denver West Parkway

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303-275-3000 • www.jisea.org Contract No. DE-AC36-08GO28308

Geospatial Optimization of 

Siting Large-Scale Solar 

Projects 

Jordan Macknick and Ted Quinby

National Renewable Energy Laboratory

Emmet Caulfield and Margot Gerritsen 

Stanford University

Jay Diffendorfer and Seth Haines 

U.S. Geological Survey

Prepared under Task No. 6A50.1010

Technical Report

NREL/TP-6A50-61375

March 2014
NOTICE

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Acknowledgments

The authors would like to thank the Joint Institute of Strategic Energy Analysis (JISEA) and its 

Innovative Research and Analysis Award Program (IRAAP) for providing the funding for this 

work. The authors would also like to extend appreciation for comments, graphics, and editing 

support to David Hester of the U.S. Geological Survey, as well as Laura Vimmerstedt, Nate 

Blair, Doug Arent, Patricia Statwick, Scott Gossett, Mary Lukkonen, and Kendra Palmer of the 

National Renewable Energy Laboratory. Any use of trade, product, or firm names is for 

descriptive purposes only and does not imply endorsement by the U.S. Government. 
iv

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Abstract

Recent policy and economic conditions have encouraged a renewed interest in developing largescale solar projects in the U.S. Southwest. However, siting some large-scale solar projects, such 

as concentrating solar power (CSP), is complex. In addition to the quality of the solar resource, 

solar developers must take into consideration many environmental, social, and economic factors 

when evaluating a potential site. This report describes a proof-of-concept, Web-based 

Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an 

optimization algorithm to inform discussions and decisions regarding the locations of utilityscale solar projects. Existing siting recommendations for large-scale solar projects from 

governmental and non-governmental organizations are not consistent with each other, are often 

not transparent in methods, and do not take into consideration the differing priorities of 

stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting 

guidelines by being user-driven, transparent, interactive, capable of incorporating multiple 

criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that 

can greatly facilitate siting decisions among multiple stakeholders. 
v 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table of Contents

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

2 CSP Siting ............................................................................................................................................. 2

2.1 CSP Siting Challenges..................................................................................................................... 2

2.2 Previous Approaches to Develop Siting Criteria ............................................................................. 2

3 Results................................................................................................................................................... 6

3.1 Overview of Siting Assistance Tool ................................................................................................ 6

3.2 How a User Interacts With the Tool ................................................................................................ 6

4 Methods................................................................................................................................................. 9

4.1 System Design ................................................................................................................................. 9

4.2 Site Selection Algorithm.................................................................................................................. 9

5 Discussion........................................................................................................................................... 14

5.1 Proof-of-Concept Tool................................................................................................................... 14

5.2 Future Directions and Recommendations...................................................................................... 14

6 Conclusions ........................................................................................................................................ 16

References................................................................................................................................................. 17

Appendix: System Technical Description .............................................................................................. 18

System Structure................................................................................................................................... 18

Operation.............................................................................................................................................. 19

Google Earth Development Client ....................................................................................................... 20

OpenCarto User Interface..................................................................................................................... 20
1 

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1 Introduction

Current demographic trends indicate a surge in population and accompanying energy demands in 

the arid U.S. Southwest (MacDonald 2010). New sources of electricity must meet this increasing 

demand and replace the retiring fleet of existing electricity generating units in the region. The 

Energy Improvement and Extension Act (2008) and the American Recovery and Reinvestment 

Act (2009) created incentives and a renewed interest in large-scale solar projects with both 

developers and investors. The acts provided investment tax credits, federal grants, possibilities 

for federal loan guarantees, and accelerated depreciation. Concentrating solar power (CSP) is one 

type of large-scale solar technology with the potential to generate considerable amounts of 

renewable electricity in the region (Mehos et al. 2009;; SEIA 2014; DOE, 2012 ). 

Decisions regarding locations of CSP facilities are complex and involve many factors that often

conflict with one another. Developers must consider a variety of factors, including solar 

resources, proximity to existing infrastructure, environmental regulations and impacts, and 

cultural heritage sites. In turn, local stakeholders, regional environmental groups, and 

government agencies also consider these factors when evaluating proposed CSP developments, 

but may prioritize these factors differently. As a result, some locations selected by developers 

may not be deemed suitable by other groups and vice versa. 

In order to facilitate CSP siting, governmental agencies and non-governmental organizations 

(NGOs) have determined zones where CSP development would be most suitable. Despite having 

similar aims, these agencies and groups have utilized different metrics and different weighting 

schemes to come to their conclusions, resulting in zones that do not overlap, and mixed signals 

being sent to CSP developers over high priority solar sites. These differing priorities led to many 

criticisms by local, regional, and national NGOs in the zones developed by the Bureau of Land 

Management (BLM), eventually resulting in some of the zones being modified (BLM/DOE 

2012). Furthermore, because fundamental assumptions and methods are not fully transparent, it 

is difficult to understand how or why different efforts produce different results. An essential 

component of decision making is effective dialogue among stakeholders to ensure all viewpoints 

are considered (Clark 2002). However, in the case of large-scale solar siting procedures, there is 

a lack of common metrics and tools that can be used among all stakeholders. 

There is a need for a common framework whereby ideas from different stakeholders can be 

considered. We have developed a proof-of-concept siting assistance tool that transparently 

weighs various factors that influence decisions of CSP plant locations and allows users to alter 

the weighting mechanisms according to their stated preferences. We provide the rationale and 

foundation for a tool utilizing existing Geographic Information System (GIS) data that can 

successfully address a subset of the factors considered in CSP siting decisions. More work is 

required to finalize the tool and expand its scope beyond CSP siting concerns to include other 

energy and mineral resource development activities. 
2 

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2 CSP Siting

2.1 CSP Siting Challenges 

Identifying suitable sites for CSP deployment requires fulfilling multiple objectives. A threshold 

of solar direct normal insolation (DNI) of around 6 kWh/m2/day is generally required to produce 

enough electricity economically. In the United States, this quality of solar insolation is generally 

only found in the Southwest (Mehos et al. 2009). In addition, large areas of contiguous land (5 to 

13 acres for every megawatt of capacity) are required to account for the size of typical proposed 

CSP facilities (BLM/DOE 2010). This land must also be relatively flat. The degree of slope 

allowed will vary based on CSP technology, but generally 5% land slope is considered the 

maximum allowed, while most developers look for slopes less than 2% (BLM/DOE 2010). 

Protected areas — national parks and preserves, wilderness areas, wildlife refuges, and critical 

habitats for endangered species — and buffer zones around these areas are excluded from 

consideration. Developers also consider proximity to existing infrastructure, such as electric 

transmission lines, roads, and urban areas, as these can significantly impact overall project costs 

and expected environmental impacts. Existing land uses and land ownership must also be 

considered, as these factors could influence how difficult it is to obtain leases and permits in 

certain areas. Other siting considerations include the likelihood of visual impacts on culturally 

significant viewsheds, the environmentally sensitive habitats in the area, and the physical and 

legal availability of water resources for plant operations. The effects of development on local 

communities related to economic development, environmental justice, and available social 

support must also be considered. 

2.2 Previous Approaches to Develop Siting Criteria

Governmental agencies and NGOs have attempted to take these multiple criteria into account to 

develop appropriate guidelines for CSP siting. Some have taken a more prescriptive approach 

and have developed maps with explicit zones suitable for development, whereas others have 

taken an exclusionary approach, listing areas that should be avoided in development. 

Mehos et al. (2009) utilized a map overlay method to showcase the amount of available land and 

solar generation potential for CSP (Figure 1). They developed the theoretical potential for CSP 

deployment by considering DNI greater than 6 kWh/m2

/day, excluding areas with land slopes 

>1%, legally protected environmental areas, urban areas, and water bodies. This land area 

corresponds to an installed CSP capacity of about 7,000 gigawatts (GW), or approximately 

16,000 gigawatt-hours (GWh) of electricity per year, approximately four times the total national 

electricity consumption in 2009 (4,119 GWh) (Mehos et al. 2009). 
3 

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Figure 1. Theoretical land potential for CSP development under solar, land, urban, water body, 

slope, and legal environmental constraints (Mehos et al. 2009). 

The Western Governors’ Association (WGA) identified geographic areas called Western 

Renewable Energy Zones (WREZ) that are suitable for solar, wind, hydropower, and geothermal 

installations (WGA/DOE 2009). These sites incorporated various metrics, including protected 

environmental areas, land slope, land ownership, transmission infrastructure, and energy 

resources (Figure 2). 
4 

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Figure 2. Western Renewable Energy Zones (WREZ) (WGA/DOE 2009). QRA = Qualified Resource 

Area. TWh = Terawatt-hour. 

The Department of the Interior proactively assigned potential BLM lands for solar project 

development and is refining a nationwide Solar Energy Development Programmatic 

Environmental Impact Statement (PEIS). The PEIS initially resulted in 24 proposed solar energy 

zones (SEZ) on BLM land; that number has been reduced to 17 ( BLM/DOE 2012) (Figure 3).

By 2030, the PEIS calls for 24 GW of utility-scale solar projects on 200,000 acres of BLM land. 
5 

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Figure 3. Proposed zones from the PEIS (BLM/DOE 2012). 

The California Energy Commission (CEC) released guidelines for siting renewable projects in 

desert areas in 2009. These guidelines, written for California but applicable to similar 

ecosystems elsewhere, recommended prioritizing already degraded lands while focusing 

primarily on identifying and avoiding environmentally sensitive areas (CEC 2009). 

In addition to proactive recommendations of solar installation locations, a number of NGOs have 

responded to these recommendations with alternative maps or guidelines. Each recommendation 

utilizes a different set of criteria, and each of their prescriptive and prohibitive recommendations 

differ in important qualitative and spatial ways. The areas excluded tend to differ, as do the 

degrees of importance they place on certain types of land. For example, some areas, such as 

wetlands, are treated in three different ways. Some organizations request wetlands development 

be prohibited (TWC 2009; WGA/DOE 2009), others ask that developers “avoid” these areas 

(CEC 2009), and still others request that developers “consider” the impacts in these areas 

(TWS 2009). 

Importantly, all of the guidelines described above are static analyses utilizing data in a simple, 

overlapping method to reveal areas suitable for development. What these studies and guidelines 

do not provide is a way to compare various potential sites within the allowable area, nor do they 

allow for alternative valuation systems to account for differences in preferences among 

stakeholders. 

The following section describes the proof-of-concept tool that incorporates these metrics in an 

optimization algorithm and has a transparent weighting system that incorporates users’ values. 
6 

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3 Results

3.1 Overview of Siting Assistance Tool

We developed a proof-of-concept for an Internet-based, interactive portal designed to improve 

the process of siting energy development by incorporating six key elements. 

• Transparency: Underlying geodata, methods, and assumptions determine the outputs of 

any decision-making process. Unfortunately, documenting these in sufficient detail to 

allow perfect replication is extremely difficult. We designed our siting tool to always 

implement the same algorithm on the same set of data but with user-defined weighting 

regimes. By recording both the selection of input variables and the resulting outputs, the 

tool maintains clarity. 

• User-driven: Differences of opinion in a planning process are difficult to integrate into a 

final decision. The tool allows every stakeholder to generate siting maps using criteria 

they specify. 

• Web-based and interactive: Harnessing web technology allows greater participation in the 

planning process, simplifies data management, minimizes the potential for differences in 

methodologies, and facilitates transparency. 

• Multi-criteria optimization, not map overlays: Spatial overlays of geodata help define and 

narrow the location choices for siting new development. However, spatial overlays have 

two shortcomings when used for decision support. First, users quickly fall into a “what 

if” trap: “What if we overlay these layers instead of those?” This issue quickly becomes 

an exercise in selecting, weighting, and overlaying maps based on value-laden criteria.

Second, decision makers get overwhelmed by a deluge of maps, each reflecting different 

combinations of data layers, without the underlying reasons for the choice of layers 

explained or justified. A preferred method is to allow users to first state their preferences 

or needs as they relate to siting new energy development and then consistently use an 

optimization algorithm to solve the siting problem. 

• Use existing data: For the majority of U.S. locations, sufficient geodata exist to support 

multi-criteria decision making. Although there are some discrepancies in geospatial 

resolution and data vintage, these do not preclude analysis. We developed a methodology 

specifically to use existing data to (1) avoid the high costs and time delays associated 

with data creation and (2) enhance transparency by taking advantage of the quality 

control and data standards associated with many existing datasets. 

• Flexibility: As new data layers are developed, they can be incorporated into versions of 

the tool. This could facilitate further analysis in specific geographic areas where more 

environmental data layers exist, for example. 

3.2 How a User Interacts With the Tool 

In the Web-based interface, users can initially select and explore the study area, view the 

underlying geodata, and move or zoom across the landscape using typical Web interface tools in 

OpenCarto (see Appendix A). The interface has a drop-down menu called “siting tool” that users 

select to begin the modeling process. 
7 

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The siting tool tab opens up a selection menu, allowing the user to specify the criteria for each 

data layer in the overall siting model. Currently, the tool uses the following layers: solar 

resource, land slope, transmission lines, existing solar energy development zones, roads, BLM

Areas of Critical Environmental Concern (ACEC), critical plant habitat, and critical fauna habitat 

(Figure 4). Each data layer has an associated cost function based on overlap (e.g., whether it 

resides in an existing solar development zone) or proximity (e.g., distance to nearest transmission 

line) to a potential site. 

Figure 4. User interface of siting assistance tool. User-defined siting criteria are shown for an 

illustrative potential development area.

For each of these data layers, a user is presented with a drop-down menu of choices. The choices 

reflect the type of information in the data layer and allow the user to specify how each layer

should be used in the decision-making process. For example, the solar resource layer allows 

users to specify a threshold amount of solar resource they require for development (in 

kWh/m2

/day). Values on the map above the specified threshold are weighted higher than values 

below the threshold. 

We created weighting regimes for the data layers primarily to reflect different types of 

mathematical functions, allowing us to test the optimization algorithm. For example, the critical 

habitat menus include the options of using either a binary weighting regime (i.e., entirely avoid 

land with critical habitat) or a cost penalty function. The road layer specifies a minimal 

acceptable distance, beyond which areas are considered unfeasible based on current economic 

conditions and market demand. Alternatively, the road function could be designed to estimate 

true costs of new road development. The flexibility of this tool to incorporate binary, cost 

penalty, and distance-related functions allows multiple types of criteria to be applied to the

datasets and incorporated into the analysis. 
8 

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After selecting criteria preferences, the siting tool outputs a map (with downloadable data) 

displaying a symbolized “score” for each pixel in a chosen area (Figure 5). The score ranges 

from 0 to100 and is based on the user-specified values for each variable. This map can be used in 

comparisons with existing projects and solar development zones. In addition, location 

information can be cross-referenced and used on other software programs designed for largescale solar analysis, such as the Solar Power Prospector (http://maps.nrel.gov/prospector) and the 

System Advisor Model (http://sam.nrel.gov). 

Figure 5. Example tool output. Green areas indicate locations with higher scores, indicating the 

best match with user-defined siting criteria. 
9 

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4 Methods

4.1 System Design

The system includes four main components (Figure 6). Users interact with the system at their 

computers via a web-based platform. Information comes to the user from two different servers. 

One delivers maps, images, and the underlying geodata using the National Renewable Energy 

Laboratory’s (NREL) OpenCarto infrastructure. The analysis server performs the site selection 

based on (1) information specified by the user and (2) the geodata existing on the map server. It

then returns the information to the user via a Web-based analysis service (WAS). The analysis 

program is implemented in C++. A final component is invisible to the user but allows the 

developers access to the system to alter code, update geodata, and perform general maintenance. 

Appendix A provides a detailed description of the information technology used in the system. 

Figure 6. Simplified system diagram

4.2 Site Selection Algorithm

After the user makes selections for the weighting regime and presses the “run” button, the site 

selection program begins. The site selection program first generates new cost surface layers 

based on the weighting criteria the user selects. These cost surfaces depend on the underlying 

function in the weighting regime and fall into four general categories: 

• Resource: Exactly one layer representing the energy resource in areal units (e.g., watts 

per square meter), as seen in Figure 7

• Areal Cost Contribution: One or more layers representing “per unit area” cost 

contributions (e.g., land acquisition cost) in unspecified areal units (e.g., dollars per acre), 

as seen in Figure 8 
10 

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• Lineal Cost Contribution: Zero or more layers representing distance-related costs (e.g., 

distance from transmission lines) in unspecified “lineal” units (e.g., dollars per mile) 

• Mask: One or more binary layers representing total exclusion zones (e.g., lakes, 

critical habitat). 

The program then combines cost surfaces for individual layers into two cumulative cost surfaces 

representing areal and distance-based costs. The cumulative areal cost surface represents the 

combination of areal costs and masking layers, while the cumulative lineal cost surface 

represents the combination of lineal cost surfaces and masking layers. This cumulative areal cost 

surface is then “flooded” by iteratively applying incrementally higher cost values and removing 

those pixels whose values fall above it, leaving contiguous areas of minimum cost. Areas of land 

too small for the footprint of the facility, determined from the user inputs, including weighting 

criteria, are removed using a segmentation algorithm, leaving only areas that meet all criteria

(Figure 9). If no areas are large enough to accommodate the facility, the threshold level is 

increased and the process is repeated until candidate sites are found. 

Finally, the map of possible locations based on areal costs, can be ranked using the lineal cost 

surface and a number of “best” solutions are sent back to the user (Figure 10). 

Figure 7. Example solar resource data. Darker pixels indicate areas of higher quality solar 

resource, which can be defined using a variety of metrics (e.g., watts per square meter).
11 

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Figure 8. Example areal surface cost layer. Darker areas indicate higher costs. Multiple areal 

surface layers can be incorporated in the decision-making process to represent costs per unit of 

area (e.g., dollars per acre) for items such as land acquisition costs and site preparation costs. 
12 

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Figure 9. Contiguous areas in included region. Areas shown are those that have successfully met 

all criteria (e.g., solar resource, land slope, transmission lines, existing solar energy development 

zones, roads, BLM Areas of Critical Environmental Concern (ACEC), critical plant habitat, and 

critical fauna habitat). 
13 

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Figure 10. Top 10 segments contiguous locations by lineal cost for facility siting based on 

segments shown in Figure 9. The segments can be ranked according to costs and a subset can be 

displayed. 
14 

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5 Discussion

The initial goals of this project were to carefully consider and build a system that could support 

the decision-making process for siting renewable energy development, particularly large-scale 

solar development. The project succeeded in conceptualizing elements critical to a decision 

support tool and developing a proof-of-concept. 

5.1 Proof-of-Concept Tool

The generic nature and broad scope of the tool makes it suitable for use by a variety of user 

groups: policymakers, utility companies, developers of large-scale renewable energy projects, 

and NGOs. User-defined weights and preferences for individual layers allow these various 

groups to test and contrast and compare individual factors, or subsets of factors, that impact a 

siting decision or optimization question. In addition, utilization of the same tool by different 

stakeholders facilitates communication and allows direct comparisons of stakeholder priorities 

and the development of potential compromises. This tool can also be used to evaluate existing 

SEZs developed by other organizations. In addition, the tool can aid in the evaluation of 

expected cumulative environmental impacts of development, given different levels of solar 

project deployment . In some cases solar development could conflict with other types of energy 

or mineral development (e.g., areas with high wind or geothermal potential, uranium deposits); 

this tool provides the opportunity to weigh and manage such risks. Importantly, this tool provides 

a common metric by which different stakeholders can communicate and express their views in a 

tangible, quantitative manner. 

5.2 Future Directions and Recommendations

Developing the proof-of-concept resulted in a number of suggestions for finalizing the tool, 

improving its performance, and enhancing its capabilities. These suggestions can be 

implemented relatively quickly and are not hurdles to finalizing the tool. 

Currently, the two main components of the tool work well: The optimization software performs 

its functions and produces appropriate results while the software running the Web interface 

creates platform for user experience. The main hurdle was linking these two pieces of the 

software so that they ran on the same server and shared inputs and outputs. Implementing the 

analysis service in the proof-of-concept phase proved more difficult than originally envisioned. 

We underestimated the large number of dependencies on external libraries and the challenge of 

integrating them smoothly. A complete implementation of the tool will use more external 

libraries. The service should preferably support the Open Geospatial Consortium (OGC) Web 

Coverage Service (WCS) rather than Web Map Service (WMS), and the Geospatial Data 

Abstraction Library (GDAL) for GeoTIFF, and should directly support a geo-enabled relational 

database management system (RDBMS), such as PostGIS. These were omitted from the proofof-concept phase due to time constraints. 

To link the two pieces of software and increase the tool’s performance, we recommend that 

future versions of this tool use modified software architecture. Computation on layers should be 

adaptive and based on the historical pattern of visitor demands in a Web-based platform, which 

might better facilitate non-linear layer combinations and cost calculation. It should also be 

possible to specify a formula for layer cost calculation and combination in a configuration file 
15 

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written in a general language, such as MathML, which could then be just-in-time (JIT) compiled 

into fast object code. The possibility of implementing the service as an extension to an existing 

open source OGC WMS/WCS server, such as GeoServer, should also be investigated.

In addition to software optimization, we recommend increasing the number of variables used in 

the optimization equation. This would improve the siting assessment functionality. For example, 

data regarding water availability, existing land use, land ownership, local permitting 

requirements and regulations, visual impact concern, and cultural impact potential could be 

incorporated into the tool. Incorporating such factors would improve the ability of this tool to 

inform real-life siting decisions. 

Because the tool is designed to facilitate discussion among stakeholders, we need feedback from 

users regarding its use and functionality. Understanding how this prototype tool elicits feedback 

from multiple stakeholder groups, such as policymakers, public utility commission 

representatives, solar developers, and environmental groups would greatly improve the scope 

and directions of future versions. Different stakeholder groups are likely to have different 

recommendations for improvement, which could be implemented in the next version of this tool. 

Solar development would likely face fewer barriers to deployment if sites were selected on nonproductive or already disturbed land parcels. These lands could be better defined and 

characterized through improvements in remote sensing techniques and further research. Such 

data could be incorporated into the tool and into the optimization equation. 

This tool could be expanded to address development of other fuels and technologies, such as 

wind, geothermal, biomass, natural gas, oil, and uranium. Detailed datasets could be incorporated 

into the tool to analyze optimum siting for these energy developments. In addition, the tool could 

also allow for comparisons between different energy technologies or identify co-location 

opportunities for various technologies. 
16 

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6 Conclusions

We developed a proof-of-concept solar siting tool that goes beyond previous siting criteria 

analyses by solving a transparent, user-definable, optimization algorithm that is Web-based and 

interactive. This project lays the foundation for future work and analyses related to determining 

suitable locations for new solar energy developments. The tool, if developed further, could assist 

policymakers and industry representatives in their solar project siting decisions by giving all 

stakeholders a common framework by which they can discuss economic and environmental 

tradeoffs associated with different locations. Furthermore, the tool could be expanded to include 

other energy and mineral resource development opportunities, such that multiple resource 

development options could be evaluated at once and compared in terms of their economic, 

environmental, and social impacts. 
17 

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References

BLM/DOE. (2010). Draft Solar Peis: Appendix M: Methodologies and Data Sources for the 

Analysis of Impacts of Solar Energy Development on Resources. DOE/EIS-0403. Washington, 

DC: U.S. Department of Energy. 

BLM/DOE. (2012). Final Programmatic Environmental Impact Statement (PEIS) for Solar 

Energy Development in Six Southwestern States. Report No: FES 12-14; DOE/EIS-0403. 

Washington, DC: U.S. Department of the Interior. 

CEC. (2009). Best Management Practices and Guidance Manual: Desert Renewable Energy 

Projects. CEC-700-2009-016-SDREV. Sacramento, CA: California Energy Commission. 

Clark, T.W. (2002). The Policy Process: A Practical Guide for Natural Resource Professionals.

New Haven, CT: Yale University Press.

DOE. (2012). SunShot Vision Study. Report No.: DOE/GO-102012-3037. Washington, D.C.: 

Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. 

MacDonald, G.M. (2010). "Water, Climate Change, and Sustainability in the Southwest." 

Proceedings of the National Academy of Sciences (107:50); pp. 21256-21262. 

Mehos, M.; Kabel, D.; Smithers, P. (2009). "Planting the Seed." IEEE Power and Energy 

Magazine (7:3); p. 7. 

SEIA. (2014). Major Solar Projects in the United States Operating, Under Construction, or 

Under Development. Washington, DC: Solar Energy Industries Assocation. Updated March 6, 

2014. 

TWC. (2009). Comments to California Energy Commission: Siting Criteria for Re Pilot 

Projects. Oak Glen, CA: The Wildlands Conservancy. 

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on the Solar Energy Study Areas for the Solar Peis. Oak Glen, CA: The Wilderness Society.

WGA/DOE. (2009). Western Renewable Energy Zones - Phase 1 Report. Washington, DC: U.S. 

Department of Energy. 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix: System Technical Description

System Structure

Data were loaded into a spatially enabled PostgreSQL database. Once loaded into the database, 

the data were publicly exposed as OGC-compliant Web Mapping Services (WMS) using 

GeoServer for use by the Web Analysis Service (WAS).

Figure A-1 shows a simplified Unified Modeling Language (UML) class diagram of the web 

analysis service; certain classes, such as exceptions, have been omitted for clarity. Essential 

supporting library packages, which are largely opaque, are shown in yellow, and “Request,” 

effectively the entry point, not implemented as a class per se, is shown in blue. 

Figure A-1. Simplified Unified Modeling Language (UML) class diagram of tool

For every service “Request,” a “DeckBuilder” is instantiated, which builds a “Deck” of “Layers” 

using both information parsed from the request via “cgicc” and from a configuration file, via 

“boost::program_options.” Each Deck contains a number of distinct types of layers (not 

represented in Figure A1, including resource, areal cost contribution, lineal cost contribution, 

mask, total area costs, and total lineal costs). 

Although “WmsLayerReader” is not the only “LayerReader”—a “FileLayerReader” and a 

“RandomLayerReader” exist for debugging and demonstration purposes — it is the 

“LayerReader” that provides the crucial analytical capability. It is sufficiently general that, in 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

principle, other concrete layer readers (e.g. one to read GeoTIFF files via GDAL) could be 

implemented and integrated into the service. 

Similarly, the “PngLayerWriter” and “JsonLayerWriter” are the current operating 

“LayerWriters,” but “LayerWriters” for other output formats could be easily implemented as 

wrappers around existing application programming interfaces (API) supporting those formats; 

the major obstacle here would be the complexity of the chosen API. The WAS implementation 

currently stands at about 2,200 non-comment lines of C++. 

Operation 

An incoming service request is parsed for client-supplied input parameters such as geographic 

bounding box, spatial reference system (SRS) identifier, and resolution. The raw string forms of 

these parameters are hashed using the MD5 cryptographic message digest algorithm to create a 

128-bit identifier with a very high probability of being unique. This identifier is a cache key: if 

an identical request has previously been serviced, the response is served from an output file 

cache and no further processing takes place. 

If there is no cache entry for the request, a “DeckBuilder” object is initiated, given the input 

parameters and the name of a configuration file. The configuration file is parsed for information 

about each layer, including type, weight, URL of the WMS server, etc. This information is 

augmented by the input parameters (SRS, bounding box, resolution) to form a WMS request for 

each layer. The WMS request is hashed, again using the MD5 algorithm, and the request is 

serviced from the input cache if possible. Otherwise, a WMS request is made to obtain raw 

layer data.

Because the bounding box and cell-size resolution are identical for each raster data layer, all of 

the layers are vertically integrated geospatially Areal and lineal cost contribution layers are 

linearly combined according to weights specified in the configuration file (or specified as input 

parameters) to yield the two total cost layers. The maximum and minimum values of the two 

layers are recorded. 

The areal total cost layer is then linearly discretized to 250 integer values. Mask layers are then 

applied and excluded pixels values are set to 255. Values between 251 and 254 inclusive are not 

used, and should never occur. This surface is then flooded by comparing the values to a 

threshold, increasing from 0. A binary solution layer records those pixels that are above (0x00) 

and below (0xff) the threshold. A de-speckling algorithm removes isolated pixels, and a 

segmentation algorithm identifies contiguous regions within the non-excluded region. 

The available resource for each region is evaluated (e.g., the summation of the product of area 

and energy density) and those regions too small to satisfy the resource requirement are 

eliminated. If no region can satisfy the resource requirement, the flood level is incremented and a 

new solution is sought until one or more regions satisfy the basic resource requirement. The 

solution is then ranked by minimum lineal cost in the region, and “best” candidate facility siting 

solutions are presented.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Google Earth Development Client

For development purposes, an AJAX-based client that uses the Google Earth browser plugin was 

written; it currently stands at approximately 1,000 non-comment lines of JavaScript, HTML

(HyperText Markup Language), and CSS (Cascading Style Sheets). In outline, it enables a 

curvilinear square bounding box to be selected in the Google Earth plugin, and by clicking “Go,” 

submits a request to the analysis service, which returns a .PNG image, which is then added to 

Google Earth as an overlay. Currently, the resolution of this image is fixed. Experiments with 

checkerboard images indicate that, were it to be scaled to the resolution actually required, it 

would have to be generalized by approximately a factor of 4 before being passed to Google Earth 

owing to scaling artifacts. In other words, although the calculations could (and, indeed, should) 

be performed at the needed resolution, the results should ideally be generalized or aggregated in 

a post-processing stage. The Google Earth plugin API requires the selection region to be drawn 

as a sequence of points representing a closed polygon; then, on every mouse-click event, the 

polygon must be deleted and redrawn anew. Thus, the responsiveness of this client is not 100% 

smooth, and remains somewhat “clunky” due to the challenge of implementing dynamic box 

selection functionality. 

OpenCarto User Interface

A web-mapping application was developed to provide a publicly accessible and functionally 

robust interface to facilitate user interaction with the WAS, as well as to visually display analysis 

results. This user interface was built upon the existing NREL-developed web-mapping 

framework called OpenCarto. OpenCarto is a collection of both open-source and custom 

software tools that provide data management, querying, visualization, and analysis capabilities 

for spatial data in a common template-based web application. In short, OpenCarto provides a set 

of tools to easily display and interact with spatial data on the web. 

OpenCarto also provides a highly extensible ExtJS-based platform that makes it easy to create 

application-specific user interface elements. This aspect of OpenCarto was leveraged to create a 

custom window to capture user-defined cost calculation layer preferences and package these 

inputs in a web request to the WAS.