A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A GIS Method for Developing 

Wind Supply Curves 

David Kline, Donna Heimiller, and Shannon Cowlin 

Technical Report 

NREL/TP-670-43053 

June 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
A GIS Method for Developing 

Wind Supply Curves 

David Kline, Donna Heimiller, and Shannon Cowlin 

Prepared under Task No. WF1F.1020 

Technical Report 

NREL/TP-670-43053 

June 2008 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Acknowledgments 

The authors gratefully acknowledge the Climate Change Division of the Environmental 

Protection Agency (EPA) for its support in this phase of the Climate Technology 

Partnership, and the previous phases that provided its foundation. The United Nations 

Development Programme provided support to China Hydropower Engineering 

Consulting Company (CHECC) for their role in this work. 

The project would have been impossible without the strong collaboration of the Chinese 

team from CHECC and allied institutions, led by Shi Pengfei and including Wang Jixue, 

Yang Chengbing, and Yang Xifeng. The wind resource data developed by the Solar and 

Wind Energy Resource Assessment project, which was sponsored by the United Nations 

Environment Programme, was also a critical element. Any errors contained in this report 

are the sole responsibility of the authors. 

 i
 ii

Table of Contents 

Acknowledgments ........................................................................................................................................ i 

Table of Contents ........................................................................................................................................ ii 

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

Project Overview.......................................................................................................................................... 1 

Methodology ................................................................................................................................................ 1 

Implementation of the Analysis ................................................................................................................. 4 

Wind Resources .........................................................................................................................4

Exclusions ..................................................................................................................................5 

Cost Measured at the Wind Farm ..............................................................................................6 

Point of Measurement ................................................................................................................6 

Cost of Energy Delivered to the Existing Grid ..........................................................................6 

Impact of Terrain on Installed Capacity ....................................................................................7 

Resulting Geospatial Supply Curve ..........................................................................................8 

The Value of the Geospatial Supply Curve .............................................................................10 

Applicability of this Approach to Wind Power Planning in China ........................................................ 11 

Building on this Effort ............................................................................................................................... 11 

Improving Underlying Data .....................................................................................................11 

Adding Analysis Capabilities ..................................................................................................12 

Coordinating with Chinese Counterparts .................................................................................13 
Introduction 

This report describes work conducted by the National Renewable Energy Laboratory 

(NREL) as part of the Wind Technology Partnership (WTP) sponsored by the U.S. 

Environmental Protection Agency (EPA). This project has developed methods that the 

National Development and Reform Commission (NDRC) intends to use in the planning 

and development of China’s 30 GW of planned wind capacity. Because of China’s 

influence within the community of developing countries, the methods and the approaches 

here may help foster wind development in other countries. 

 

Project Overview 

NREL has worked closely with its counterpart, the China Hydropower Engineering 

Consulting Company (CHECC), in developing this analysis and planning methodology. 

The work has also been coordinated with the Energy Bureau of NDRC to ensure that the 

results fit their planning purposes. The analysis provides a geographic and economic 

assessment of wind resources in the Zhangbei region, which yields a “geospatial supply 

curve” or GSC. The GSC gives not only the wind supply curve—supply price as a 

function of quantity supplied—but also the geographic locations that correspond to any 

given point on the supply curve. The GSC can answer three key questions regarding the 

planning of new wind capacity in Zhangbei: (1) How much wind power is available at or 

below a given price? (2) Conversely, given a desired level of installed capacity, how 

much will the delivered energy cost? (3) Which locations can supply energy at or below 

a given price? The answers to these questions provide a foundation for a more 

comprehensive regional planning framework that would address additional infrastructure, 

planning, and policy questions. Because it has been accepted as a planning methodology 

by the NDRC Energy Bureau, the GSC can play an important role in supporting China’s 

planned 30 GW of additional wind power. 

Methodology 

This section describes the methodology developed for use in this study, its 

implementation, and the adjustments that were made because of limitations in the 

available data. 

The goal of this method is to produce the geospatial supply curve (GSC), which has two 

parts. The first part is a standard supply curve, giving the supply available within the 

Zhangbei region at any given price (or, conversely, the marginal price for a given level of 

supply from Zhangbei).1

 The second element, the geospatial portion, is a geographic 

information system (GIS) that associates a map with each point on the supply curve. The 

map shows the GIS cells that are included at that level of supply. Equivalently, the map 

shows all the locations that can deliver energy at or below the corresponding price. 

 

1

 The supply curve is expressed in units of installed nominal capacity. The GIS system contains data that 

could also be used to calculate the annual energy generation at any given level of supply. Annual 

generation will vary with the level of supply, since the capacity factor decreases as the supply increases and 

less favorable sites are included. 

 1
Figure 1 presents an overview of the methodology used to develop the GSC. The right 

column shows the geographic inputs; the left column describes the technical, economic, 

and finance inputs; and the middle column provides a conceptual overview of the flow of 

the calculations. Note that the shapes of the boxes distinguish between types of variables. 

The boxes with curved edges describe quantities that are considered “maps,” i.e., they 

have a value for each “cell” or geographic grid point. The boxes with all straight edges 

represent other kinds of data, or calculation processes. 

The analysis was originally intended to proceed as follows: The calculations begin by 

combining the wind power data with the power curve of the wind turbines to calculate the 

capacity factor of a turbine, hypothetically located at each cell. Together with cost and 

finance parameters, those capacity factors determine the levelized cost of energy (LCOE) 

at each cell, without accounting for any transmission costs (“local LCOE”). Information 

on how the transmission cost depends on the location of the cell, together with and other 

parameters, determines the cost, including transmission cost (“LCOE delivered to the 

grid”). In the final step, the GIS software aggregates the map data to develop the GSC. 

The methodology described above and illustrated in Figure 1 is an idealized version of 

what was actually carried out. As described in Section IV, data availability limited the 

analysis. The shaded portions of Figure 1 indicate calculations omitted because of the 

data limitations. Accordingly, the supply curve presented below represents the local 

LCOE rather than the LCOE delivered to the grid. 

 2
Calculation 

process 

Other input 

or output 

data 

Data or 

results in 

map form 

Wind 

speed 

data 

LCOE 

delivered 

to grid 

Turbine 

power 

curve data 

Capacity 

factors 

Local 

LCOE 

Aggregate 

across all 

cells

Turbine cost 

and finance 

data 

GIS DATA Calculated 

Results 

Technical and 

Economic Data 

Terrain 

data 

Legend 

Map 

results 

Supply 

curve 

results 

Exclusion 

zones 

Transmission 

cost data 

Figure 1. Overview of Methodology 

 3
Implementation of the Analysis 

This section provides more detail on the implementation of the methodology described 

above and presents the results. It also describes the data sources and the adaptation of the 

methodology because of the limited data available. 

Wind Resources 

The wind resource information used in this study was developed by NREL with input 

from the CHECC, as part of the Solar and Wind Energy Resource Assessment (SWERA) 

project in 2005.2

 A detailed report of the data and methodology used to create the wind 

resource assessment is available on the SWERA Web site at http://swera.unep.net. The 

wind resource is represented as annual wind power density (W m-2) or, equivalently, 

wind power class at 50 m above ground, with a spatial resolution of 1 km2. Zhangbei’s 

wind resource distribution is shown in Figure 2. 

Figure 2. Wind Resource Distribution in Zhangbei 

This analysis considered only resource levels of class 3 and higher. The resource 

information was divided into four subclasses for each wind power class to allow for finer 

delineation of wind capacity factor and separation of cost (see Table 1). 

 

2

 SWERA was sponsored by the United Nations Environmental Programme. 

 4
Table 1. Wind Power Class Intervals Used to Develop GSC 

Power Class 

Subclass 

Wind Power Density 

(W m-2) at 50 m 

Quarter Power 

Class 

Wind Power Density 

(W m-2) at 50 m 

3.1 300 – 324.9 5.1 500 – 524.9 

3.2 325 – 349.9 5.2 525 – 549.9 

3.3 350 – 374.9 5.3 550 – 574.9 

3.4 375 – 399.9 5.4 575 – 599.9 

4.1 400 – 424.9 6.1 600 – 649.9 

4.2 425 – 449.9 6.2 650 – 699.9 

4.3 450 – 474.9 6.3 700 – 749.9 

4.4 475 – 499.9 6.4 750 – 799.9 

7.1 >= 800 

Exclusions 

There are many factors that determine whether an area can be developed for power 

generation, many of which can be evaluated only at a site-specific level. However, this 

regional-scale analysis can eliminate areas unlikely to be available for development, 

based on land-use and environmental factors. As shown in Figure 3, these areas include 

water bodies, areas around village sites, and areas adjacent to the Great Wall historical 

area. A global dataset of nationally protected areas was also reviewed, but none of those 

areas fell within Zhangbei. 

Figure 3. Zhangbei Wind Resource Exclusions 

 5
Other areas that should be excluded were discussed, but were not identified in this 

analysis because the data were not available. Those exclusion areas include nationally 

designated croplands and mining areas. Modifying the exclusions used in the analysis 

could be accomplished quickly if data becomes available in a GIS-compatible format. 

Cost Measured at the Wind Farm 

The analysis assumes development of the Zhangbei prospect areas by private developers, 

as is contemplated in current plans. (In fact, concessions already have been sold to 

private concerns for some parts of Zhangbei.) Current policy calls for the wind farm 

developers to supply power to the national grid at a feed-in tariff determined during the 

bidding process. To model this policy regime, the cost at the wind farm is determined by 

solving for the feed-in tariff that will yield an agreed-upon internal rate of return (IRR) to 

the private developer. The value used here is a nominal IRR of 8% per annum, calculated 

on the after-tax cash flow to the developer. The required feed-in tariff was obtained by 

iteration in a spreadsheet model of after-tax annual cash flow under applicable Chinese 

tax rules, which was developed by CHECC. The cost of energy at the wind farm is 

calculated for each wind power subclass, for each of the four types of turbines and for the 

average of all four turbines. 

Point of Measurement 

The cost of energy from new capacity on an electric grid depends on where the cost is 

measured, because it depends on the transmission facilities that need to be added. A full 

consideration of the issues involved in estimating those costs is beyond the scope of this 

effort. 

Cost of Energy Delivered to the Existing Grid 

The original plan for this effort called for a relatively simple approach to the transmission 

cost question that would estimate the cost “delivered to the transmission system.” Such 

an estimate begins with a levelized cost of energy at the wind farm, i.e., including only 

the capital cost of the turbine and its annual operating cost without any incremental 

transmission costs. As mentioned above, the cost is represented by the required feed-in 

tariff. The cost of energy at each point on the map is then increased as a function of the 

distance to the existing transmission line and the cost per MW-mile to connect to it, 

which may depend on the terrain. 

However, data limitations prevented this analysis from developing a model that would 

give that cost as a function of distance and terrain. Accordingly, the price shown on the 

supply curve is simply the cost measured at the wind farm, described above.3

 

3

 All wind locations in Zhangbei are located less than 50 km (30 mi.) from the existing 110 and 220 kva 

transmission lines. Accordingly, the corresponding cost of connecting to the grid is expected to be 

relatively small. As briefly mentioned above, the complete consideration of required transmission system 

additions would impose further complexities and data requirements. That analysis would require 

considering upgrades to the existing high-tension transmission system, which would in turn require an 

understanding of the capacity available on that system. 

 6
Impact of Terrain on Installed Capacity 

The amount of installed wind capacity that can be placed in a given area will be 

influenced by factors such as terrain, land use, pattern of wind direction (particularly of 

higher-speed events), and the turbines used. This analysis assumed a base density level 

of 5 MW of installed capacity per km2. This assumption represents a 10-rotor-diameter 

by 5-rotor-diameter turbine spacing across the entire area not excluded. At the 

suggestion of CHECC, the base installed capacity was varied as a function of the terrain 

slope and land-cover pattern that may impact development (see Table 2). 

The slope of each 1 km2 area was calculated using global digital elevation data. The 

density of turbines is generally a decreasing function of slope. In Zhangbei, the slope 

values range from 0-10%, and the installed capacity was set using the values in Table 2. 

The higher slopes occur along the southern border of Zhangbei, in higher wind class 

areas. 

Table 2. Impact of Land Slope on Wind Turbine Density 

Percent 

Slope 

Installed 

Capacity 

(MW km-2) 

0 – 3 5 

3 – 5 3 

5 – 10 2.5 

The land-cover pattern in Zhangbei is predominantly crop or grasslands, with a small area 

of shrub land. At the suggestion of CHECC, areas designated as crop or grasslands were 

modified to be 80% available and forested areas are assumed to be 50% available. Shrub 

lands were given an intermediate value of 65% availability. These calculations resulted 

in installed wind capacity values between 2 and 4 MW km-2 for areas that were not 

completely excluded earlier. This distribution (see Figure 4) becomes the foundation for 

constructing the GSC. 

 7
Figure 4. Zhangbei Estimated Installed Capacity 

Resulting Geospatial Supply Curve 

The GSC that results from the above analysis provides two kinds of information: a supply 

curve and geographic information linked to that supply curve. The supply curves 

resulting from the work so far (see Figure 5) show the four different wind turbine types 

and the average value.4

 

4

 Although DZh77 and GW77 have significantly lower costs of energy CHECC wanted to see the supply 

curves for all 4 wind turbines and for the average of all four. There may be other considerations besides 

what is captured in the cost of energy calculations used here that could dictate the use of one turbine rather 

than another. 

 8
Available Wind Supply Curve - Zhangbei

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0 2 6,000 8,000 10,000 12,000

Installed Capacity (MW)

Tariff with VAT (yuan per kWh) 

GE77

DZh77

S64

GW77

Turbine Avg

,000 4,000

Figure 5. Zhangbei Available Wind Supply 

 

The geographic information is illustrated in Figure 6. Consider one point on the DZh77 

supply curve, showing that 2,400 MW are available at a price of 0.5 Yuan/kWh 

($.067/kWh) or less (indicated by arrow in Figure 5).5 The GIS portion of the analysis 

shows the locations that supply the wind power for that point on the supply curve. For 

any point on the supply curve, the GIS software can be used to develop a map analogous 

to Figure 6, showing which locations supply the power corresponding to that quantity and 

price. When linked to the geographic information shown in Figure 6, the supply curve 

becomes the geospatial supply curve. 

 

5

 The quantity referred to here as “price” is actually the required feed-in tariff, as described above. 

 9
Figure 6. Zhangbei Locations Supplying Power 

The Value of the Geospatial Supply Curve 

The ability of a GIS to spatially represent and relate different datasets enables 

development of the geospatial supply curve. The evaluation of potential installed wind 

capacity is based on terrain, land-use, and exclusion factors that are independent of the 

wind resource distribution. The cost of energy for an individual site depends on the wind 

resource. Integrating these components by spatially cross-referencing the quantity and 

the cost provides the data used to build the supply curve. Using a GIS system in this way 

also provides a mechanism for modifying the supply curve to account for other factors 

that may modify the cost, especially those that are spatial in nature such as distance to 

transmission lines, distance to load centers, proximity to hydropower resources, and local 

taxes or incentives. 

Building the supply curve within a GIS provides flexibility for viewing the cost 

information related to the different GIS components. The costs can be summarized by 

power class, by land-use category, or other criteria. It can focus on smaller regions, or be 

aggregated to represent larger regions. Overall, it is a flexible mechanism for building a 

supply curve. 

 10
Applicability of this Approach to Wind Power Planning in 

China 

The geospatial supply curve methodology provides the economic basis for planning the 

development of wind prospect areas such as Zhangbei. The Energy Bureau of NDRC 

intends for CHECC to integrate an enhanced version of this methodology into a 

comprehensive framework for addressing the regional planning issues required to 

implement China’s plans for 30 GW of new wind installations by 2020. NDRC’s 

planned use of the methods developed here represents the most important contribution of 

this effort. It also affords the opportunity to continue to work with CHECC and NDRC 

to refine the methodology and broaden the scope to address additional regional planning 

issues. 

In discussions about the design of this effort, NDRC requested that the methodology be 

broadened cover all the regional planning issues that need to be addressed in pursuing the 

30 GW goal. Shortly before the time of this writing, NDRC asked that a refined version 

be applied to a 10 GW project in Gansu Province. CHECC will be responsible for 

refining the methodology to apply it in Gansu and disseminating it so that provincial 

government officials and others can apply it to development plans for many additional 

wind prospect areas. 

As in the United States, responsibility for new electricity projects in China is shared 

between the national and provincial governments. The provincial governments will have 

some input regarding the location of wind parks within their provinces, and will also be 

responsible for some of the infrastructure requirements, such as new roads. In addition, 

added transmission capacity will have to be coordinated with the China State Grid 

Corporation and other entities responsible for the national grid. The methodology 

developed during this project provides an economic and geographic foundation for 

extensions that would address the planning and analysis requirements of both the 

provincial governments and the grid operators. NREL hopes to continue its collaboration 

with CHECC to build on the basic geospatial supply curve to meet those requirements, as 

described in Section VI. 

Building on this Effort 

As stated earlier, the NDRC plans to use this method as the basis for analysis of many 

other wind provinces, and for support to provincial stakeholders in assessing and 

addressing additional regional planning issues. The work to date provides a valuable 

proof-of-concept, but refinements and enhancements will be necessary to fully address 

NDRC’s requirements for a regional planning methodology. Those refinements and 

enhancements are considered next. 

Improving Underlying Data 

The geospatial supply curve developed in this project is based on the following sources: 

wind resource data developed by NREL and Chinese partners; wind turbine cost and 

 11
performance data provided by CHECC; and geographic information that includes the 

locations of roads, existing transmission lines, and exclusion zones where wind farms 

cannot be sited, also provided by CHECC.6

 

The geographic data contains the most significant gaps and approximations that should be 

addressed in future refinements and extensions. Most importantly, the transmission 

system data available for this analysis was adequate only for a limited representation of 

the costs of additional transmission infrastructure, as discussed in Section IV. Enhanced 

data on location, capacity, and loading characteristics of the existing grid system will be 

necessary for the next stages of this analysis. There is also no consideration in the 

current analysis for road infrastructure requirements, because the data on the existing 

road network was inadequate. Finally, the information on excluded areas could be 

improved, particularly in the areas of land use and officially designated cropland. 

The wind data used here is appropriate to support this level of analysis, because it was 

developed in accordance with generally accepted international standards for regionalscale wind resource assessment as part of SWERA. Other prospect areas, such as Gansu, 

may require additional wind resource assessment. In addition, project developers will 

want to conduct their own more detailed resource studies for micro-siting of turbines and 

for refining the economic analysis—those studies need not be conducted by the public 

sector. 

Chinese partners may want to update the cost data underlying the cost-of-energy 

calculations, or even the algorithm for the calculation. Those updates are easily 

incorporated into an updated geospatial supply curve at any time. 

Adding Analysis Capabilities 

In extending the capabilities of the current methods, to address more of the requisite 

regional planning issues, the most important element relates to the handling of 

transmission infrastructure costs; as mentioned above, the transmission data available to 

this effort this were quite limited. 

One capability that CHECC mentioned as desirable is the inclusion of pumped storage to 

help level the wind supply, reducing its variability. It would be not be difficult to 

incorporate such an option and to compare the cost with and without pumped storage. 

However, effectively evaluating the benefits of pumped storage would require a much 

richer model of both transmission cost and load flow on the transmission system. Both 

the data and the approach to those models would require close cooperation with the China 

State Grid Corporation, which operates the grid. A refined analysis will also require 

seasonal and diurnal wind resource profiles to compare to hydropower deployment. 

The strategy for enhancing the representation of transmission could be organized around 

a series of scenarios, each of which would correspond to a point on the geospatial supply 

curve. Given the point on the supply curve, the GIS portion would detail the locations of 

 

6

 Exclusion zones include national parks and historical zones such as the Great Wall, cropland, water 

bodies, and developed areas. 

 12
 13

the wind farms contributing to the indicated supply. From that basis, load-flow modeling 

or other techniques would provide estimates of the costs of new transmission 

infrastructure. Further extensions could estimate the ancillary costs related to large wind 

farms, due to additional spinning reserve requirements and other requirements of grid 

integration. 

Coordinating with Chinese Counterparts 

Building on the present effort will require careful coordination with a much broader 

range of Chinese partners, including NDRC, the China State Grid Corporation, provincial 

planning officials, provincial energy regulators, and wind farm developers. It would be 

best to continue the work in the same mode pursued to this point: developing methods 

and techniques jointly with Chinese partners and developing an independent capacity in 

the Chinese counterpart organizations. 
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14. ABSTRACT (Maximum 200 Words)

This report describes work conducted by the National Renewable Energy Laboratory (NREL) as part of the Wind 

Technology Partnership (WTP) sponsored by the U.S. Environmental Protection Agency (EPA). This project has 

developed methods that the National Development and Reform Commission (NDRC) intends to use in the planning 

and development of China’s 30 GW of planned wind capacity. Because of China’s influence within the community of 

developing countries, the methods and the approaches here may help foster wind development in other countries. 

15. SUBJECT TERMS

NREL; David Kline; Donna Heimiller; Shannon Cowlin; China; geographic information system; GIS; Wind Technology 

Partnership; WTP; U.S. Environmental Protection Agency; EPA; Nation Development and Reform Commission; 

NDRC; wind resources; Zhangbei; China Hydropower Engineering Consulting Company 

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