GIS-BASED SPATIAL ANALYSIS FOR LARGE-SCALE SOLAR POWER ANDTRANSMISSION LINE ISSUES: CASE STUDY OF WYOMING, U.S.

Rebecca HottDepartment of Energy & Mineral Engineering

The Pennsylvania State University221 MRL Building

University Park, PA 16802email: rmh5227@psu.edu

Ron SantiniDepartment of Geography

The Pennsylvania State University2217 EES Building

University Park, PA 16802email: ras301@psu.edu

Dr. Jeffrey BrownsonDepartment of Energy & Mineral Engineering

The Pennsylvania State University224 MRL Building

University Park, PA 16802email: nanomech@psu.edu

ABSTRACT

Efforts to incorporate renewable energy sources intocurrent and future electricity regimes have beenbrought about due to the challenge of an increasingdemand for electricity worldwide, retirement of agingconventional generation facilities, along with theconsequences of greenhouse gas emissions from existingconventional energy sources. This paper presents adecision support tool to locate potential sites forlarge-scale solar power projects focusing ontransmission line issues on a state level. GeographicInformation Systems (GIS) and Multi-Criteria DecisionAnalysis (MCA) were combined to complete a sitesuitability analysis. GIS and MCA were incorporatedto develop a hierarchical process for assigning sitesuitability to land resources, mapped with respect totechnical, environmental and economic parameters. Aseries of maps was created with GIS software toillustrate possible locations for large-scale solar powerprojects on two levels with respect to transmissionissues. Resulting locations were analyzed according topopulated areas and existing transmission lines.Wyoming was chosen as a case study based on itslocation as an outlying region to the south-west, largeopen land areas, and significant annual solar resource(between 4.5 and 6 kWh/m2/day total average annualirradiation) using solar resource maps from the

National Renewable Energy Laboratory (NREL).Wyoming was also chosen because of the state’s longhistory of high energy production (i.e. coal from thePowder River Basin) and unusually high energydemand per capita (no. 1 in the nation).

1. INTRODUCTION

According to NREL’s national solar resource maps it isknown that the best solar radiation in the United Statesencompasses the south-west. The next step is to lookat outlying regions, which can be denoted as secondary‘quality’ insolation locations. The state of Wyoming islocated in this arc of outlying regions. With an open,barren landscape it possesses essential qualities forlarge-scale solar power systems. Along with these keyqualities comes the issue of transmitting electricitygenerated from large-scale solar power systems. This iswhere the transmission issue becomes important.

2. BACKGROUND

For the creation and utilization of a Solar-ElectricTransmission Model it is important to understand themain concepts of GIS, MCA, centralized solar powergeneration and the current energy situation for the

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study state or location.

2.1 Overview of GIS

A Geographic Information System (GIS) is composedof three integrated elements: geography, informationand systems. Geography refers to the real world andthe spatial realities that comprise it. Data is theinformation, and systems represent the computertechnology and support infrastructure [1]. GISperforms numerous functions but it can best bedescribed as “a computer-based technology andmethodology for collecting, managing, analyzing,modeling and presenting geographic data for a widerange of applications” [1].

Three main uses of GIS are Data Integration, SpatialAnalysis and Visualization. Data Integration involveslinking geography with data, which can be in the formof tables, images or scanned documents. In this specificapplication, data from various sources can be combined.Spatial analysis is performed to analyze the topological,geometric or geographic properties of an entity. Thefinal presentation of data depicts a visual display ofrelationships between entities [1]. All three have beenutilized within this model and analysis. ArcMap 10.0 c©

was chosen as the main GIS software because ofaccessibility, user-friendliness and available tools.

Spatial data and attributes are the two fundamentalcomponents of geographic data used in GIS. Spatialdata has a specific location according to latitude andlongitude in a world geographic referencing system orin an address system. Attributes are descriptivecharacteristics that represent the non-spatial data.Raster and vector data comprise the two primaryformats of spatial data. Raster data is based on a gridstructure where each cell represents a single identitysuch as a number or text label. Vector data iscomprised of data elements such as points, lines andpolygons. This data appears as map-like drawings orfeatures that are continuous or analog [1]. Both rasterand vector data have been incorporated into thisanalysis.

2.2 Overview of MCA

The purpose of multi-criteria decision analysis (MCA)is to simplify complex issues involving multiple criteriaby ranking all of the possible scenarios to determine anoptimal alternative [2]. GIS-based multi-criteriaanalysis involves two types of evaluation methods:“Boolean overlay operations and weighted linearcombinations (WLC)” [3]. Boolean overlay involves

assessing criteria by thresholds of suitability to createBoolean maps. These maps are then combined usinglogical operators such as AND (intersection) and OR(union) [4]. The AND operator results in a set solutionwhere a variable either meets the criterion or not. TheOR operator is more liberal; even if the variable doesnot meet the criterion it will still be considered in theresults [5]. This is known as “ORness.”

Weighted linear combination standardizes criteria (orfactors) to a common numeric range, which are furthercombined by weighted averaging. This method is quitedifferent than Boolean overlay. A low score from onecriterion can be compensated by a high score fromanother criterion; this feature is known as trade-off [4].Boolean overlay was utilized within this analysis.

2.3 Central Solar Power Generation

From a nature point of view, solar energy is considereda low-density energy [6]. For this reason, utilizing solarenergy on a centralized, large scale proves beneficial.Central generation refers to every aspect of the powergenerating capability being encompassed in one singlearea. Large-scale refers to the size of the power projectand is generally interpreted as having a solar powercapacity of 1 megawatt (MW) to 10MW. Specifically,solar power projects greater than 10MW and rangingup to several gigawatts (GW) are further classified asvery large-scale (VLS) systems [6].

Centralized solar power generation projects can beincorporated into the electricity grid throughtransmission lines and utility companies or used on astand-alone basis. Stand-alone systems provideelectricity to the immediate surrounding area where asgrid-tied systems can supply electricity to far offdistances [6]. Typically a stand-alone system is used inremote areas where there is no access to the electricitygrid. There are two levels to this analysis. The firstlevel analyzes resulting locations based on populatedareas. If near commercial and residential areas, it couldbe used as a stand alone system or connected to anexisting line. The second level analyzes resultinglocations based on their proximity to currenttransmission lines.

2.4 Energy Production & Use in Wyoming

Several of the largest fossil fuel deposits in the UnitedStates are found in the vast geological basins ofWyoming. It has been estimated that Wyoming’srecoverable coal reserves are second to Montana’s, andit’s natural gas reserves are second to those found in

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Texas along with the state’s substantive crude oilreserves [7]. It is recognized that Wyoming’s economyis based on the coal mining, oil and gas industries. Thestate ranks no. 1 in energy consumption per capita [7],however, the aggregate energy demand is low. This isdue to the energy-intensive economy and industrialsector [8]. Using Wyoming as a case study will show aninteresting view between the state’s conventionalenergy history and it’s ability to provide renewableenergy to the commercial and residential sectors. Amap of Wyoming can be found below (see Fig. 1).

Fig. 1: Map of Wyoming, U.S.

3. METHODOLOGY

Multiple variables were used to analyze the sitesuitability for large-scale solar power systems withrespect to the two levels mentioned previously insection 2.3: (1) analysis of suitable locations based onpopulated areas and (2) analysis of suitable locationsbased on existing transmission lines. A list of thevariables used in the Solar-Electric Transmission Modelcan be found in Table 1.

Table 1: GIS VARIABLES

Variable Data Type Vector File

Aspect RasterCity Vector PointCounty Vector PolygonLandcover RasterSlope RasterSolar Potential RasterState Vector PolygonTransmission Lines Vector Line

3.1 Slope and Aspect/Azimuth

Elevation data from 2009 was obtained as a NationalElevation Dataset (NED) from the U.S. GeologicalSurvey (USGS) Seamless Server. Eight separate fileswere downloaded from 45.10◦N to 40.88◦S and from-111.13◦W to -103.80◦E. Elevation data is raster (grid)with a resolution of 1 Arc Second, which isapproximately 30 meters. The Mosaic Tool found inthe Data Management Toolbox for a Raster Dataset inthe GIS software was used to combine the eight filesinto a single file. The resulting raster was then clippedto the boundary of the state shapefile. This step wascompleted beforehand and not included in the model,Fig. 2, due to tool processing time constraints.

From the resulting elevation file a slope and aspectanalysis was applied to the raster. Slope and aspect areboth important when constructing a large-scale solarpower system. Slope refers to gradient or steepness of asurface. Aspect refers to the direction of which theslope faces. It is equivalent to azimuth with valuesbetween 0 and 359. A value of 0 or 360 is north, 90 iseast, 180 is south and 270 is west. From combiningprevious research by Charabi [2] and Arnette [9] a slopeless than 5◦ was deemed suitable for this analysis aswell as a slope between 5 and 15◦ with aspect valuesbetween 112.5 and 247.5 (south-east to south-west). Inorder to perform Boolean operations, suitable areaswere assigned a value of 1 while unsuitable areas wereassigned a value of 0.

3.2 Land Cover

Land cover data from 2006 was also obtained from theUSGS Seamless Server. Two raster files with aresolution of 30 meters were downloaded from 45.10◦Nto 40.88◦S and from -111.13◦W to -103.80◦E. Onceagain the Mosaic tool was used to combine the filesalong with the Clip tool to cut the data to the state’sboundary. Table 2 lists the individual land coverclasses, GIS value and whether the land cover class issuitable for the location of large-scale solar powersystems. The GIS value is an arbitrary value part ofthe National Land Cover Dataset Classification Systemgiven to each individual land cover class.

As can be seen in the table, land cover classes thatrepresent open, barren, grassland types were deemedsuitable and re-assigned a value of 1 while developed,forested or wetland areas were deemed unsuitable andre-assigned a value of 0. Once again, values of 0 and 1were used to perform Boolean operations.

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Fig. 2: GIS-based Solar-Electric Transmission Model.

Table 2: LAND COVER DATA

Class Value Suitable

Open Water 11 NoPerennial Ice/Snow 12 NoDeveloped, Open Space 21 YesDeveloped, Low Intensity 22 NoDeveloped, Medium Intensity 23 NoDeveloped, High Intensity 24 NoBarren Land 31 YesDeciduous Forest 41 NoEvergreen Forest 42 NoMixed Forest 43 NoShrub/Scrub 52 YesGrassland/Herbaceous 71 YesPasture/Hay 81 YesCultivated Crops 82 NoWoody Wetlands 90 NoEmergent Herbaceous Wetlands 95 No

3.3 Solar Potential

Global Horizontal Irradiance (GHI) and Direct NormalIrradiance (DNI) raster files were obtained from NREL.GHI data is from 2007 with a 10km resolution andrepresents the annual average solar resource potentialavailable to a flat-plate collector oriented horizontal tothe earth’s surface. PV solar resource potential valuesrange between approximately 3.8 and 5.8 kWh/m2/dayfor Wyoming, representing an adequate solar resource.DNI data is from 2005 with a 10km resolution andrepresents the annual average solar resource potentialavailable to concentrating solar collectors that track thesun throughout the day. CSP resource potential values

are slightly better than PV resource potential valueswith a range of 4.3 to 6.3 kWh/m2/day.

The Clip tool was used to obtain a raster file of bothGHI and DNI within the state boundary of Wyoming.The symbology was also changed for each file using acolor ramp of yellow to orange to depict the solarresource.

3.4 Population Areas

Two shapefiles representing population areas weredownloaded from the Bureau of Land Management’sWyoming site and the Wyoming GeoLibrary,respectively. These vector files consist of a county(polygon) file from 1999 and city (point) file from 1996.

3.5 Transmission Lines

Major power and transmission line data throughout thewestern United States and Canada from 2004 wasobtained through the Wyoming GeoLibrary. Power andtransmission lines were extracted within the state’sboundary and converted into another vector (line) file.

3.6 Solar-Electric Transmission Model

Using the model builder in ArcMap 10.0 c© aSolar-Electric Transmission Model was created toanalyze the solar potential with respect to transmissionline issues (see Fig. 2). The first step involveddetermining suitable locations for large-scale solarpower systems. As previously stated in section 3.1,slopes under 5◦ and slopes between 5 and 10◦ with asouth-east, south or south-west aspect are suitable.Raster files for these locations were combined using the

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Raster Calculator and the Boolean OR operator. Theprocess for determining suitable land cover wasdescribed in section 3.2. Combining suitable areas ofslope and land cover was completed with the BooleanAnd operator in the Raster Calculator tool. Thisprocess can be followed on the bottom half of Fig. 2.

As stated in Section 2.3 there are two main levels tothis analysis. The first level analyzes suitable locationsbased on commercial and residential areas. Toemphasize the distance factor, a multiple ring bufferwas created for the city shapefile. A buffer starting atthe center of each city and spaning 30 miles (≈ 48.28km) outward, in 5 mile (≈ 8.05 km) increments, wascreated.

The second level analyzes resulting locations withrespect to current transmission lines. Once again, toemphasize the distance factor the multiple ring buffertool was used on the transmission line shapefile.Starting on each transmission line, a 30 mile (≈ 48.28km) buffer with 5 mile (≈ 8.05 km) increments wasgenerated.

4. RESULTS

The solar resource (GHI and DNI), population areasand transmission line files were overlayed with thesuitable location file (see Fig. 3). Figs. 4 through 7depict these results, respectively. As can be seen inthese images many suitable locations coincide withhigher levels of the solar radiation and are in closeproximity to cities and current transmission lines.

Fig. 3: Suitable areas for Large-scale Solar Power.

Fig. 4: Suitable areas overlayed with GHI data.

Fig. 5: Suitable areas overlayed with DNI data.

The combination of GIS and MCA to create aSolar-Electric Transmission Model is a useful decisionsupport tool in modeling potential sites for large-scalesolar power projects with respect to transmission issueson a state level. This model can further be fit to otherstates or areas of interest. From this case study it canspecifically be seen that Wyoming possesses an amplesolar resource with the means to transmit electricityfrom solar power projects to support the commercialand residential sector within the state’s boundary.

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Fig. 6: Suitable areas overlayed with population areas.

Fig. 7: Suitable areas overlayed with current transmis-sion lines.

Further research could be completed on a smaller scale.For instance, a specific location within the state couldbe analyzed in greater detail. Economic parameterscould be taken into consideration to determine theoptimal route for connecting a large-scale solar power

system with the transmission system. Also, newtransmission lines could be projected for suitablelocations not in close proximity to current lines.

5. ACKNOWLEDGMENTS

Ryan Baxter — Instructor, Geography, PSU

Ryan Hippenstiel — MGIS student, Geography, PSU

Brownson Research Group

The Pennsylvania State University, the College of Earthand Mineral Sciences, and the John and Willie LeoneFamily Department of Energy and Mineral Engineering.

6. REFERENCES

(1) B. E. Davis, GIS: A Visual Approach, 2nd ed.,Albany, NY: Delmar Thomson Learning, 2001, (2) Y.Charabi and A. Gastli, “PV site suitability analysisusing GIS-based spatial fuzzy multi-criteriaevaluation,” Renewable Energy, vol. 36, pp. 2554-2561,2011, http://www.sciencedirect.com.ezaccess.libraries.psu.edu/science/article/pii/S0960148111000760 (3) J.Malczewski, “Ordered weighted averaging with fuzzyquantifiers: GIS-based multicriteria evaluation forland-use suitability analysis,” International Journal ofApplied Earth Observation and Geoinformation, vol. 8,pp. 270-277, 2006, http://www.sciencedirect.com/science/article/pii/S0303243406000031 (4) H. Jiangand J. R. Eastman, “Application of fuzzy measures inmulti-criteria evaluation,” GIS International Journal ofGeographic Information Science, vol. 14, pp. 173-184,2000, http://www.tandfonline.com/doi/pdf/10.1080/136588100240903 (5) J. R. Janke, “Multicriteria GISmodeling of wind and solar farms in Colorado,Renewable Energy, vol. 35, pp. 2228-2234, 2010,http://www.sciencedirect.com.ezaccess.libraries.psu.edu/science/article/pii/S096014811000131X (6) K. Komoto,M. Ito, P. van der Vleuten, D. Faiman, and K.Kurokawa (eds.), Energy from the Desert: Very LargeScale Photovoltaic Systems: Socio-economic, Financial,Technical and Environmental Aspects, Sterling, VA:Earthscan, 2009 (7) “Wyoming Analysis,” 2009,http://205.254.135.24/state/state-energy-profiles-analysis.cfm?sid=WY (8) “Wyoming Energy Potential:Wyoming’s Carbon Footprint,” 2005, http://www.e-redux.com/states/statedetail.php?id=1161#ElectricityLink (9) A. N. Arnette, and C. W. Zobel,“Spatial analysis of renewable energy potentila in thegreater southern Appalachian mountains,” RenewableEnergy, pp. 2785-2798, 2011, http://www.sciencedirect.com.ezaccess.libraries.psu.edu/science/article/pii/S0960148111001960

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