Solar Power Potential in Shrinking Cities:

A Case Study of Cleveland, Ohio

Brian Beyeler

Kent State University

Abstract:

Shrinking cities in Ohio with a surplus of vacant land now have a unique opportunity to help

combat climate change by becoming producers of green energy. Solar power in Ohio’s shrinking

cities is now a viable option versus fossil fuels due to improved technologies, lower installation

and equipment costs, and promising potential for solar energy. Therefore, there is a need to

perform detailed and reliable estimation of harvestable solar radiation in Ohio’s shrinking cities.

This study explores modeling solar power potential on vacant land in Cleveland, Ohio through

use of a geographic information system.

Introduction:

Climate Change, Fossil Fuels, and Ohio’s Energy Profile

“Last fall, as world coal consumption and world carbon emissions were headed for new

records, the Intergovernmental Panel on Climate Change issued its latest report. For the first time

the panel estimated an emissions budget for the planet—the maximum amount of carbon we can

release if we don’t want the temperature rise to exceed 2 degrees Celsius, a level which many

scientists consider a threshold of serious harm. The IPCC concluded that we have already

emitted more than half our carbon budget. On our current path, we’ll emit the rest in less than 30

years (Nijhuis, 2014).”

In Ohio, the majority of electricity is generated using nonrenewable fossil fuels (Figure

1). The use of these resources contributes to climate change (Nijhuis, 2014). Coal is used to

generate 69.7 percent of the electricity in Ohio. Approximately 16 percent of the electricity in

Ohio is produced using natural gas and other gases.

Petroleum generates approximately 1 percent of

Ohio’s electricity. All of these fossil fuels are

burned to produce heat, which converts water into

high-pressure steam. The steam turns the blades of

turbines that are connected to a generator that

converts mechanical energy into electricity (Public

Utilities Commission of Ohio, 2014).

In the last few decades, laws have been

passed in the United States to reduce the emissions

of sulfur dioxide, nitrogen oxides, and soot

particles. “But carbon dioxide (CO₂), the main

cause of climate change, is a problem on an

entirely different scale. In 2012 the world emitted a

record 34.5 billion metric tons of CO₂ from fossil

fuels. Coal was the largest contributor (Nijhuis,

2014).”

“Clean” coal is a delusion. Cleaning coal

captures carbon dioxide and other pollutants. These

pollutants need to be disposed of somehow, so the waste is injected back into the earth by

compressing CO₂ into sandstone formations. Sudden releases of CO₂ can be lethal, particularly

when the gas collects and concentrates in a confined space. “The first American power plant

Figure 1. Ohio's Energy Profile. Public Utilites Commission of Ohio, 2014.

designed to capture carbon is scheduled to open at the end of this year. The Kemper County coal-

gasification plant in eastern Mississippi will capture more than half its CO₂ emissions and pipe

them to nearby oil fields.” Even if all of the emissions in coal could be captured and disposed of

effectively without danger to humans and the environment, there are still other social and

environmental consequences. “Just look at West Virginia, where whole Appalachian peaks have

been knocked into valleys to get at the coal underneath and streams run orange with acidic water

(Nijhuis, 2014).”

Cheap natural gas has reduced the demand for coal in the United States. As gas prices in

the U.S. near record lows, some coal power plants are converting to natural gas. Gas is a cleaner

source of energy compared to coal, but it still contributes to 21percent of global fossil fuel

emissions of CO₂ (Nijhuis, 2014). A relatively new process called horizontal hydraulic

fracturing (fracking) is responsible for the price decrease. Fracking has been used since the

1940’s, but the new and improved horizontal method is far larger and riskier. The impact of

fracking on the environment and human health is yet to be determined. An average of 5,283,441

gallons of water and 52,834 gallons of acids, biocides, scale inhibitors, friction reducers and

surfactants are used in one fracking well (Howarth, et al, 2011) .

Shrinking cities: A unique opportunity to combat climate change

In recent years planners have started to study how cities shrink and grow. Over the last

fifty years, many cities with populations over 100,000 have shrunk by at least 10 percent. The

American mortgage crisis and international recession that started in 2007 has made cities shrink

at an even faster rate (Hollander, et al., 2009). This has resulted in a large amount of property

abandonment which has contributed to increased crime and has forced local economies into

decline. Furthermore, “vacant and abandoned properties also pose fiscal challenges of property

maintenance and management while a dwindling tax base caused by the loss of residents and

businesses makes it extremely difficult to address the increasing social and service needs of the

remaining population (Logan & Schilling, 2008).”

Planners now need to embrace the opportunity to re-imagine shrinking cities and their

development (Hollander, et al., 2009). Shrinkage can provide fertile ground for citywide

greening strategies. Improved green energy technologies can help cities transition to a green

economy by converting their vacant and abandoned properties into new economic opportunities.

The implementation of these greening strategies can help to revitalize urban environments,

generate revenue, create jobs, and stabilize local economies. “Transforming our green

infrastructure vision into action will require planners and policymakers to revamp old

approaches, craft innovative programs, and test new models (Logan & Schilling, 2008).”

Although attempts to attain energy self-reliance in the United States at the country level

have been widely discussed, it should first be explored at a community level. Cities occupy less

than 3 percent of the earth’s land surface, consume 75 percent of total global energy, and

produce 80 percent of all greenhouse gas emissions (Grewal & Grewal, 2012). To develop green

economies and self-reliance, cites first need to develop local policies and plans that are tailored

to meet the needs of the community within its own resource base. “When applied to energy, the

concept is that a community should consider local geography and natural resource availability

when proposing solutions to meet its energy demands (Grewal & Grewal, 2012).”

Solar Panels in Ohio

Ohio law contains an alternative energy portfolio standard that requires that 25 percent of

electricity sold by Ohio’s electric distribution utilities or electric service companies must be

generated from alternative energy sources by 2025. One half of these energy facilities must be

located in Ohio. A minimum of 12.5 percent of this energy must come from renewable energy

sources such as solar, wind, biomass, and hydropower. A minimum of .5 percent must come

from solar resources (Public Utilities Commission of Ohio, 2014).

Solar panel prices have been drastically reduced over the last 25 years due to

technological advancement. Competitive labor rates from solar companies have driven the cost

of labor down as well (Solar Energy Industries Association, 2013).

Although many people think Ohio would not be an ideal place, the potential for solar

energy use is promising. Ohio averages four to five peak sun hours daily. This accounts for the

varying weather patterns that occur in Ohio throughout the year. With this level of sun energy,

the state gets approximately 60 percent of the energy of Arizona and an estimated 40 percent

more energy than Germany, which is one of the world’s leaders in solar energy production. Even

when there isn’t full sun available, solar panels still generate electricity. Germany is a great

example of this. The country is one of the cloudiest nations in the world yet it still produces 1

percent of its total electricity through solar energy (Public Utilities Commission of Ohio, 2014).

In Ohio, solar power is now a viable option versus fossil fuels due to the decrease in price

of solar panels and installation, improvement in panel technology and efficiency, and promising

potential for solar energy. Shrinking cities in Ohio with a surplus of vacant land have a unique

opportunity to help meet and even exceed the 2025 goal of .5 percent solar energy for the state.

Therefore, there is a need to perform detailed and reliable estimation of harvestable solar

radiation in Ohio’s shrinking cities. This study explores modeling solar power potential on a

vacant lot in Cleveland, Ohio through use of a geographic information system.

Methods:

Data Collection:

Many vacant homes are being demolished in Cleveland, but often vegetation and other

structures still exist around them. Solar power estimation can be done using a digital elevation

model (DEM), but that would only analyze the bare ground. In order to analyze vacant lots in

Cleveland that have surrounding

structures and vegetation that

may cast shadows on solar

panels, a digital surface model

(DSM) is needed. In this study a

DSM was created from Light

Detection and Ranging (LiDAR)

data that was obtained from the

Ohio Geographically Referenced

Information Program (Figure 2).

LiDAR is an optical remote-

sensing technique that uses laser light to densely sample the Figure 2. LiDAR point data from OGRIP.

surface of the earth, producing highly accurate x, y, and z measurements (ArcGIS 10.1 Help,

2013).

A shapefile containing vacant homes and lots in Cleveland, OH was obtained from the

Cleveland City Planning Commission. To showcase the usefulness of a DSM, a vacant parcel

with vegetation surrounding it was chosen as the input for this analysis. Parcel 12428008

(located at 41.482602, -81.635475) is a 3.23 acre lot located on the east side of Cleveland on

East 75th

Street (Figure 3). The parcel is owned by the Cleveland Land Bank.

The following steps found in Iler (2012) were used to convert raw LiDAR point data into

a DSM raster for input in the Area Solar Radiation tool in ArcMap:

1. LiDAR point data was converted to a Triangulated Irregular Network (TIN) using the LAS to

TIN tool found in the 3D Analyst toolset (Figure 4). A TIN model is used to create a 3D surface

out of individual LiDAR points by connecting the edges (lines) to generate a triangle network

(ArcGIS 10.1 Help, 2013).

2. The TIN to Raster function in the 3D analyst toolset was used to create the DSM for the

analysis. In the conversion process natural neighbor interpolation method was used and the DSM

was given a cell size of 1 to produce a detailed surface. The result was a raster with elevation

values for bare ground, trees, and other objects (Figure 5).

Figure 3. Parcel 12428008 imagery.

Figure 4. TIN for parcel 12428008.

Analysis:

The DSM that was created from LiDAR data is the input for the Area Solar Radiation tool in

ArcMap. The DSM is georeferenced in the Ohio State Plane North Coordinate system, so the

tool knows what latitude to calculate for.

Modeling Incoming Solar Radiation:

Before moving on to analyze the DSM for area solar radiation, to better understand the

process behind solar radiation estimation a paraphrased explanation and images from ArcGIS

10.1 Help has been included below.

Incoming solar radiation originates from the sun and is

intercepted at the earth's surface as direct, diffuse, and

reflected components (Figure 6). Direct radiation is an

unimpeded line from the sun. Diffuse radiation is scattered

by atmospheric constituents, such as clouds and dust.

Reflected radiation is reflected from surface features. The

sum of the direct, diffuse, and reflected radiation is called

total or global solar radiation.

The Area Solar Radiation tool in ArcMap is used to

calculate the insolation across an entire landscape. The tool

does not include the reflected component because it is such

a small percentage of the total isolation value. The

calculation of area solar radiation includes four steps.

1. The calculation of an upward-looking hemispherical viewshed based on topography:

The viewshed is a raster representation of the entire sky that is visible or obstructed when viewed

from a particular location. A viewshed is calculated by searching in a specified number of

directions around a location of interest and determining the maximum angle of sky obstruction,

or horizon angle. Figure 7 depicts the calculation of a viewshed for one cell. Horizon angles are

calculated along a specified number of directions and used to create a hemispherical

representation of the sky. The resultant viewshed characterizes whether sky directions are visible

Figure 6. Solar Radiation. ArcGis 10.1 Help, 2013.

Figure 5. DSM for parcel 12428008.

(shown in white) or obstructed (shown in gray). The viewshed is shown overlaid on a

hemispherical photograph to demonstrate the theory.

2. Overlay of the viewshed on a direct sunmap to estimate direct radiation:

The direct solar radiation originating from each sky direction is calculated using a sunmap in the

same hemispherical projection as the viewshed. A sunmap displays the sun track, or apparent

position of the sun as it varies through the hours of day and through the days of the year. The

sunmap consists of sectors defined by the sun's position at particular intervals during the day

(hours) and time of year (days or months). The sun track is calculated based on the latitude of the

study area and the time configuration that defines sunmap sectors. For each sunmap sector, a

unique identification value is specified, along with its centroid

zenith and azimuth angle. The solar radiation originating from

each sector is calculated separately, and the viewshed is

overlaid on the sunmap for calculation of direct radiation.

Figure 8 shows a sunmap for 45º N latitude calculated from

the winter solstice (December 21) to summer solstice (June

21). Each sun sector (colored box) represents the sun's

position using 1/2 hour intervals through the day and monthly

intervals through the year. The image is in the same

hemispherical projection as the upward-looking viewsheds.

The position of the sun is represented as it moves across the

sky during the time of day and time of year.

3. Overlay of the viewshed on a diffuse skymap to estimate diffuse radiation:

To calculate diffuse radiation for a particular location, a

skymap is created to represent a hemispherical view of the

entire sky divided into a series of sky sectors defined by

zenith and azimuth angles. Each sector is assigned a unique

identifier value, along with the centroid zenith and azimuth

angles. Diffuse radiation is calculated for each sky sector

based on direction (zenith and azimuth). Figure 9 is a

skymap with sky sectors defined by 8 zenith divisions and

16 azimuth divisions. Each color represents a unique sky

sector, or portion of the sky, from which diffuse radiation

originates.

Figure 9. Skymap. ArcGis 10.1 Help, 2013.

Figure 8. Sunmap. ArcGis 10.1 Help, 2013.

Figure 7. Viewshed calculation. ArcGis 10.1 Help, 2013.

4. Repeating the process for every location of interest to produce an insolation map:

During the insolation calculation, the viewshed raster is overlaid with the sunmap and skymap

rasters to calculate diffuse and direct radiation received from each sky direction. The proportion

of visible sky area in each sector is calculated by dividing the number of unobstructed cells by

the total number of cells in each sector. Allowance is made for partially obstructed sky sectors.

Figure 10 illustrates the overlay of a viewshed on a sunmap and a skymap. Solar radiation is

calculated by summing direct and diffuse insolation for each cell.

The time configuration for the tool can be set for one day, multiple days, special days

(summer solstice, equinox, or winter solstice), or for a whole year. For this study the whole year

of 2013 was used. Default values were used for the topographic and radiation parameters to

simulate a generally clear sky. After running the Area Solar Radiation of the area of interest all

values were summed using the Zonal Statistics tool in ArcMap.

Results:

The total output measurement is

in watt-hours per meter squared

(Wh/m2). To do further

calculations, conversion to

kilowatt hours (kWh) is necessary

because it is the common

measurement used by energy

companies when electricity is

sold. To convert, the sum of all

cell values (see Figure 11) was

divided by 1000. The estimated

total area solar radiation for the

year of 2013 was 153,192,000 kWh. However, not all of this energy can be harvested. Solar

panels on the market today only capture between 10 and 20 percent of incoming solar radiation.

This was taken into consideration by calculating harvestable kWh for 10, 15, and 20 percent

efficient panels (Table 1).

Figure 10.Overlay of viewshed on direct and diffuse calculations. ArcGis 10.1 Help, 2013.

Figure 11. Area solar radiation and sum of all values for the year of 2013 on parcel 12428008.

The average yearly electricity use per home in Ohio is about 10,200 kWh (Public Utilities

Commission of Ohio, 2014). To see how many homes that this particular vacant lot could

potentially power for the year of 2013, the harvestable kWh were divided by the average

electricity use per home in Ohio (Table 1).

The average price of electricity in Cleveland for the year of 2013 was $0.12 (Public Utilities

Commission of Ohio, 2014). To estimate potential revenue that the solar panels on this lot could

generate, the homes powered were multiplied by 10,200 kWh, and then multiplied by the

average cost of electricity per kWh for the year (Table 1).

Solar panels typically have a lost energy value in between 23 and 31 percent due to snow, dust,

wire losses, inverter losses, and temperature losses (Wisconsin Department of Energy). To

account for this, a value of 27 percent was calculated (Table 2).

Table 2. Homes powered and revenue potential by panel efficiency with

27% losses

Panel Efficiency 20% 15% 10%

Homes powered 2193 1645 1096

Potential revenue $2,683,924 $2,012,943 $1,341,962

The results above assume that the entire 3.23 acre lot can be covered with solar panels. This is

very unlikely due to the orientation of panels. A buffer zone with a fence and foliage would be

necessary to mask the view from surrounding areas and to keep thieves out, which would result

in a loss of useable area as well. More realistically, one half of the land would be utilized (Table

3).

Table 3. 50% of values in table 2 to project values for half of land used

Panel Efficiency 20% 15% 10%

Homes powered 1096 883 548

Potential revenue $1,341,962 $1,080,792 $670,981

The above values are all from just one of many vacant properties in this particular neighborhood

(Figure 12).

Table 1. Harvestable kWh, homes powered, and potential revenue by

panel efficiency

Panel Efficiency 20% 15% 10%

Harvestable kWh 30,638,400 22,978,800 15,319,200

Homes powered 3004 2253 1502

Potential revenue $3,676,608 $2,757,456 $1,838,304

Discussion:

Default values were used in the Area Solar Radiation tool in ArcMap for the amount of

radiation that passes through the atmosphere (transmittivity), and the proportion of normal

radiation flux that is diffuse. These defaults assert that sky is generally clear on most days.

Historical weather data could be analyzed to find a more accurate input for those radiation

parameters.

Data for the initial cost and maintenance of solar panels for a project of this size was not

readily available. Utilizing solar energy does require an initial investment that is generally not

recouped for a number of years. In order for widespread use of solar power in Cleveland to work,

money would have to be borrowed or granted to finance the initial investment. Leasing or

selling land to private investors and electric providers are avenues that should be explored as

well.

The LiDAR data, although suitable for this study, is from 2006. Current LiDAR data

would be needed for an accurate large scale analysis as the landscape is constantly changing in

large cities. In shrinking cities such as Cleveland, buildings are being demolished by the day due

to abandonment and vacancy. Therefore, the vacant parcel data acquired for this study was out of

date the day after it was received. Due to the rapid economic, social, and physical changes that

have taken place in Cleveland, there is an urgent need for timely and accurate geographic data

collection and verification. Planners require reliable data to implement viable citywide greening

initiatives that will lead to revitalization. Unfortunately, with tightened budgets due to lost tax

revenue, shrinking cities do not have the funds or personnel needed to collect and process data

for implementation of plans that will lead to revitalization. Grants from the state and federal

government should be made available to shrinking cities to accelerate recovery through viable

citywide greening initiatives.

Conclusion:

Shrinking cities in Ohio with a surplus of vacant land now have a unique opportunity to help

combat climate change by becoming producers of green energy. Planners need to be open to

Figure 12. Neighborhood vacancies. Parcel 12428008 shown in green. Source: Cleveland City Planning Commission.

implementing citywide greening strategies that can bring economic revitalization and stability.

This study has shown that solar power is now a viable financial option in the state of Ohio that

can help to combat climate change, and could be useful to help revitalize shrinking cities.

Through use of a geographic information system detailed models of solar radiation can be

produced to estimate solar power generation. Estimated revenue from solar power can be derived

from those models as well.

Further research should focus on ways to improve the solar power estimation process by

analyzing historical weather data to create an accurate input for the radiation parameters. Further

research should also explore creative ways to finance green energy in shrinking cities.

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