Cost­Benefit Analysis of Photovoltaic Solar Energy Use in an “Average” US Commercial Building

Elvin Bora Wu Hao

Nathan Mariano Deniz Ozensoy Eduardo Zulueta

Abstract

The potentially drastic effects of climate change call for reduction of greenhouse gas emissions, a major source of which is energy consumption by buildings in the commercial sector. Solar energy is an especially promising renewable energy source for such buildings. Previous analyses have attempted to assess the economic viability of solar energy for buildings on a large regional scale or have looked at the potential benefits for specific buildings or neighborhoods; few have addressed the cost/benefit profile presented to an “average” building owner considering installation of solar panels. In this analysis, we compare the costs of solar powered energy to the costs of traditionally sourced energy for a building over a 28­year period. The results show that at current prices and rates of energy usage, solar panels are marginally zmore beneficial than traditional sources of the extended time period, suggesting that new building owners should seriously consider installation of solar panels in their individual cases. This study presents valuable insight into the economic situation facing individual building owners and by extension, the commercial building sector of the United States as a whole.

1. Introduction

Climate change resulting from anthropogenic greenhouse gas (GHG) emissions is widely

agreed to be one of the most severe problems facing mankind (IPCC, 2014). If global

temperatures increase by more than 2 degrees Celsius above pre­Industrial level (a mostly

agreed­upon limit for the maximum acceptable increase; Priddle, 2014), the consequences could

be devastating­­potentially including species going extinct, coastal cities underwater, and

increasingly severe and frequent extreme weather events (storms, drought, wildfires, etc.).

Greenhouse gas emissions have continued to increase despite of international policy efforts to

curb such emissions, such as the Kyoto Protocol (Olivier et al., 2014). The energy sector

represents roughly two­thirds of all GHG emissions worldwide (Priddle, 2014), and within the

United States, 41% of total energy consumption was in residential and commercial buildings in

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2014 (“How much energy…”, 2015). Thus, improving the energy efficiency of buildings could

potentially yield significant reductions in GHG emissions.

Solar photovoltaic (PV) technology is perhaps the best renewable energy technology with

which this could be achieved. It has the least negative impact on the environment (Solangi et al.,

2011) and has a number of other advantages, such as silent energy generation, avoidance of

transmission losses due to the generation of energy at the place of usage, and lower costs when

compared to other renewable energy methods (Brito et al., 2011). Given its modular nature, it is

also uniquely suited to integration with building construction.

There is a robust literature that has explored the potential for use of solar panels to

generate energy for buildings. Hestnes (2000) was perhaps the first to provide a comprehensive

look at the possibilities, whereas Henning (2007) assessed the feasibility of using solar panels for

air conditioning specifically. A number of papers have used 2D modeling (Brito, 2010;

Winginton, 2010) and 3D modeling (Santos et al., 2014; Redweik, Catita, and Brito, 2013; Catita

et al., 2014) to assess the potential energy yield of specific buildings and neighborhoods with

installation of solar panels. However, in focusing only on where solar panel installation would be

most efficient, these studies have tended to neglect the cost­benefit aspect of the problem, instead

assuming that solar is a worthwhile investment and attempting to develop methods to identify the

places where it would be most effective. Even if a place is particularly suited for solar energy,

the panels would only actually be installed if the they were more economically efficient than the

alternative options (assuming that building owners would not install the panels for altruistic

reasons). For a long time, installation costs for PV solar panels were prohibitively high, leading

to limited implementation, but improvements in technology and implementation of government

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incentive policies mean that the question of cost­effectiveness of solar panels is one that can

legitimately be addressed.

On this subject, there is also a fairly robust literature. Authors have performed

cost­benefit analyses of PV energy generation when used in a number of settings, including Italy,

Spain, India, Canada, island nations, and desert climates (Orioli and Di Gangi, 2013;

Fernández­Membrive, Lastra­Barro, and Tolón­Becerra, 2015; Kohle, Kohle, and Joshi, 2002;

Branker, Pathak, and Pearce, 2011; Kaldellis et al., 2008; Shaahid and Elhadidy, 2006). Different

authors have approached the problem of cost­benefit analyses of PV from various angles, with

some focusing on the effects of a state’s policy incentives on the profitability of solar systems

(Spertino, Di Leo, and Cocina, 2013; Fernández­Membrive, Lastra­Barro, and Tolón­Becerra,

2015), others assessing the costs and benefits of specific variants of PV technology (Azzopardi et

al., 2011; Emmott, Urbina, and Nelson, 2011; Roes et al., 2009; Powell et al., 2012), and others

comparing costs between different regions (Mateus and Oliveira, 2008; Peters et al., 2011). The

level of analysis also varies from case studies of specific buildings (Zhai et al., 2006; Desideri,

Proietti, and Sdringola, 2009) to assessments of the potential of PV systems to service entire

regions (Fthenakis, Mason, and Zweibel, 2008; Branker, Pathak, and Pearce, 2011).

Yet, upon review, it emerges that few studies have attempted to draw general conclusions

about the economic viability of solar PV from the perspective of owners of individual buildings

in a given region. As mentioned, studies have tended to neglect the cost­benefit analysis by

looking only at ways to model the potential energy yield of a given building, assessed the

viability of solar PV at a regional level, or have performed cost­benefit analyses on specific

buildings, not attempting to draw general conclusions. Furthermore, the studies that have

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attempted to assess whether solar panels installed on individual buildings could prove

cost­effective for owners in a specific region (Orioli and Di Gangi, 2013; Ramadhan and Naseeb,

2010; Mateus and Oliveira, 2008; Ren et al., 2009; Li, Boyle and Reynolds, 2011) have not

looked at the case of the United States. Orioli and Di Gangi (2013) comprehensively assess the

economic viability of solar PV in a southern Italian city, whereas Ramadhan and Naseeb (2010)

take a similar approach for Kuwait. Ren, Gao, and Ruan (2008) and Li, Boyle, and Reynolds

(2011) analyze the economic viability of solar PV installed in residential properties in Japan and

Ireland, respectively. Martinez­Cesena, Azzopardi, and Mutale (2012) propose a general

methodology for assessment of domestic PV systems and use the United Kingdom as their case

study. Chong et al. (2011) assess the viability wind­solar hybrids in urban high­rises around the

world, while Mateus and Oliveira (2008) compare PV economics in buildings for Rome, Berlin,

and Lisbon.

In this study, we attempt to address this problem by performing a cost­benefit analysis of

a solar PV system installed on a commercial building in the United States. Using national­level

projections of solar PV costs and energy prices, we assess the economic viability of a

commercial building using solar panels as its energy source over a 28­year period. This analysis

is intended to provide broader insights into the status of solar PV as a potential energy source for

buildings in the US than a study of a particular building or neighborhood would provide. Our

analysis suggests that for the average building in the United States, solar PV prices are now low

enough that solar constitutes a viable long­term investment. For individual cases, this means that

buildings in conditions favorable to solar panels (low energy usage, high irradiation, etc.) should

find solar PV to be an especially attractive option.

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The first section outlines the methodology of the cost­benefit analysis and the data used.

Then, we discuss the results and discuss potential costs and benefits that were not considered in

our model. Finally, we examine policy implications and draw conclusions.

2. Methodology and Data

To assess the economic viability of solar cell PV technology over the lifetime of

commercial sector buildings, we conducted a cost­benefit analysis on an average commercial

building having its energy provided by solar panels. This entailed calculating the total costs

incurred through installation and maintenance of a solar energy system over a given time period

and comparing these costs to those of energy provided by traditional fossil fuel sources. The net

result (benefits minus costs), which can be negative, yields the results for an individual building.

Even though the median lifespan of a building in the commercial sector is between 50

and 60 years (D&R International, Ltd, 2012), we have limited the time span of our analysis to 28

years due to data restrictions. Commercial sector buildings are any building that provides

services in education, food sales, food service, health care, lodging, mercantile, office space,

public assembly, public order and safety, religious worship, warehouse and storage, as well as

other service (besides food and retail) (D&R International, Ltd, 2012). In our analysis of the

average agent, we use the median size for a building in the commercial sector: 2500 sq feet

(D&R International, Ltd, 2012). To calculate the building’s energy consumption, we use the

commercial sector average energy consumption per square foot as projected by the Annual

Energy Outlook 2015. We also assume that the energy consumption of the average building in

the commercial sector lasts for 12 uninterrupted hours per day. We use this assumption to

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calculate the rate at which energy is consumed per day (our data provides us with absolute

quantities of energy consumed annually), which allows us to estimate the approximate amount of

photovoltaic cells that need to be installed to provide the necessary flow of energy. Given the

amount of energy consumed per day, we estimate the price of generating this energy through

solar power with pricing data from the US Department of Energy SunShot program, which

include yearly projections over the period of analysis. These data account for nationwide solar

prices, which includes regional variations in financial incentives and local market prices. A more

refined study would also consider annual fluctuations in day light received by the photovoltaic

cells based on season and location to more precisely pinpoint the production capacity required by

the photovoltaic cell farm.

The data consist of projections through 2040 of energy prices for the commercial

sector(coal, natural gas, distilled fuel, electricity), commercial sector energy usage information,

and carbon emissions provided by the Annual Energy Outlook 2015, which is published by the

United States Energy Information Administration (U.S Energy Information Association, 2015).

We also use data provided by the US Department of Energy SunShot program to provide

estimations of current and future prices of PV solar systems. Finally, we employ a technical

support document on the Social Cost of Carbon for Regulatory Impact Analysis 2013 to calculate

commercial sector wide impact of carbon emissions on the US economy (Interagency Working

Group on Social Cost of Carbon, United States Government, 2013). Since all prices were

reported in 2013 dollars in the Annual Energy Outlook 2015 report, we also report our findings

in 2013 dollars. Where prices were stated in a different year’s values, we converted the prices to

their 2013 equivalents using actual inflation values.

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There are a number of assumptions made for the analysis. First, we assume that PV

module costs are going to stabilize in the near future, as predicted by the SunShot program

(Feldman, et al., 2014). Unless soft costs associated with installation of PV systems such as labor

costs change significantly, the overall price of PV systems will also be stable, which is what we

ultimately assume. We also assumed that the PV cells will have to be renewed every 20 years (an

expectation below industry expectation), and will produce 1% less energy per year (Center for

Alternative Technology, n.d.). Furthermore, we assumed that the current PV cell technology is

advanced enough to provide the energy demands of any new building in the commercial sector.

This assumption is justified by the wide variety of “zero energy” buildings that already meet all

their energy needs from renewable sources, mainly solar power. Examples of such buildings in

the United States are the Environmental Tech Center at Sonoma State University and the Adam

Joseph Lewis Centre for Environmental Studies at the Oberlin College (U.S. Department of

Energy Energy Efficiency and Renewable Energy, 2008). The Sun Ship is another prominent

example of a zero energy commercial building, located in Freiburg, Germany (Heinze & Voss,

2009).

It is debatable whether an already­constructed building could be retrofitted to be

completely solar powered, given that a significant portion of energy savings in the zero energy

buildings are achieved by using thicker windows and otherwise better isolation that reduces heat

loss. However, we do not consider retrofitting costs, which are difficult to specify on average.

We are also primarily concerned with new building construction. Similarly, we do not compare

any differences in construction expenses that might be necessary for zero energy buildings, such

as improved insulation (Hernandez & Kenny, 2009). The primary intent is to focus on how solar

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energy costs compared to fossil fuel costs; thus, the analysis would be unnecessarily complicated

by consideration of other energy­saving measures that could be used in conjunction with either

energy source.

The saving reported are not discounted for factors such as how much a given agent

discounts future expenditures. Furthermore, since there are no reliable projections for inflation

over the middle­ to long­term time future, we assume that the economy performs close to the

FED goal of 2% inflation per year in calculating the projected value of savings and costs (Board

of Governors of the Federal Reserve System, 2015).

4. Results

Our cost­benefit analysis of the average agent presented on the appendix shows that a

median sized commercial building that operates at the sector average energy intensity cuts even

in terms of lifetime operational energy expenditure. In other words, theoretically powering a

typical commercial building solely using distributed photovoltaic solar power costs roughly the

same as powering that building with traditional sources of energy. However, this does not

include the economic value of the reduced carbon emissions achieved by the agent. Some plots

describing our findings are presented below; please refer to Appendix 1 for the full results.

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Chart 1: Total Yearly Costs of an Average Commercial Building Depending on Source of Energy

Chart 2: Cumulative Costs of Traditional Sources of Energy v Photovoltaic Energy

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Chart 3: Comparison of the Cumulative Present Value of the Cost of Traditional Sources of Energy v

Photovoltaic Energy

5. Discussion

Our data shows that under our group assumptions, installation of PV solar panels is

economically advisable by a small margin to the average building owner when solar costs are

compared to traditional fossil fuel costs. While this obviously does not mean that PV solar is

economically viable for every new building being constructed in the United States, it does imply

that most agents should at least consider the option when making decisions about their energy

sources. Buildings constructed in conditions favorable to solar panels (low energy usage, high

irradiation, etc.) should seriously consider installation, since it is likely to be economically

advantageous to them. Individuals who derive utility from contributions to environment could

find a positive benefit from using solar panels, even if it is actually economically

disadvantageous for their case.

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On this subject, there are a number of potentially relevant factors that do not enter into

our cost­benefit analysis. If we extrapolate this finding to the commercial sector, this implies that

solar panels could be tremendously advantageous for the sector as a whole. On a scale this large,

installation of solar panels could yield significant non­economic benefits in the form of avoided

carbon costs. A private owner of an individual building is unlikely to internalize these benefits in

their cost­benefit calculation since they are of a public nature, but they are definitely relevant to

consider when looking at potential adoption of solar panels on a large scale. Solar energy also

avoids additional GHG emissions produced in the transferral of fossil fuel energy from source to

destination. The data show that almost twice the amount of traditionally sourced electrical energy

provided to the commercial sector is lost in the transfer of the energy from the source to its

destination (U.S Energy Information Association, 2015). We calculated the potential economic

cost of continued carbon emissions by the commercial building sector in terms of dollars per

metric ton, using the emissions data from the 2015 Annual Energy Outlook (Interagency

Working Group on Social Cost of Carbon, United States Government, 2013; see Appendix 2 for

calculations). The analysis showed carbon emissions would cause $932 billion dollars in

damages to the economy nation wide over the 28 year period of analysis. If we were to include

this estimated cost under the savings associated with transitioning to photovoltaic cell

technology, then the nationwide cost of using PV cells to supply energy is only 77% of the

projected energy costs with the current composition of the energy market.

Furthermore, installers of solar panels benefit from improved property values, since

future owners would benefit from the avoided costs of traditionally sourced energy. Thus, the

owner could potentially recuperate some of the costs of installations if the building were to be

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sold. Using solar as a building’s source of energy also reduces dependence on volatile fuel

prices, which fluctuate greatly from year to year and impact the profitability of the building. In

this way, solar energy reduces future uncertainty. Lastly, in many states, business owners are

allowed to apply accelerated depreciation accounting to their solar power investments,

effectively resulting in additional tax credits.

However, there are several reasons why rates of adoption of solar panels are currently

low and may continue to be low into the near future. These reasons can also be thought of as

costs that are not considered by in our cost­benefit analysis. Though we have showed that the

investment in solar panels is marginally beneficial over a 28 year period, discounting of future

benefits in favor of avoiding present costs might mean that agents would be unwilling to incur

the large up­front costs necessary for installation of solar panels. The relatively small, but

ongoing payments incurred by fossil fuel energy might be more appealing, even if they are

higher than the costs of solar panels in the long­term. This can be alleviated by the increasing the

availability of payment schemes that distributes over time the installation costs. This can likely

be accomplished by the market through the agency of financial intermediaries such as banks.

However, given the substantial benefit to the US economy as large, a central solution with active

involvement of government agencies to encourage the adoption of PV technologies should be

strongly considered.

Furthermore, over the last couple years, oil prices have surprisingly plummeted. This

was due to technological developments such as fracking, making previously unreachable or

otherwise uneconomical to excavate oil sources economically feasible to exploit, and the recent

softening of US policies that hindered the export of US oil. As a result of these changes­­one

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technological, the other policy­driven­­international supply of oil suddenly increased and prices

crashed. Even though the model used by the EIA in the Annual Energy Outlook 2015 expects oil

prices to recover immediately and increase a great deal before 2040, this may not actually occur,

given that oil prices are still falling. Furthermore, technological innovations that replace, or

increase the efficiency with which we use fossil fuels may also keep the downward pressure on

fossil fuel prices. In the case that fossil fuel prices do not increase as estimated, then energy

prices will be lower than the model estimates, and PV cells would be relatively costlier.

Path dependence could also play a role. This refers to the idea that it is less costly to for

an individual to continue with the status quo than it is to acquire the information and resources

necessary to implement a new system­­in this case, PV solar panels for one’s building. Though

these are one­time costs, when combined with the large upfront economic cost of solar panels, it

can make for a very costly transition.

6. Policy Implications

Some of the costs impeding adoption of solar panels could potentially be offset by quality

policymaking. There are a number of potential policy options that the US government has

available to encourage investment in commercial solar PV systems. In this section, we focus on

three policy areas that directly affect the total cost of commercial PV systems and the impact of

high start­up costs on the solar market.

One of the most important issues surrounding distributed solar in the United States is the

regulatory support of the Third Party PPA model of solar panel ownership and use. This

ownership model allows building­owners to purchase electricity at prices lower than retail levels

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while avoiding the large initial capital expenditures needed to buy and install the solar panels

(Solar Power Purchase Agreements, 2011). The model is set up as follows: a building owner

interested in utilizing roof space to generate solar powered electricity, but who does not want to

spend the large upfront costs needed to install the panels, will sign a power purchasing

agreement with a solar project developer who builds, owns, and operates the solar panels on the

customer’s building. The developer would then sell the electricity back to the customer at the

rate stipulated by the PPA, which is generally at or lower than retail prices (Solar Power

Purchase Agreements, 2011).

PPA contracts address one of the largest barriers to entry associated with commercial

solar PV system investments: high start­up costs. As our cost­benefit analysis illustrated, the

high cost of buying the solar panels and installing them vastly outweighs the costs of maintaining

the equipment. Thus, while our calculated present value of the total cost of a PV system is

approximately equal to that of a typical retail­powered building, buying typical retail power

offers the additional advantage of spreading out costs over time and may thus disincentivize

potential investors who do not have access to cheaper sources of capital. By taking on a PPA

agreement, building owners do not have to deal with the huge start­up costs because they would

essentially replicate the cost of energy they would be paying per year if they chose to pay for

typical retail power. The payments that the end­user (building­owner) would make are typically

less than or equal to typical retail rates with slight price increases related to inflation and

decreases in the efficiency of the PV equipment. Thus, in a typical PPA contract made today, the

building owner would be paying roughly similar amounts to the solar power provider, as they

would have paid to typical utilities.

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As of 2010, 25 states have imposed the same regulations on third party developers of

solar panels as on utility companies, effectively making PPA contracts unprofitable due to the

increased administration costs associated with the regulations. (Kollins, Speer, and Cory, 2010).

Although running a thorough cost­benefit analysis of deregulating PPA contracts is outside the

scope of our study, state governments who have currently not deregulated the businesses of solar

power third party developers should strongly consider doing so because of the tremendous

start­up costs related to illustrated by our cost­benefit analysis. As solar power has reached

similar cost levels to grid­backed energy over a long­term investment, the most pressing concern

for potential investors is no longer overall lifetime cost but solar PV’s high start­up costs, and

policies that may spread out lifetime investment costs should be supported.

Another key policy issue surrounding distributed generation is the mechanism by which

commercial and residential owners of PV systems can sell electricity generated by panels back to

utility providers. The main systems currently in place around the world are feed­in­tariffs (FIT)

and net metering. In a net metering system, which is the main system in place in the USA, an

electric meter records the excess electricity produced by the PV systems. If the amount of PV

generation is larger than consumption at the end of a billing period, the household or building

owner is paid for the excess PV­generated electricity at retail electricity prices or with an

equivalent credit. If the amount of electricity consumed is higher than amount of electricity

generated, then the building owner is billed at retail prices (“Net Metering”, 2011).

In a feed­in tariff system, the government mandates electric utilities and grid companies

to purchase all electricity produced by PV system owners at a set rate. The building owner then

purchases all of the electricity they consume at standard electricity rates, which are lower than

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the rates at which the electricity was originally sold (Yamamoto, 2012). The difference between

these two rates yields profits for solar energy producers (subsidized by the utilities companies),

with further profits made if the building produces more electricity than it consumes.

Feed­in­tariff systems have not caught on in the US, with only 6 states having adopted FIT

policies as of 2013, but are the most widely used renewable energy policy in the world (“Feed­in

Tariffs”, 2014).

Governments considering a change in their metering systems should understand how

these policies affect the energy costs of commercial PV system owners. Thus, we have modeled

out the net energy costs of a commercial building owner under a net metering system vs. those

costs under a FIT system. The graph is shown below:

Chart 4: Comparison of net Energy Cost of PV with Net Metering policy, Feed in Tariffs policy, and market prices

The costs under a net metering system are identical to the previous graph showing the

cost of PV because in our model, we assume that the building produces as much energy as it

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consumes; if it produced excess energy, the building would accrue profits. A feed­in tariff results

in gradually declining energy costs as the building owner profits by selling their energy to utility

owners at above­retail prices. Here, we modeled a FIT that results in payments of 10% above the

retail electricity price, leading to a lower cost of solar energy over a 28­year period based on

current prices versus retail prices. As we see by the gradual decline of net energy costs, this

system is best understood as a subsidy incentivizing production once the system is installed. The

main drawback with FIT versus other policies is that they do not directly address high start­up

costs. High start­up costs could be argued to be more pressing than merely reducing the total net

energy cost over the life of the investment, especially now, since current systems under net

metering are already just as cheap on average as retail energy payments. On the other hand, FIT

schemes encourage cheaper financing and so indirectly deal with high start­up costs as well.

The final policy we discuss in this section is the 30% investment tax credit that is

scheduled to reduce to 10% after 2016. Since no subsidies are incorporated into the original

analysis, the orange line on the previous graph represents the costs associated with a solar PV

system without the tax credit. Below, we show the effect of an extended 30% tax credit,

assuming that the installation costs all fall under property eligible for the tax credit:

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Chart 5: Comparison of Net Energy costs w/ITC

We see that the 30% tax credit, as a stand­in effectively for all direct rebates, reduces the

time it takes for the PV of typical retail power costs to surpass the PV of photovoltaic power

more than the feed­in­tariff because it directly addresses high start­up costs. Additionally, a

direct rebate may encourage investment even more than the deregulation of PPA contracts

because building owners still receive all the additional benefits that come with owning PV

systems outright (including increasing property values, accelerated depreciation benefits, and

others).

However, the scope of this project does not include an in­depth analysis of which policies

should be prioritized, and so one should not conclude that rebates should absolutely be the

number one priority for governments. Indeed, there are other factors involved that have made

feed­in­tariffs the first priority and most effective policy in Europe above rebates. One of these

factors may be that retail electricity prices in Europe are on average much higher than in the US,

so more costs are saved under a FIT system than with rebates. The prioritization of policies is

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also heavily dependent based on what type of commercial buildings is being considered

(high­energy vs. low­energy) and the retail prices in each state.

7. Conclusion

Current retail energy prices in the US and the record­low cost of solar PV power have

made it so that PV power is a realistic option for many commercial building owners even without

large subsidies or feed­in­tariffs. This information suggests that the most pressing matter to

commercial solar investors is the large start­up costs associated with these investments, and that

an extension of the ITC and deregulation of PPA contracts may prove helpful with this issue.

However, in states with low retail electricity prices, feed­in­tariffs may be necessary to further

promote investment in solar energy nationwide. Studies that should be considered to expound

upon our findings include: analyses of investors’ prioritization of start­up costs vs. total cost over

the life of a PV system, the potential negative effects of deregulating PPA contracts, and what

specific areas in the US would most benefit from a feed­in­tariff based on retail energy prices.

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Page 28

APPENDIX 1: Cost-Benefit Analysis for an Average Commercial Building

TAB1:

Average Commercial Building Energy Consumption

TAB2: Commercial Sector Energy Consumption by Source

TAB3:

Average Commercial Building Energy Consumption by Source

TAB4: Cost of Energy for the Average Building

TAB5:

Average Commercial Building Lost Electricity

TAB6: Cost of PV Installation

TAB7:

Results of the Analysis

Year Delivered Energy Consumption (Thousand btu per sqft)

Total Energy Consumption (Thousand btu)

[A] [B]

2012 99.84 249,607.852013 104.88 262,205.582014 107.21 268,021.662015 104.25 260,615.752016 103.33 258,324.412017 102.66 256,649.742018 102.05 255,122.112019 101.18 252,956.622020 100.02 250,045.492021 98.88 247,204.462022 98.00 245,011.832023 97.32 243,293.312024 96.80 241,996.502025 96.32 240,790.962026 95.90 239,761.532027 95.64 239,088.562028 95.53 238,825.042029 95.50 238,757.922030 95.40 238,511.932031 95.23 238,069.212032 94.98 237,437.632033 94.71 236,780.092034 94.44 236,111.952035 94.22 235,541.762036 94.00 234,994.132037 93.78 234,445.002038 93.51 233,784.492039 93.18 232,937.872040 92.79 231,976.09

Average Commercial Building Energy Consumption

[A] The Annual Energy Outlook 2015 (AEO2015) by the U.S. Energy Information Administration (EIA), Table A5 Commercial sector key indicators and consumption. Accessible at http://www.eia.gov/oiaf/aeo/tablebrowser/. [B] = [A] * 2500sqft 2500 sqft is the size of an average commercial building, according to EIA, 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables, Oct. 2006, Table A1, p. 1-2. (http://buildingsdatabook.eren.doe.gov/TableView.aspx?table=3.2.5).

Natural Gas (quadrillion btu)

Distillate Fuel (quadrillion btu)

Electricity (quadrillion btu) Total (quadrillion btu) Natural

Gas(%)Distillate Fuel

(%) Electricity (%)

[A] [B] [C] [D] [H] [E] [F] [G] [I]2012 2.97 0.36 4.53 7.86 9.24 17.10 37.79% 4.56% 57.65% 204.12%2013 3.37 0.37 4.57 8.30 9.42 17.72 40.59% 4.41% 55.00% 206.19%2014 3.54 0.38 4.63 8.55 9.52 18.07 41.43% 4.39% 54.17% 205.64%2015 3.32 0.35 4.66 8.32 9.50 17.82 39.87% 4.15% 55.98% 204.09%2016 3.29 0.35 4.70 8.34 9.51 17.84 39.52% 4.16% 56.33% 202.44%2017 3.31 0.35 4.72 8.37 9.44 17.82 39.52% 4.13% 56.34% 200.09%2018 3.33 0.34 4.75 8.42 9.48 17.90 39.48% 4.07% 56.45% 199.43%2019 3.32 0.34 4.79 8.45 9.59 18.05 39.26% 4.03% 56.71% 200.16%2020 3.30 0.34 4.82 8.45 9.68 18.13 39.00% 3.99% 57.01% 200.81%2021 3.28 0.33 4.84 8.46 9.71 18.17 38.77% 3.96% 57.28% 200.46%2022 3.27 0.33 4.87 8.48 9.74 18.22 38.61% 3.91% 57.48% 199.85%2023 3.28 0.33 4.91 8.51 9.79 18.31 38.50% 3.85% 57.65% 199.53%2024 3.29 0.32 4.95 8.56 9.85 18.41 38.39% 3.79% 57.82% 199.11%2025 3.29 0.32 4.99 8.60 9.88 18.49 38.30% 3.73% 57.97% 198.20%2026 3.30 0.32 5.03 8.65 9.93 18.58 38.20% 3.67% 58.12% 197.55%2027 3.32 0.31 5.07 8.71 9.98 18.69 38.19% 3.61% 58.20% 197.00%2028 3.36 0.31 5.11 8.78 10.02 18.80 38.24% 3.54% 58.22% 196.21%2029 3.39 0.31 5.15 8.85 10.08 18.93 38.30% 3.47% 58.22% 195.58%2030 3.43 0.30 5.19 8.92 10.13 19.06 38.41% 3.41% 58.19% 195.19%2031 3.45 0.30 5.23 8.99 10.18 19.17 38.45% 3.35% 58.21% 194.64%2032 3.48 0.30 5.27 9.05 10.24 19.29 38.46% 3.29% 58.25% 194.32%2033 3.51 0.29 5.31 9.11 10.30 19.41 38.48% 3.23% 58.29% 193.97%2034 3.53 0.29 5.35 9.18 10.36 19.55 38.50% 3.17% 58.32% 193.58%2035 3.57 0.29 5.40 9.26 10.43 19.69 38.53% 3.12% 58.35% 193.08%2036 3.60 0.29 5.46 9.34 10.51 19.85 38.55% 3.06% 58.39% 192.61%2037 3.64 0.28 5.51 9.43 10.59 20.02 38.56% 3.00% 58.44% 192.16%2038 3.67 0.28 5.56 9.51 10.67 20.19 38.56% 2.94% 58.50% 191.81%2039 3.69 0.28 5.61 9.58 10.74 20.33 38.53% 2.89% 58.58% 191.33%2040 3.71 0.27 5.66 9.65 10.80 20.45 38.49% 2.84% 58.67% 190.82%

Commercial Delivered Energy Consumption by Source

Year

Commercial Delivered Energy Percentage by Source

Commercial Sector Energy Consumption by Source

Lost Electricity Total Energy Generated (quadrillion btu)

Lost Electricity as percentage of Delivered Electricity(%)

[A], [B], [C] The Annual Energy Outlook 2015 (AEO2015) by the U.S. Energy Information Administration (EIA), Table A5 Commercial sector key indicators and consumption. Data accessible at http://www.eia.gov/oiaf/aeo/tablebrowser/. [D] = [A] + [B] + [C] ; [E] = [A]/[D] ; [F] = [B]/[D] ; [G] = [C]/[D] ; [I] = [H]/[C]

Natural Gas(%) Distillate Fuel (%) Electricity (%) Natural Gas (million btu)

Distillate Fuel (million btu)

Electricity (million btu) Total

[A] [C] [D] [E] [F] [G] [H][B1] [B2] [B3]

2012 249,607.85 37.79% 4.56% 57.65% 94.33 11.38 143.89 249.61 204.12% 293.722013 262,205.58 40.59% 4.41% 55.00% 106.43 11.57 144.20 262.21 206.19% 297.342014 268,021.66 41.43% 4.39% 54.17% 111.05 11.78 145.20 268.02 205.64% 298.582015 260,615.75 39.87% 4.15% 55.98% 103.91 10.82 145.88 260.62 204.09% 297.722016 258,324.41 39.52% 4.16% 56.33% 102.08 10.74 145.50 258.32 202.44% 294.562017 256,649.74 39.52% 4.13% 56.34% 101.44 10.61 144.60 256.65 200.09% 289.332018 255,122.11 39.48% 4.07% 56.45% 100.73 10.38 144.01 255.12 199.43% 287.192019 252,956.62 39.26% 4.03% 56.71% 99.32 10.19 143.45 252.96 200.16% 287.122020 250,045.49 39.00% 3.99% 57.01% 97.51 9.99 142.55 250.05 200.81% 286.252021 247,204.46 38.77% 3.96% 57.28% 95.84 9.78 141.59 247.20 200.46% 283.822022 245,011.83 38.61% 3.91% 57.48% 94.61 9.57 140.84 245.01 199.85% 281.462023 243,293.31 38.50% 3.85% 57.65% 93.67 9.37 140.26 243.29 199.53% 279.852024 241,996.50 38.39% 3.79% 57.82% 92.90 9.17 139.93 242.00 199.11% 278.602025 240,790.96 38.30% 3.73% 57.97% 92.22 8.99 139.59 240.79 198.20% 276.662026 239,761.53 38.20% 3.67% 58.12% 91.60 8.80 139.36 239.76 197.55% 275.312027 239,088.56 38.19% 3.61% 58.20% 91.30 8.63 139.16 239.09 197.00% 274.152028 238,825.04 38.24% 3.54% 58.22% 91.33 8.46 139.03 238.83 196.21% 272.802029 238,757.92 38.30% 3.47% 58.22% 91.45 8.29 139.01 238.76 195.58% 271.882030 238,511.93 38.41% 3.41% 58.19% 91.60 8.13 138.78 238.51 195.19% 270.892031 238,069.21 38.45% 3.35% 58.21% 91.53 7.97 138.57 238.07 194.64% 269.712032 237,437.63 38.46% 3.29% 58.25% 91.31 7.81 138.31 237.44 194.32% 268.772033 236,780.09 38.48% 3.23% 58.29% 91.11 7.65 138.01 236.78 193.97% 267.702034 236,111.95 38.50% 3.17% 58.32% 90.91 7.50 137.71 236.11 193.58% 266.572035 235,541.76 38.53% 3.12% 58.35% 90.76 7.34 137.44 235.54 193.08% 265.372036 234,994.13 38.55% 3.06% 58.39% 90.60 7.18 137.21 234.99 192.61% 264.282037 234,445.00 38.56% 3.00% 58.44% 90.41 7.03 137.01 234.45 192.16% 263.272038 233,784.49 38.56% 2.94% 58.50% 90.15 6.88 136.76 233.78 191.81% 262.322039 232,937.87 38.53% 2.89% 58.58% 89.74 6.73 136.46 232.94 191.33% 261.102040 231,976.09 38.49% 2.84% 58.67% 89.29 6.59 136.10 231.98 190.82% 259.69

Commercial Sector Delivered Energy Percentage Energy Consumption by the Average Building

[B]

Average Commercial Building Energy Consumption by Source

YearsTotal Energy Consumption of Average Building(Thousand

btu)Lost Electricity as percentage of

Delivered Electricity Lost Electricity

(million btu)

[A] from Tab1 [B] from Tab2 [G] from Tab2 [C] = [B1]*[A] ; [D] = [B2]*[A] ; [E] = [B3]*[A] [F] = [C] + [D] + [E] [H] = [E] * [G]

Natural Gas Distillate Fuel Electricity Natural Gas Distillate Fuel Electricity Total Natural Gas Distillate Fuel Electricity Total

[E]

2012 8.16 26.75 29.99 94.33 11.38 143.89 249.61 769.50 304.56 4,315.89 5,389.94 5,389.942013 8.12 26.68 29.72 106.43 11.57 144.20 262.21 864.48 308.59 4,285.54 5,458.61 5,349.442014 8.58 25.66 30.02 111.05 11.78 145.20 268.02 952.51 302.20 4,358.20 5,612.91 5,390.632015 8.49 18.37 29.76 103.91 10.82 145.88 260.62 882.63 198.81 4,341.45 5,422.88 5,103.982016 8.52 20.09 30.36 102.08 10.74 145.50 258.32 869.54 215.79 4,417.52 5,502.85 5,075.652017 8.53 20.33 30.51 101.44 10.61 144.60 256.65 865.27 215.66 4,411.80 5,492.72 4,964.992018 8.57 20.38 30.32 100.73 10.38 144.01 255.12 863.53 211.59 4,365.58 5,440.71 4,819.612019 9.06 20.67 30.59 99.32 10.19 143.45 252.96 900.13 210.53 4,388.31 5,498.97 4,773.802020 9.55 20.97 31.14 97.51 9.99 142.55 250.05 931.29 209.34 4,438.84 5,579.47 4,746.812021 9.88 21.36 31.69 95.84 9.78 141.59 247.20 946.56 208.92 4,486.60 5,642.08 4,704.072022 10.05 21.86 31.90 94.61 9.57 140.84 245.01 951.19 209.18 4,493.21 5,653.59 4,619.392023 10.24 22.30 32.16 93.67 9.37 140.26 243.29 959.07 208.84 4,510.68 5,678.60 4,547.032024 10.38 22.75 32.16 92.90 9.17 139.93 242.00 963.87 208.62 4,499.65 5,672.14 4,451.032025 10.54 23.22 32.49 92.22 8.99 139.59 240.79 971.96 208.65 4,534.69 5,715.29 4,395.192026 10.69 23.72 32.57 91.60 8.80 139.36 239.76 978.95 208.80 4,539.56 5,727.31 4,316.342027 10.63 24.23 32.70 91.30 8.63 139.16 239.09 970.62 209.07 4,550.60 5,730.29 4,232.222028 10.51 24.76 32.78 91.33 8.46 139.03 238.83 959.78 209.44 4,557.03 5,726.24 4,144.642029 10.46 25.29 32.62 91.45 8.29 139.01 238.76 956.62 209.74 4,534.39 5,700.74 4,043.662030 10.40 25.84 32.59 91.60 8.13 138.78 238.51 952.28 210.11 4,522.35 5,684.74 3,951.662031 10.53 26.42 32.58 91.53 7.97 138.57 238.07 964.17 210.59 4,514.74 5,689.50 3,875.882032 10.68 27.00 32.71 91.31 7.81 138.31 237.44 974.85 210.92 4,524.85 5,710.62 3,812.452033 10.83 27.64 32.88 91.11 7.65 138.01 236.78 986.72 211.52 4,538.28 5,736.52 3,753.152034 10.96 28.27 33.00 90.91 7.50 137.71 236.11 996.72 211.90 4,544.78 5,753.39 3,688.912035 11.12 28.91 33.14 90.76 7.34 137.44 235.54 1,009.67 212.19 4,554.18 5,776.04 3,629.362036 11.31 29.58 33.22 90.60 7.18 137.21 234.99 1,024.32 212.42 4,558.21 5,794.96 3,568.422037 11.52 30.26 33.41 90.41 7.03 137.01 234.45 1,041.18 212.69 4,577.47 5,831.34 3,519.012038 11.80 31.02 33.75 90.15 6.88 136.76 233.78 1,063.88 213.34 4,615.56 5,892.78 3,484.962039 12.21 31.75 34.12 89.74 6.73 136.46 232.94 1,095.45 213.64 4,656.25 5,965.34 3,457.322040 12.62 32.45 34.52 89.29 6.59 136.10 231.98 1,126.97 213.84 4,697.90 6,038.72 3,429.85

TOTAL: 164,519.31 125,239.39

Year

[B][A] [C]

Commercial Sector Energy Prices by Source (in 2013$/million btu) Energy Consumption by the Average Building (in million btu) Cost of Energy for the Average Building (in 2013$s)

Present Value of Cost of Energy

[A] The Annual Energy Outlook 2015 (AEO2015) by the U.S. Energy Information Administration (EIA), Table A3 Energy Prices by Sector and Source, United States. All prices are in 2013 dollars. Data accessible at http://www.eia.gov/oiaf/aeo/tablebrowser/. [B] from Tab3 [E] For PV, used discount rate as 0.98: http://www.federalreserve.gov/faqs/economy_14400.htm [C] = [A] * [B] for each source

YearCommercial Sector electricity prices (in

2013$s/million btu)Lost Electricity

(million btu)Cost of Lost Electricity

(2013$s)PV of Cost of Lost

Electricity

[A] [B] [C] [D]

2012 29.99 293.72 8,809.70 8,809.702013 29.72 297.34 8,836.46 8,659.732014 30.02 298.58 8,961.99 8,607.102015 29.76 297.72 8,860.32 8,339.262016 30.36 294.56 8,942.76 8,248.522017 30.51 289.33 8,827.44 7,979.312018 30.32 287.19 8,706.15 7,712.282019 30.59 287.12 8,783.45 7,625.142020 31.14 286.25 8,913.60 7,583.362021 31.69 283.82 8,993.69 7,498.472022 31.90 281.46 8,979.72 7,337.092023 32.16 279.85 9,000.02 7,206.602024 32.16 278.60 8,959.12 7,030.372025 32.49 276.66 8,987.60 6,911.672026 32.57 275.31 8,968.10 6,758.742027 32.70 274.15 8,964.62 6,620.992028 32.78 272.80 8,941.40 6,471.762029 32.62 271.88 8,868.26 6,290.452030 32.59 270.89 8,827.39 6,136.232031 32.58 269.71 8,787.43 5,986.292032 32.71 268.77 8,792.71 5,870.092033 32.88 267.70 8,802.74 5,759.242034 33.00 266.57 8,797.81 5,640.902035 33.14 265.37 8,793.44 5,525.332036 33.22 264.28 8,779.51 5,406.252037 33.41 263.27 8,795.91 5,308.022038 33.75 262.32 8,853.12 5,235.702039 34.12 261.10 8,908.86 5,163.292040 34.52 259.69 8,964.38 5,091.55

TOTAL: 257,407.71 196,813.40

Average Commercial Building Lost Electricity

[A] from Tab4[B] from Tab4[C] = [A] * [B]

Pricing for PV installations of large commercial systems ($/kw) [A] 3,890Number of Hours of Energy Consumption [B] 4,380

Energy Demanded at Installation (kwh) 87,000.00Energy Demanded at Installation (kw) 19.86

Installation Costs (2014 $) [A] 77,267.12Installation Cost(2013 $) 75,721.78

Yearly Maintenance Cost ($) [C] 19.00

YearYearly Energy

Consumption by Average Building (million btu)

Yearly Energy Consumption by Average

Building (kw)

Yearly Energy Consumption Divided by Working Hours of

PV (kw/h)Total Cost (Installation +

Maintenance) Present Value of Total Costs

2012 249.61 73,152.83 16.70 75,740.78 75,740.782013 262.21 76,844.86 17.54 19.00 18.622014 268.02 78,549.39 17.93 19.00 18.252015 260.62 76,378.93 17.44 19.00 17.882016 258.32 75,707.40 17.28 19.00 17.522017 256.65 75,216.61 17.17 19.00 17.172018 255.12 74,768.90 17.07 19.00 16.832019 252.96 74,134.26 16.93 19.00 16.492020 250.05 73,281.09 16.73 19.00 16.162021 247.20 72,448.47 16.54 19.00 15.842022 245.01 71,805.87 16.39 19.00 15.522023 243.29 71,302.22 16.28 19.00 15.212024 242.00 70,922.16 16.19 19.00 14.912025 240.79 70,568.86 16.11 19.00 14.612026 239.76 70,267.16 16.04 19.00 14.322027 239.09 70,069.93 16.00 19.00 14.032028 238.83 69,992.70 15.98 19.00 13.752029 238.76 69,973.03 15.98 19.00 13.482030 238.51 69,900.94 15.96 19.00 13.212031 238.07 69,771.19 15.93 19.00 12.942032 237.44 69,586.09 15.89 75,740.78 50,565.152033 236.78 69,393.39 15.84 19.00 12.432034 236.11 69,197.57 15.80 19.00 12.182035 235.54 69,030.47 15.76 19.00 11.942036 234.99 68,869.97 15.72 19.00 11.702037 234.45 68,709.04 15.69 19.00 11.472038 233.78 68,515.46 15.64 19.00 11.242039 232.94 68,267.34 15.59 19.00 11.012040 231.98 67,985.47 15.52 19.00 10.79

TOTAL: 151,994.56 126,695.46

Cost of PV Installation

[A] U.S. Department of Energy SunShot Initiative 2014 Photovoltaic System Pricing Trends NREL/PR-6A20-62558; http://www.nrel.gov/docs/fy14osti/62558.pdf [B] We assume 12 hour work days 1million btu = 293.07103866 kW [C] http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html Other assumptions: PV system will change once every 20 years, prices will be more or less stable in the long run (U.S. Department of Energy SunShot Initiative 2014). Therefore, a second installation cost is calculated for 2032.

Present Value of Cost of Energy for an Average Commercial Building from 2012-2040 (in 2013 dollars) 125,239.39Present Value of Cost of PV system for an Average Commercial Building from 2012-2040 (in 2013 dolars) 126,695.46

Results of the Analysis

APPENDIX 2: Economic Impact of Carbon Emissions

Years 5.0% avg 3.0%avg 2.5% avg2012 9.79 31.15 48.95 932.57 29,049.66 29,049.662013 9.79 32.04 49.84 968.00 31,014.78 30,394.492014 9.79 32.93 50.73 990.87 32,629.50 31,337.372015 10.68 33.82 51.62 972.72 32,897.52 30,962.882016 10.68 34.71 53.40 958.26 33,261.24 30,679.112017 10.68 35.60 54.29 952.45 33,907.27 30,649.492018 10.68 36.49 55.18 955.91 34,881.23 30,899.272019 10.68 37.38 56.07 970.63 36,282.30 31,497.592020 10.68 38.27 57.85 978.89 37,462.22 31,871.472021 11.57 39.16 58.74 979.02 38,338.32 31,964.492022 11.57 40.05 59.63 981.69 39,316.50 32,124.442023 11.57 40.94 60.52 986.66 40,394.01 32,344.752024 12.46 41.83 61.41 992.23 41,504.80 32,569.512025 12.46 42.72 62.30 994.19 42,471.71 32,661.692026 13.35 43.61 63.19 997.86 43,516.76 32,796.052027 13.35 43.61 64.08 1,002.50 43,718.89 32,289.422028 13.35 44.50 64.97 1,006.91 44,807.29 32,431.412029 14.24 45.39 65.86 1,011.96 45,932.92 32,581.222030 14.24 46.28 67.64 1,016.47 47,042.26 32,700.732031 15.13 47.17 68.53 1,019.02 48,067.07 32,744.862032 15.13 48.06 69.42 1,022.64 49,147.90 32,811.532033 16.02 48.95 70.31 1,026.28 50,236.21 32,867.332034 16.02 49.84 71.20 1,030.65 51,367.38 32,935.262035 16.91 50.73 72.09 1,036.71 52,592.20 33,046.172036 16.91 51.62 72.98 1,042.96 53,837.84 33,152.282037 17.80 52.51 74.76 1,047.44 55,001.05 33,191.202038 17.80 53.40 75.65 1,051.90 56,171.66 33,219.662039 18.69 54.29 76.54 1,054.19 57,232.20 33,169.922040 18.69 55.18 77.43 1,056.70 58,308.61 33,117.90

TOTAL: 1,260,391.27 932,061.15

Annual SCC values: 2010 - 2050 (2013$ per metric ton of C02)

Carbon emissions (million

metric tons)

Economic worth of Carbon emissions

reduced (million $'s using 3.0% discount rate)

Present Value of Economic worth of Carbon emissions reduced (million

$'s using 3.0% discount rate)

Economic Impact of Carbon Emissions