A 34-page special report on universities in California …...project’s financing. Wind and solar, at high levels of penetration, can also reduce wholesale power prices. d. A large

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NRG Energy Case Study

HECMBAStudentsYeeThengNg,Sathvik

Ganesan,andVeljkoBrciccreatewindandsolar

financialmodelforNRGthatcouldchangeenergy

asweknowit.

A34-pagespecialreportonuniversitiesin

Californiaandrenewableenergy March 17, 2017

Team: ÉnergieHEC • Yee Theng Ng •Sathvik Ganesan •Veljko Brcic

2

Table of Contents

I. Executive Summary

3

II. Project Evaluation

4

III. Financial Model

9

A. Cost Model

9

B. Revenue Model

10

C. Sensitivity Analysis

11

IV. Financing Structure

13

V. Impact

14

VI. Conclusion

14

VII. Appendices

15

3

I. Executive Summary

This paper is divided in four key sections. First, we explain the decision process behind the

selection of the 4-campus university in California project as our target. Afterwards, we explain

why we chose Solar PV and Wind as our energy choices. Then, we get into the details of the

financial models, explaining the assumptions, references, and calculations behind it. We

conclude the paper with the model results.

The result of our financial model indicate that NRG would be able to provide the distributor,

California ISO, a margin of 20-30%, essentially charging a tariff between 10 and 11.5 cents per

kWh. Using a combination of 30% solar system and 70% wind system, NRG would be able to

generate an IRR between 12.2 to 14.3% for this university project.

4

II. Project Evaluation

To select the most feasible energy project to build our financing model, we formulated a

decision matrix that considers the following factors:

1. Government subsidies - Availability, amount and sustainability of subsidies. Energy

subsidies mechanisms varies within the different US states; most states have

implemented feed-in tariffs, short term and long term tax incentives. Federal tax credit

for investment in solar, fuel cells, wind, geothermal, micro turbines, biomass and

combined heat and power. The state provides tax credits of 10% to 30% for eligible

technologies, with level of credit based on technology, for projects beginning

construction prior to 2020. Reduced credit available to qualifying projects beginning

construction in 2020 and thereafter.

2. Utilization. Depending on the nature of the project, we will consider whether the facilities

can support peak load (i.e. Supporting conventions power sources in times of peak usage)

or base load generation (i.e. for continual usage) for the energy market. Another usage

consideration is the flexibility of the energy facility or system (i.e. Ability to store energy

economically) to match resource availability for power use at the facility. Different

projects may require different requirements for flexibility, depending on cost -benefit

calculations as well as the length/ duration of energy use and the overall intensity at the

facility (day vs night energy consumption).

3. Resource and grid availability. For the energy system to function optimally, the existing

grid needs to avoid over-loading, so as to ensure optimal utilization during peaks and

troughs generated by any energy source. Availability of the clean energy sources,

5

proximity and access to transmission lines in the state or region will also allow strategic

deployment of the resources.

We gave the above three criteria more weightage as these are the key issues that affect the

overall financing costs of any clean energy project.

4. Cost savings/ Economies of scale. Generally, the bigger projects will have better

economies of scale over the project lifetime, especially when most energy projects incur

significant capital and development costs at the outset. Also, if taking into account

installation, operation and maintenance costs, larger projects tend to derive more

economic benefits in the longer term. Another way to consider cost savings would be the

value of carbon emissions avoided by zero or low-carbon generation in the energy system

should the project be implemented.

5. Price / willingness to pay. The price of the energy system also plays an important role to

support the implementation of the project. In cases where it is difficult to pass on costs to

consumers and the energy provider or manager of the project has to bear a huge bulk of

the costs, it may be difficult to justify for the project if the net project returns are not

significant.

6

Based on the above criteria, we have come up with a weighted scoring system that considers the

four projects which NRG has provided, as summarised below:

• 2,000 bed, 5 megawatt hospital in New Jersey (NJ)

• 4-campus, 25 megawatt university in California (CA)

• 40 megawatt community in New York (NY)

• 30 megawatt data centre in Texas (TX).

A high score would imply a higher chance for selecting the project. A detailed project evaluation

analysis is found in Appendix A.

NJ CA NY TX

Government

subsidies 3 9 9 9

Utilization 12 12 6 6

Resource and grid

availability 6 12 3 9

Economies of scale 4 8 6 4

Willingness to Pay 2 2 3 4

Total average score 5.4 8.6 5.4 6.4

7

Based on the above scoring, we picked the 4-campus, 25 megawatt university project in

California for NRG to embark on. California is an ideal location for deploying clean energy

sources. NRG is already recognised as one of the state’s largest providers of clean energy

solutions. Picking this university project will leverage on NRG’s existing capabilities in

California since it currently has various utility-scale and distributed solar facilities in California.

According to Bloomberg New Energy Finance (BNEF), elevated renewable output will have a

large impact on California energy markets. We have chosen to go for solar and wind generation

for the California project due to the following reasons:

a. Wind and solar energy are clean fuel sources. Both types of energy do not pollute the air

like conventional power plants that rely on combustion of fossil fuels, such as coal or

natural gas. Wind turbines don't produce atmospheric emissions that increase health

problems like asthma or create acid rain or greenhouse gases. According to the Wind

Vision Report, wind has the potential to reduce cumulative greenhouse gas emissions by

14%, saving $400 billion in avoided global damage by 2050.

b. Wind generates for all hours and is inexhaustible, lowering both peak and off-peak

prices, while solar can provide energy during the day, and can drag down on-peak prices.

Wind’s higher capacity factor can complement solar energy in powering the university.

For as long as the sun shines and the wind blows, the energy produced can be harnessed

to send power across the grid.

c. Wind and solar power are cost effective. Both are one of the lowest-cost renewable

energy technologies available today, with power prices offered by newly built wind farms

averaging 2 cents per kilowatt-hour, depending on the wind resource and the particular

8

project’s financing. Wind and solar, at high levels of penetration, can also reduce

wholesale power prices.

d. A large proportion of solar and wind projects will be implemented over the coming year

in California, and this will drive higher renewable energy capacity, lowering prices. This

is true especially for Southern California, where much of the new solar capacity is

located. Between November 2016 and September 2017, BNEF expects California solar

capacity will increase by about 1.4 GW. Most of the new solar capacity in California will

be concentrated in Kern and Los Angeles counties. California is a top generator of

electricity from wind energy, producing more than 6% of the U.S. total wind resources.

California also has considerable solar potential, given its ideal geographic location. In

2014, California became the first state in the nation to get more than 5% of its utility-

scale electricity generation from solar energy.

9

III. Financial model

To create an economically feasible project, we used a project finance model to calculate the

potential and optimal IRR of the project. The financial model of the project takes into account

the fundamental assumptions about the project such as plant parameters (engineering / design),

costs, tariffs, interest rates, production levels. In terms of output, the model provides us with the

forecasts of cash flows before financing in annual intervals, while taking tax options into

consideration to provide pro-forma financial statements for future years. Based on those

forecasts, we can also calculate some ratios.

Key elements of business models for electricity generators from clean or renewable energy

sources are the revenue streams, cost structure and the way it is financed as elaborated below:

A. Cost model

There are few operational expenses for the clean energy project as maintenance fees tend to be

low, although some technologies may require major maintenance half-way through the lifetime

of the plant - for instance inverters in solar plants may need to be replaced well before the

modules. The cost model allows us to combine input costs (e.g. capital, operation and

maintenance expenses) for wind, solar, and both systems working together. The costs were

calculated based on the peak capacity required for the plant to support an energy demand of 25

MW on the university campus. Tax only needs to be paid after the investment has been fully

depreciated. All other flows are dependent on the way the project has been financed.

10

B. Revenue model

The most significant revenue stream comes from selling electricity to the grid, either at a fixed

price (guaranteed feed-in tariff) or a market price. If the installation is not grid-connected, the

savings from not having to purchase electricity from other sources improve net income in the

same way. Another income stream comes from tax benefits such as production tax credits or

investment tax credits. The value of the tax credits1 depends on the tax capacity of NRG.

We calculated potential revenue that can be derived from the sale of electricity to California ISO

(CAISO), who would eventually distribute power to the university campuses. It is adjusted for

appropriate plant capacities and capacity factors2 of both systems, as well as annual capacity

degradation. We used a solar photovoltaic (thin-film) 100 MW and Wind Class 3 technology

system for this project and projected for it to run on a 25-year life cycle. In determining the

profits arising from the project, we factored a straight-line depreciation rate of 4% for the plant

as well as tax credits to be provided by the federal/state government.

1 NewsolarthermalandPVplantsareeligibletoreceivea30%investmenttaxcreditoncapitalexpendituresif

underconstructionbeforetheendof2019,andthentaxcreditstaperoffto26%in2020,22%in2021,and10%

thereafter.Newwind,geothermal,andbiomassplantsreceivea$23.0/MWh($12.0/MWhfortechnologiesother

thanwind,geothermalandclosed-loopbiomass)inflation-adjustedproductiontaxcreditovertheplant’sfirstten

yearsofserviceiftheyareunderconstructionbeforetheendof2016,withthetaxcreditforwinddecliningby

20%in2017,40%in2018,60%in2019,andexpiringcompletelyin2020.

2Therelevantplantcapacitiesforsolarandwindsystemsusedinourprojectmodelare35MWpand42MWp

respectively.therelevantcapacitiesfactorsofthesolarandwindsystemsare22%and42%respectively.

11

The extracted financial results are shown below, with the full results shown in Appendix B:

C. Sensitivity analysis results

The optimal combination of energy system to generate a healthy projected IRR is a 30% - 70%

combination of solar and wind energy respectively, giving a potential IRR 13.2%. The

potential IRR was derived from the mid-case margin of 25% that NRG needs to provide the

distributor California ISO to achieve a balance between an optimal IRR for NRG and margins

received by CASIO. This will require NRG to charge a tariff of 10.8 cents per kWh.

The detailed analysis is found below:

-2,000,000

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Chart1:ProjectedFinancialResultsof4-Campus

Universityproject

Netoperatingexpenses EBITDA Profitaftertaxes Revenue

-50%

0%

50%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Chart2:Projectedfinancialresultsof4-Campus

UniversityProject

EBITDA(%revenue) Profitbeforetaxes(%revenue)

12

% solar in the energy system % wind in the energy system Project IRR

10% 90% 13.2%

20% 80% 12.7%

30% 70% 13.2%

40% 60% 12.7%

The optimal combination of energy system to generate a healthy projected IRR is a 30% - 70%

combination of solar and wind energy respectively, which gives us a potential IRR of

13.2%. This figure is well within our targeted IRR of between 12 to 14%. This is based on the

tariff of 107.8 $/MWh, which is the mid case in the scenario below.

At the same time, to determine the relevant tariffs for NRG to charge for the project, we worked

backwards using the latest California commercial electricity tariff rate at $143.7/MWh 3and

conducting a sensitivity testing on the margins that NRG should provide the distributer, CAISO.

Our results showed that to achieve a balance between an optimal IRR for NRG and margins

received by CASIO, NRG needs to provide the distributor California ISO with a margin of 20-

30%, by charging a tariff between 10 and 12 cents per kWh.

3 http://www.eia.gov/state/data.php?sid=CA#Prices

13

Margins to California ISO Final tariff charged by NRG ($/MWh) Projected IRR

15% 122.1 15.3%

20% 115.0 14.3%

25% 107.8 13.2%

30% 100.6 12.2%

35% 93.4 11.1%

IV. Financing Structure

NRG has been financing their projects through a combination of long-term debt, corporate

bonds, equity, and others. For this project, we are using a 50 - 50 split for equity and structured

debt. Given that the project has secured cash-flows from the agreement with California ISO

(which help predict a stable repayment schedule) and it has a major asset that can be used as

collateral, NRG is in a strong position to secure debt. Normally, a company looking for these

structures need to bring some of the investments to the table. We made an assumption that NRG

would contribute with 50% of the Capital investment while obtaining the remaining 50% through

a loan. Another alternative, not demonstrated on this simulation, is to partially finance the project

through senior corporate bonds. This alternative could potentially be a cheaper alternative for the

firm and it has been used before.

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V. Impact

a. To environment

The combination of wind and solar will have a significant impact to the environment. The

outcome of the plant will be generating approximately 150k MWh. This is enough electricity to

power 9000 houses for a year, and almost 20,000 cars off the road. At this rate, the project could

potentially avoid 90,000 tons of carbon emissions.

b. To university.

While college and university campuses across US are, in aggregate, responsible for only about

3% of the total greenhouse gas (GHG) emissions emitted by the U.S., implementing a clean

energy system across the university will allow it to create a commitment to a sustainable energy

future and drive social change. Students and university staff can witness real-time carbon

footprint reduction at locations across campus - sport complexes, parking structures, academic

buildings, administration offices and maintenance facilities. The cost savings from the lower

emissions and energy use can be used to invest in other university-wide projects, including those

related to driving sustainability.

VI. Conclusion

The final results of our built model show us that a hybrid project of 30% solar and 70% wind can

generate a positive IRR of 14.7%. The capital investment required would be approximately

$54M and it would generate an EBIT of roughly 32%. NRG would be able to provide the

distributor, California ISO, a margin of 25%, essentially charging a tariff of 10.8 cents per kWh.

Based on these results, we would recommend moving forward with this project.

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VII. Appendix A: Detailed project evaluation

Averagescoreweightage

Criteria NJ California NY Texas Weightage NJ CA NY TX

Government

subsidies/

regulations

1 3 3 3 3 3 9 9 9

Utilisation/

predictable

demand

4 4 2 2 3 12 12 6 6

Resource/grid

availability2 5 1 3 3 6 12 3 9

Cost/

Economiesof

scalebenefits

2 4 3 2 2 4 8 6 4

Price/

willingnessto

pay

2 2 3 4 1 2 2 3 4

5.4 8.6 5.4 6.4

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Qualitative evaluation

1. Four Campus / One university in California

We examined the feasibility for NRG to implement an energy system for a four-campus

university in California. We concluded that this was the most suitable project for NRG,

achieving a score of 9.6 on our decision matrix. Our findings are summarized as below.

Government subsidies and regulation: 3

California state government has been a pioneer in the development of renewable energy in the

United States and has constantly partnered with renewable energy developers to help them

develop projects faster, cheaper, and more efficiently. Moreover, the government has set

aggressive targets for the state to achieve 33% of renewable energy by 2020 and 50% by 2033.

The state provides financial and technical assistance for the development of renewable energy

projects, but it currently does not provide grants or tax rebates for utility level programs. These

grants are currently available at a federal level.

Utilisation and predictable demand: 4

Universities have significant variability on their energy requirements throughout the day. It’s

expected that the energy consumption would be higher during the day than during the night.

Fortunately, given that the number of students can be estimated throughout the year, we can

estimate the amount of energy each campus would require. On top of powering the campus

buildings, the university will have energy requirements for the administration, faculty, and

research. The latter potentially have a strong demand of energy. Overall, a rough estimate can be

achieved.

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Resource/ grid availability: 5

California has one of the most developed grids in the United States and the world. Over the last

decade, NRG has been able to develop more than 20 projects in California, spread among wind,

solar, and natural gas, representing almost 20% of the MW produced under NRG’s portfolio. The

company’s vast experience and presence in the province is a major advantage to the development

of this project, hence the 5/5 score.

Cost/ EOS benefits: 4

The project has significant potential economies of scale given the savings derived in switching

from conventional energy sources to cleaner energy sources.

Price/ willingness to pay:

California distributes energy through different ISOs. The price changes across the region but

maintains a rough estimate of $0.15 per KWh, making it one of the most expensive states in

electricity distribution. The low score comes from the lack of flexibility on the price as electricity

as it is consolidated and distributed across the state.

2. Hospital in New Jersey

We examined the feasibility for NRG to implement an energy system for a 2,000 bed, 5

megawatt hospital in New Jersey. We concluded that this project would rank the lowest in terms

of implementation feasibility (a score of 5.4 based on our decision matrix). Our findings are

summarized as below.

Government subsidies and regulation 1

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New Jersey's renewable energy program does offer rebates for some clean energy types.

Unfortunately, legislators made the decision to back solar power exclusively with the Solar

Renewable Energy Certificates (SREC) market. That means the cost of solar panels in New

Jersey may be a bit higher at the outset. The SREC is sold separately from the electricity and

represents the "solar" aspect of the electricity that was produced. The value of an SREC is

determined by the market subject to supply and demand constraints. SRECs can be sold to

electricity suppliers needing to meet their solar Renewables Portfolio Standard (RPS)

requirement. The market is typically capped by a fine or solar alternative compliance payment

(SACP) paid by any electricity suppliers for every SREC they fall short of the requirement.

In addition, New Jersey Governor Chris Christie’s proposed budget for the next fiscal year

would allocate $152 million out of the clean-energy fund to other purposes, primarily to pay the

state’s and New Jersey Transit’s energy costs. This would suggest that clean energy projects may

not gain much traction in receiving government subsidies.

Utilisation and predictable demand 4

Hospitals consume large amounts of energy because of how they are run and the many people

that use them. They are open 24 hours a day; thousands of employees, patients, and visitors

occupy the buildings daily; and have sophisticated energy needs such as heating, ventilation, and

air conditioning (HVAC) systems control the temperatures and air flow controls. In addition,

many energy intensive activities occur in these buildings: laundry, medical and lab equipment

use, sterilization, computer and server use, food service, and refrigeration. As such, there is high

predictable demand for the energy system.

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Resource/ grid availability 2

New Jersey is a major consumer of petroleum products, and the petroleum-dependent

transportation sector consumes more energy than any other sector in the state. New Jersey

depends on nuclear power and natural gas for 90% of its net electricity generation, with

renewable energy supplying less than 5% of net generation.

Solar power became the state's leading renewable energy source in 2015, supplying two-thirds of

net renewable electricity generation, while biomass facilities supplied nearly all the rest of the

state's net renewable electricity generation. New Jersey has adopted a renewable portfolio

standard (RPS) that will require nearly one-fourth of net electricity sales to come from renewable

energy resources by 2021. Specific solar and offshore wind requirements are included in the

standard. In February 2016, more than 43,000 solar photovoltaic (PV) facilities were installed

around the state on residential and business rooftops, with solar capacity totaling nearly 1,267

megawatts from distributed generation and 377 megawatts from utility-scale generation.

Commercial solar PV farms also have been built, and the state's two largest farms each have

capacities of 19.9 megawatts. At the end of 2015, New Jersey was fourth among the states in

installed solar PV capacity. Only a small share of New Jersey's renewable electricity is generated

by wind. New Jersey's best wind power potential is found offshore along its coast, and many of

the projects are still in planning stages.

Thus, for NRG to harness other forms of clean energy in New Jersey (e.g. solar, wind, biomass),

NRG needs to expand its existing asset portfolio of serving the natural gas utility market in New

Jersey, which may take time.

20

Cost/ EOS benefits 2

The savings derived in switching from conventional energy sources to cleaner energy sources for

a hospital in Jersey is minimal, because of the need for secure, reliable source of energy to run

the facilities. Natural gas was the most common main space heating fuel, which was used by 74

% of hospitals in the US, followed by district heat, 20 %.

Price/ willingness to pay 2

New Jersey averaged the 10th-highest electricity prices in the nation in 2015, while natural gas

prices are one of the lowest in the country at $9.11/thousand cu ft. As such, there will likely be a

huge inertia to have a hospital switch develop a cleaner energy system (that shifts away from

conventional sources e.g. natural gas) since it can be economically unattractive and difficult for

costs to be passed on to its patients.

3. Community in New York

We examined the feasibility for NRG to implement an energy system for a 40-megawatt

university in New York. We concluded that this project would rank the joint lowest in terms of

implementation feasibility (a score of 5.4 based on our decision matrix). The added benefit here

is the potential to impact the community through this project and increase reputation of NRG.

Our findings are summarized as below.


New York state offers renewable energy incentives in the form of net energy metering system as

well as the production tax credits. New York offers 25% tax credits for solar and wind projects.

Net metering ensures that one gets paid a fair price for the solar electricity that one sends into the

21

grid. Any credits for excess solar power that one accrues in a ‘credit bank’ can be used in future

months (usually winter) if your solar energy system produces less electricity.

In 2010, New York regulators set a goal of obtaining about 8.5% of new renewable generation

from small, customer-sited facilities, such as solar photovoltaic (PV) and solar thermal systems,

fuel cells, anaerobic digester systems, and wind installations. New York is consolidating its

renewable portfolio standard (RPS), energy efficiency portfolio standard (EEPS), and other clean

energy mandates under a program called Reforming the Energy Vision (REV). REV is intended

to create a flexible utility business model that offers increased incentives for renewable and

distributed electricity generation as well as consumer incentives for efficiency and distributed

generation. The REV program details were being developed during 2016. The REV sets state

goals for 2030 of obtaining half of all electricity sold in the state from renewable sources,

reducing energy-related greenhouse gas emissions 40% from 1990 levels, and reducing energy

consumption by buildings 23% from 2012 levels.

Utilisation and predictable demand 2

The utilization rate of electricity in a community is highly variable. One would expect high

consumption of energy during the day while a low consumption during the night. However, that

is a generic assumption, which is subject to variation depending on the city itself. Low utilization

rates will be detrimental to NRG’s business model. Hence, we rated NY low in this category due

to unpredictable demand of electricity in the community. Moreover, the efficiency of the

technology is likely to be low due to unpredictable weather patterns and presence of tall

buildings.

22


More than half of New York’s energy is supplied from other states and Canada. About four-fifths

of net renewable generation in New York comes from hydroelectricity, with small but growing

amounts from wind, biomass, and solar sources. Besides, NRG has no renewable energy plants

set up in New York. Thus, for NRG to harness other forms of clean energy in New York (e.g.

solar, wind, biomass), NRG needs to expand its existing asset portfolio of serving the existing

natural gas and hydroelectricity supplied areas, which may take time.



a community in New York is pretty good due to the net energy metering system. However, from

the NRG perspective, the costs and benefits from economies of scale is likely to be minimal until

they are able to set up more plants and supply increased energy to New York.


New York residents are currently charged 17.75 cents/kWh, which is significantly higher than

the US average of 12.75 cents/kWh. The willingness to pay is likely to be pretty high for that

reason. However, due to the substantial costs involved and low technological efficiencies in New

York, NRG is unlikely to make enough profit margins at that price. As such, there will likely be

a huge inertia to have a community such as New York to switch to a cleaner energy system (that

shifts away from conventional sources e.g. natural gas) since it can be economically unattractive.

23

4. Data centre in Texas

We examined the feasibility for NRG to implement an energy system for a 30-megawatt data

centre in Texas. We concluded that this project would rank the second highest in terms of

implementation feasibility (a score of 6.4 based on our decision matrix). The added benefit here

is the potential to impact the community through this project and increase reputation of NRG.

Our findings are summarized as below.


In 1999, the Public Utility Commission of Texas first adopted rules for the state's renewable

energy mandate. In 2005, the state legislature amended the mandate to require that 5,880 MW, or

about 5% of the state's electricity capacity, to come from renewable sources by 2015. Lawmakers

also set a goal of 10,000 MW of renewable capacity by 2025, including 500 MW from sources

other than wind. Texas surpassed the 2015 goal in 2005 and the 2025 goal in 2009, almost

entirely with wind power. Renewable energy sources contributed one-tenth of the state's net

electricity generation in 2015, and that amounted to nearly one-sixth of U.S. electricity from all

non-hydroelectric renewable sources. Texas produced more non-hydroelectric renewable

generation than any other state in the nation. The government provides 30% tax credits to

commercial projects. These initiatives allowed us to rate Texas as medium in this category.

24

Utilization and predictable demand 2

The energy efficiency in data centres is a significant problem as demand of data centres may

fluctuate a lot. While rising energy costs are an incentive for greater efficiency, the pressure to

keep up with technological developments and the rapid pace of operational change mean that

energy efficiency is often a lower priority. Studies show that average server utilization remained

static at 12 to 18 percent between 2006 and 2012. This underutilized equipment not only has a

significant energy draw but also is a constraint on data center capacity. An estimated 20 to 30

percent of servers in large data centers today are idle, obsolete, or unused but are still plugged in

and operating in “on” mode, consuming energy doing nothing. At its peak, the demand is

significantly higher than its average. Owing to this reason, we rated the data centre project low in

this category.


While solar energy in Texas is not very economically friendly due to existing lack of solar

projects, there are a huge number of wind projects in Texas. Wind accounts for nearly all of the

electricity generated from renewable resources in Texas. In 2011, Texas was the first state to

reach 10,000 MW of installed wind generating capacity. At the end of 2015, Texas had more

than 18,500 MW of wind capacity installed. However, NRG itself does not have wind plants set

up in Texas. Due to these reasons, we rated resource availability as 3.

25



a data centre in Texas is pretty good due to the government incentives. However, from the NRG

perspective, the costs and benefits from economies of scale is likely to be minimal until they are

able to set up more plants in Texas.


Most of these data centres belong to huge technology companies such as HP, Microsoft and

Facebook. We envision that we can charge high prices to these companies, who are willing to

pay good prices for reliable and clean energy. Moreover, by adopting renewable energy projects

for the data centres, these companies stand to benefit by increasing their own reputation by

minimizing carbon emissions. Owing to these reasons, we rated the category very high.

26

Appendix B: Financial model input and output

Inputs–OverallProject

Revenuemodel

ProjectCapacity MWp 39.5

PowerGeneration MWh/MWp 3146

AnnualDegradation Per 0.4%

Costmodel

Operations&Maintenance

·Fixed $ $1,604,613

·Inflation Per 2.50%

·Variable $ $353,904

·Insurance Per 2.50%

PlantandMachinery

·Capitalinvestment $/MWp $1,375,513.68

·SLMDepreciationrate per 4.0%

·MaximumDepreciable

valueper 100.0%

27

Inputs–SolarPlant

Revenuemodel

ProjectCapacity MWp 35

CapacityFactor per 21.70%

PowerGeneration MWh/MWp 1900.92

AnnualDegradation per 0.55%

Costmodel


·Fixed $ 591,300

·Variable $ 249,660

·Insurance Percent 0.3%

PlantandMachinery

·Capitalinvestment $/MWp 1,509,198



valueper 100.0%

28

Inputs–WindPlant

Revenuemodel

ProjectCapacity MWp 42

CapacityFactor per 42%

PowerGeneration MWh/MWp 3679.2

AnnualDegradation per 0.30%

Costmodel


·Fixed $ 2,038,890

·Variable $ 398,580

·Insurance Percent 0.3%

PlantandMachinery

·Capitalinvestment $/MWp 1,318,221



valueper 100.0%

29

Appendix C - Output / Financials

IncomeStatement

Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13

Revenue 6,358,278 8,445,913 8,414,241 8,382,688 8,351,253 8,319,935 8,288,736 8,257,653 8,226,687 8,195,837 8,165,102 8,134,483 8,103,979 8,073,589

Expenses

FixedO&M 1,203,460 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613

VariableO&M 265,428 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904

InsuranceCost 1,396,257 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498

NetOperatingExpenses

2,865,145 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015

EBITDA 3,493,134 5,078,899 5,047,226 5,015,673 4,984,238 4,952,921 4,921,721 4,890,638 4,859,672 4,828,822 4,798,088 4,767,468 4,736,964 4,706,574

Depreciation 1,631,442 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255

Interest 271,907 1,060,437 1,006,056 951,674 897,293 837,473 772,216 701,520 625,386 549,252 462,242 364,355 266,469 163,144

WCinterest 56,092 111,974 111,554 111,136 110,719 110,304 109,890 109,478 109,068 108,659 108,251 107,845 107,441 107,038

Profitsbeforetaxes 1,533,693 1,731,232 1,754,361 1,777,607 1,800,970 1,829,888 1,864,360 1,904,385 1,949,963 1,995,656 2,052,339 2,120,012 2,187,799 2,261,137

Profit aftertaxes/Retainedearnings

1,073,585 1,211,862 1,228,053 1,244,325 1,260,679 1,280,922 1,305,052 1,333,069 1,364,974 1,396,959 1,436,637 1,908,011 1,969,019 2,035,023

30

IncomeStatement(cont.)

Year 14 15 16 17 18 19 20 21 22 23 24 25

Revenue 8,043,313 8,013,150 7,983,101 7,953,165 7,923,340 7,893,628 7,864,027 7,834,536 7,805,157 7,775,888 7,746,728 1,929,419

Expenses

FixedO&M 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 1,604,613 401,153

VariableO&M 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 353,904 88,476

InsuranceCost 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,408,498 1,371,775

NetOperatingExpenses 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 3,367,015 1,861,405

EBITDA 4,676,298 4,646,136 4,616,087 4,586,150 4,556,326 4,526,613 4,497,012 4,467,522 4,438,142 4,408,873 4,379,713 68,015

Depreciation 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 2,175,255 543,814

Interest 54,381 - - - - - - - - - - -

WCinterest 106,636 106,237 105,838 105,441 105,046 104,652 104,260 103,869 103,479 103,091 102,704 102,319

Profits beforetaxes 2,340,025 2,364,644 2,334,993 2,305,453 2,276,024 2,246,706 2,217,497 2,188,398 2,159,408 2,130,526 2,101,753 -578,118

Profit aftertaxes/Retainedearnings

2,106,022 2,128,179 2,101,494 2,074,908 2,048,422 2,022,035 1,995,747 1,969,558 1,943,467 1,917,474 1,891,578 -463,787

31

BalanceSheet

Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13

Assets

CurrentAssets 3,329,143 4,590,551 5,866,128 7,155,964 8,460,148 9,510,668 10,583,328 11,410,115 12,266,831 13,153,562 13,534,197 14,905,936 16,338,043 17,563,609

FixedAssets 52,749,946 50,574,690 48,399,435 46,224,179 44,048,924 41,873,668 39,698,413 37,523,157 35,347,902 33,172,646 30,997,391 28,822,135 26,646,880 24,471,624

TotalAssets 56,079,089 55,165,241 54,265,563 53,380,143 52,509,072 51,384,337 50,281,741 48,933,272 47,614,732 46,326,209 44,531,588 43,728,072 42,984,923 42,035,233

EquitiesandLiabilities

TotalShareholders’

Equity28,264,279 29,476,141 30,704,194 31,948,519 33,209,199 34,490,120 35,795,172 37,128,241 38,493,215 39,890,174 41,326,812 43,234,823 45,203,842 47,238,865

TotalLiabilities 27,814,810 25,689,100 23,561,369 21,431,624 19,299,873 16,894,217 14,486,569 11,805,031 9,121,517 6,436,034 3,204,777 493,249 (2,218,919) (5,203,632)

TotalSEandLiabilities 56,079,089 55,165,241 54,265,563 53,380,143 52,509,072 51,384,337 50,281,741 48,933,272 47,614,732 46,326,209 44,531,588 43,728,072 42,984,923 42,035,233

32

BalanceSheet(cont.)

Year 14 15 16 17 18 19 20 21 22 23 24 25

Assets

CurrentAssets 18,859,539 22,896,063 26,905,270 30,887,263 34,842,145 38,770,016 42,670,978 46,545,133 50,392,580 54,213,419 58,007,751 58,084,436

FixedAssets 22,296,369 20,121,113 17,945,858 15,770,602 13,595,347 11,420,091 9,244,836 7,069,580 4,894,325 2,719,069 543,814 -

TotalAssets 41,155,908 43,017,176 44,851,128 46,657,866 48,437,492 50,190,107 51,915,814 53,614,713 55,286,904 56,932,488 58,551,565 58,084,436

EquitiesandLiabilities

TotalShareholders’

Equity49,344,888 51,473,067 53,574,560 55,649,468 57,697,890 59,719,925 61,715,672 63,685,230 65,628,697 67,546,171 69,437,749 68,973,962

TotalLiabilities (8,188,979) (8,455,891) (8,723,432) (8,991,602) (9,260,398) (9,529,817) (9,799,858) (10,070,517) (10,341,792) (10,613,682) (10,886,184) (10,889,527)

TotalSEandLiabilities 41,155,908 43,017,176 44,851,128 46,657,866 48,437,492 50,190,107 51,915,814 53,614,713 55,286,904 56,932,488 58,551,565 58,084,436

33

CashFlowStatement

Year 0 1 2 3 4 5 6 7 8 9 10 11 12

Cashflowfrom

Operations960,031 2,629,827 2,643,962 2,658,188 2,672,503 2,690,714 2,712,820 2,738,822 2,768,718 2,798,702 2,836,386 3,827,458 3,887,794

Cashflowfrom

Investment54,381,387 - - - - - - - - - - - -

Cashflowfrom

Financing55,356,904 (1,363,193) (1,363,179) (1,363,165) (1,363,152) (1,635,045) (1,635,032) (1,906,925) (1,906,912) (1,906,898) (2,450,699) (2,450,686) (2,450,673)

NetCashflowofthe

year1,935,548 1,266,634 1,280,783 1,295,023 1,309,351 1,055,669 1,077,788 831,896 861,806 891,803 385,687 1,376,772 1,437,121

CashFlowStatement(cont.)

Year 13 14 15 16 17 18 19 20 21 22 23 24 25

Cashflowfrom

Operations3,953,128 4,023,460 4,044,953 4,017,605 3,990,359 3,963,216 3,936,174 3,909,234 3,882,395 3,855,657 3,829,019 3,802,481 84,803

Cashflowfrom

Investment- - - - - - - - - - - - -

Cashflowfrom

Financing(2,722,566) (2,722,553) (3,471) (3,458) (3,445) (3,432) (3,419) (3,406) (3,393) (3,381) (3,368) (3,355) (3,343)

NetCashflowofthe

year1,230,562 1,300,907 4,041,482 4,014,147 3,986,914 3,959,784 3,932,755 3,905,828 3,879,002 3,852,276 3,825,651 3,799,125 81,460

34

Appendix D: References https://www.eia.gov/http://www.energy.ca.gov/https://about.bnef.com/https://solarpowerrocks.com/new_jersey/#incentiveshttp://www.njspotlight.com/stories/17/02/28/crafting-a-multifaceted-energy-policy-targeted-beyond-governor-s-term/http://www.srectrade.com/srec_markets/

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