NRG ENERGY CASE STUDY

HEC Paris J17 Team Pascal Lim

Sanjay Munka Albert Priori

MARCH 8, 2017

HEC PARIS

FRANCE

Table of Contents

1. Introduction ......................................................................................................................... 2

2. Datacenter Requirements and Constraints .......................................................................... 2

3. Clean Technology mix selection .......................................................................................... 3

3.1. Fuel Cells CHP ................................................................................................................ 4

3.2. Solar PV and batteries ..................................................................................................... 6

3.3. Resiliency and reliability .................................................................................................10

4. Financing structure ............................................................................................................12

5. Powering the data center: final design and financial model ................................................14

5.1. Decision-making process ...............................................................................................15

5.2. Financial results .............................................................................................................16

6. Conclusion .........................................................................................................................19

1. Introduction

NRG is the leading integrated power company in the US with over 51 GW of generation

capacity1. In 2015, NRG’s power generation in the North American market stood at 123 TWh,

with US CO2e emissions1 at 87 million metric tonnes. NRG is a pioneer in the drive towards

cleaner energy with renewable sources representing about 9% of their total generation portfolio1.

It has aggressive sustainability goals of reducing its carbon emissions 50% by 2030 and 90% by

20501.

To achieve its ambitious emissions goals, NRG is increasingly focusing on cleaner energy.

However, unlike established technologies, project financing for renewable or distributed energy

systems remains a significant obstacle. This proposal helps overcome this obstacle by adopting a

creative financial model that enables the development of a resilient clean energy distributed

generation system for a 30 MW data center in Texas, helping NRG meet its sustainability

goals at the same time.

2. Datacenter Requirements and Constraints

Data centers require energy throughout the day, without specific requirements for a particular

time of the day; making their energy demand constant.

Critical load and HVAC load are the two most important figures crucial in designing the

electrical system that will power the data center. Studies2 have shown that typically data centers

have 52% of their overall loads represented by critical load, comprising mainly of server racks &

routers and 30% represented by HVAC load. HVAC extracts heat generated by all equipment,

lighting, personnel and weather. Further, 6% of the overall load represents UPS’s operating at

88% efficiency and 12% represents a combination of lighting and protection equipment loads

such as fire, security & monitoring systems. Fig 2.1 illustrates loading requirements for a data

center.

Figure 2.1: Typical electrical sizing for a data center

3. Clean Technology mix selection

We evaluated several clean technologies for this project, summarized below:

● Gas Turbines: Typically the most reliable and robust solution for this application.

However, inefficiency at part load and high emissions made it a far from ideal solution

for NRGs mid-term and long-term goals.

● Wind Turbines: Good and cost effective renewable solution. However, non-homogeneous

wind patterns in Texas cause high energy production volatility. For this reason, to ensure

resilience, a 100% backup would be necessary, increasing CAPEX significantly.

● Biomass: High CO2 emissions. Furthermore, the suitability of such technology is

strongly related to a short fuel supply chain that does not seem possible in Texas based on

a preliminary analysis.

● Battery Storage: Proven and reliable technology that facilitates the effective utilization of

intermittent renewable sources and reduces the need for peak generation capacity.

Technology is still expensive but costs are reducing1 year on year.

● Fuel Cells: High efficiency, can run continuously and are a good complement to

renewables, since they are able to provide base load power. Certain types are well suited

to CHP applications.

● Solar PV: Clean and sustainable technology requiring low maintenance and increasingly

lower investment costs. However, performances are weather dependent.

To increase reliability, resilience and reduce greenhouse gas emissions, a combination of fuel

cells in CHP configuration, PV and battery storage was chosen for the project. The Installed

capacity of each technology has been set to maximize project IRR and minimize carbon

emissions.

3.1. Fuel Cells CHP

Fuel cells can provide high efficiency and low emissions energy over a broad load profile.

Among the different available technologies, the Molten Carbonate Fuel Cells (MCFC) was

chosen for its fuel flexibility, efficiency and high operating temperatures that makes it ideal for a

CHP application.

1https://www.bloomberg.com/news/articles/2017-02-21/big-batteries-coming-of-age-prompt-bankers-to-

place-their-bets

Figure 3.1.1: MCFC operating process2

A large portion of the power consumed by a datacenter is essentially converted to heat, for this

reason its cooling requirements are high. Fuel Cells in combination with an absorption chiller

driven by exhaust heat will also provide cooling to the datacenter, reducing client’s air

electricity requirements and consequently costs.

The CHP configuration increases overall energy efficiency from 42.5% (electric generation only)

to 74.8% (thermal and electric generation altogether). The following simplified flowchart shows

the CHP working principle and main figures in terms of electricity and cooling production for

100 units of energy (natural gas) entering the fuel cell.

Figure 3.1.2: Fuel Cell CHP General Flow Chart

2 http://www.fuelcelltoday.com/technologies/mcfc

Natural Gas has been chosen instead of hydrogen, since it’s a cheaper and more available fuel:

for this reason, the system produces carbon dioxide. However, thanks to the CHP configuration,

CO2 releases are calculated with a thermal credit, since supplementary emissions to generate the

useful thermal power by an on-site boiler are avoided.

Taking into account thermal credits, CO2 emissions can be lowered from 939 to 559 lbs/MWh.

The performance, costs and emissions for MCFC that will be used in our financial model can be

found in the table 3.1.1.

MCFC FUEL CELLS

Electric Efficiency %, HHV 42.5%

Thermal Efficiency (%, HHV) %, HHV 32.3%

Overall Efficiency (%, HHV) %, HHV 74.8%

Installed Cost $/kW $4,600

O&M Cost, excluding fuel ¢/kWh 4.0

CO2 Emissions Electric Power Only lbs/MWh 939

CO2 Emissions in CHP with thermal credit lbs/MWh 559

Table 3.1.1: MCFC Key Figures3

3.2. Solar PV and batteries

The design of the photovoltaic and battery system has been optimized to match the daily and

yearly production profile with the Data center load profile. To smoothen the daily PV production

profile, a single-axis tracking system has been preferred to a static one. Furthermore, to reduce

the seasonal production gap, the tilt of the panels has been increased to 55°.

3 https://energy.gov/sites/prod/files/2016/09/f33/CHP-Fuel%20Cell.pdf

Figure 3.2.1: Production profile of one PV MWp installed

With tracking systems, the panels power density (kWp per square meter) can be optimized,

hence more efficient polycrystalline silicon technology have been chosen over thin film.

Conversion rate (DC Electricity over Solar Ration kW) for this type of technology is around

18%.

All the data related to photovoltaic power production are supported by a PVsyst study (appendix

3.1). The photovoltaic & battery storage association is designed as a complement of the fuel

cells power production. Therefore, the power output profile of the association should also be a

constant over time.

According to our simulations, a 1 MWP PV system in Texas will produce on average 2,89 MWh

per day during the worst month (November), as illustrated by figure 3.2.2.

Figure 3.2.2: November Load Profile for PV and batteries

The batteries are sized to store the energy that cannot be immediately consumed by the

datacenter (grey line). This energy is then restituted by the battery during night time and

inadequate weather conditions (orange line). In order to absorb the totality of PV excess

production of one particular day, the battery storage needs to be sized to 66%4 of the total energy

produced that day. Therefore, because of our choice to dimension the system for an average day

of November, 1.93 MWh of battery storage (66% of 2.89 MWh) is required for a 1 MWp. This

association will ensure constant power for a 0,125 MW production.

However, this combination does not guarantee perfect autonomy from the network and energy

will still be sold and bought on the grid, as illustrated by the following table.

4 This figure has been computing from the following inputs: battery storage losses, inverter losses

avoided and PV average production profile in November.

Table 3.2.1: Power balance of PV & battery system for 1 MWp PV

1 kWp of Solar PV

Daily average production in November kWh 2.89

Installed Cost $/kW 1400

O&M Cost, excluding fuel ¢/kWh 0.5

CO2 Emissions Electric Power Only lbs/MWh 0

Table 3.2.2: Performance, costs and emissions for PV

The Battery costs, life-time and efficiency are dependent on the technology selected5. The final

solution has been selected in order to optimize the financial results.

Batteries Lead Acid Li-ion

Cycle Efficiency % 80% 92%

Life time years 8.2 4.1

Installed Capacity Cost $/kWh 378 350

Cost of storage $/kWh 0.32 0.32

CO2 Emissions Electric Power Only lbs/MWh 0 0

Table 3.2.23 Performance, costs and emissions for batteries

5 https://www.solarchoice.net.au/blog/levelised-cost-of-storage-compare-battery-value

3.3. Resiliency and reliability

It is crucial for data centers to have a reliable energy source and be protected from network

disruptions. Energy resiliency and reliability in data centers are usually achieved by traditional

UPS configuration to ensure critical load feeding.

Fig 3.3.1: typical UPS configuration in data center

However, our generation mix produces DC electricity. We can improve the traditional

configuration by an alternative configuration illustrated in Figure 3.3.2. Individual fuel cells and

PV panels supply DC power to the critical load independently, thereby mitigating the risk of

overall power failure.

The loss of any one power generation unit will always be < 1.4 MW (the size of a single fuel

cell). This loss will be immediately compensated by a combination of increased batteries output

and eventual transient load shedding of non-critical AC users. The impact on event for the Data

center will be a temporary partial change from Inverter to Grid supply for non-critical loads

(HVAC and other).

Figure 3.3.2: Alternative configuration in data center with DC power

Bloom energy estimates the availability of similar solutions to be 99.998%6.

The solution is not fully independent from the grid since external power support will be needed

30% of the days7 to meet the data center demand. We considered that protection against long

term grid outages (>2 hours), given its additional costs, should be conditioned by the client risk

aversion profile.

An event similar to the NorthEast BlackOut of 1965 would have less than 30% chances to result

in a power failure for the data center (depending on time and day of the event). However, if the

client decides to implement such protections, he will only need to connect his own batteries to

the DC line.

6

http://c0688662.cdn.cloudfiles.rackspacecloud.com/downloads_pdf_Bloomenergy_Mission_Critical.pdf 7 This figure has been computed on the basis of PVsyst daily production results for the year 1990 with the

weather data of Austin.

4. Financing structure

Financing structures for renewable energy projects range from the regular bank loan and parent

company equity scheme, to more industry-specific structures, taking advantage of the energy

Investment Tax Credit (ITC) offered by the US government for projects that involve

photovoltaic solar panels. This ITC is a tax credit to the power company of up to 30% of the total

capital required for building the solar panels. However, in most cases including ours, the project

company does not have enough tax liability to fully benefit from the tax credit.

Table 4.1: ITC rate per technology and installation year

Source: https://energy.gov/savings/business-energy-investment-tax-credit-itc

In order to fully take advantage of the ITC though, the project company (called “developer”) can

partner with another company (called the “Tax equity investor”) with enough tax liability to

benefit from this tax credit. The required capital to be deployed for funding the project is for the

most part largely provided by the tax equity investor (for example 80%), the developer providing

the remaining amount (20%). However, in order for this partnership to be even more attractive

for the tax equity investor, the cash flows originating from the project will be distributed in a

90%/10% split, skewed towards the tax equity investor who initially receives 90% of the cash

distribution until its rate of return reaches the pre-agreed hurdle rate (15% including ITC) while

the developer receives the remaining cash flow. Once the hurdle rate is achieved, the distribution

flips and 90% of the cash distribution now flows to the developer, who can decide to buy the tax

investor’s equity out. Using this structure, the capital provided by the developer is small but the

rate of return is rather large, making this structure very attractive for both parties.

The tax equity investor can then claim almost all of the ITC credited for the project in order to

reduce its overall tax liability as a company.

The financing structure can be further improved with loans for funding the initial required

capital. Such debt will be repayed through the cash flows generated by electricity sales. The

interest rates for such projects are not projected to be very high (around 5 to 7%) because the

project provides assets as collateral and the demand carries a very low level of risk, as prices will

be locked in through a Price Purchase Agreement (PPA) and as global demand for internet data

will be on the rise for the foreseeable future, ensuring the long term sustainability of the data

center. It is true that tax equity investors generally would rather steer away from project debt

because senior debt takes precedence over equity in a liquidation scenario. However, the project

relatively low level of uncertainty and the projected returns are the best incentive in this scenario.

The final result is displayed in figure 4.1, illustrating the interaction between the different actors

in the financial structure:

Figure 4.1: Finance and cash distribution structure

Risk can even further be reduced by locking in natural gas prices through the purchases of call

options with the Henry Hub natural gas as underlying. The purchase of such derivative products

will be reflected in the financial model.

5. Powering the data center: final design and financial model

In order to produce the financial model, some key assumptions need to be made (see appendix

5.1). The model created can however accommodate for a variety of scenario in order to select

the best parameters.

Using the assumed values, the full financial reporting statements were computed and projected

for the next 20 years: income statement, balance sheet and cash flow statement. The cash flows

from the cash flow statement were used in order to determine the rates of return that each

investor gains.

5.1. Decision-making process

The model was built to determine the financial impact of the technical design choices as

described in the first part of the document. The financial outputs are the ultimate drivers behind

the technology mix and selection.

The first question to be answered is the technology mix that will be required to provide enough

energy for the 30 MW data center. In order to attract tax equity investors, the ITC amount needs

to be significant enough and consequently, the CAPEX used for energy from photovoltaic has to

be significant too. Figure 5.1.1 summarizes the energy provided by the solar and batteries

combination based on the remaining energy requirement complementing the fuel cells. Because

the heat from fuel cells is used to produce cooling, the final demand from the data center

decreases.

Figure 5.1: ITC, PV and batteries requirements as a function of fuel cell output for data center

As shown by Figure 5.1, the ITC amount drastically reduces with increasing fuel cell output. In

order to attract the tax equity investor, it was decided that $10 million would be the minimum

amount of ITC achieved (equivalent to a $33.3 million CAPEX spend for solar PV). The $10

million mark for ITC corresponds to a fuel cell energy output of around 20-21 MW.

5.2. Financial results

A table showing different pricing and debt/equity scenarios can be found in appendix 5.2. The

table shows that because of the large CAPEX required for batteries, the project return is largest

when the proportion of fuel cells output is maximized. Therefore the optimal proportion knowing

the constraints ($10 million ITC and maximized return) is for a fuel cells output of around 21

MW. In this case, the PV solar equipment is designed to provide 21.7 MW at peak in order to

charge batteries with a capacity of 43.4 MWh, enough to continuously provide 2.7 MW, the

remaining demand not met by the fuel cells output.

The PPA price is then determined by the lowest number possible that generates an attractive

return. Indeed, the pricing structure should still reflect and capture the customer’s willingness to

pay.

Figure 5.2: Project internal rate of return compared to PPA price for different debt and equity

proportions

The minimum PPA price in order to reach the tax equity hurdle rate (15% including ITC)

given by the model is $0.115/kWh and corresponds to a debt and equity proportion of

respectively 80% and 20%. The tax equity investor reaches this rate for its invested capital of

$24.2 million as soon as the first quarter of the fifth year, at which point the distribution flips.

NRG’s internal rate of return based on this flip becomes 32%, for an initial invested capital

of $6.05 million.

These numbers can be compared with the project IRR of 13.9% (assuming all the equity was

provided by one investor only).

Figure 5.3: Representation of cashflows with time

Figure 5.3 illustrates the distribution of all the cash flows with time per quarter. The peaks

correspond to the summer time when solar PV is able to gather more sunlight.

Even though the required PPA price is high compared to the average price of electricity paid in

Texas, the customer has resiliency thanks to a dedicated distributed energy system that also emits

significantly less carbon than the national average.

6. Conclusion

The proposed project provides a reliable and resilient energy source to the datacenter. Value has

also been added through the optimization of the power originating from fuel cells, through the

use of cooling in order to reduce the data center demand, while solar and batteries provide power

with no carbon emissions. A creative financing structure was also set up in order to determine

what proportion of each technology was to be used in order to generate maximum returns for all

the different actors, with the full use of the ITC, leading to the following mix:

17% of the energy is produced by PV and 83% is produced by fuel cells. The carbon footprint of

the project can be calculated:

559 x 83% + 0 x 17% = 465 lbs/MWh

The project features a 70% reduction of CO2 emissions compared to NRG’s current portfolio,

with an average 1559 lbs/MWh.

These environmental performances will be sufficient to drive NRG’s effort to reduce by 50%

current emissions. Future significant price reductions are also expected in fuel cells and battery

storage technology; with an increase in hydrogen penetration in the energy market, allowing

NRG to meet its longer term emissions goals.

APPENDIX 3.1: PVSyst study

APPENDIX 5.1: Main assumptions made in the financial model

Table: Debt information for the proposed technical solution

Debt arrangement fees 2%

Construction financing interest rate 7%

Interest rate on cash 0.50%

Table: CAPEX and OPEX information for the proposed technical solution

Batteries CAPEX costs were calculated based on required capacity.

Table: Other financial assumptions made for the model

APPENDIX 5.2: IRR output tables for different debt/equity and PPA price scenario