Private equity financing of renewable energy: A study of … · 2016. 8. 11. · Figure 6: IRR vs WACC for exit in years 3,7 and 1225 Figure 7: IRR vs feed-in tariff and tariff degression

Private equity financing of renewable energy:

A study of profitability and feasibility in European markets

Author: Burak Kakdaş

Program: MSc in Finance and Private Equity, 2015-2016

Course: FM410: Private Equity

Word Count��6,476

“The copyright of this dissertation rests with the author and no quotation from it or

information derived from it may be published without prior written consent of the author.”

Burak Kakdas - Private equity financing of renewable energy

1

Adı-Soyadı Burak Kakdaş

Referans No. JM- 122 Sözleşme No. TR2012/0136.08.01/122

Başvuru Yaptığı Sektör

(Kamu-Üniversite-Özel Sektör) Üniversite

Başvuru esnasında Bursiyerin Bağlı

Olduğu Kurum

Bilkent Üniversitesi

Bursiyerin Bağlı Olduğu Kurumun İli Ankara

Başvuru esnasında Bursiyerin Bağlı

Olduğu Kurumdaki Unvanı Öğrenci (Lisans)

Çalıştığı AB Müktesebat Başlığı Mali hizmetler

Öğrenim Gördüğü Ülke Birleşik Krallık - İngiltere

Şehir Londra

Yabancı Dil İngilizce

Üniversite Londra İkstisat Okulu

Fakülte Finans

Bölüm Finans

Program Adı MSc Finans ve Özel Sermaye

Programın Başlangıç/Bitiş Tarihleri (örn.

Ekim 20…./Eylül 20….)

Eylül 2015/Haziran 2016

Öğrenim Süresi (ay) 9

Tez/Araştırma Çalışmasının Başlığı Yenilenebilir enerjinin özel sermayeyle

finansmanı: Avrupa marketlerinde karlılık ve fizibilite çalışması

Danışmanının Adı/Soyadı Dr. Juanita Gonzalez-Uribe

Danışmanının E-posta Adresi [email protected]


2

Name/Surname

Burak Kakdas

Reference No. JM - 122 Contract No. TR2012/0136.08.01/122

Applied From

Public Sector/University/Private Sector

University

Institution on the date of application Bilkent University

City of the Institution on the date of

application

Ankara

Title Student (Undergradute)

Related EU Acquis Chapter Financial Services

Country of Host Institution United Kingdom – England

City of Host Institution London

Language of the Programme English

Name of the Host Institution London School of Economics (LSE)

Faculty Finance

Department Finance

Name of the Programme MSc Finance and Private Equity

Start/End Dates of the Programme

(i.e.September 20…./October 20….)

September 2015/ June 2016

Duration of the Programme (Months) 9

Title of the Dissertation/ Research

Study

Private equity financing of renewable

energy: A study of profitability and

feasibility in European markets

Name of the Advisor Dr. Juanita Gonzalez-Uribe

E-mail of the Advisor [email protected]


1

Özet

Kurulu güneş ve rüzgar enerjisi güç santrallerinin satınalma senaryosu için genel bir kaldıraçlı

satın alma modeli (leveraged buyout-LBO) geliştirildi. LBO modeline verilen girdilere göre

gelir tablosu verilerini ayarlamak adına, kurulu yenilenebilir enerji güç santralleri için bir

karlılık modeli önerildi ve uygulandı. Aynı şekilde, satınalma ve çıkış sırasında değerleme

çarpanlarını santral nitelikleri ve diğer faktörlere göre ayarlayabilmek adına, yenilenebilir güç

santrallerinin değerlemesi indirgenmiş nakit akışı (discounted cash flow-DCF) modeliyle

yapıldı. Santral nitelikleri, piyasa değişkenleri, politik değişkenler ve yatırım stratejisinin iç

karlılık oranına (internal rate of return-IRR) etkisi LBO modeli kullanılarak analiz edildi. Her

değişkenin analizi üzerine yatırım ve politika tartışması yapıldı. Son olarak, Avrupa’da

bulunan birbirinden farklı ve varsayımsal dört santralin kaldıraçlı satınalma senaryoları göz

önüne alındı. Bu senaryolar için değişkenler santralde kullanılan teknolojinin nitelikleri,

piyasa durumu ve santralin bulunduğu ülkedeki politik etkenler göz önüne alınarak seçildi.

Geliştirilen LBO modeli kullanılarak, her senaryoda elde edilebilecek azami iç karlılık oranı

ve bu oranı elde eden optimal strateji değişkenleri tespit edildi. Bu analiz üzerinden

varsayımsal santrallerin özel sermaye veya altyapı yatırım fonu stratejileri için uygunluğu

tartışıldı.


2

Abstract

A generic LBO model is implemented for the buyout scenario of an operating solar or wind

energy power plant. A profit model is proposed and implemented for an operating renewable

energy plant in order to adjust income statement items for any input given to LBO model.

Similarly, a DCF valuation of a renewable power plant is implemented in order to adjust

valuation multiples in entry and exit for plant properties and other factors. Effect of plant

properties, market factors, policy measures and deal strategy on IRR is analysed using the

LBO model. Investment and policy implications over each factor’s analysis are discussed.

Finally, hypothetical LBO scenarios of four different plants in Europe are considered. The

factors for these scenarios are chosen to represent characteristics of the technology used in

plant, market conditions and policies of the country plant is located. Maximum IRR

achievable from the asset and the deal strategy leading to that IRR is found using the LBO

model. Whether each of these hypothetical plant is a suitable target for private equity or

infrastructure fund strategy is discussed.


3

TableofContents

1. Introduction 6

2. MethodologyandImplementation 8

2.1. Profitmodel 8

2.2. DCFValuationandImpliedMultiples12

2.3. LBOModel 15

3. AnalysisandDiscussionofFactorEffectsonIRR 16

3.1. Capacityfactor 19

3.2. Plantage 20

3.3. O&MCostsandOperationalImprovements 22

3.4. Capitalcosts 24

3.5. WACC 25

3.6. Feedintariffandtariffdegressionrate 26

3.7. Taxrate 28

3.8. Depreciationschedule29

3.9. Interestrateondebt 31

3.10. Leverageratioandcashsweep 32

4. LBOscenariosofrenewableenergypowerplantsinEurope 34

5. Conclusion 38

6. Bibliography 39


4

List of Tables

Table 1: Capacity factors of wind and solar power technologies (NREL-Utility Scale Energy

Technology Capacity Factors, 1 June 2016) 9

Table 2: Operational and maintenance costs of wind and solar power technologies (Freris and

Infield 2008, p196) 11

Table 3: Capital costs of wind and solar power technologies (Kost et al 2013, p10) 13

Table 4: Summary of inputs for all four plants in Europe considered for LBO 35

Table 5: Maximum IRR for LBO of solar PV plant in Portugal 36

Table 6: Maximum IRR for LBO of CSP plant in Spain 36

Table 7: Maximum IRR for LBO of onshore wind farm in France 37

Table 8: Maximum IRR for LBO of offshore wind farm in Germany 37


5

List of Figures

Figure 1: EV/EBITDA multiples implied by plant age 17

Figure 2: IRR vs capacity factor 19

Figure 3: IRR vs plant age in entry for 20%,50% and 80% capacity factors 20

Figure 4: IRR vs O&M for 0%,10% and 20% operational improvement scenarios 22

Figure 5: IRR vs capital cost for still depreciating and fully depreciated plants 24

Figure 6: IRR vs WACC for exit in years 3,7 and 12 25

Figure 7: IRR vs feed-in tariff and tariff degression 26

Figure 8: IRR vs tax rate for depreciating and fully depreciated plants 28

Figure 9: IRR vs age of plant with 20% capacity factor under different depreciation schedules

29

Figure 10: IRR vs age of plant with 80% capacity factor under different depreciation

schedules 30

Figure 11: IRR vs interest rate on debt for different investment horizons 31

Figure 12: IRR vs leverage for different cash sweep ratios 32

Figure 13: IRR vs cash sweep rate for exit in 5,7 and 12 years 33


6

1. Introduction

The purpose of this study is to analyse the profitability of private equity deals that target built

and operating wind and solar power plants. A generic leveraged buyout (LBO) model is

implemented to analyse different scenarios. As the goal is to create a generic LBO model to

analyse different investment scenarios, P&L and valuation multiples also also had to be

adjusted for each case. Thus, profit model and valuation parameters are also modelled

explicitly.

Operating renewable energy plants have relatively simple operations. With some simplifying

but realistic assumptions, a simple profit model can be implemented. In Section 2.1, the

implemented profit model of a renewable energy power plant is explained. Relevant power

economics terminology and underlying laws of physics are also introduced in this section.

Given the financials of a firm, LBO analysis requires valuation multiples at entry and exit.

This is typically done using an EV/EBITDA multiple, which has been the method followed in

this study as well. Usually, the valuation multiple is extracted from an evaluation of

comparable firms or transactions. However, this approach is unfitting for this study, because

unlike other firms, power plants have predictable and finite lifespan.

Assuming other factors determining multiples are constant or are averaged, and given that

revenues and costs of an operating renewable energy plant are relatively stable over its

lifetime, finite lifespan of a plant implies a rapidly declining valuation multiple as the plant

ages. Thus, a fixed entry and exit multiple pair implied by a market study and simply adjusted

for expectations will be erroneous. Further, controlling for the age of plant and other crucial

factors that may affect valuation multiple is impractical in a market study, because renewable

power firms that are sold in transactions or are traded in the market generally include a

portfolio of plants in different locations, developed in different times, using different

technologies and different energy resources which often are not all renewable.

Contrary to comparable analysis method, fundamental analysis using Discounted Cash Flows

method is perfectly fit for valuation of renewable energy power plants since cash flows are


7

highly predictable. Thus, I have implemented a DCF model to find the enterprise value of a

plant throughout its lifespan given plant characteristics and market conditions.

Implementation of DCF model is explained in Section 2.2. EV/EBITDA multiples implied by

DCF valuation throughout the lifespan of the plant are used as inputs for the LBO model.

With plant financials, EV/EBITDA multiples and some further assumptions in place, the LBO

model is implemented which allows analysing different investment strategies by changing

leverage, operational improvements and cash sweep parameters and observe IRR over

different investment horizons. Implementation of LBO model is explained in Section 2.3.

In Section 3, the effects of plant characteristics, market conditions, policy variables and deal

strategy on IRR are studied using models developed. Investment and policy implications of

observations are discussed for each factor that affects IRR.

Finally, in Section 4, LBOs of four hypothetical plants are analyzed using factors that are

taken from real life plants and academic studies. Plants considered are a solar Photovoltaic

(PV) plant in Portugal, a concentrated solar power (CSP) plant in Spain, an onshore wind

farm in France and an offshore wind farm in Germany. Whether the plant is a good target for

private equity funds, which aim to achieve above 15%-20% IRR in 5-7 years, or infrastructure

funds, which aim for 10-15% IRR in 10-15 years, is discussed for each plant (Smith 2016,

p2).


8

2. Methodology and Implementation

Throughout the analysis, all cash transactions are assumed to occur at the end of the financial

year for simplicity.

2.1. Profit model

The revenue generated by a power plant depends on its electricity energy output, denominated

in Megawatt-hours (MWh), and price at which unit electricity is sold, denominated as

currency/MWh.

There are three factors that determine the output: nameplate capacity, capacity factor and

degradation rate. Nameplate capacity, expressed in Megawatts (MW), gives the maximum

power output of a power station if the plant is operated all the time with full capacity and

under perfect conditions (EIA, Accessed on 1 June 2016). In short, nameplate capacity gives

the potential output of a plant by design.

Power potential of the plant decreases over time since it gradually wears-out. The rate at

which a plant’s maximum potential decreases is called “degradation rate” (Jurdan and Kurtz

2013, p1).

Although the plant is designed to deliver nameplate capacity, its actual output depends on

physical, operational and technical factors. This effect is captured by the “capacity factor” of

the plant, which is the ratio of actual output of a plant to its nameplate potential at any given

time (US Nuclear Regulatory Commission, 1 June 2016). Capacity factor varies according to

energy resource, technology deployed and location of the plant. Table 1 presents ranges of

capacity factor observed today for different solar and wind technologies.


9

Technology Min Median Max

Wind, onshore 26% 38% 52%

Wind, offshore 31.52% 39% 45%

Solar, photovoltaic (PV) 16% 20.30% 30%

Concentrated solar power (CSP) 25.30% 38.50% 85%

Table 1: Capacity factors of wind and solar power technologies (NREL-Utility Scale Energy Technology Capacity Factors, 1 June 2016)

Given nameplate capacity (N), degredation rate (d) and capacity factor at time t, C(t) power

capacity of a renewable plant at time t is given by

! " = %× 1 − ) *×+ " (-.)(01. 1)

Electric power is the rate at which electrical energy is transferred by an electric circuit. Power

and energy are related with time, so the electric energy generated by a plant from time t0 to t1

is calculated as follows:

034567 "8, ": = ! " )"*;

*<(01. 2)

The SI unit of energy is Joule (J), however Watt-hour is generally used when referring to

energy for everyday use (1 Megawatt-hour = 1 MWh = 106 Watt-hour).

Assuming all degradation occurs at the end of the year and using the average capacity factor

across the year, power of a plant is constant throughout the year. Then, annual (energy) output

of a plant is calculated as follows:

0* = !*×365)A7B×24ℎEF5B)A7 = !*×8760 .A"" − ℎEF5 (01. 3)


10

Replacing Pt with Eq. 1, output of a plant (energy produced in a year) is given by

0* = %×(1 − ))*J:×+×8760ℎEF5B -.ℎ 01. 4

where N is the nameplate capacity and d is the degradation rate.

Electricity is generally traded in wholesale energy exchanges and electricity prices are

characterised by high volatility since demand and supply itself is very volatile throughout the

day and across seasons. However, renewable energy projects mostly enjoy a fixed price

structure over a long term contract thanks to investment incentive schemes called “feed-in

tariffs” offered by most European countries. Each country in Europe determines tariff rates

separately. Offered rates often change within the same country as well according to harvested

resource, installed technology and capacity of the plant. These schemes may also include a

“tariff degression”, which means that the offered price will decrease over time. Thus, a feed-

in tariff structure with tariff degression mechanism is adopted for this study. Under this

structure, the electricity price at year t is given by

K* = K×(1 − L)*J:(01. 4)

where p is the original feed-in tariff (price at first year of plant’s operation) in €/MWh and L

is the rate at which tariff decreases over years.

With output and price models in place, the revenue generated by the plant at year t is

calculated as follows:

M4N43F4* = K*×0* = K×(1 − L)*J:×%×(1 − ))*×+×8760 € (01. 5)

The cost structures of wind and solar plants are pretty simple since there is no fuel or other

input required for production. The costs of running a power plant are referred to as

Operational and Maintenance (O&M) costs, which is quoted for per MW nameplate capacity.

O&M includes insurance, rent and rates set by the local administrative authority, as well as

the costs of labour and materials used for operations and maintenance (Freris and Infield


11

2008, p196). O&M costs depend on renewable resource harvested, technology used in the

plant and country. Estimates of O&M costs for wind and solar technologies in Europe are

presented in Table 2.

Technology O&M(€ cents /kWh)

Wind, onshore 0.9-1.5

Wind, offshore 1.5-3

Solar, photovoltaic (PV) 0.15-0.8

Concentrated solar power (CSP) 1.8-3.15

Table 2: Operational and maintenance costs of wind and solar power technologies (Freris and Infield 2008, p196)

Assuming O&M costs increase with inflation, the cost of running a power plant at year t is

calculated as follows:

+EB"* = P&- ×(1 + S)*J:×%(01. 6)

where O&M is the cost of running a one MW capacity power plant at first year of operation

given in €m/MW and S is the prevailing inflation rate.

The difference between equations 5 and 6 give the gross profit at year t, which concludes

profit model implemented for this study.


12

2.2. DCF Valuation and Implied Multiples

DCF analysis requires knowing Free Cash Flows (FCF) and a discount rate. Calculating FCF

at each year requires knowledge of Earnings Before Interest and Tax (EBIT), tax rate,

depreciation, capital expenditures (CAPEX) and change in net working capital (∆NWC) at

each year.

A power plant requires a small inventory consisting of spare parts and tools to be used for

maintenance, as O&M includes materials required for maintenance work. However, this is

negligible for the purpose of this study, given the majority of O&M is related to rent and

insurance and maintenance needs of renewable energy plants are predictable since these are

reliable systems. Receivable and payable accounts are also assumed to be zero, since

electricity is fed into the transmission network and sold as it is generated. Thus, net working

capital of plant is assumed to be zero throughout the operational life of a plant.

A renewable power plant requires a significant investment upfront at development phase.

However, once deployed it mostly requires regular maintenance work which is already

accounted for in O&M. Although some technologies such as solar photovoltaic panels may

require some further CAPEX in their mid-life due to some electric hardware replacements,

these do not represent a huge cost compared to the scale of the cash flows and is negligible for

the purpose of this analysis. Thus, CAPEX is also assumed to be zero throughout the

operational life of a power plant.

Amount of investment required per MW nameplate capacity during the development phase is

referred as “capital cost” (sometimes “overnight capital cost”), denominated in € per MW

nameplate capacity. Capital cost includes the cost of the plant, land acquisition (unless land is

rented), grid connection and initial financing costs (Freris and Infield 2008, p196). An

estimate range of capital costs for different renewable technologies for Europe is given in

Table 3.


13

Technology Minimum (€/kW) Maximum (€/kW)

Wind, onshore 1000 1800

Wind, offshore 3400 4500

Solar, photovoltaic (PV) (utility scale) 1000 1400

Concentrated solar power (CSP) 2500 7000

Table 3: Capital costs of wind and solar power technologies (Kost et al 2013, p10)

Part of total upfront investment spent on electric and other hardware is capital expenditure.

Following the analysis on International Renewable Energy Agency’s Renewable Energy Cost

Analysis this ratio is set to 83% for solar and wind plants for the rest of this study (Taylor,

Daniel and So 2015).

Since CAPEX during operation phase of a plant is zero, amount of Plant Property and

Equipment that will be depreciated is set by the CAPEX incurred during development phase

of the plant, which will be a fraction of total upfront investment. A straight line depreciation

method is assumed with zero salvage value. Years until full depreciation is left as a variable,

since accelerated depreciation schemes are used in some countries as an investment incentive.

Noting that O&M costs include all the operating expenses of a power plant except for

depreciation, we can calculate EBIT by

0TUV* = W5EBB!5EXY"* − Z4K54[YA"YE3* = M4N43F4* − +EB"* − Z4K54[YA"YE3*(01. 7)

With these assumptions in place, depreciation, EBIT, EBITDA, and FCF figures can be

calculated for each year. Given CAPEX and NWC is zero, FCF of a plant at year t is

calculated as follows

\+\* = 0TUV× 1 − ] + Z*(01. 8)

where Dt is depreciation at year t.


14

For discount rate, Weighted Average Cost of Capital (WACC) figures provided in levelised

cost study of renewable energy technologies by Fraunhofer Institut of Renewable Energy

Technologies are used (Kost et al 2013, p11). This paper provides WACC estimates for

different renewable resources, technologies and locations assuming 60-80% leverage, which

makes provided WACC rates suitable for DCF valuation of a plant that is subject to an LBO.

With FCF and discount rate in place, Enterprise Value (EV) at year t>1 of a renewable energy

power plant that started operating at year 0 is calculated as follows

0 *̂ =\+\*

1 +._++ *

`

*

(01. 9)

where T is the lifetime of the power plant in years which changes according to technology

used in the plant.

Dividing EVt with EBITDAt gives the EV/EBITDA multiple implied by DCF valuation of the

plant which has T-t years of operational life left, which is given as an input to LBO model.


15

2.3. LBO Model

Given entry and exit multiples implied by DCF valuation in addition to all the information

and parameters that were discussed up to now, implementing LBO model requires setting

sources and uses of funds, determining the debt schedule and management stake.

I assumed that all shares of plant are bought and existing debt is paid down at entry. Plant is

assumed to have no excess cash before buyout. Transaction costs are assumed to be 2% of the

Enterprise Value. Thus, Total Uses of Funds equals EV plus transaction costs or 102% of

entry EV.

A single tranche of amortizable debt is assumed to finance the deal together with Sponsor

Equity. Leverage, interest rate on debt and cash sweep are kept as variables. Any available

cash that is not used for debt repayment is assumed to be paid as dividend to shareholders at

the year it is generated.

While the revenues of the plant are set, there may be room for operational improvements in

form of cost reductions. Operational improvements are modelled as an instantaneous percent

deduction over O&M costs for simplicity.

A plain vanilla management option of 5% is assumed.

Tax loss carry forwards are not allowed.

Given these assumptions and required inputs, LBO model is implemented and IRR figures for

investment horizon between 1 to 15 years are obtained.


16

3. Analysis and Discussion of Factor Effects on IRR

The list of inputs required for analysis are divided into categories of plant properties, market

variables, policy variables and deal strategy. The complete list of inputs and their values for

benchmark case are given below. Benchmark value of each parameter is chosen from its

actual range such that there will be no bankruptcy situation during analysis, which is a totally

different scenario and not in the scope of this analysis.

• Plant properties

o Nameplate potential (MW) = 100MW

o Degradation rate (%) = 0.5%

o Plant lifetime (years) = 25 years

o Plant age at entry (years) = 11 years

o Capacity factor (%) = 40%

o Operational and Management (O&M) Costs (€m/MW) = 0.35 €cents/kWh

o Capital costs (€m/MW) = 1.2 €m/MW

o CAPEX per € capital cost (%) = 83%

o WACC (%) = 5%

• Market Variables

o Inflation (%) = 2%

o Interest rate on debt (%) = 4%

• Policy variables

o Feed-in tariff (€/MWh) = 50 €/MWh

o Tariff degression (%) = 0%

o Tax rate (%) = 30%

o Depreciation horizon (years) = 10 years

• Deal strategy

o Leverage (%) = 80%

o Cash sweep (%) = 100%

o Operational improvements (%) = 0%


17

Input figures of benchmark case are preserved throughout this section, unless stated

otherwise. Also, whenever there is no specific mention of it, an investment life of 7 years is

assumed.

Plant properties, market variables and policy variables are first given to DCF model, which

generates EV/EBITDA multiples to be used in the LBO model. EV/EBITDA multiple for

plants of different age are presented in Figure 1 below. Notice how multiple decreases as

plant ages. This illustrates the shortcoming of traditional market implied EV/EBITDA

approach which was discussed before.

In secondary axis of the same figure, the ratio of EV/EBITDA multiple at exit to

EV/EBITDA multiple at entry for an investment life of 7 years is given. Notice that this ratio

is always below 100% and it decreases as plant age increases. This observation will also be

essential in evaluating results obtained in this section. Also note that after age-18, the ratio is

zero because the EV/EBITDA multiple at exit is zero since the plant will have already

completed its lifetime prior to exit.

Figure 1: EV/EBITDA multiples implied by plant age

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0,00

2,00

4,00

6,00

8,00

10,00

12,00

0 5 10 15 20 25

ExitMultip

le/En

tryM

ultip

le

EV/EBITD

A

PlantAge

EV/EBITDAvsPlantAge

EV/EBITDA Exitmultipletoentrymultipleratiofor7yearsofinvestmentlife


18

Once multiples are obtained, all these inputs are fed into the LBO model for IRR analysis.

The purpose of this section is to observe how IRR responds to different factors. How IRR

responds to changing each factor is studied. Thus, the nominal values observed for IRR are

not as important, as the values are not chosen to represent investment cases. Also, the test

range for factors are chosen to cover all possible range, even if it may be somewhat

unrealistic in to understand the behaviour of the full behaviour of IRR in response to that

factor for the same may be observed at more conservative values with another set of inputs.


19

3.1. Capacity factor

IRR increases with capacity factor at a decreasing rate as observed in Figure 2. This implies

that while private equity investors will always prefer a higher capacity factor plant, after

reaching about 40% increasing capacity factor will not be as lucrative.

Figure 2: IRR vs capacity factor

Considering median capacity factor for all four technologies are below 40%, an active

financial sponsor involvement in renewable power generation market can further motivate

better use of capital in renewable energy industry. By giving an early exit option to good

developers who find energy efficient locations and build plants with higher capacity factors,

financial sponsors may release equity to good developers who can use it to invest in new

plants.

4,00%

4,50%

5,00%

5,50%

6,00%

6,50%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

IRR

CapacityFactor

CapacityfactorvsIRR


20

3.2. Plant age

The capacity factor of the plant changes how IRR responds to plant age in entry, as observed

in Figure 3.

For medium and high capacity factor plants, IRR is maximum when the plant is new. Then,

IRR decreases until plant age reaches 10 because the ratio of exit to entry multiple decreases

with plant age. Once the plant gets fully depreciated at age 10, the depreciation tax shield

goes away which makes debt tax shield more valuable and boosts IRR. Then, IRR keeps

decreasing slowly till age 18, again due to decreasing exit to entry multiple ratio. Finally, at

age 18 IRR makes a big downwards jump and becomes negative because the remaining useful

lifetime of plant is less than 7 years.

Figure 3: IRR vs plant age in entry for 20%,50% and 80% capacity factors

-20%

-15%

-10%

-5%

0%

5%

10%

0 2 4 6 8 10 12 14 16 18 20

IRR

Plantage(years)

IRRvsageofplantinentryfordifferentcapacityfactors

CF=20% CF=50% CF=80%


21

The movement of IRR with respect to age once plant is fully depreciated is similar for low

capacity plants. However, IRR for a low capacity factor plant is negative until full

depreciation, then it makes a big positive jump. This happens because a lower capacity factor

plant has lower output, lower revenues and lower EBIT. In fact, 20% capacity factor plant has

negative EBIT with benchmark inputs. On the other hand, depreciation is not affected by

capacity factor. Thus, the debt tax shield is not valuable for low capacity factor plants, which

is a significant source of value creation for an LBO deal, until they get fully depreciated.

Following this comparison, a financial sponsor should aim to buy a younger plant given that it

has a sufficiently high capacity factor, so that the effect of decaying valuation multiple is

minimized. On the other hand, if the plant has a low capacity factor, it shouldn’t even be

considered for an LBO until it gets fully depreciated. For the benchmark case, this threshold

capacity factor is 24% beyond which EBIT is positive from age-1 and IRR response to plant

age is as in 50% or 80% capacity factor cases in Figure 3.


22

3.3. O&M Costs and Operational Improvements

Figure 4: IRR vs O&M for 0%,10% and 20% operational improvement scenarios

When there is no operational improvement, increasing O&M has no effect on IRR because

O&M costs are fairly priced during both entry and exit. Thus, IRR vs O&M is a flat line for

no operational improvement case in Figure 4. In cases with operational improvements, IRR

increases with O&M since higher initial costs implies higher nominal savings whose present

value will be higher under same level of operational improvements.

IRR is observed to be very sensitive operational improvements. A 10% operational

improvement brings 18% IRR and a 20% operational improvement brings 28% IRR with the

benchmark case, which has only 7% IRR without any improvements. So, finding badly

operated plants and turning them around pays of very well, provided that financial sponsor is

capable of identifying such plants and achieving operational improvements.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50

IRR

O&M(€cents/kWh)

IRRvsO&Mfordifferentoperationalimprovementscenarios

Noimprovement 10%O&Msaving 20%O&Msaving


23

If a financial sponsor does not plan any operational improvements, O&M costs should not

affect its decision to invest in a plant, since these costs are predictable for wind and solar

plants and are fairly priced during transactions. On the other hand, financial sponsors who

aim to create value by implementing operational improvements better invest in technologies

with higher O&M costs like offshore wind and concentrated solar power plants. Also,

considering that these technologies are less mature, their potential cost reductions are greater.

Finally, financial sponsors with operational expertise should try to buy plants with higher

O&M costs within each technology group, so that resources they spend to realise operational

improvements will have a larger payoff.


24

3.4. Capital costs The effect of capital cost on IRR is observed for capital cost range implied by Table 3 for

fully depreciated and still depreciating plant cases which is controlled by changing the age of

plant while keeping depreciation horizon constant.

Figure 5: IRR vs capital cost for still depreciating and fully depreciated plants

Figure 5 illustrates the effect of capital costs on IRR for cases where plant is fully depreciated

and not, which is implied by controlling plant age. For fully depreciated plants, capital cost

has no effect on IRR, which is expected as then they have no influence over valuation

multiples. However, if plant is not fully depreciated, IRR decreases with increasing capital

costs because higher capital costs imply high depreciation tax shield, which makes debt tax

shields less valuable and decreases IRR.

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0

IRR

Capitalcosts(€/kW)

IRRvsCapitalCosts

Stilldepreciating Fullydepreciated


25

3.5. WACC Increasing WACC in this model would imply that overall risk (operational and financial risk

combined) of the acquired plant is higher.

Figure 6: IRR vs WACC for exit in years 3,7 and 12

As WACC increases IRR increases for exit in each year which makes sense since expected

return should increase as risk of investment increases. The mechanism in this model that

creates this relation is through exit to entry valuation ratio. Higher WACC makes

EV/EBITDA decrease at a slower rate as plant gets older. Thus, the ratio of exit multiple to

entry multiple increases, which brings higher IRR.

Understanding the relationship between risk level of a plant and IRR given investment

lifetime is important to determine whether a plant is a good target for a specific fund strategy.

For instance, in Figure 6, a plant whose WACC is between 7-7.5% gives an IRR between

10.4%-11.27% in 12 years, which means that it is suitable for infrastructure fund strategy.

The same plant gives below 15% IRR for exits in years 5 and 7, which makes it a bad target

for private equity funds.

-10%

-5%

0%

5%

10%

15%

20%

25%

2% 3% 4% 5% 6% 7% 8% 9% 10%

IRR

WACC

IRRvsWACC

Exitinyear5 Exitinyear7 Exitinyear12


26

3.6. Feed in tariff and tariff degression rate

Figure 7: IRR vs feed-in tariff and tariff degression

Regardless of tariff degression rate, IRR increases at a decreasing rate as feed-in tariff

increases. Sensitivity of IRR to feed-in tariff decreases as tariff degression rate decreases.

Figure 7 shows for 0-3% tariff degression rate, a feed-in tariff beyond €80/MWh does not

change IRR that much. Noting that €80/MWh is much less than what is offered in most

European countries, this observation has an important policy implication. If policy makers

aim to motivate private equity investment in renewable energy resources, while the existence

of feed-in tariff structure is very effective as it increases the debt capacity of power plants by

removing revenue volatility, the amount offered is not as important. Further, the lower tariff

degression rate is, the less important is the amount offered.

Thus, the cost of feed-in tariff schemes to governments may be decreased if private equity

investors instead of developers are targeted with these schemes. Increasing LBO activity may

0%

1%

2%

3%

4%

5%

6%

7%

8%

20 40 60 80 100 120 140

IRR

Feedintariff(€/MWh)

IRRvsfeed-intariffandtariffdegression

0%degression% 1%degression 2%degression 3%degression


27

increase overall investment in renewable energy resources by extending exit options for

developers. However, whether private equity targeted feed-in tariff schemes would be as

effective as schemes that target developers directly is unclear, and the answer is out of scope

and beyond the reach of this study.

On the other hand, IRR is very sensitive towards tariff degression rate. In fact, a tariff

degression rate higher than inflation causes EBITDA to decrease over time. Coupled with the

effect of decreasing valuation multiple, this effect can quickly destroy IRR. Thus, a feed-in

tariff scheme with high tariff degression rate will be a strong disincentive for LBOs.


28

3.7. Tax rate

Figure 8: IRR vs tax rate for depreciating and fully depreciated plants

For fully depreciated plants, increasing tax rate increases IRR since it makes debt tax shield

more valuable.

For plants that are not fully depreciated yet, depreciation tax shield comes into play in

addition to debt tax shield. Higher tax rate increases the value of remaining depreciation tax

shields and thus the entry multiple increases. This effect decreases IRR. However, increasing

tax rate still makes debt tax shields more valuable. The trade-off of these two forces gives a

bell shaped curve as the one observed in the figure above.

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

0% 10% 20% 30% 40% 50% 60% 70% 80%

IRR

Taxrate

IRRvsTaxRate

Stilldepreciating(Age=6) Fullydepreciated(Age=11)


29

3.8. Depreciation schedule

The effect of changing depreciation horizon depends on the capacity factor of the plant. With

an accelerated depreciation schedule, LBO of plants with low capacity factor becomes

feasible at an earlier age since this happens only after plant is fully depreciated as discussed

before. It is also possible to achieve higher IRR from these plants under accelerated

depreciation schedule. Figure 9 illustrates both of these observations. So, an accelerated

depreciation schedule would increase LBO activity in countries where plants have low

capacity factor (such as solar plants in the UK).


For high capacity factor plants, effect of depreciation schedule depends on the plant age. An

accelerated depreciation schedule decreases the IRR at the very early ages of the plant as

increased depreciation tax shields remove the tax benefit of debt. However, accelerated

depreciation schedule wipes out depreciation tax shields at an earlier age. This boosts IRR

significantly as there are more cash flows that can benefit from debt tax shield and earlier

investment to a fully depreciated plant allows capturing a higher exit to entry multiple ratio.

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

0 2 4 6 8 10 12 14 16 18 20

IRR

Plantage(years)

IRRvsAgeofplantwithdifferentdepreciationschedules(20%capacityfactor)

Fulldepreciationin5years Fulldepreciationin10years


30

Thus, IRR increases for mid-aged plants under a shorter depreciation horizon. These effects

are illustrated in Figure 10. An accelerated depreciation tax shield would increase LBO

activity over mid-aged high capacity plants, while it would make young high capacity plants

less attractive for private equity investors.


6%

7%

7%

8%

8%

9%

9%

10%

10%

11%

0 2 4 6 8 10 12 14 16 18

IRR

Plantage(years)

IRRvsAgeofplantwithdifferentdepreciationschedules(80%capacityfactor)

Fulldepreciationin5years Fulldepreciationin10years


31

3.9. Interest rate on debt

Figure 11: IRR vs interest rate on debt for different investment horizons

In general IRR decreases as interest rate on debt increases as expected, since this makes debt

financing more costly. However, IRR becomes less sensitive to interest rate on debt as the

investment life increases as observed in Figure 10. This implies that infrastructure fund

strategy is more resilient to conditions on debt financing markets.

-10%

-5%

0%

5%

10%

15%

0% 2% 4% 6% 8% 10%

IRR

Interestrateondebt

IRRvsInterestRateonDebtfordifferentinvestmenthorizons

5years 7years 12years


32

3.10. Leverage ratio and cash sweep

Figure 12: IRR vs leverage for different cash sweep ratios

IRR increases with leverage as expected since a less equity is required during entry and more

debt tax shield is generated by adding more debt to balance sheet.

Notice in Figure 11, decreasing cash sweep increases IRR significantly. With 80% leverage

for instance, decreasing cash sweep to 75% increases IRR by 1%. Decreasing cash sweep to

50% increases IRR for another 2%, to a total of 10%. This makes sense since decreasing cash

sweep means financial sponsor will get back a greater portion of its equity early on and a

smaller amount of equity will be exposed to the negative effect of decreasing EV/EBITDA

multiple.

As discussed before and observed in Figure 1, the ratio of exit to entry multiple decreases as

investment life increases. Thus, a lower cash sweep gets more desirable if investment life is

longer. In that sense, infrastructure funds whose strategy is to hold assets for an extended

00%

05%

10%

15%

20%

25%

40% 50% 60% 70% 80% 90% 100%

IRR

Leverage

IRRvsLeverageandCashSweep

50%cashsweep 75%cashsweep 100%cashsweep


33

time, should decrease cash sweep and pay more dividends. However, note that decreasing

cash sweep increases the likelihood of bankruptcy, especially if the plant will be hold for

longer. Lower cash sweep becomes feasible while avoiding bankruptcy at lower leverage

ratios. Figure 12 displays IRR response to cash sweep for different investment horizons.

Minimum cash sweep is kept at 65% in to avoid bankruptcy prior to an exit in 12 years.

Figure 13: IRR vs cash sweep rate for exit in 5,7 and 12 years

So, there is a trade off between these two IRR boosting strategies: high leverage and low cash

sweep. Since IRR sensitivity to cash sweep increases with investment life, it makes sense to

decrease cash sweep at cost of decreasing leverage to an extent for longer investment life. On

the other hand, increasing leverage is more effective in increasing IRR than cash sweep for

shorter investment life. However, there is a tradeoff regardless of investment life and holistic

effect of these two factors should be analyzed to maximize IRR.

6,0%

6,5%

7,0%

7,5%

8,0%

8,5%

9,0%

9,5%

60% 65% 70% 75% 80% 85% 90% 95% 100% 105%

IRR

Cashsweeprate

IRRvsCashSweepRatefordifferentinvestment lives

Exitin5years Exitin7years Exitin12years


34

4. LBO scenarios of renewable energy power plants in Europe

For this part of the study, some hypothetical investment scenarios are considered for four

different technologies applied to plants in four different European countries: a solar PV plant

in Portugal, a CSP plant in Spain, an onshore wind farm in France and an offshore wind farm

in Germany.

Depreciation horizon is assumed to be 5 years so that wind farms with 20 year total lifetime

will still be operational after an investment life of 12 years. Also, plant is assumed to be 6

years old so that it is fully depreciated, which makes it a more suitable target for LBO.

Inflation is set to average of last 10 years in country of plant. Tax rate is set to corporate tax

rate in target country.

For capital and O&M costs, average of the range given for each technology in Tables 2 and 3

are used except for offshore-wind case. The offshore wind farm with average O&M (2.25

€cents/kWh) did not make sense to operate in any financing scenario, so I have considered the

case with minimum O&M cost offshore wind farm instead.

Values for total lifetime of plant, interest rate on debt and WACC are taken from Fraunhofer

Institut of Solar Energy’s study on levelised cost of renewable energy technologies (Kost et al

2013, p11). For plants in Spain and Portugal, values provided for “regions with high solar

irradiation” are used for corresponding solar technologies. For WACC of onshore wind farm

in France, the spread between German and French 10-year government bonds, 0.35% (on 31

May 2016), is added to WACC provided for an onshore wind farm in Germany.

Solar PV plant’s capacity factor is set to 21.52%, which is the average capacity factor for

solar PV plants in Portugal (Energy Matters, 2 May 2014). Capacity factor of Andasol-1

Power Station, 41.5%, is used for CSP plant in Spain (NREL SAM Case Study, 1 June 2016).

For onshore wind farm in France, average capacity factor of onshore windfarms in France

measured in 2011 is used (Chabot 2012, p2). Capacity factor of Baltic-1 offshore wind farm

(44%) is used for the case of offshore wind farm in Germany (Renewables International, 29

April 2013).


35

Tariff rates and tariff degression rates that applies to each case is taken from Policy and

Measures Database of International Energy Agency (EIA, 31 May 2016).

5% operational improvement is assumed for cases of CSP and offshore wind plants, which are

less mature technologies. For onshore wind and solar PV plants, which are more established

technologies, no operational improvement is considered. Table 4 gives a summary of all

inputs for each case.

Scenario

Solar PV Plant

in Portugal

CSP Plant in

Spain

Onshore wind

farm in France

Offshore wind

in Germany

Nameplate

potential 100 MW 100 MW 100 MW 100 MW

Degradation 0.5% 0.5% 0.5% 0.5%

Plant lifetime 25 25 20 20

Plant age 6 6 6 6

Capital costs 1200 €/kW 4750 €/kW 1400 €/kW 4200 €/kW

O&M Costs 0.475

€cents/kWh

2.475

€cents/kWh 1.2 €cents/kWh

2.25

€cents/kWh

Capacity factor 21.52% 41.5% 27% 44%

WACC 6.8% 9.7% 6.25% 9.8%

Inflation 1.42% 1.66% 1.25% 1.37%

Interest rate on

debt 6% 8% 4.5% 7%

Feed in tariff 257 €/MWh 270 €/MWh 82 €/MWh 150 €/MWh

Tariff

degression 0% 0% 0% 5%

Tax rate 21% 25% 33.3% 29.72%

Operational

improvements 0% 5% 0% 5%

Table 4: Summary of inputs for all four plants in Europe considered for LBO


36

With these inputs, IRR is maximized for exits in 5,7 and 12 years while controlling for

bankruptcy, interest coverage ratio and debt to EBITDA ratio from entry to exit. Bankruptcy

is prevented by making sure that EV never falls beginning-of-period debt level. Interest

coverage ratio is kept above 1.5 and debt-to-EBITDA ratio is kept below 6 throughout

investment life. Above 80% leverage ratio is not allowed.

Maximum IRRs for different investment lives are presented with optimal leverage and cash

sweep combinations that leads to them for each plant scenario in tables below.

Investment life 5 years 7 years 12 years

Leverage 77% 77% 78%

Cash sweep 10% 10% 39%

IRR 11.64% 13.26% 12.90%

Table 5: Maximum IRR for LBO of solar PV plant in Portugal

With an IRR below 15% for exits in 5-7 years, solar PV plant in Portugal is not a good

investment for private equity funds. However, it may be a feasible investment for an

infrastructure fund as it can generate 12.9% IRR with an exit in 12 years.




IRR 25.07% 25.95% 22.76%

Table 6: Maximum IRR for LBO of CSP plant in Spain

The CSP plant in Spain looks like a lucrative investment for private equity strategy with

above 25% IRR for exits in both 5 and 7 years. It is possible to obtain up to 22.76% IRR in 12

years as well with this plant. High IRR implies that this project comes with a high risk, so it

may not be a suitable target for infrastructure fund strategy. Nonetheless, an infrastructure

fund may still bid for this plant if it considers lower operational improvements or simply


37

because higher IRR would not hurt. After all, while infrastructure funds aim for 10-15% IRR,

overall fund performances beyond 15% are very common (The 2016 Q1 Preqin Quarterly

Update: Infrastructure, p11).




IRR 13.77% 13.42% 11.57%

Table 7: Maximum IRR for LBO of onshore wind farm in France

The case of onshore wind farm in France is pretty similar to solar PV in Portugal. It is a

feasible investment for an infrastructure fund, but would not pass the hurdle rate test of a

private equity fund.


Leverage 80% 80% -

Cash sweep 77% 80% -

IRR 11.93% 8.02% 0%

Table 8: Maximum IRR for LBO of offshore wind farm in Germany

LBO of offshore wind farm in Germany is not feasible for either fund strategy. For exits in 5-

7 years, IRR is too low for private equity funds. For an exit in 12 years, it is impossible to

make any profit. The critical factor here that makes the case of offshore wind farm in

Germany so different than other three plants is the degression rate of the feed-in tariff. This

observation was made earlier in Section 3. 5% tariff degression rate is much higher than

inflation rate of 1.37%, which makes EBITDA decrease over time. Combined with the

decreasing EV/EBITDA multiple throughout investment life, this hurts IRR significantly. The

longer the investment life is, the bigger is this effect as observed in this case.


38

5. Conclusion

Feasibility and profitability of LBOs of solar and wind power plants depend on plant

properties, financing conditions, government policies and structure of schemes that aim to

incentivise investment in renewable energy resources. Further, whether the plant is a good

LBO target depends on the fund strategy.

Plants with a capacity factor above 30% would be desirable for private equity funds in

general, while infrastructure funds can afford buying plants with 20-30% capacity factors as

they aim for a lower IRR. LBOs of plants with low capacity factor are feasible only after they

become fully depreciated. Plants with high capacity factor better be bought early on to capture

a higher exit to entry valuation multiple ratio. Running and capital costs of a plant alone are

not crucial for IRR on their own, as they are priced during both entry and exit. However, high

capital costs decrease IRR on LBOs of plants that are not fully depreciated yet by decreasing

the value of debt tax shields.

Feed-in tariff schemes that offer fixed electricity prices to renewable energy plants help

increase IRR significantly by increasing debt capacity of plants as they remove revenue

volatility. Increasing the tariff rate does not effect IRR that much after a point, which may be

considered to decrease cost of these schemes to governments by targeting private equity

investors instead of directly targeting developers. Degression of tariff rates hurts IRR

significantly, and a positive tariff degression rate is a very strong disincentive for LBOs of

these plants.

Operational improvements are a significant source of value creation in LBOs of renewable

plants, as IRR is very sensitive to decreasing running costs. Decreasing cash sweep rate

increases IRR significantly as it removes the negative effect of decaying valuation multiple

due to finite lifetime of plant. Thanks to low volatility of revenues and costs, these plants

have high debt capacity and benefit of debt tax shield is significant which implies a high

leverage for these deals. However, rapidly decreasing enterprise value of the plant as it ages

imposes a trade-off between increasing leverage and decreasing cash sweep rate to avoid


39

bankruptcy. Thus, overall effect of these two deal factors should be analysed in determining

the deal strategy to obtain maximum IRR from investment.

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Survey, 2016

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