risk-return profile of offshore wind investments -...
TRANSCRIPT
Author: Diogo Matias Belo (ID: db86356)
Academic Supervisor: Torben Smith Petersen
Master Thesis - Semester 2 -
2011
Risk-return profile of Offshore Wind Investments
An alternative for institutional investors
Aarhus School of Business
MSc Finance and International Business
2
Table Of Contents
1. Introduction .................................................................................................................. 6
2. Infrastructure Investments ............................................................................................ 9
2.1. The Asset-Classes Setting: Infrastructure ............................................................. 9
2.2. Offshore Wind Energy......................................................................................... 10
2.3. The financial gap of offshore wind energy .......................................................... 11
2.4. Investment Vehicles ............................................................................................ 12
3. Previous Research ...................................................................................................... 16
4. Hypothesis .................................................................................................................. 19
H1: Investments in Offshore Wind Funds have a long-term time horizon ................ 19
H2: Investments in Offshore Wind Funds require high initial capital outflows ........ 20
H3: Investments in Offshore Wind Funds present stable cash inflows ...................... 20
H4: Investments in Offshore Wind Funds are low-risk and low-return investments . 21
5. Data ............................................................................................................................. 23
5.1. General Assumptions: ......................................................................................... 23
- Investment Costs ......................................................................................................... 23
- O&M costs ................................................................................................................... 24
- Electricity Production .................................................................................................. 24
- Turbine Lifecycle ......................................................................................................... 25
- Discount Rate .............................................................................................................. 25
- Taxes ............................................................................................................................ 26
- Depreciation ................................................................................................................ 26
5.2. Country-Specific: ................................................................................................ 27
- Denmark (FIT): ............................................................................................................. 27
- Ireland (FIT): ................................................................................................................ 27
- Netherlands (FIT): ........................................................................................................ 28
- Sweden (TGC): ............................................................................................................. 28
- UK (TGC): ..................................................................................................................... 29
3
5.3. Electricity Price: .................................................................................................. 29
6. Methodology ............................................................................................................... 34
Monte Carlo Simulation ............................................................................................. 34
Portfolio Financing ..................................................................................................... 38
Performance Indicators ............................................................................................... 41
- Return .......................................................................................................................... 41
- Standard Deviation ...................................................................................................... 42
- Time-length ................................................................................................................. 42
- Downside Risk ............................................................................................................. 43
7. Empirical Results ........................................................................................................ 44
H1: Investments in Offshore Wind Funds have a long-term time horizon ................ 44
H2: Investments in Offshore Wind Funds require high initial capital outflows ........ 46
H3: Investments in Offshore Wind Funds present stable cash inflows ...................... 48
H4: Investments in Offshore Wind Funds are low-risk and low-return investments . 53
Sensitivity Analysis .................................................................................................... 65
9. Further Research ......................................................................................................... 72
References ...................................................................................................................... 74
Appendices
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
4
Table Index
Table 1: Descriptive statistics of day-ahead trading spot electricity prices on of the last 2 years .............. 31
Table 2: Description of the variables used in the model and Condition on Microsoft Excel ..................... 36
Table 3: Performance indicator per characteristic of the investment explained ......................................... 41
Table 4: Duration analysis (All countries).................................................................................................. 45
Table 5: Statistics on the initial costs of each of the fund’s farms ............................................................. 46
Table 6: Standard Deviation of free cash flows generated by the Infra fund ............................................. 48
Table 7: Cross Country Analysis of standard deviation according to the Low scenario on electricity price
.................................................................................................................................................................... 51
Table 8: Cross Country Analysis of standard deviation according to the Intermediate scenario on
electricity price ........................................................................................................................................... 51
Table 9: Cross Country Analysis of standard deviation according to the High scenario on electricity price
.................................................................................................................................................................... 51
Table 10: Standard Deviation of free cash flows generated by the Infra fund, without the incentive
systems established by governments .......................................................................................................... 52
Table 11: Default Frequency of free cash flows generated by the Infra fund............................................. 53
Table 12: NPV of the European Infra fund according to the different electricity price scenarios .............. 54
Table 13: Default Frequency of free cash flows generated by the Infra fund, without the incentive system
established by governments ........................................................................................................................ 55
Table 14: Cross Country analysis of default frequencies of cash flows according to the Low scenario on
electricity price ........................................................................................................................................... 57
Table 15: Cross Country analysis of default frequencies of cash flows according to the Intermediate
scenario on electricity price ........................................................................................................................ 57
Table 16: Cross Country analysis of default frequencies of cash flows according to the High scenario on
electricity price ........................................................................................................................................... 57
Table 17: Annualized Rate of Return of the cash flows generated by the European Infra fund ................. 58
Table 18: IRR of the European Infra fund .................................................................................................. 59
Table 19: Annualized Rate of Return of free cash flows generated by the Infra fund, without the incentive
system established by governments ............................................................................................................ 62
Table 20: IRR of the Infra fund, without the incentive system established by governments ..................... 62
Table 21: Cross Country analysis of IRRs according to the Low scenario on electricity price .................. 63
Table 22: Cross Country analysis of IRRs according to the Intermediate scenario on electricity price ..... 63
Table 23: Cross Country analysis of IRRs according to the High scenario on electricity price ................. 64
Table 24: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the Low
Scenario on electricity prices ...................................................................................................................... 66
Table 25: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the Intermediate
Scenario on electricity prices ...................................................................................................................... 67
5
Table 26: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the High
Scenario on electricity prices ...................................................................................................................... 67
6
Risk-Return Profile of Offshore Wind
Investments
An alternative for institutional
investors
ABSTRACT
This paper analyzes the possibility of Offshore Wind Farms being financed by private
institutional investors, by depicting a risk-return profile for this type of investments.
The model here designed is supported by a Monte Carlo simulation on 1000 cash flows
of 37 farms from 5 countries combined in a European Infra Fund. The statistical
analysis of the results provides evidence of the potential of this particular asset to meet
institutional investors’ profiles given three scenarios on electricity prices. Final results
indicate a low level of volatility (1,85%) added to low downside risk and reasonable
return potential (11,09%) revealed by average IRR, and complemented by an evidenced
duration of approximately 13 years in the worst case scenario. Comparing to previous
done on other asset classes, this performance is superior to that of bonds, equities or
real-estate.
1. Introduction
When Markowitz firstly defined a quantitative method for asset allocation, the exercise
led to a series of developments that ultimately brought us Modern Portfolio Theory.
Accordingly, diversification throughout the whole set of assets in the world would result
in benefits on the risk-return maximization process, but that would require identifying
and characterizing every asset in the planet in what can labeled as a Dantesque task.
7
Ultimately, every deal made on earth would have to be registered including art, cars,
fruits or even food, in order to derive a risk-return profile and include the asset in the
complete investment opportunity set. This way every rational investor would be allowed
to find the optimal asset mix in which to invest his money.
With this paper I purpose myself to contribute to a better definition of the investment
opportunity set by studying the risk-return profile of a particular asset in which little
research has been conducted on: Offshore Wind Investments.
Infrastructure assets are just recently starting to be characterized in terms of a risk-
return outline and it is often argued as being a specific type of investment offering great
potential. As being one of the most crucial infrastructure investments nowadays,
Offshore Wind farms are becoming increasingly commercial and thus, demanding
larger and larger stakes of investors’ money in order to substitute pollution intensive
power plants such as coal or gas –based, or the dangerous nuclear plants. Europe has
defined it as playing a major role in the short-to-medium-term future production of
greener-energy but the current financial environment and the budgetary constraints on
several peripheral countries constitute major impediments to the full development of the
technology. A particular interesting solution to solve for this financial gap in Offshore
Wind is to allow for large institutional investors such as pension funds, insurance
companies, banks or wealthy individuals to see the benefits of this investment and
persuade them to allocate a share of their wealth to it.
In this paper I am going to develop a simplistic model to generate cash flows for 37 real
Offshore Wind farms out of 5 European countries, which will then be combined and
predicted with a Monte Carlo simulation that will allow for a more significant analysis
of the results. The objective is to be able to design a European Infra Fund which will be
the owner of the totality of the farms as of 2011, given the incentive systems presently
active in those nations. The fund’s performance will then be measured according to a set
of indicators that will allow for reaching conclusions on the risk-return profile of these
investments. In order to do so, a few assumptions have to be established in what
respects to the construction of the model itself and the parameters included, such as
Load Factors, O&M costs, Initial Investments or Electricity prices.
8
According to 3 scenarios on average electricity prices, €55, €75 and €95 per MW/h,
final results reveal solid conclusions: Offshore Wind shows consistent 11-15% IRRs
throughout the 20 years investment period; it also leads to low cash flow volatility as
measured by standard deviation, with figures averaging from 1,70%-1,85% in the worst
case scenario, which is indicative of a very positive risk profile compared with other
asset classes such as Equities, Bonds, Real-Estate or Commodities. Furthermore,
duration analysis concludes that there is a natural long-term prominence for these
particular assets with figures around 13 to 17 years, and low probabilities for value
destruction for the investor – no default frequencies or negative NPVs in any of the
scenarios, given a 5% risk-free rate.
Finally, the analysis of the results proves the power of diversification achieved as the
average results of 1000 generated solutions reveal less volatility and default risk than
otherwise isolated analyzed countries.
Conclusions lead towards Offshore Wind Investments being able to establish
themselves as a substitute for corporate bonds in a strategic asset allocation context for
fund managers seeking guaranteed returns with low levels of risk for a relatively long
period of time.
The remaining of the paper is organized in the following way: section 2 develops on the
background setting of Infrastructure as an asset class and the Offshore Wind industry
dilemmas; section 3 reveals what was previously written by other researchers; sections
4 and 5 denote the Hypothesis and the Data used respectively; section 6 allow for the
understanding of the Methodology and section 7 announces the Empirical Results of
this study; finally section 8 sums up with the Conclusions and section 9 states Further
Research needed to complement the work here performed.
9
2. Infrastructure Investments
2.1. THE ASSET-CLASSES SETTING: INFRASTRUCTURE
Historically, equity and fixed-income have been the most predominant asset classes
when deciding on which securities to allocate wealth to. Nevertheless, different profiles
and the appearance of new products in which to invest have led investor to explore other
asset classes. This, directly related with the recent development and opening of
regulations and the need for achieving greater degrees of portfolio diversification,
allowed for the establishment of some fundamental alternatives. Amongst the most
traditional alternative investments there are Private-equity, Commodities and Real-
Estate. Some authors consider space for other modern alternative investments due to
very particular profiles, including Hedge-Funds, Managed Futures and Distressed
Securities. These alternative asset-classes are today approached with a substantial
importance as these specific markets are less efficient in terms of information, thus
presenting greater opportunities for generating wealth (Maginn et al. 2007).
Although there is no consensus among authors on whether to consider Infrastructure as
a different asset class or rather as a sub group of Real-Estate or Private-Equity, one
thing that is certain is that infrastructure deals have very particular characteristics.
Infrastructure assets are not clearly defined but usually they are considered to cover
essential services for social progress, in developed or developing countries, thus
reaching several sectors of the economy – transportation (ports, airports, toll roads and
tunnels), communication (cable networks, towers), social welfare (hospitals, schools,
courts) and utilities (energy distribution networks, water, waste, power plants) (Inderst
2010).
Since it is ordinarily up to a countries’ government to define which infrastructures are
most needed by the public, infrastructure assets possess natural monopolistic features
(Ramamurti & Doh 2004) due to legislation and limited competition resulting from the
large up-front capital investments required. Hence, these are assets typically featuring
low elasticity demands which imply also the generation of stable long-duration future
cash flows. As these assets have long but finite lives, they match investors with
10
relatively predictable long-duration liabilities, such as pension funds or insurance
companies (Sawant 2010a). Inderst (2010) synthesizes some often pointed attractive
financial characteristics of Infrastructure Assets:
- long-term stable cash-flows
- good inflation hedge
- low sensitivity to swings in the economy and markets
- low correlation of returns with other asset classes
- relatively low default rates
- sustainability (renewable energy)
According to the OECD (OECD 2007) the combination of developed and developing
countries will demand for around EUR 48 trillion between 2005 and 2030 in
infrastructure investment. In developing countries there is an increasing need for these
assets due to population growth, which is particularly high in countries such as India,
Brazil, Angola or China. Bitsch et al. (Bitsch, Buchner & Kaserer 2010) imply that
more people automatically will use more the existing infrastructure but also, with the
expected level of population growth maintaining the same rates, there will be further
needs for new assets. In addition, even though developed countries present decreasing
populations, because of having started with the development of infrastructure much
sooner, they will further on continue to demand for replacing the aging infrastructure.
European economies and the US have been establishing the pace in technological terms
but, although progress enables evolutions it also requires supplementary spending, as
for example in the establishment of wind energy parks.
2.2. OFFSHORE WIND ENERGY
Europe established bold targets on green energy production for meeting the required
greenhouse emissions’ goal defined in Kyoto. As an example, in the UK, one of the
most determined European countries in terms of predicted wind energy investment, its
Renewable Energy Strategy defines a target of 15% gross final energy consumption to
come from renewable sources by 2020, almost 7 times higher the level of 2008. This
ambitious target imply 30% green power generation which would mean approximately
11
additional 27GW of renewable capacity divided in 12GW out of offshore wind farms
and 11GW of onshore projects (Hurley & O'Regan 2010).
Wind energy is seen as playing a crucial part in the achievement of the specified targets
because of being one of the lowest-cost renewable sources (Loring 2007). Specifically,
Offshore Wind has a decisive role because of its superior efficiency due to higher and
smoother wind speeds, combined with the avoidance of negative visual impact or noise
and land limitations of onshore projects.
Although, approved Offshore projects in the UK are, at this stage, clearly beyond the
target line with already approved projects of 50GW, the roll-out rate of projects for
2009 was 0,3 GW and, in order to meet the targets stated above of 12GW, it would be
needed to complete approximately 1.1GW per year, that is almost 4 times more projects
being successfully set up.
The scenario of the industry presented nowadays is quite the same for the rest of Europe
and the path to follow in the future is still incognito.
2.3. THE FINANCIAL GAP OF OFFSHORE WIND ENERGY
Several limitations have been restricting the full expansion of Offshore Wind Energy:
for example, deficiencies on the supply chain (producers of wind turbines), project
planning delays and restricted accesses to grid connections have not been helping the
development of the market. However, the most significant barrier has been the difficulty
in securing pre-construction financing (Hurley & O'Regan 2010).
As usual, high needs for infrastructure funds are accompanied by the lack of financial
resources. Governments of emerging countries are presented with the dilemma of albeit
already difficult economic situations, soaring shortages of infrastructure force them to
act and provide for the needs of their people. In developed countries the situation is not
any easier: aging populations are a burden to several governments with public pension
systems and, also, they see themselves struggling with budgetary deficits as we are
currently witnessing with the sovereign debt crisis of the peripheral countries of Europe
(Greece, Ireland, Portugal, Spain…). Governments do not want to be responsible for
12
raising taxes or collecting fees (Beeferman 2008) and they would rather prefer to hand
the development of such infrastructures to private entities.
In the specific case of the UK market, the above mentioned 12 GW of offshore power
would cost approximately EUR 44 billion until 2020, which reveals an extensive need.
Further, the offshore wind market in Europe faces large competition in regards to
funding requirements: among the energy sector, Ofgem estimates that in the UK alone,
EUR 220 Billion of investment are needed for the coming decade on several
infrastrcutures, averaging EUR 18 Billion per year – this is double the amount budgeted
by the 6 biggest utilities plus the National Grid in the UK for annual capital
expenditures, combined (Hurley & O'Regan 2010).
The concern with securing pre-construction financing in offshore wind developments is
very much associated with the risks, namely construction risks, and the current less-
than-optimal banking and investment climate (Hurley & O'Regan 2010). Offshore
projects have only been able to reach capital after an existing farm is up and running,
hence presenting a track record of its operations. This is a great difference in respect to
Onshore Wind Farms which have perceived less complexity in financing projects.
This difference in terms of risk between Offshore and Onshore Wind Farms is highly
representative of an important issue when evaluating Infrastructure as an asset class: the
heterogeneity of projects among the definition. As argued by Sawant (2010a), some of
the investment sectors comprised as infrastructure-related are fast growing industries
and exposed to high technology risk, thus leading to higher cash flow volatility on the
cost side. Sawant (2010a) points that alternative energies (wind, solar, biogas, etc…) or
information technology infrastructures are fast-growing but technologically risky assets
which can be better suited for venture capital investors’ profiles.
2.4. INVESTMENT VEHICLES
Private sector participation in infrastructure increased exponentially throughout the
1990s (Gausch, Laffont & Straub 2007). This is seen as the result of more private
13
investors willing to explore the opportunities of this type of deals, together with the
recent reforms of developing countries (generally highly populated and with large needs
of these assets) to allow the private management of both pension funds and
infrastructure (Vives 1999).
There are several forms of investing in infrastructure differing in relation to the
minimum-capital requirement, time-horizon of the investment and liquidity (Bitsch,
Buchner & Kaserer 2010):
- One of the oldest methods is direct investment, which can translate in a long time
horizon, in some cases reaching 99 years (Beeferman 2008), thus involving large
liquidity, political and regulatory risk. The requirement of high-amounts of capital sorts
out this type of investment from smaller institutions, allowing only the participation of
insurance companies or pension funds, which have funding availability for surpassing
these investment needs. Amongst some of the more common structures of direct
investment there are, for example, Public Private Partnerships (PPPs) and Project
Finance structures.
- In order to overcome these high initial capital requirements, the traditional approach
by some pension funds has been to invest directly in publicly traded shares or bonds
of utility companies – in the case of the energy sector (Inderst 2010). Listed securities
provide for less liquidity risk and allow for achieving a greater degree of diversification
in a simpler mode. However, this also entails some problems related with the fact that,
by participating in the equity stake of these companies, institutional investors are
exposing themselves to the risk associated with operations, marketing and sales – the
whole supply chain of the utility company – which often is not in an investor’s interest,
generally simply looking for the infrastructure side of it.
- Finally, another alternative for investors is to opt on indirect investment through listed
and unlisted infrastructure funds. Infra Funds provide for a great diversification of
geography and business risk compared to investment in utilities’ stocks or direct
investments. Also, small investors with less capital available are not excluded from
14
participating in infrastructure assets as capital requirements are smaller (Bitsch,
Buchner & Kaserer 2010).
In this paper we are going to focus on this last approach. Infra Funds were initially
designed in Australia in the mid-1990 but have grown remarkably since then in Canada
and, throughout the beginning of the new century, in Europe and Asia.
In Australia, the country which is probably most developed in terms of infrastructure
investment acceptance, superannuation funds (the same as European pension funds),
have driven significant increases in capital flowing to listed and unlisted infrastructure
or listed infrastructure companies. 116 unlisted infrastructure funds were recorded to
have raised a figure of about €83 Billion and, by the end of 2007, they were looking to
raise further €81 Billion. Unlisted funds counting is reaching 20 as of 2009 and marking
on a capitalization of €21 Billion. Figures from the end of 2008 reveal a volume of
approximately €52 Billion managed by 19 infrastructure managers for pension funds.
As a clear sign of the increasing importance of Infra Funds, Inderst states that about 150
pension funds around the globe acknowledged to have started investing in infrastructure
funds and to have established 2%, 3%, 5% or more to their strategic asset allocation.
However, the global amount of funds allocated to infrastructure investments is still
below 1%, out of the whole basket of world assets (Inderst 2010)
The reason for following such an approach is that it purely focuses on infrastructure
while not demanding commitments of large amounts of capital and providing for better
diversification than direct investments, thus annulling business risks, geography risks
and one entity full-control of the assets -related risks.
The Fund here designed is structured in a way that portfolio farms to which the fund
commits itself are decided by the board, according to the mandate, and a position is
established (in the ownership – equity). Then, as the underlying company created
specifically to control the farm (typically an SPV) receives the payoff from its
operations, cash starts out-flowing from the SPV to the fund, which will collect the
value received in its accounts and distribute it to each participant of the fund.
Throughout the procedure, and until the money has finally reached the participant – say
a pension fund, a bank, an insurance company or a wealthy individual – there will
certainly be fees or taxes to take into consideration, hence making cash flows between
15
portfolio companies and fund diverse from those between the fund and its investors.
Although, being a reality, the complexity of tax systems among countries, the different
rates of taxation applied to different investors and the large range of fees collected by
fund managers makes turns that analysis into an aspect outside the objective of this
paper. Further adding, management fees charged by the funds will also not be analyzed
here.
Particularly, I am going to analyze cash flow amounts generated by a fund composed of
5 countries and 37 underlying companies owning a total Offshore Wind Farm project
which will pay the respective amount to the fund and then this fund will distribute it to
its investors. The final objective is to understand with statistical significance what is the
risk and return generated for the fund if owning 100% participations in the 37 farms.
16
3. Previous Research
Literature often points features which lean infrastructure assets for being a very
attractive investment, nevertheless previous research is far from being conclusive.
Besides, there are no studies on Infra Funds specialized in Offshore Wind Farms or
even in Wind Farms both Offshore and Onshore, which makes it hard to compare with
the analysis here performed.
Inderst (Inderst 2010) analyses Infrastructure investments as an alternative and starts by
stating its growing importance in the world. Inderst poses the question on whether it can
be seen as an alternative asset class or rather a different vehicle for investments in
Private Equity, Real-Estate or even Equities, by trying to define a risk-return profile
which would definitely allow for its inclusion in one of these classes, or else create a
new one. The analysis on performance is divided as of listed and unlisted funds: as of
listed funds, indices tend to show better performances than global stock market indices
until the late 2000s, when performance drops below the benchmark indices. Volatility is
seen as high, sometimes higher than those of general stocks and dividend yields are also
above average which is mainly attributed to the inclusion of utility stocks in the
analysis. As for unlisted funds, Inderst points out that the history of these particular
funds is quite short and data is reluctantly made public. Hence, the latter combined with
the wide variety in nature of unlisted infrastructure funds and the difficulty in accepting
a universal benchmark performance measure, leads to much less reliable conclusions.
Bitsch et al. (Bitsch, Buchner & Kaserer 2010) perform a much more detailed study and
focus specifically on deals on infrastructure in order to be able to qualitatively compare
them to non-infrastructure deals. This paper provides an important guideline for the
study here presented as it analyses cash flows returned to the investor and intends to
establish a risk return profile, although not looking for an analysis on a fund’s
perspective. Final conclusions reveal some support for infrastructure investment: default
risk seems to be lower for infrastructure than for non-infrastructure, even though no
evidence of more stable cash flows is found (conversely to what is often pointed).
Moreover, average and median returns seem to be higher for infrastructure deals both
measured by IRR and investment multiples. On the long-term nature of this type of
17
investments, duration analysis shows no evidence of differences when compared with
non-infrastructure deals.
It is important to mention, however, that Bitsch et al. run a study based on the broad
range of the definition of infrastructure while I am going to focus solely on Offshore
Wind deals.
Both Peng & Newell (Peng & Newell 2007) and Finkenzeller et al. (Finkenzeller,
Dechant & Schäfers 2010) conduct studies based on a 10 years’ analysis limited to Q2
2006 and Q4 2007, respectively, and reveal that average annual returns on infrastructure
are higher than those of bonds, properties and equities, but volatility does not seem to
present the same consensus. While Peng & Newell find that volatility of unlisted
infrastructure (5,8%) is lower than stocks’ (11,0%), it is higher than for bonds (4,1%)
and direct property (1,5%), Finkenzeller et al., however, refer that the annualized
standard deviation of the returns for direct investment is 10,4%, which is higher than the
one found for bonds (4,1%), property (7,9%) and equities (9,5%). Inderst points out an
important critic to both studies by mentioning that the period analyzed is probably not
the most appropriate to infer any conclusions on risk and return, due to the reason of
being prior to the credit crunch of 2007.
In order to understand the extraordinary degree of variation amongst results from
previous authors, it is decisive to understand the findings of another study run by
CEPRES (CEPRES 2009) which is a center that collects information on private equity
deals, registering the cash flows generated, both in and out to the investors, among
them, on deals in which the underlying asset is an infrastructure (bridges, schools, toll
roads, power plants, etc.). According to this data center, realized direct infrastructure
transactions finalized by unlisted funds present an average and median IRR of 48,0%
and 14,3% respectively. This is incredibly different from those numbers from Peng &
Newell or from Frikenzeller et al, especially if one takes in consideration that CEPRES
numbers are IRRs and those by other authors are annualized rates of return, which do
not take into consideration costs on time value of money.
18
Finally, two authors set a future path to follow by presenting very interesting analysis
on this topic. Vives (Vives 1999), makes a theoretical analysis on some of the reasons
which can lead pension funds to participate in infrastructure deals, mainly exploring the
option of project finance-based investment, much like the paper I am here presenting.
Vives, focus on the emerging market countries from Latin America and finishes with an
analysis on the implications for Europe and the U.S. of private participation in both the
mandatory pension system and infrastructure investment.
Also exploring the emerging markets’ opportunities, Sawant (Sawant 2010a) dissects
the possibilities presented by infrastructure project bonds. Although the analysis I am
presenting in this paper is based on the pure equity composition of capital, the usual
approach in the industry is still supported on syndicated loans, and project bonds might
present, in the future, a reliable alternative. Sawant concludes that the risk-return profile
is still not rewarding for investors, although revealing some interesting properties in an
investor’s point of view: low correlations with equity indices and very stable cash
flows.
As mentioned above, none of the studies conducted previously focus in this particular
type of infrastructure I am going to analyze here, which, although making it more
difficult to set comparisons, provides to this paper with added value for presenting an
alternative approach in the set of pension funds and other institutional investors
committing their money to infrastructure specific listed or unlisted funds.
19
4. Hypothesis
In order to solve for the offshore wind financing gap, the technology has to make its
case to an international corporate investment context. Investors have to be convinced as
to the attractive properties of this type of deals, with the aim of establishing in their
mandates a strategic allocation position in this specific infrastructure. As Inderst (2010)
mentions in his article, there are some points often argued in favor of infrastructure
assets:
- long-term cash-flows
- stability/low volatility
- low risk and low return cash flows
- relatively low default rates
- good inflation hedge
- low sensitivity to swings in the economy and markets
- low correlation of returns with other asset classes
- sustainability (renewable energy)
It is difficult to determine what characterizes an asset class and this paper will not try to
establish infrastructure as an alternative or, even more, offshore wind energy
investments. The goal of this paper is to analyze that such investment characteristics
often postulated are real in Greenfield offshore wind projects from several European
countries if these were to be combined by a fund. Hence, the following hypothesis are
tested:
H1: INVESTMENTS IN OFFSHORE WIND FUNDS HAVE A LONG-TERM TIME HORIZON
By definition, Offshore Wind Farms have to be long-term directed, with a minimum
expected years of living for turbines of around 20 years stated by suppliers. Most
recently some turbines have started being produced as to generate energy for 25 years
long.
20
Although having a life span of around 20 years, because of being a relatively recent
technology, there is still not enough evidence of how long exactly a turbine can last.
Nonetheless, even if one assumes that a turbine will go on to 20-30 years, it does not
mean that investors will hold this asset in their portfolio for as long as the total lifetime
of a turbine. Thus, as a crucial part of the assumptions, I am considering that a fund is
sponsoring each farm from its very beginning until it is shut down, which will be
approximately 20 years later.
Although the results might be very dependent on these assumptions, I am going to
perform an analysis of the duration of these cash flows as to establish its characteristic
and understand whether this type of investment is consistent with the profile of
institutional investors.
H2: INVESTMENTS IN OFFSHORE WIND FUNDS REQUIRE HIGH INITIAL CAPITAL
OUTFLOWS
Investments in offshore wind farms vary broadly in terms of size, number of turbines
and its capacity. I am going to analyze the magnitude of the initial capital outflow
totaled by those farms studied in this paper and compare it to other securities. As it is an
expensive technology and it is a generalized procedure in this industry for suppliers to
deliver key-in-hand solutions, one would expect higher initial costs than for other asset
classes.
Given the study here conducted, capital outflows are dependent on the established
assumptions on initial costs per MW/h installed but real data is used to formulate the
total capacity installed, thus, these figures will be estimated with reasonable certainty.
H3: INVESTMENTS IN OFFSHORE WIND FUNDS PRESENT STABLE CASH INFLOWS
As being one of the most interesting properties, stable cash flows are crucial for
investors such as pension funds or insurance companies which often present reasonably
stable and predictable liabilities as well. Hence, it is useful for this type of investors to
search the market looking for assets that produce cash flows matching their liabilities.
21
As it is stated by Inderst (2010), it should be expected to encounter less volatility in
Infrastructure investments due to a reasonably inelastic demand and its monopolistic
features. Nonetheless, Offshore Wind Investment is a very specific type of asset which
presents technological risk and is exposed to weather characteristics and electricity spot
prices. Therefore it will be interesting to reach a conclusion on the risk profile of these
particular investments in infrastructure.
H4: INVESTMENTS IN OFFSHORE WIND FUNDS ARE LOW-RISK AND LOW-RETURN
INVESTMENTS
In order to understand the risk for equity investors, although studying the volatility of
cash flows, I am going to analyze the default frequencies of Offshore Wind
Investments. This will allow one to conclude on the likelihood of a project destroying
value to its sponsor. One should expect to see lower default frequencies than for other
investments, for instance in non-infrastructure assets.
Besides, I will measure the return achieved and compare it to other asset classes in order
to understand if one can state the lower payout this investment produces. Typically it is
argued that these deals provide low, bond-like returns, which is also consistent with the
argument that there is a smaller risk.
These results can prove themselves determinant for a fund’s board which is analyzing
whether to invest in an Infra Fund such as the one being constructed here, as they give
one an idea on what is the overall profile of these investments.
Further hypothesis testing would be of value for an institutional investor in order to
reach a complete conclusion on the risk-return characteristics of the asset – for instance,
its relation with the macroeconomic environment and the claim that these particular
investments provide inflation-linked returns. Nevertheless, any empirical evidence
depicted from the returns here generated would be pure coincidence as no links to the
economy are included in the construction of the model here designed. For example, the
actual consequences of electricity price variability could somehow transmit the effects
22
of supply and demand influences which can be the basis for a link to Inflation Rates,
GDP growth and Public Equity Markets; also, actual costs in O&M activities carry the
influences of the macroeconomic environment such as the volatility in oil prices; but
neither of those real data indicators are introduced and thus, an empirical study would
not provide for reliable conclusions on this. However, Appendix E presents the material
necessary for a possible time-series OLS regression and the SPSS outcome which
reveals a jointly significance F-test that is not statistically significant, as predicted,
while each of the t-tests on each Beta also reveals no statistical significance. The
econometric study is conducted relating the average return attained by a sample of
several funds throughout several years with macroeconomic measures. For instance
from 1999 to 2010, one would collect data on annual GDP growth rates (GDP), annual
inflation rates (INFLATION) and average yearly return on a public equity market proxy
(EQUITY)1.
This would be one of the options on how to look for any relationships between the
variables. The other would be by looking at a panel data set which could study several
funds (or deals on individual farms, as Offshore Wind Funds are not available in a
statistically significant number of observations) throughout the years. Again, the study
would not make sense in modeled figures as the ones here presented, since any
correlation (or the lack of it) would not be a reliable conclusion on the actual numbers.
Further research on this particular aspect would bring significant value to the industry.
1 Data source: Nordea Statistics
23
5. Data
For the specific purpose of running the study in this thesis, a set of data is generated.
The data generation aims to simulate an Offshore Wind Infra Fund that would collect in
its portfolio, from 2011 on, 37 newly established offshore wind farms and thus, after
analyzing its balance sheet, one can deduct the risk-return characteristics of such an
investment. By comparing its risk-return characteristics with those presented by other
asset classes, it will make it possible to understand how attractive it would be for mutual
funds, pension funds, insurance companies, banks or wealthy individuals to invest in the
fund.
In order to run the study, a set of data is generated based on the combination of cash
flows produced by 37 farms in 5 countries: Denmark, United Kingdom, Ireland, Sweden
and The Netherlands. The data is built on real figures for the number of turbines and
capacity installed in each farm. Nonetheless, the generation is dependent on some
crucial assumptions, both general and country-specific.
5.1. GENERAL ASSUMPTIONS:
Five main parameters are identified by Morthorst et al. (2009) in analyzing wind power
economics: investment costs (including foundations and grid connection), operation
and maintenance costs (O&M), electricity production, turbine lifetime and discount
rate. In this paper I am going to follow this framework and search the literature in order
to find reliable assumptions for the latter parameters. Furthermore, I am going to
include taxes and depreciation as other crucial parameters for the computation of free
cash flows.
- Investment Costs
Morthorst et al. state that on average, investment costs on a new offshore wind farm
near-shore are expected to be in the range of € 2,0–2,2 million/MW. Therefore, for the
initial computation, I am going to assume an initial investment of €2,2 million per
24
MW/h of capacity installed as to depart from a worst-case in terms of initial capital
required.
- O&M costs
Furthermore, in their paper, Morthorst et al. use 16€ per MW/h installed yearly as
operations and maintenance (O&M) costs for the farms. The value for O&M costs is
stated to account for the average expenditure of insurance, regular maintenance, repair,
spare parts and administration. Some countries have predefined in their standards that
land rental is part of O&M costs while others have assumed it to be a cost considered in
the initial investment. Thus, to simplify the analysis, we are going to assume it to be
included, for all of the farms, in the O&M costs, using the assumed value of 16€ per
MW/h as an average.
Besides, O&M costs are modeled as being “pegged” to the load rate generated for each
year. The idea is that the effective number of hours per year that a turbine is working is
related with the O&M costs: the smaller the load rate, the more likely there was need to
perform maintenance as of increased downtime, reflecting it as losses in the economical
results. According to this methodology, the same random number generated for the load
rate is taken for a normal distribution of the O&M costs with a standard deviation of
one quarter of the average. The lack of modeling in this area according to previous
authors and a need for inducing variability in these costs stimulate for the need to create
the referred method.
- Electricity Production
Electricity production depends on the turbines’ capacity, the number of turbines and the
load rate. Besides the first 2 variables, which are given by the real data set of farms in
the 5 countries, for computing the electricity generation per farm, one as to assume a
load rate, which is the percentage of the total possible outcome a turbine can produce
that is effectively generated. This percentage varies according to wind conditions and
25
down time, but according to the DECC (Department of Energy and Climate Change
2011) a load factor of around 40% is acceptable as an average.
As it is a volatile factor throughout the lifetime of the turbine, to increase the reality of
the simulation, I am going to use Microsoft Excel 2007 to generate a load factor
following a normal distribution with an average of 40% and standard deviation of 10%
for each year. There is no evidence in previous literature on which distribution a load
factor of an offshore wind turbine follows, and thus, the normal distribution is applied
for the simplicity of the model, which should allow for some uncertainty regarding the
average mentioned above of 40%.
- Turbine Lifecycle
In assuming the life cycle of an offshore wind turbine, even though there have not been
so many turbines reaching its life expectancy of 20 years stated by the main supply
chain players due to the relative infancy of offshore wind energy, several authors use
the 20 years mark as the usual maturity for the device (Schleisner 2000) & (Morthorst et
al. 2009). Nevertheless, a sensitivity analysis on the life expectancy of a wind turbine is
performed.
- Discount Rate
The discount rate should reflect the risk the investor is taking when financing the
offshore wind farm. As this technology is still relatively new, the risks are considered to
be fairly high and those companies responsible for the project have to manage risks
regarding construction, technology failures, O&M costs and volume (wind conditions-
related).
Also, the debate on an appropriate discount rate opens up the discussion to capital
structure decisions affecting the required rate of return, depending on the amount of
debt (leveraged position) and equity. As this is not the point of the discussion for the
paper, every farm is assumed to be completely financed by equity investors which,
hence, have all the risk in their hands. The only risk that is shared is the one passed on
26
to insurance companies (costs of insurance included in O&M costs) and to suppliers
when setting up the farms (which usually commit themselves to present key-in-hand
solutions, although the price paid to turbine suppliers as the initial investment is already
taking into consideration such risks).
Morthorst (2009) assumes a discount rate range between 5-10, mainly required to
compute net present values (NPV), productivity indexes (PI), and other valuation
measures. For the purpose of this simulation, I am going to assume a discount rate of
5%, which is clearly above a risk free interest rate in Europe at present (Euribor 3
months equal to 1,556% as of 01-07-20112), but still reasonable taking into
consideration the risks involved in these projects.
- Taxes
Although tax rates vary from country to country, I am going to assume one “universal”
tax rate for the 5 countries used in this simulation as it is more interesting to see what
happens to risk and return when performing a sensitivity analysis on the model. Hence,
a corporate tax rate on the profit generated by each farm of 25% is assumed. One as to
bear in mind that there is also individual taxes to take into consideration when a
participant of the fund “cashes” its share or receive payoffs. However, that tax rate
depends both on the country of analysis and on the type of investor, as there are
different tax rates for pension funds or banks and other institutional investors, or even
on an individual investor. Due to the complexity of such analysis, it is behind the scope
of this paper to evaluate this issue.
- Depreciation
Depreciation is also considered in this simulation as it is part of Free Cash Flows’
computation. In this case, I am going to start with the assumption that, at the end of the
2 Data Source: Eurostat
27
lifecycle of a turbine (20 years, by definition), there is no money from the remaining of
the farm, in the ocean, but there is also no costs from taking turbines down and shutting
off the farm. Depreciation can become a crucial variable mainly as it poses the issue of
whether a Farm should be dismantled in the end of its commercial life or rather
renewed, replacing old turbines by new ones and take advantage of the already
established grid connections and turbine foundations.
As a part of the free cash flow generation, the revenue stream is dependent on the
incentive system used in each country. The report developed by Mott Macdonald (Mott
Macdonald 2011) describes several incentive policies of various European countries and
resumes the main aspects of each system.
5.2. COUNTRY-SPECIFIC:
- Denmark (FIT):
For Denmark, the system is based on a tender offer on the premium feed-in tariff (FIT).
For being conceded the exploration companies make a bid for the premium tariff they
require and the most competitive wins. The most recent lease is based on a total amount
of spot electricity price plus premium tariff equal to approximately 84€ per MW/h
which applies to a maximum of 10TW or 20 years, whatever comes first (International
Energy Agency 2011).
- Ireland (FIT):
The Irish system for supporting Offshore Wind is based on a Feed-in tariff offered
through a Power Purchase Agreement (PPA) for up to 15 years and maximum until
2024 (European Renewable Energy Council 2009). Since February 2008, the premium
tariff plus the spot price offered are set to the amount of 140€ MW/h. Nevertheless, the
framework establishes a contract for difference (CfD) which means that, throughout the
lifetime of the PPA, whenever the reference price on the spot electricity market is below
the PPA price, the support is indeed provided, but during times in which the sport price
28
is superior to the PPA price, the payment flows backwards and the price received is still
the 140€ per MW/h mentioned above (RES LEGAL 2011).
- Netherlands (FIT):
In the Netherlands, the system is also based on a feed-in tariff in which a tender offer is
made by competitive bidders (Mott Macdonald 2011). As of 2009, there is a base rate of
186 € per MW/h up to 15 years and to a maximum amount of €2,645 Million in total to
the whole investment. In this particular system, the Dutch Government establishes a
fixed premium price which has an option for an higher price when spot markets are
above the FIT agreed upon (International Energy Agency 2011). Thus, it acts as a floor
on the price received, which can be higher if the spot market presents more favorable
conditions.
- Sweden (TGC):
In Sweden there is a tradable green certificate system, a market-based subsidy system in
which the government establishes a requirement for utility companies to own these
certificates according to a legal quota on their volume of sales. In order to do so, either
utility companies (consumers of electricity) produce their own energy from renewable
sources or use the certificates’ market to buy them from someone who does. As the
system allocates 1 certificate per each Megawatt-hour of renewable electricity
produced, the system favors the cheapest method of producing electricity from
renewable sources.
According to a report by the Swedish Energy Agency (Jöhnemark, Östberg &
Johansson 2009), there is a 10-15 year support period which can go up to 2030. The
limitation is due to the fact that the 15 year support period is assumed to be the limit for
plants to be commercially viable. After this period, they should be able to produce
renewable electricity at a profit, without the need for the subsidy from certificates.
Green and Vasilakos (Green & Vasilakos 2011) state that a tradable green certificate in
Sweden was worth approximately €32 (300 SEK) in 2009.
29
For the purpose of simulating the economical viability of an Offshore Wind project, the
percentage quota on sales established by law in Sweden is not relevant, but I am going
to assume the average price per TGC mentioned above, as it is a crucial part of the
computation.
- UK (TGC):
The UK incentive system is denominated Renewable Obligation Certificate (ROC) and,
as in Sweden, it is also a TGC system. Nonetheless, contrarily to what happens in
Sweden, the system is banded, which means that different technologies are allocated
different amounts of ROCs. Although a lot of divergence in terms of what is written to
be the incentive system in the UK, the ultimate revision as approved 2 ROC’s per
MW/h for Offshore Wind projects, with each ROC trading at an average of €54 (£48
GBP) per MW/h (Mott Macdonald 2011).
As a result, the mentioned incentive will be assumed when computing the free cash
flows of UK farms.
From the analysis of several articles and institutional reports, it is possible to affirm that
incentive systems are very complex tools. Several countries’ governments support R&D
for this technology through grants and funding, which will here be ignored due to the
complexity that is quantifying such incentive. Moreover, there are tax incentives and
levy exemptions for some farms of bigger proportions in a quantity of the countries
analyzed, but again the purpose of this paper is not to study the different support
systems for Offshore Wind projects in the EU.
5.3. ELECTRICITY PRICE:
Europe has long been preparing for a unified internal electricity market. This liberalized
electricity market aims for the introduction of cross-country competition according to a
uniform legal framework, in order to maximize production and trading efficiency. Final
30
consumers will benefit from a unified system in which efficiency gains will secure
lower prices (Domanico 2007).
A web-based tool provided by Nordea, Nordea Statistics, part of the program e-Markets,
made it possible to gather data on electricity prices from Nordpool Spot for the last 10
years (from 05/06/2001 to 03/06/2011). Nordpool is an electricity power exchange for
several markets in the north of Europe, being today one of the most active regional
electricity markets in the world. It is the largest and with the longest history in Europe
and Wind Power is already traded on it (Holttinen 2005). Intuitively, electricity
exchanges reproduce the equilibrium between supply and demand, replacing the old
government influence in this market.
The data set returns Elspot prices in Euros for day-ahead trading in which, buyers
evaluate how much electricity they will need to meet the requirements of their
customers in the following day and how much they are willing to offer for those MWs
of power (bid price – hour by hour). The same way, sellers quantify the amount of
power they will be able to generate and at what price is it financially optimal for them
(ask price – hour by hour).
According to the legislation encountered for the 5 countries analyzed, the main goal of
the incentive systems is to provide initial support for this relatively new technology as
to develop its implementation. Hence, governments aim for its independency from tax
payers and self-sufficiency based on market equilibrium, for instance based on power
exchanges or bilateral contracts.
As the purpose of the paper is not to model electricity prices but instead to analyze cash
flows’ risk return on Offshore Wind Investments, 3 scenarios for the average spot
electricity prices, exchange traded, are built for the next 20 years: low (average
electricity price = €55), medium (€75) and high (€95) scenarios. These values are based
on a basic statistical analysis of Nordpool Spot prices (see Table 1) which indicate that
on average for the last 2 years, the average spot price was €55,50. Furthermore, the
analysis reveals a tendency, from 2001 to 2011, for prices to consistently increase,
31
coming from an annual average of €21 in 2001 and reaching a value of 60€ (3 times
higher) in the first half of 2011 (see Figure 1). The analysis also reveals a standard
deviation of approximately €15 in the last decade which we are going to use for
computing a normally distributed electricity price throughout the life cycle of the farm.
Finally, the analysis also shows that, in the last decade, there were 15 days in which
electricity prices rose above €95, with an all time maximum of €134,80. Appendix A
contains further detailed statistical information on the breakdown of electricity prices
for the last 10 years.
Statistics 2011 & 2010
Min 20,67 €
Average 55,50 €
Max 134,80 €
Decil 10% 44,08 €
Decil 90% 71,87 €
Quartil 1 (25%) 47,49 €
Quartil 2 (50%) 51,30 €
Quartil 3 (75%) 62,97 €
Quartil 4 (100%) 134,80 €
Number of Observations 519
Standard Deviation 12,82 €
Number of days above 95 6
Table 1: Descriptive statistics of day-ahead trading spot electricity prices on of the last 2 years
32
Figure 1: Illustrative graph on the path of electricity prices throughout the last 10 years and annual
average price
This increase in electricity prices is probably reflecting not only an increase in demand,
but also the awareness of the European population that there is a need to invest in
cleaner sources of energy, although the costs may be higher. The most recent nuclear
disaster in Japan led several European nations (such as Germany and Switzerland, for
example) to decree the immediate discontinuing of nuclear power investment and
further investment in other environmentally friendly fonts of power.
Government support through incentives and facilitated funding reveals exactly this idea,
as to spread the costs of a cleaner environment through society by having population’s
taxes as font. Hence, it is reasonable to assume that the future will likely bring a further
increase in prices until the extent that these renewable sources of energy, such as
Offshore Wind, become cheaper, due to technological development, and, ultimately
more efficient, driving prices to an inflexion point.
-
20,00
40,00
60,00
80,00
100,00
120,00
140,00
05-06-2001 05-06-2003 05-06-2005 05-06-2007 05-06-2009
Electricity Price Average per year
33
As being a benchmark due to the high liquidity and volume traded, I am going to use
the scenarios presented above based on electricity prices of Nordpool and assuming a
future liberalized and further open European electricity market (even though countries
such as the UK, Ireland and the Netherlands do not trade primarily on Nordpool, it
serves as a proxy).
34
6. Methodology
The simulation3 combines cash flows from farms in different countries in order to
simulate the risk diversification that can be achieved through a fund that combines
several farms, from different countries, in its portfolio. For instance, as we cannot store
electricity or wind (in comparison to other commodities such as oil, gas or coal), an
electricity generator owning a wind farm cannot control if the wind is blowing at
enough speed at a specific location, but can prevent shortages by offsetting the losses of
power in one location with the above average power generated at another.
After summing the cash flows produced by the 37 farms mentioned in the previous
section, one is presented with the cash flows the Infra Fund investors are going to face.
Nonetheless, this is not enough for stating with certainty, given the established
assumptions, that those will be the results for the sponsors. Thus, I then run that result
1000 times through a Monte Carlo Simulation.
MONTE CARLO SIMULATION
The methodology of Monte Carlo Simulation certifies the process by assuring a
procedure for sampling random outcomes of the Infra Fund. Monte Carlo is based on
the Markov Process and accordingly, the return of the project (Infra Fund) is not
dependent from past performance and only the present value of the project matters for
predicting what will happen in the future.
John Hull (Hull 2009) describes the process as for returns on stocks, in which the only
dependent variable is the Stock Price itself (S) at present:
3 All the calculations here conducted were done using Microsof Excel 2007 and PASW Statistics 18, also
known as SPSS.
35
or
Consequently, is the change in the stock price (S) in a established time interval ( )
and is a randomly generated number following a normal distribution with mean zero
and standard deviation 1. The parameter is the expected rate of return per unit of time
and is the volatility parameter. Given that
is the return of the stock and
represents the expected value of this return according to a unit of time, if is the
stochastic part of the return, then the variance of the stochastic component is .
Thus, the standard deviation of the return is (Hull 2009).
In our analysis there are 3 variables following a stochastic process which use the
expression = RAND( ) in Excel, hence, producing a random sample matrix of numbers
between 1 and 0. As stated above, for simplifying the simulation, all 3 variables O&M
Costs, Load Factor and Spot Market Electricity Price were assumed to follow a Normal
Distribution and therefore, the function =NORMSINV(RAND( )) executes the
necessary reproduction.
The model here developed depends on both stationary and stochastic variables (see
Table 2), being assumed as stationary the ones that tend to be more stable throughout
time, such as Corporate Tax Rate, Expected Lifecycle, Discount Rate or the Incentive
System from each country. Those which are observed as being highly volatile variables,
presenting different values on a daily basis (such as Electricity Prices and Load
Factors), I take as an assumption here the mean and standard deviation (constants) – as
argued on the previous section – per annum. Same for O&M costs, which, although not
making sense to state that O&M costs vary on a daily basis, its annual average is
volatile, thus, justifying the need for being a stochastic variable.
36
For instance, Load Factor is assumed to follow a Normal Distribution with Mean ( =
40% and a Standard Deviation ( = 10%.
In this exercise ( time variation is assumed to be always 1 as the rates here
considered are already pre-established as being annualized.
By repeatedly simulating movements on the Load Factor, in the limit, as (the set of
random numbers generated between 0 and 1) are independent numbers from each other,
a complete probability distribution (Normal, in this specific exercise) at the end of the
time here analyzed, can be derived.
Variable Condition
Investment Costs (per MW/h installed) Assumption
O&M Costs Assumption
Expected Life Cycle (No. of Years) Assumption
Total Capacity Real Data
Total Possbile Outcome per annum (MW/h) = Total Capacity * 24 * 365
Initial Investment = Investment Costs * Total Capacity
Average Maintenance Costs (MC) = O&M Costs * Total Possible Outcome
Std. Deviation of Maintenance Costs = 0,25 * Maintenance Costs
Maintenance Costs (observed) = NORMSINV((1-LF);(1-Average MC); Std MC)
Average Load Rate (LR) Assumption
Std. Deviation of Load Rate = 0,25 * Load Rate
Load Factor (LF) = NORMSINV(RAND( ); Average LR ; Std LR)
Discount Rate Assumption
Corporate Tax Rate Assumption
Residual Value Assumption
Depreciation = (Initial Investment-Residual Value)/Expected Life Cycle
Electricity Price (EP) Changing Assumption
Std. Deviation of Electricity Price Assumption
Electricity Price (Observed) = NORMSINV(RAND( ); Average EP ; Std EP)
Incentive System Changing Assumption (Country Dependent)
Table 2: Description of the variables used in the model and Condition on Microsoft Excel
37
The computation of Free Cash Flows of the European Infra Fund (EIF) follows the
following formula:
where
Corporate Tax rate
Depreciation amount of each farm
Total Revenues of each farm
Total Costs of each farm
Total Revenue is dependent on the formula:
where
Load Factor achieved each year, which follows a Normal distribution with mean
and standard deviation , besides being dependent, as mentioned above, on the
randomly generated
Output produced each year
Electricity Price, following a Normal distribution with mean and standard
deviation , and also dependent on (another randomly generated set of numbers)
Government’s Incentive per year, depending on the Output generated by the
farm(O); on the year the farm is running (t); and the support Fee (F) established by law
38
Total Costs is represented by the equation:
where,
Initial Investment
= Operation & Maintenance Costs, following a Normal distribution with mean
and standard deviation , and also dependent on (randomly generated)
It is important to mention that the set of final results here presented show only one of
the several possible outcomes for the Infra Fund. Each time Excel is asked to create a
new set of random samples , a new pattern of load factors will lead to completely new
IRR, Average Returns, Standard Deviations, Durations, NPVs and Default Frequencies.
There are some limitations that have to be considered taking into account the simplistic
nature of the model for generating cash flows: for instance, there are still no studies
stating a distribution followed by load rates or maintenance costs based on passed
observed cases of Offshore Wind Farms, and, even though there are several studies on
electricity prices, the complexity of those methodologies takes need for developing
another paper just to set ground on how to model that 1 variable for this study.
PORTFOLIO FINANCING
Portfolio Financing derives from project finance, in which the security for the
investment is the project itself. The typical deal is arranged through an SPV (Special
Purpose Vehicle) created for the specific purpose of owning and managing the Offshore
Wind Farm. As no company or person is liable for the money, the only guarantee
39
investors have is the total cash flows the project will generate. According to Morthorst
(Morthorst et al. 2009), it works like “a giant property mortgage” in which the house is
collected by the investors if the payments are not repaid.
Project finance can be established for both debt or equity deals, in which the first thing
to do is to derive with reasonable certainty the likely cash flows that the project will
generate. In case of equity holders, the sponsors of the SPV will receive cash flows
according to the percentage ownership and are subject to the volatility of the profit
generated. For debt holders, the only difference is that repayments are designed to be
somehow stable throughout the lifetime of the loan. Equity holders hold a call option on
the profits and in some years they might not want to exercise the call (there will be no
more profit to distribute to sponsors as debt holders got the whole piece), but other
years, profits will grow and then equity investors will see the benefits of the call.
Analyzing figure 1, typically the SPV (here designated Wind Farm Ltd) owned by one
or several companies (joint venture), recurs to debt financing through a lending
syndicate consisting of one leading Bank institution (Bank A) which organizes the loan
and then searches for other banks willing to get involved in the operation (Banks B, C
and D).
Figure 2: Typical wind farm financing structure (source: Garrard Hassan at Morthorst et al. 2009)
A different approach for structuring the financing of such projects is Portfolio
Financing, already much in use and the one we are interested in for this study. In
40
Portfolio Finance, the owner of several wind farms is financed itself identically to the
SPV in project finance, with the difference that it bundles together several farms,
usually separated by a considerable geographic distance, and dependent on a different
range of turbine types and suppliers. In here, the security is conferred by the whole
portfolio of farms. The idea underneath the conception of this type of financing is that it
reduces risks associated with this technology, such as design faults of a specific
supplier, fiscal incentives-country specific dependency or wind shortfalls in particular
years. The latter is a crucial aspect as it can be seen in figure 3.
By grouping together several farms in one portfolio, the average wind speed of the
projects provides a much more stable line than each of the projects itself. This is an
attractive feature for investors as predictability and low volatility are highly rated
characteristics.
Hence, portfolio finance itself provides a tool for reducing risk at the same time that
benefits financial institutions (banks) which usually prefer big deals, as the due
diligence they are obligated to perform does not change significantly.
In this study, I am going to perform an analysis based on an organization (or a group of
sponsors in equity terms) owning a portfolio of wind farms in different European
countries with installed turbines provided by several suppliers. Thus, the analysis will
allow one to reduce or perhaps even annul technological risk and political risk.
Figure 3: The geographical effect of portfolio financing (source: Marco et al. 2007)
41
PERFORMANCE INDICATORS
In order to be able to compare the results here obtained with the ones achieved by
previous authors, I am going to use a set of measures for defining the risk-return profile
of Offshore Wind Infrastructure Investments (see Table 3).
- Return
As of return, Internal Rate of Return – IRR – and annualized rate of return - a measure
of return which does not take into consideration time values of money, thus presenting
the return of the cash flow generated over the initial capital requirement on each year –
are going to be used. IRR is usually defined as the return which equates a set of cash
flows occurring at different points over time to zero. IRR is also a very widely used
measure, for instance in private equity deals, but also in measuring performance of
projects or deals by funds’ managers on taking a decision on which asset classes to
invest (Luckett 1984).
Characteristic of the investment
Performance Indicator
Return: IRR (Internal Rate of Return)
Annualized Rate of Return
Risk: Standard Deviation
Time length: Macaulay Duration
Downside Risk: NPV
Default Frequency
Table 3: Performance indicator per characteristic of the investment explained
42
- Standard Deviation
Furthermore, standard deviation is used as a measure of volatility, which gives us a
proxy of risk. The calculation is based on all cash flows from each SPV (Offshore Wind
Farm) to the Infra Fund. For example, a small amount of capital generated by the
several farms here gathered in one year combined with an immense amount in the next
year increases the risk measure (Cumming & Walz 2009).
- Time-length
As of the purpose of analyzing the time horizon of Offshore Wind Farms’ investments,
the measure here used is annual Macaulay’s duration, which is a measure of the
project’s value volatility and the average time to discontinuity of cash flows (paid or
received). I am going to use Macaulay’s duration as defined by Fabozzi (Fabozzi 2010),
but adapted to a project’s specificities instead of a bond’s:
where:
= Cash Flows at time t
= Residual Value
Duration presents interesting properties namely the fact that when all other factors are
constant, the longer the maturity, the greater the duration. Also, the lower the cash
flows, the greater the duration.
By the use of this measure it is also possible to compare this investment’s maturity with
the one presented by other assets competing for the money of institutional investors.
43
- Downside Risk
In order to measure the downside (or default) risk, two measures here defined are also
used: first, NPV as it easily establishes a time value of money-adjusted measure for
defaulting. Accordingly, as I am assuming a risk-free interest rate of 5% in this paper,
and NPV will evaluate if the Infra Fund will add value for its sponsors, given that they
could at least invest their cash in a riskless alternative which would provide them 5%
return on top of the investment.
Moreover, a second measure is used which creates a multiple for each fund combination
according to the cumulated distributions returned to the investors as a proportion of the
cumulative paid-in capital. Bitsch et al. (Bitsch, Buchner & Kaserer 2010) use a similar
measure and thus, provide for a method to scrutinize those deals that return a multiple
smaller than 1, which would translate in a smaller amount of distributions returned than
capital invested by the fund to the owner of the several farms. Hence, a frequency
smaller than 1 means money lost by sponsors of the fund.
44
7. Empirical Results
Using the data and methodology described previously the analysis leads to the
following results:
H1: INVESTMENTS IN OFFSHORE WIND FUNDS HAVE A LONG-TERM TIME
HORIZON
For testing the differences in time horizon, as mentioned in section 4, I am going to
analyze the duration of the cash flows generated by the Infra Fund composed by the
combination of 37 farms in 5 countries. It is expected that durations will be largely
dependent on the assumption established of a 20 years life-cycle for wind turbines
according to the suppliers’ technological information. Duration, which according to the
purpose of this paper, is appropriately defined by Davis (Davis 2001) as the average
time to discontinued cash flows.
As a result, based on Table 4, we can state that regardless of the electricity price
scenario assumed, the duration of the cash flows will be lower than the maturity of 20
years assumed. Moreover, duration is increasing together with electricity price: the low
scenario of 55,00€ on average per year, throughout the 20 years, produces a duration of
13,33 years, which is considered a long-term perspective investment, when compared
with investments in which the sight is established as to hold assets for a few months, 1
year or even 5 years; as electricity price increases, duration is also increasing and out of
the 1000 cash flow results produced by the simulation at a price of 95,00€, a maximum
duration is found for a fund with 19,26 years, which is fairly high.
Bitsch et al., (Bitsch, Buchner & Kaserer 2010) in their paper on infrastructure deals,
found that numbers are not significantly different from those of non-infrastructure deals.
Comparing the results, they found that in their analysis, deals on non-infrastructure
investments average a duration of 50,83 months, which translated to years means an
average of approximately 4 years. This is considerably different from the results here
presented although it is not a surprising result due to the long average life span of
infrastructure, as argued by Inderst (Inderst 2010), and the assumptions pre-established
45
in this analysis. Metrick and Yasuda (Metrick & Yasuda 2010) affirm that deals done by
private equity-type funds usually present durations in the range of 10-12 years average,
which is similar to the numbers here presented.
Scenario
Measure: Duration Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 13,3308 15,3679 17,7078
Median 13,3365 15,3599 17,7191
Standard Deviation 0,4169 0,4389 0,4626
Minimum 11,9392 14,2594 16,0814
Maximum 14,6412 16,8288 19,2601
Table 4: Duration analysis (All countries)
Moreover, durations with these proportions are suitable for institutional investors such
as pension funds and insurance companies looking for long-term directed portfolios,
mainly for those funds using immunization of liabilities as a strategy in asset allocation
decisions, as indicated by Davis (Davis 2001).
Pension Funds and Life insurance companies are usually crucial investors in long-dated
bonds, such as 20-30 years sovereign bonds and ultimately, as governments reintroduce
the ultra-long paper issuance (30-50 years), of even longer duration assets. This is due
to the new international accounting standards, which more clearly expose interest rate
risk on liabilities in the balance sheet, but also to the projections of longer-living
populations in Europe, indicating that demand for long-paper will continue
(Blommestein 2007). Blommestein also states that not only pension funds and life
insurance companies are frequent clients of such issuances, but also relative value
driven-accounts, such as banks, hedge funds or endowment funds (private universities).
46
H2: INVESTMENTS IN OFFSHORE WIND FUNDS REQUIRE HIGH INITIAL CAPITAL
OUTFLOWS
It is also frequently mentioned that infrastructure assets have higher capital
requirements than other types of investments.
As of the analysis here presented, assumptions lead to the formation of a fund composed
of 5 countries and 37 farms which in total require an up-front investment of € 6.867
Million. This is a very high figure mainly because we are here simulating a fund which
would buy 100% participations in 37 Offshore Wind farms, a technology on its own
already expensive. Furthermore, the high number of farms leads to an increased
expenditure. Even though the results here presented are based on the assumption of such
an expense, it is important to understand the set of data we are dealing with: for instance
each farm, based on real data, leads to an average initial investment of €186 Million,
although the maximum of the sample is €1.100 Million and the minimum is €4 Million.
Thus, there is a very large disparity among projects, as proven by the figure of standard
deviation (€223 Million) – see Table 5.
Each Farm
Average 185.608.648,65 €
Median 132.000.000,00 €
Maximum 1.100.000.000,00 €
Minimum 4.400.000,00 €
Standard Deviation 222.700.552,18 €
Table 5: Statistics on the initial costs of each of the fund’s farms
Notably, the data set is still composed of many small farms (see Figure 3), which push
the average and median towards small figures. These smaller farms were set as of the
beginning of this technology and therefore, as the industry is developing itself towards
47
mature age, it is expected that larger fields will be created, of considerable commercial
size, thus, leading to larger demands of capital to set up one unique farm.
Figure 4: Frequency Distribution of Farms’ Initial Costs (in Millions of Euros)
Initial investments of this nature, requiring a high amount of capital, can only be a
possibility for institutional investors, which can, for instance, sponsor the creation of the
fund and then make it become available for everyone to invest in (listing the fund in a
public stock exchange) accessing a wider set of capital and loosening up the capital
requirement. Nonetheless, if the fund were to remain private, as it is drawn in this
analysis, it would mean a very large up-front cost for its sponsors.
Bitsch et al. (Bitsch, Buchner & Kaserer 2010) conclude that, on average, Infrastructure
deals present initial costs of about USD 22 Million (around €15,5 Million) which is
clearly lower than the €186 Million here stated. Nonetheless, their analysis includes not
only power plants (from renewable and non-renewable sources), but also bridges, toll
roads and other kinds of infrastructure investments. The most important aspect to depict
is that it is found that infra deals are more than double in capital requirement than non-
infra deals, meaning that, as expected Infrastructure investments require larger up-front
investments.
48
Hence, one cannot reject the null hypothesis that Investments in Offshore Wind Farms
require high initial capital outflows.
H3: INVESTMENTS IN OFFSHORE WIND FUNDS PRESENT STABLE CASH INFLOWS
One of the most important aspects in determining the risk-return profile of a particular
asset type is the variability of its cash-flows. As mentioned in previous sections, it is
often pointed that Infrastructure assets exhibit low cash flow volatility, much like bond
investments.
In order to conduct that analysis, I am going to use the standard deviation of the capital
generated by the Infra Fund as a measure for the volatility of the cash inflows of the
investment.
Scenario
Measure: Standard Deviation Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 1,85% 1,76% 1,80%
Median 1,84% 1,76% 1,79%
Standard Deviation 0,27% 0,29% 0,30%
Minimum 0,98% 0,82% 0,84%
Maximum 2,77% 2,79% 2,66%
Table 6: Standard Deviation of free cash flows generated by the Infra fund
Cumming and Walz (Cumming & Walz 2009) conduct a study in which they also
analyze variability in cash flows by the usage of standard deviation, although scaled by
the initial investment required on each project. In the study here presented, though, the
initial investment is the same for all the cash flows analyzed. Thus, there is no point in
scaling the standard deviations encountered as the scaling factor would produce the
same results. This is due to the design of the fund, which is composed of the same 37
farms, with assumed initial investments established, which is then simulated 1000
49
times, but without variation on the initial investment figure – the same fund, just 1000
possible results.
The analysis revealed on Table 6 is based on the computation of standard deviations for
each of the 1000 simulated Infra Fund’s cash flows for the 20 years period. Average
numbers show a relatively small figure, annualized, for each of the 3 scenarios of
electricity price, with the highest number being of 1,85% for the Low scenario.
Furthermore, the volatility of these figures is also relatively small, accounting for only
0,30% in the High scenario. The maximum figure here presented is of 2,79% standard
deviation per annum, which is still a fairly low number in terms of risk.
An overview of sovereign bonds performance in terms of volatility reveals that, out of a
sample starting from the year of 1973 and reaching all the way through 2008, the last
decades have been less volatile than the longer timeframes. Analysis of government
bonds show that overall, annual standard deviations record a figure of 4,8% – 5%.
Comparing these numbers to the ones presented by other asset classes’ profiles, one can
depict the magnitude of the standard deviation here achieved: Real-Estate, leads to
volatilities rounding the 15-16%, Hedge Funds for 8%-15% and Commodities 12%-
20% (Bekkers, Doeswijk & Lam 2009).
It is important to understand that this type of investment cannot compare to government
bonds and it will probably never constitute a substitute in terms of investment class,
mainly due to the potential risk of default that is taken as granted by investors as being
far smaller for a government as to fail to meet its obligations, than for an electricity
power project fund. Ultimately, one has to recall that the numbers here presented are
dependent on the incentive system guaranteed by governments themselves.
Nonetheless, Government Bonds provide a good benchmark for comparing all other
asset classes with, namely in terms of risk.
When comparing this asset with Corporate Bonds, it is crucial to distinguish between
low-grade and high-grade bonds: Kinn (1994) finds evidence, in a 28 years study of
both types of bonds, that standard deviation of the returns provided by corporate bonds
are in the range of 8%-10%, more precisely, 8,64% for low-grade and 9,42% for high-
grade bonds. These results are also clearly above the ones found according to the study
50
here conducted, which, even when looking at the results on a sole country perspective
(see tables 7, 8 and 9), are still slightly underperforming in what respects the standard
deviation averages encountered.
In order to understand the power of technological and political diversification induced
by the Infra Fund here constructed, Tables 7, 8 and 9, show the standard deviation
figures constructed in this study, but through a country specific approach. Accordingly,
the study divides the farms in relation to the country they belong to and an Infra Fund is
assembled per country. Thus, the Danish Infra Fund is based on 13 Offshore Wind
Danish Farms, the Dutch is based on 4 Offshore Wind Dutch Farms, and so on.
The most important fact to take out of the analysis of the tables is that, when
constituting a merely national fund, in contrast to a European fund, results in terms of
volatility are much worst, specifically for the Dutch and Irish Funds. All of the figures
here presented are higher than those of the European fund, regardless of the electricity
price scenario.
Moreover, in some countries volatility becomes larger as the average electricity price
increases, such as Sweden and the UK; in the Netherlands and Ireland, however, the
study reveals an inverse relationship and standard deviations are decreasing as
electricity prices increase.
Figure 5: Diversification effect in the figure of standard deviation across scenarios on electricity price.
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
7,00%
8,00%
9,00%
Low Intermediate High
Diversification Effect on the Risk Measure: Standard Deviation
All
DK
IR
NL
SE
UK
51
Figure 4 makes it easier to understand: the first line from the bottom (in dark-blue) is
the lowest standard deviation achieved and belongs to the European Infra Fund,
combining the 5 countries here analyzed.
LOW Scenario: Country Analysis
Measure: Standard Deviation DK IR NL SE UK
Average 2,27% 7,35% 8,40% 3,69% 2,35%
Median 2,25% 7,35% 8,38% 3,70% 2,34%
Standard Deviation 0,36% 0,98% 0,72% 0,58% 0,40%
Minimum 1,02% 4,59% 5,98% 1,85% 1,18%
Maximum 3,41% 10,35% 10,78% 5,63% 3,75%
Table 7: Cross Country Analysis of standard deviation according to the Low scenario on electricity price
INTERMEDIATE Scenario: Country Analysis
Measure: Standard Deviation DK IR NL SE UK
Average 1,96% 6,42% 7,34% 3,84% 2,55%
Median 1,96% 6,39% 7,34% 3,81% 2,53%
Standard Deviation 0,31% 0,97% 0,76% 0,63% 0,43%
Minimum 0,93% 3,35% 4,92% 1,86% 1,07%
Maximum 3,03% 9,68% 9,68% 6,02% 4,01%
Table 8: Cross Country Analysis of standard deviation according to the Intermediate scenario on
electricity price
HIGH Scenario: Country Analysis
Measure: Standard Deviation DK IR NL SE UK
Average 2,04% 5,79% 6,40% 4,10% 2,75%
Median 2,03% 5,73% 6,37% 4,08% 2,73%
Standard Deviation 0,32% 0,94% 0,77% 0,70% 0,46%
Minimum 1,01% 2,86% 4,13% 2,20% 1,59%
Maximum 3,04% 8,96% 8,85% 6,92% 4,51%
Table 9: Cross Country Analysis of standard deviation according to the High scenario on electricity price
52
Conversely to what one would expect, an analysis on the fund’s performance given that
NO incentive system would be operational (see Table 10), leads to the conclusion that
standard deviations become even smaller. This is opposed to what is often argued by
professionals in this industry and government members when defending the support fees
established: one of the most crucial reasons for giving incentives to Offshore Wind
Farms, besides economical viability, is predictability in the cash flows outputted by the
farms, which are expected to stabilize with the inclusion of a fixed price for the power
generated.
Scenario: NO Incentive
Measure: Standard Deviation Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 1,04% 1,12% 1,20%
Median 1,04% 1,11% 1,20%
Standard Deviation 0,17% 0,19% 0,20%
Minimum 0,59% 0,49% 0,63%
Maximum 1,79% 1,75% 1,78%
Table 10: Standard Deviation of free cash flows generated by the Infra fund, without the incentive
systems established by governments
Nevertheless, this is a false argument as some of the incentive systems are not
establishing a fixed price throughout the lifecycle of the farm (or partially). Instead,
they provide for an additional fee on top of (volatile) electricity prices. This is the case
for the UK, Denmark and Sweden, which in this analysis are the single most
representative countries with 14, 13 and 5 farms each, totaling 32 farms out of the 37
composing the European Infra Fund. Thus, these countries, regardless of the support
system type – FiT or TGC – do not institute a fix price per MW/h generated, applying
instead an incentive to the outcome produced, which, as it adds up to the spot market
electricity prices, will not provide for more stable cash flows.
53
H4: INVESTMENTS IN OFFSHORE WIND FUNDS ARE LOW-RISK AND LOW-RETURN
INVESTMENTS
Infrastructure investments are perceived as presenting low levels of risk, and therefore,
with Offshore Wind farms being an infrastructure investment, I am going to analyze if
this hypothesis can or cannot be rejected.
Several critics have been used on the power of standard deviation to measure and define
risk. For example, it is frequently declared that standard deviation does not reflect the
true underlying portfolio risk unless the data used is reflective of future performances;
also, statistical significance of standard deviation on cash flows is dependent on a large
number of observations; another important argument is that standard deviation only
takes into consideration expected risk and not potential risk (Wander & D'Vari 2003).
The first two arguments are surmounted by the nature of this simulation which is one of
predicting cash flows through a model for the future performance of a fund, based on a
large (Monte Carlo) simulation. The latter is overcome through the use of another
measure on downside risk: default frequency.
Recalling what was inferred in the previous section, the multiple generated by the 1000
simulations on the European Infra Fund provides the cumulated distributions returned to
the investors as a proportion of the cumulative paid-in capital. A multiple smaller than
1, would mean that there was a smaller amount of distributions returned to the investors
than the capital invested by the fund.
Scenario
Measure: Defualt Frequency Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 2,4467 2,7993 3,2071
Median 2,4460 2,7985 3,2078
Standard Deviation 0,0688 0,0717 0,0785
Minimum 2,2319 2,4998 2,9067
Maximum 2,6534 3,0473 3,4613
Smaller than 1 0% 0% 0%
Table 11: Default Frequency of free cash flows generated by the Infra fund
54
According to Table 11, the European Infra Fund does not generate any multiple smaller
than one. This is proven also by the minimum figures for any of the electricity price
scenarios. Another important aspect is that, given the lowest of the scenarios here
presented, the cumulated distributions of capital to investors more than double the initial
investment. At an average price of €95,00 per MW/h the return reaches 3 times the cost
of establishing the farm.
Although these results are good indicators on the performance of the farms, they are
based on the computation of annual free cash flows which do not consider time value of
money. Default frequencies reveal the total amount of Euros returned to investors at any
given point in time, no matter how far in time it happens. Therefore, Table 12 shows the
performance of the European Infra Fund according to NPV.
Scenario
Measure: NPV Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 3.785.251.769,08 € 5.224.449.212,42 € 6.943.091.204,14 €
Median 3.783.842.067,64 € 5.231.102.594,14 € 6.953.491.307,40 €
Standard Deviation 306.503.792,63 € 316.767.008,62 € 347.340.042,64 €
Minimum 2.819.102.990,39 € 3.992.168.569,33 € 5.678.151.196,71 €
Maximum 4.719.053.604,80 € 6.276.189.467,67 € 8.022.855.224,08 €
Negative NPV 0% 0% 0%
Table 12: NPV of the European Infra fund according to the different electricity price scenarios
Results in Table 12 present the NPVs for the project of a European Infra Fund if
considering 1000 simulations of its free cash flows. NPV analysis allows for the
consideration of time value of money, which according to our assumption, is supposed
to be possible to reinvest the cash flows at least at a 5% risk-free rate. As predicted,
NPVs are increasing as electricity prices increase. Proving the good perceptions from
the previous table, also no negative NPVs are revealed by this measure, indicating solid
results.
55
Another approach that can reveal crucial findings is the one of NO incentive systems
included in the computation of Free Cash Flows (see Table 13).
Scenario
Measure: Default Frequency Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 0,4364 0,9970 1,5190
Median 0,4376 0,9960 1,5191
Standard Deviation 0,0452 0,0531 0,0533
Minimum 0,2787 0,7829 1,3505
Maximum 0,5674 1,1563 1,7242
Smaller than 1 100,00% 52,40% 0,00%
Table 13: Default Frequency of free cash flows generated by the Infra fund, without the incentive system
established by governments
The results are self explanatory in what rspects to the importance of governmental
support. Looking at table 12 one can infer that only if electricity prices were to rise to an
annual average level of €95,00 per MW/h, the default frequencies would be zero. That
is, only in the highest scenario investors could be guaranteed to add value to their
money. The low scenario indicates that in all of the 1000 simulations, every result
would lead to a default frequency smaller than one, and only approximately 50% (500
cases) in the overall simulation would mean creation of value in the intermediate
scenario, although the average figure is also below one, but very close.
Finally, tables 14, 15 and 16 reveal the results on a country basis, which is indicative of
the success of each country’s incentive system. One cannot conclude that the incentive
system is good on the sole basis of the percentage of simulations which result in a
multiple above one. It is vital that the incentive system provides for the necessary
support of the technology without affecting the sole strive for efficiency – otherwise,
tax payers will bear the unnecessary costs of the badly used support.
56
Firstly, and as expected, we can observe that multiples are rising as average electricity
prices increase, for every country. Moreover, if electricity prices were to follow the path
of the last 2 years (Low scenario), only 2 countries would present multiples smaller than
one: Denmark (with 13,37%) and Sweden (23,30%) . At an intermediate level for
electricity, only Denmark still presents farms with multiples below one and only 1,20%
out of the 1000 simulations. The high level shows no default frequencies.
These results are not surprising in the view that Denmark and Sweden, although
applying 2 different types of support, FiT and TGC respectively, present the most
modest incentives in terms of overall money added to electricity price. The Danish
system adds €29,00 per MW/h to spot market electricity prices, while the Swedish
system adds €32,00 on average. Other countries are more generous and provide for an
average additional €108,00, as the UK, or the Netherlands, which fixes a price of
€186,00 throughout the lifetime of the support system.
These differences are also reflected by the proportion assumed by the multiples for the
UK, which are much higher than the other countries in general, reaching return figures
of more than 3 times (Low and Intermediate) and 4 times (High) the initial costs. The
same for the Netherlands, which on the two highest scenarios is already returning 3
times the invested amount. Again, the analysis on the default frequencies does not take
into consideration for time value of money. Appendix A provides for a detailed
statistical description on the NPV results given a risk-free interest rate of 5%.
This disparity in terms of incentives creates the risk of investing in Offshore spots
which do not offer maximum wind conditions. Thus, investors’ funds will be deviated
from possible better places in terms of wind conditions in Denmark or Sweden, to be
placed in British less optimal farms.
Another way to interpret the results is to think of the externality provided by the green
source of energy constructed: the English population might value more the non-
polluting source of electricity than the Danes or the Swedes (although historical facts
and people’s characteristics do not suggest that).
57
LOW Scenario: Country Analysis
Measure: Default Frequency DK IR NL SE UK
Average 1,0721 1,8253 2,8784 1,0894 3,1800
Median 1,0725 1,8170 2,8719 1,0909 3,1776
Standard Deviation 0,0684 0,2291 0,1909 0,1415 0,1084
Minimum 0,8765 1,0896 2,2309 0,6705 2,8390
Maximum 1,2671 2,5504 3,4362 1,5518 3,5372
Smaller than 1 13,37% 0,00% 0,00% 23,20% 0,00%
Table 14: Cross Country analysis of default frequencies of cash flows according to the Low scenario on
electricity price
INTERMEDIATE Scenario: Country Analysis
Measure: Default Frequency DK IR NL SE UK
Average 1,1862 2,0441 3,0481 1,6200 3,6637
Median 1,1856 2,0373 3,0499 1,6159 3,6632
Standard Deviation 0,0842 0,2412 0,1885 0,1573 0,1134
Minimum 0,8974 1,3365 2,4226 1,1074 3,3024
Maximum 1,4412 2,7885 3,6709 2,0774 4,0546
Smaller than 1 1,20% 0,00% 0,00% 0,00% 0,00%
Table 15: Cross Country analysis of default frequencies of cash flows according to the Intermediate
scenario on electricity price
HIGH Scenario: Country Analysis
Measure: Default Frequency DK IR NL SE UK
Average 1,5219 2,2292 3,1726 2,1197 4,1475
Median 1,5184 2,2371 3,1671 2,1155 4,1471
Standard Deviation 0,0923 0,2444 0,1895 0,1703 0,1230
Minimum 1,2359 1,4138 2,4730 1,4486 3,8133
Maximum 1,7950 2,9906 3,7392 2,6880 4,5908
Smaller than 1 0,00% 0,00% 0,00% 0,00% 0,00%
Table 16: Cross Country analysis of default frequencies of cash flows according to the High scenario on
electricity price
58
Once again the results here disclosed reflect the power of diversification as default
frequencies in the low scenario for the European Infra Fund combining every farm from
the 5 countries revealed no multiples below one. In the last tables presented we can see
2 countries which if standing alone, would not present the same level of certainty as if
grouped with other European nations.
Further scrutinizing the results comprising the performance of the Infra Fund designed
in this paper, I analyze the IRR produced by the Monte Carlo simulation and the
annualized rate of return.
Scenario
Measure: Annualized Rate of Return Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 12,23% 14,00% 16,04%
Median 12,23% 13,99% 16,04%
Standard Deviation 0,34% 0,36% 0,39%
Minimum 11,16% 12,50% 14,53%
Maximum 13,27% 15,24% 17,31%
Table 17: Annualized Rate of Return of the cash flows generated by the European Infra fund
Beginning with the results on the Annualized Rate of Return achieved by this
investment (see Table 17), the returns increase as electricity price scenarios become
higher: the low scenario returns 12,23% to investors while the high scenario indicates a
return of 16,04%. The standard deviation of these results, throughout the 1000
simulations, are low and reach a maximum of 0,39%, meaning that we are presented
with secure figures. Recalling what was stated in the previous section, the annualized
rate of return is constructed based on the annual cash flow outputted divided by the
initial investment. This translates in a number that does not account for time value of
money, thus generally overstating the return actually generated by the investment, as the
capital initially invested could be returning at a risk-free asset at least 5% (assumption).
59
Nonetheless, the calculation of annualized rates of return is useful in the sense that it is
often a measure used for computing the return achieved by other asset classes such as
equities or real-estate, particularly when the investment horizon is short-term directed.
Scenario
Measure: IRR Low: 55,00€ Intermediate: 75,00€ High: 95,00€
Average 11,09% 13,04% 15,33%
Median 11,08% 13,02% 15,33%
Standard Deviation 0,50% 0,51% 0,56%
Minimum 9,47% 11,38% 13,68%
Maximum 12,67% 14,61% 17,01%
Table 18: IRR of the European Infra fund
In order to better understand the actual return achieved one shall analyze the results on
the IRR (see Table 18). The breakdown of the table allows one to understand the
Internal Rate of Return of the project which is the European Infra fund composed of 37
Offshore Wind Farms. The low scenario on electricity price, as expected, generates on
average, out of the 1000 simulations, the lowest figure in terms of return, 11,09%,
which rises to 15,33% in the high scenario of €95,00 per MW/h. Moreover, one can
depict that performance throughout the 1000 simulations’ results are fairly steady and
reveal a maximum standard deviation of approximately 0,56%.
This means that, supposing electricity prices would remain stable for the next 20 years
at an average €55,00, results on Offshore Wind Farms’ investments would provide with
guaranteed 10% plus returns which is a very good performance, given the low risk
encountered by measuring standard deviations of cash flows, which was, for the low
scenario, of 1,85%.
In an analysis of the compensation attributed by this rate of return, given the volatility
of the investment, the calculation of the coefficient of variation would return a figure of
60
16,64%. Furthermore, given a risk-free rate of 5%, the low scenario conducts to a
single-period Sharpe ratio of:
Besides the positive figure in the result, indicating that the excess return is higher than
one unit of risk, the value of 3,30 leads to the interpretation that the risk premium is
more than 3 times the risk of investing in this asset.
When comparing the results here presented with the analysis conducted by Bitsch et al.,
they have concluded, as expected, that Infrastructure deals significantly outperform non-
infrastructure investments. Both papers done by Peng & Newell and Finkenzeller et al.
reveal returns of 14,1% and 12,1%, respectively, which are consistent with the
annualized rate of return of 12,23% here found for the low scenario on electricity prices.
Regarding the latter 2 papers, the biggest differences in terms of findings is related to
volatility. The results found by these authors situate standard deviation as being 5,8%
(Peng & Newell 2007) and 10,4% (Finkenzeller, Dechant & Schäfers 2010), which are
both much higher values than those here shown. This difference can be associated with
the assumptions here established in terms of electricity prices which in reality, over the
last decade, were subject to spikes in particular points in time, boosting prices up to
€134,80, and during 6 days in the last 2 years, the spot prices revealed trading at above
€95,00 per MW/h.
However, previous authors show evidence of IRRs reaching 34%-48% (CEPRES 2009),
which is a very different figure from the ones here depicted, and also from the 2 authors
mentioned before. Nonetheless, Inderst (Inderst 2010) states that pension funds are very
cautious in attributing target figures to return and volatility for infrastructure
investments. For instance, in the context of asset-liability modeling, values of 9%-10%
are attributed to return and 7%-8% to standard deviation. Again, this is somehow
61
similar to the numbers for return presented in this study but significantly different in
what respects to volatility.
In order to better comprehend the Sharpe Ratio derived above, a comparison to other
asset classes is required. Hodges et al. (Hodges, Taylor & Yoder 1997) indicate that for
long-term periods of investment, Corporate Bonds, for instance lead to a Sharpe ratio of
1.02, while common stocks only reach a maximum figure of 0,903. Once again this is
an argument in favor of Offshore Wind fund investments, with these presenting higher
returns per unit of risk. Even so, one has to bear in mind that these results are driven by
the low values of volatility here revealed, which, according to other authors’ findings,
seem to be too low. Nevertheless, the fund here designed is purposely built to diversify
away some of the natural, technological and political risk, while at the same time
avoiding some of the market risk by being defined according to the assumptions pre-
established as being solely composed by equity and without debt holders’ participation.
Moreover, in Kinn’s paper (Kinn 1994) the author finds evidence of returns for high-
grade and low-grade returns averaging 6,57% and 7,99%, respectively. Given the
standard deviation already explored in the previous section of 8%-10%, the overall
picture is clearly less favorable for corporate bonds than for Offshore Wind
Investments. Deriving a reward/risk ratio indicates that reward reaches a maximum of
90% of the risk, thus largely surpassed by the performance of the assets here analyzed:
Only in times of declining interest rates, do corporate bonds seem to resemble the
performance of Offshore Wind Investments: although still presenting more or less the
same values of standard deviation, average returns dramatically increase reaching
Return/Risk ratios of 3,04 and 3,20 for high and low -grade respectively.
62
Interesting to see is what happens to the numbers on both annualized rate of returns and
IRR when removing the incentive system (see tables 19 and 20).
Scenario
Measure: Annualized Rate Return Low:
55,00€ Intermediate:
75,00€ High:
95,00€
Average 2,18% 4,98% 7,59%
Median 2,18% 4,98% 7,60%
Standard Deviation 0,23% 0,27% 0,27%
Minimum 1,34% 3,91% 6,75%
Maximum 2,84% 5,78% 8,62%
Table 19: Annualized Rate of Return of free cash flows generated by the Infra fund, without the incentive
system established by governments
Scenario
Measure: IRR Low:
55,00€ Intermediate:
75,00€ High:
95,00€
Average #NUM! -0,04% 4,36%
Median #NUM! -0,04% 4,33%
Standard Deviation #NUM! 0,51% 0,42%
Minimum #NUM! -2,19% 3,06%
Maximum #NUM! 1,48% 5,92%
Table 20: IRR of the Infra fund, without the incentive system established by governments
As predicted, by removing the support system on every country included in the analysis,
all the scenarios show lower results. When looking at table 20 we can see that the
column of the low scenario of €55,00 does not state any values. This is due to highly
negative results. Only at the intermediate scenario we can see some improvements, still
averaging a negative 0,04% return, and even at the high scenario, results are not bright
with 4,36% average IRR in the 1000 simulations.
63
Results for the annualized rate of return are significantly better but still below the risk-
free rate of 5% for the low and intermediate scenario, 2,18% and 4,98% respectively.
Thus, it is clear that if electricity prices were to remain stable for the next 20 years at the
level of 2010 and 2011, Offshore Wind Farms could not be financially self-sustained
and would always need governments’ support, unless in the unlikely scenario of
electricity prices doubling the present value and reaching prices of around €100,00 per
MW/h.
Finally, a country-by-country analysis of the returns leads to an appropriate perception
of the effectiveness of each country’s incentive systems and ultimately allows one to
conclude for the power of diversification (see Tables 21, 22 and 23).
LOW Scenario: Country Analysis
Measure: IRR DK IR NL SE UK
Average 0,74% 9,44% 17,10% #DIV/0! 14,93%
Median 0,75% 9,54% 17,10% #DIV/0! 14,88%
Standard Deviation 0,71% 2,18% 1,58% #DIV/0! 0,78%
Minimum -1,42% 1,85% 12,03% #DIV/0! 12,90%
Maximum 2,82% 16,24% 22,28% #DIV/0! 18,15%
Table 21: Cross Country analysis of IRRs according to the Low scenario on electricity price
INTERMEDIATE Scenario: Country Analysis
Measure: IRR DK IR NL SE UK
Average 1,72% 10,11% 17,25% 5,67% 17,59%
Median 1,74% 10,16% 17,22% 5,65% 17,56%
Standard Deviation 0,76% 2,07% 1,58% 1,29% 0,85%
Minimum -1,00% 3,76% 12,07% 0,96% 15,45%
Maximum 4,07% 17,86% 23,79% 9,08% 20,40%
Table 22: Cross Country analysis of IRRs according to the Intermediate scenario on electricity price
64
HIGH Scenario: Country Analysis
Measure: IRR DK IR NL SE UK
Average 4,38% 10,68% 17,35% 9,26% 20,25%
Median 4,37% 10,64% 17,32% 9,21% 20,21%
Standard Deviation 0,71% 1,94% 1,56% 1,32% 0,89%
Minimum 1,99% 4,60% 10,56% 3,95% 17,58%
Maximum 6,50% 16,20% 22,29% 14,30% 23,25%
Table 23: Cross Country analysis of IRRs according to the High scenario on electricity price
As stated before, Sweden and Denmark are the 2 countries with the most modest
incentives to this technology: Danish and Swedish farms seem to not be profitable for
investors at the present level of electricity price (according to this model). Additionally,
Danish results seem to be only satisfactory at a level of electricity prices of €95,00. On
the opposite point, the UK and Netherlands are the ones with the most prominent
support fees, revealing solid IRRs at the low scenario (17,10% and 14,9% respectively).
A curious result evidenced by the analysis of the tables is that the two countries in this
sample that use a TGC system for supporting Offshore Wind (Sweden and the UK) are
the ones with the most notable variability across scenarios. Sweden reveals very
negative IRRs given the low scenario, then jumps to an average 5,67% return and
finalizes, at the high scenario, with 9,26% per annum, which is already a very good
number. Similarly, the UK presents average IRRs of 14,93%, 17,59% and 20,25% for
the Low, Intermediate and High scenario respectively. Oppositely, Ireland and the
Netherlands present much more modest ranges, irrespectively of good or bad
performances. Ireland goes from 9,44% to 10,11% and 10,68%; the Netherlands from
17,10% to 17,25% and finishing with 17,35%.
65
These results might indicate that the TGC system confers higher volatility to
performance results of Offshore Wind Farms, being more exposed to variations in spot
market electricity prices, while FiT systems might concede higher stability to returns.
Figure 6: Diversification effect on IRRs across scenarios on electricity price
Figure 5 presented above reveals, once again, the power of the diversification effect.
Across all the 3 scenarios, there is a big disparity in terms of cash flows presented by
each country’s fund. By compounding the cash flows in a single European fund, the
results are smoother and within the range of 10%-15% IRRs.
Further results on the cross country analysis of Annualized Rates of Return are
presented in Appendix C.
SENSITIVITY ANALYSIS
In the future, the industry for Offshore Wind is expected to suffer some improvements
leading to higher efficiency gains both in terms of O&M costs and Initial investment
requirements. Both costs are expected to fall from the actual assumed €16,00 per MW/h
of total possible output annually, to an average of €13,00. Also, Initial investment costs
are predicted to decrease with the development of this technology to around
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
Low Intermediate High
Diversification Effect on IRR
ALL
DK
IR
NL
SE
UK
66
€1.800.000,00 per MW/h capacity installed (Morthorst et al. 2009). In order to
understand what would be the implications of such changes to the IRRs encountered in
this paper, I will perform a sensitivity analysis based on 5 hypotheses. Tables 24, 25 and
26 resume the findings.
Due to limitations of the computer used to run the calculations, it was not possible to
run the sensitivity analysis through the 1000 simulations on the results of the European
Infra Fund. Hence, these results are for the performance of 1 fund only and depart from
initial IRRs different from the ones presented in this section. Nevertheless, these
findings are still indicative of the variations on the IRR of the 37 farms’ fund given the
changes in those 2 cost drivers.
As we can see, when both initial investment and O&M costs decrease, IRRs increase (as
expected). Interesting to understand is the percentage increment in each scenario:
accordingly, in the low scenario the IRR increases less (4,42 percentage points more)
than in the intermediate (4,47) or high scenario (5,12). Thus, one can state that the
reduction in 3€ per MW/h in O&M costs and €400.000,00 of investment costs results in
a significant difference in the returns achieved - for instance, for the low scenario it is
almost a 50% increase in the IRR.
Present IRR: 10,43% 10,00 € 13,00 € 16,00 € 19,00 € 22,00 €
1.400.000,00 € 21,19% 19,59% 17,92% 16,20% 14,40%
1.800.000,00 € 16,19% 14,85% 13,46% 12,00% 10,47%
2.200.000,00 € 12,83% 11,66% 10,43% 9,14% 7,78%
2.600.000,00 € 10,37% 9,31% 8,20% 7,02% 5,78%
3.000.000,00 € 8,48% 7,49% 6,46% 5,37% 4,21%
Table 24: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the Low
Scenario on electricity prices
67
Present IRR: 12,88% 10,00 € 13,00 € 16,00 € 19,00 € 22,00 €
1.400.000,00 € 24,39% 22,86% 21,29% 19,68% 18,01%
1.800.000,00 € 18,80% 17,55% 16,26% 14,92% 13,53%
2.200.000,00 € 15,10% 14,01% 12,88% 11,71% 10,50%
2.600.000,00 € 12,41% 11,43% 10,42% 9,37% 8,27%
3.000.000,00 € 10,34% 9,45% 8,52% 7,54% 6,53%
Table 25: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the Intermediate
Scenario on electricity prices
Present IRR: 15,78% 10,00 € 13,00 € 16,00 € 19,00 € 22,00 €
1.400.000,00 € 28,58% 27,10% 25,59% 24,05% 22,48%
1.800.000,00 € 22,10% 20,90% 19,67% 18,42% 17,13%
2.200.000,00 € 17,86% 16,83% 15,78% 14,70% 13,58%
2.600.000,00 € 14,82% 13,91% 12,97% 12,00% 11,00%
3.000.000,00 € 12,51% 11,68% 10,82% 9,94% 9,02%
Table 26: Sensitivity Analysis on O&M costs (x-axis) and Initial Investment (y-axis) for the High
Scenario on electricity prices
Furthermore, ranges of variation ceteris paribus, that is, holding one of the variables
here analyzed constant, lead to the conclusion that variations of €400.000,00 in initial
investment bring bigger changes to the IRRs than the differences of €3,00 per MW/h on
O&M costs. In a comparison of the low scenario the range of variation holding the
O&M costs at a level of €10,00, the IRR achieved with €3.000.000,00 in Initial costs is
approximately 13 percentage points smaller than the IRR at €1.400.000,00. Although,
this sensitivity analysis here conducted does not compare the two drivers using the same
unit measure, this can lead to the conclusion that the performance of Offshore Wind is
still largely dependent on the Initial Costs. Besides electricity price, which is a crucial
driver, as proved above in H4, also the values of Initial Investment are critical.
68
IRRs here obtained are revealing the power of innovation. Accordingly, in such a
scenario of reduced costs for maintenance and construction, the whole return picture
changes which will certainly influence investors’ decisions4.
4 Although the construction of the several spreadsheets was prepared for further sensitivity analysis, for
instance, regarding duration and standard deviation effects relative to modifications of Turbine Life Cycle
years, Corporate Tax Rate and Load Factor, the capacity of the computer used is not sufficient to satisfy
the needs of the software and thus, the outcome is not reasonable. Therefore, the results were excluded
from the paper as they do not appear to translate reliable information. The main reason is that the numbers
I am departing from, according to a particular present scenario, are not revealed by the sensitivity
analysis, as they should. Also, some unreasonable variations appear with IRRs decreasing and then
increasing again according to higher Life Cycles, or higher Electricity Prices, which also does not make
sense. Nevertheless, the tables generated were included in appendix D and F (virtual format).
69
8. Conclusions
The construction of the risk-return profile of Offshore Wind Investments here presented
is dependent on crucial assumptions, specifically on the cash flow generation through
the built-in model. Nevertheless some very interesting conclusions can be portrayed.
A European Infra Fund combining the cash flows of 37 real existing farms distributed
through 5 countries allows for achieving a higher level of technological, natural and
political risk diversification.
The returns obtained by the statistical analysis of the cash flows generated by the fund
indicate an average IRR of 11,09% given the average (present) electricity price of
€55,00, a figure that increases substantially with the raise of electricity prices.
Compared to previous empirical evidence, the number appears to be somehow
congruent. Some authors even refer the number as being reasonably close to returns
provided by stocks.
Furthermore, volatility of cash flows seems to be fairly low, reaching values of 1,85%
standard deviation per annum, which is lower than any of the figures presented for other
important alternative asset classes, such as Real-Estate or Corporate Bonds. This might
be connected to the simplified nature of the model generating the cash flows, but it is
also reflecting the pleasant characteristic of the fund as being country-diversified. An
analysis of a sole-country fund for each of the 5 nations here studied indicates much
higher standard deviations, which combined result in this lower value.
The analysis of the duration of the cash flows generated and the downside risk also
indicate very attractive features: results reveal a minimum duration of 13 years for a
buy-and-hold investor that keeps the investment throughout the whole lifetime of the
asset (20 years). Moreover, downside risk reveals that, even according to the low
scenario on electricity price, there are no default frequencies in the 1000 simulations ran
and no negative NPVs.
Even though these results might not hold for all the infrastructure assets it seems like
they hold to this particular one. This is one of the critics often pointed to the defendants
70
of infrastructure as an asset class: that it is a very heterogenic class, with a large
variance of results in respect to risk-return profile for different types of infrastructures,
thus it is important to differentiate each asset type according t its particular specificities.
The numbers here revealed fit the profile of pension funds, insurance companies and
even banks as possible institutions interested in investing in Offshore Wind: long-term
durations matching their long-term liabilities, low probability of default, with relatively
steady secure cash flows, and interesting rates of return. Even so, it does not seem fair
to state that Offshore Wind Investments can be seen as substitutes of Sovereign Bonds
due to the widely accepted feeling of security brought by these assets and the statute
achieved by securities such as U.S. or German government bonds of such confidence
that these are often considered as the proxy for a benchmark of risk-free rate, that is,
zero risk. However, Offshore Wind Investments might be able to establish themselves,
in the future, as an interesting alternative for institutional investors, compared to the
properties featured by Corporate Bonds or Real-Estate.
The analysis of the profit margin per MW/h also suggests that wind can be an
economically viable source of energy, although, as things stand, it is dependent on
governmental support systems. Scenario analysis shows that, in the event of an increase
in the average wholesale electricity price, Offshore Wind generation can become self-
sustained in financial terms (although the increase would have to reach 2 times the price
practiced today).
Besides the high influence of electricity prices, Offshore Wind Investments’
performance is also largely dependent on the Initial Costs: a sensitivity analysis here
conducted suggests that if construction costs drop as predicted in the future, the IRRs
achieved might increase in about 50%, benefiting the prospective investment profile
even further.
If we consider the costs on the environment and public health that other non-renewable
sources of energy imply, than perhaps wind energy is already economically self-
sustained, as subsidies are reflecting an externality that is hard to measure in a
quantitative way. In order to explain these results, it is important to comprehend that the
technology is still quite recent and the riskiness of the construction phase of such farms
brings initial investment up to very high figures that drag down returns without
71
governmental support. However, governments appear to be willing to bear this
technological and natural risks, instead of pollution or nuclear risks and, keeping it this
way, Offshore Wind Investments seem to be an attractive asset in which to invest our
money.
72
9. Further Research
On an academic perspective it would be interesting to develop further studies on how to
model the load rate of an Offshore Wind Farm according to real data provided by
already established farms. Previous researchers have developed methodologies for
modeling wind but load rates would take into account both weather conditions and
down-times for turbines. Thus, there is space for a higher knowledge in terms of this
particular parameter that affects the financials of this type of investment.
Also, O&M costs’ models are lacking in facilitating the analysis of what happens as we
reach the end of the lifecycle of a turbine. As of the infancy of this technology, many
farms haven’t reached a mature age and cannot provide real data for running such
studies. Nonetheless, in the next few years, as this type of data is being released a lot
would be gained with records revealing which distribution approximates the costs’
expense in Operations & Maintenance on Offshore Wind farms.
The most interesting study would be to run an analysis on historic deals by funds
investing in Offshore Wind uniquely taking into consideration the capital structure of
the special purpose entity owning each farm and the percentage participation of each
sponsor. CEPRES is a privately held center that collects private equity cash flow
information and classifies each deal according to type of business. Initial contacts with
this center assured me that this type of data could not be made public unless for
Governmental Institutions. This deal analysis, however, could provide for a clear idea
on the profitability of the investment, the amount of cash flowing in and out and its
uncertainty. It would also allow one to understand what has been the decision by many
fund managers about whether to buy majority participations or simply minority interests
in farms, hence understanding the ultimate objectives of these funds in respect to
assuming control over farms. Finally, a cross-section study regressing the average
returns of each deal on macroeconomic environment indicators such as GDP growth
rates or Public Equity Markets would have made it possible to further analyze the often
claimed low correlation with the general economy and the stock market. Also, the
inclusion of a variable for inflation could prove the link of these assets with inflation
rates, thus justifying the argument for the good inflation hedging properties.
73
Moreover, little is known on the possibility of issuing bonds on portfolio financing
projects of Offshore Wind. Sawant (Sawant 2010a) tries to add something in what
respects to this topic but there is shortage of information due to the lack of classification
of bonds as being based on infrastructure assets or not, and furthermore, on which type
of infrastructure. Besides the heterogeneity among infrastructure investments, some
authors also indicate room for another distinction between Greenfield and Brownfield
investments, classifying also in terms of entry stage of participation. Offshore Wind
Funds partially financed through the issuance of bonds could also be organized in
indexes and provide for additional diversification potential.
Finally, Offshore Wind Farms are nowadays a reality only for Europe, the U.S. and
partially in Australia, but the remaining of the countries are lagging behind in the
development of this technology. Nonetheless, the future will lead to the development of
farms in emerging economies and other developing countries. Thus, it would be of
value to conduct a study such as the one here designed on the financial viability of an
Offshore Wind Fund in the developing world, given the political, social and economical
conditions of emerging markets with very large populations and high population growth
rates, which can boost the returns up perhaps without adding significant risk to these
projects.
74
References
Beeferman, LW 2008, 'Pension Fund Investment in Infrastructure: A resource paper',
Occasional Paper Series, vol 3, no. Pensions and Capital Stewardship Project - Labor
and Worklife Program, pp. 1-78.
Bekkers, N, Doeswijk, RQ & Lam, TW 2009, 'Strategie Asset Allocation: Determining
the Optimal Portfolio With Ten Asset Classes', Journal of Wealth Management, vol
12(3), pp. 61-77.
Bitsch, F, Buchner, A & Kaserer, C 2010, 'Risk, Return and Cash Flow Characteristics
of Infrastructure Fund Investments', EIB Papers, vol 15:1, pp. 106-136.
Blommestein, HJ 2007, 'Pension funds and the evolving market for (ultra-)long
government bonds', Pensions: An International Journal, vol 12(4), pp. 175-184.
CEPRES 2009, 'Infrastructure Private Equity', Center of Private Equity Research,
Munich, Germany.
Chan, C, Forwood, D, Roper, H & Sayers, C 2009, 'Public Infrastructure Financing: An
International perspective', Productivity Comission Staff Working Paper, Australian
Government, Melbourne.
Cumming, D & Walz, U 2009, 'Private Equity Returns and Disclosure Around the
World', Journal of International Business Studies, vol 41(4), pp. 727-754.
Davis, EP 2001, 'Portfolio Regulation of Life Insurance Companies', Financial Market
Trends, OECD, Paris.
Department of Energy and Climate Change 2011, 'Review of the generation costs and
deployment potential of renewable electricity technologies in the UK', Study Report,
DECC, Ove Arup & Partners Ltd, London.
Domanico, F 2007, 'Concentration in the European electricity industry: The internal
market as solution?', Energy Policy, vol 35, pp. 5064-5076.
European Renewable Energy Council 2009, 'Renewable Energy Policy Review:
Ireland', RES National Policy Reviews, European Renewable Energy Council.
75
Fabozzi, FJ 2010, Bond Markets , Analysis and Strategies, 7th edn, Pearson Prentice
Hall, Upper Saddle River, New Jersey.
Finkenzeller, K, Dechant, T & Schäfers, W 2010, 'Infrastructure: a new dimension of
real estate? An Asset Allocation Analysis', Journal of Property Investment and Finance,
vol 28(4), pp. 263-274.
Gausch, JL, Laffont, J-J & Straub, S 2007, 'Concessions of Infrastructure in Latin-
America: Government Led Renegotiation', Journal of Applied Econometrics, vol 27, pp.
1267-1294.
Green, R & Vasilakos, N 2011, 'The Economics of Offshore Wind', Energy Policy, vol
39 (2), pp. 496-502.
Hodges, CW, Taylor, WL & Yoder, JA 1997, 'Stocks, Bonds, the Sharpe Ratio and the
Investment Horizon', Financial Analysts Journal, vol 53(6), pp. 74-80.
Holttinen, H 2005, 'Optimal electricity market for Wind Power', Energy Policy, vol 33,
pp. 2052-2063.
Hull, JC 2009, Options, Futures and Other Derivatives, 7th edn, Pearson Prentice Hall,
Upper Saddle River, New Jersey.
Hurley, M & O'Regan, R 2010, 'Meeting the 2010 rrenewable energy targets: Filing the
offshare wind financing gap', Energy, Utilities and Mining, PricewaterhouseCoopers,
London.
Inderst, G 2010, 'Pension Fund Investment in Infrastructure: What have we learnt?',
Pensions: An International Journal, vol 15:2, pp. 89-99.
International Energy Agency 2011, Policies and Measures Database, viewed 30 July
2011, <http://www.iea.org/textbase/pm/index.html>.
Jöhnemark, M, Östberg, R & Johansson, M 2009, 'The Electricity Certificate System',
Swedish Energy Agency, Edita Communication.
Kinn, J 1994, 'nravelling the Low-Grade Bond Risk/Reward Puzzle', Financial Analysts
Journal, vol 50(4), pp. 32-42.
76
Loring, JM 2007, 'Wind Energy Planning in England, Wales and Denmark: Factors
influencing project success', Energy Policy, vol 35, pp. 2648-2660.
Luckett, PF 1984, 'ARR vs. IRR: A Review and An Analysis', Journal of Business
Finance and Accounting, vol 11(2), pp. 213-231.
Maginn, JL, Tuttle, DL, Pinto, JE & McLeavey, DW 2007, Managing Investment
Portfolios: A Dynamic Process, 3rd edn, John Wiley & Sons, Inc., Hoboken, New
Jersey.
Marco, JM, Circe, T & Guillermo, G 2007, 'Towards determination of the wind farm
portfolio effectbased on wind regimes dependency analysis', World Wind Energy
Conference, Mar de La Plata, Argentina.
Metrick, A & Yasuda, A 2010, 'The economics of Private Equity Funds', Review of
Financial Studies, vol 23(6), pp. 2303-2341.
Morthorst, PE, Auer, H, Garrad, A & Blanco, I 2009, 'Wind Energy - The facts, Part III:
The Economics of Wind Power', Intelligent Energy - Europe, Executive Agency for
Competitiveness and Inovation, European Wind Energy Association.
Mott Macdonald 2011, 'Accelarating Deployment of Offshore Renewable Energy
Technologies', Reneable Energy Technology Deployment, International Energy
Agency, Glasgow.
OECD 2007, 'Infrastructure to 2030, Volume 2, Mapping Policy for Electricity, Water
and Transport', Organisation for Economic Co-operation and Development, Paris.
Orr, RJ 2007, 'The Rise of Infra Funds', Project Finance International - Global
Infrastructure Report 2007, Project Finance International, Zell am Harmersbach,
Germany.
Peng, HW & Newell, G 2007, 'The Significance of Infrastructure in Investment
Portfolios', Pacific Rim Property Reaserch Journal, Fremantle, Australia.
Ragwitz, M, Held, A, Stricker, E, Krechting, A, Resch, G & Panzer, C 2010, 'Recent
experiences with feed-in tariff systems in the EU: A Research Paper for the
77
International Feed-In Cooperation', Ministry for the Environment, Nature Conservation
and Nuclear Safety, International Feed-In Cooperation, Karlsruhe.
Ramamurti, R & Doh, JP 2004, 'Rethinking Foreign Infrastructure Investment in
Developing Countries', Journal of World Business, vol 39:2, pp. 151-167.
RES LEGAL 2011, Legal Sources on Renewable Energy, viewed 30 July 2011,
<http://www.res-legal.de/en/search-for-countries.html>.
Sawant, RJ 2010a, 'Emerging Market Infrastructure Project Bonds: Their Risks and
Returns', Journal of Structured Finance, vol 15:4, pp. 75-83.
Sawant, RJ 2010b, 'The economics of Large Scale Infrastructure FDI: The case of
Project Finance', Journal of International Business Studies, vol 41, pp. 1036-1055.
Schleisner, L 2000, 'Life cycle assessment of a wind farm and related externalities',
Renewable Energy, vol 3 (1), pp. 279-288.
Snyder, B & Kaiser, MJ 2009, 'Offshore wind power in the US: Regulatory issues and
models for regulation', Energy Policy, vol 37, pp. 4442-4453.
Uppenberg, K, Strauss, H & Wagenvoort, R 2011, 'Financing Infrastructure', A review
of the 2010 EIB Conference in Economics and Finance, European Investment Bank,
Luxembourg.
Vives, A 1999, 'Pension Funds in Infrastructure Project Finance: Regulations and
Instrument Design', The Journal of Structured Finance, vol 5:2, pp. 37-52.
Wander, B & D'Vari, R 2003, 'The Limitations of Standard Deviation as a Measure of
Bond Portfolio Risk', Journal of Wealth Management, vol 6(3), pp. 35-38.
Wells, LT & Gleason, ES 1995, 'Is Foreign Infrastructure Investment Still Risky?',
Harvard Business Review, vol 73:5, pp. 44-54.