Int. J. Industrial and Systems Engineering, Vol. 14, No. 4, 2013
A systems model for commercialising emerging
technology: wave energy farm system (WEFS) case
study
Oscar Bonilla*, Donald N. Merino,
Michael Raftery, Tamara Wainer and
Rashmi Jain
School of Systems and Enterprises,
Stevens Institute of Technology,
Castle Point on Hudson, Hoboken, NJ 07030, USA
E-mail: oscarbonilla100@gmail.com
E-mail: dmerino@stevens.edu
E-mail: michaelwraftery@hotmail.com
E-mail: tamarawainer@hotmail.com
E-mail: rashmijain@verizon.net
*Corresponding author
Abstract: This paper proposes a systems life cycle model that could be used to
analyse the commercial feasibility of emerging technologies development. The
model is based on a comprehensive review of the literature on the key factors
impacting the success or failure in commercialising technology. The proposed
systems model includes a technical, economic and operational domain and
covers a project’s planning, project implementation and feedback, analysis and
control phases.
This model was applied to a case study, a wave energy technology, with a
concentration on the economic domain. The economic domain includes an after
tax analysis model, which a typical company uses to assess economic
feasibility and a sensitivity analysis that identifies the most important attributes.
The internal rate of return (IRR) and other figures of merit (FoMs) were used to
show whether the project meets standard business criteria. This case shows how
financial leverage significantly improves a project’s economic attractiveness.
Keywords: technology commercialisation; systems model; commercialising
emerging technology; renewable energy; wave/ocean energy farm; systems
engineering; after tax analysis; ATA; engineering economics; internal rate of
return; IRR; net present value; NPV; life cycle costs; LCC/EUAC.
Reference to this paper should be made as follows: Bonilla, O., Merino, D.N.,
Raftery, M., Wainer, T. and Jain, R. (2013) ‘A systems model for
commercialising emerging technology: wave energy farm system (WEFS) case
study’, Int. J. Industrial and Systems Engineering, Vol. 14, No. 4, pp.441–462.
Biographical notes: Oscar Bonilla is a PhD student of engineering
management at Stevens Institute of Technology. He obtained his Bachelor in
Automation Engineering from La Salle University, Colombia, Masters in
Industrial Processes’ Automation from Los Andes University, Colombia, and
Masters in Business Administration from Stevens Institute of Technology,
Hoboken – New Jersey. He has over ten years of professional experience in
industry areas like six sigma, project management, maintenance management,
Copyright © 2013 Inderscience Enterprises Ltd.
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lean manufacturing, continuous processes’ improvement, and project financial
analysis. Currently, he serves as financial analyst for the information
technology industry.
Donald N. Merino is a Tenured Full Professor and the Alexander Crombie
Humphreys Chaired Professor of Economics of Engineering at Stevens Institute
of Technology. He is the Founder Emeritus of the undergraduate Bachelor of
Engineering in Engineering Management and the Executive Master in
Technology Management programme at Stevens. He won the Morton
Distinguished Teaching Award for Full Professors at Stevens, was awarded the
ASEM and ASEE Bernard Sarchet Award, and received two centennial
certificates from the ASEE in Engineering Economics and Engineering
Management. John Wiley published his book, The Selection Process for
Capital Projects and he has published over 50 refereed journal articles and
conference papers and over 30 research reports.
Michael Raftery is Founder and CEO of Seahorse Power and Research
Engineer at the Center for Maritime Systems at Stevens Institute of
Technology. He is an active member of the Technical Advisory Group for the
National Renewable Energy Lab and advises on standards for the wave energy
industry. He is a member of the Society of Naval Architects and Marine
Engineers and holds a Masters of Ocean Engineering from Stevens Institute of
Technology and Bachelors in Oceanography from Humbolt State University.
He is a former US Navy Deep Sea Diver and Underwater Construction
Technician.
Tamara Wainer is a Director in Global Risk Management at a Big 4 public
accounting firm and has held several positions in tax consulting in the USA and
Amsterdam. She is a graduate of the Executive Master in Technology
Management (EMTM) programme at Stevens Institute of Technology with an
MBA and Bachelor in Economics and Politics from Brandeis University.
Rashmi Jain is an Associate Professor of Systems Engineering at Stevens
Institute of Technology. She has over 15 years of experience in information
technology (IT) systems. Prior to joining Stevens, she was with Accenture
(formerly known as Andersen Consulting). Over the course of her career, she
has been involved in leading the implementation of large and complex systems
engineering and integration projects. She has done lectures internationally at
Keio University, overseas Chinese Institute of Technology (OCIT), and Indian
Institute of Technology, Delhi. She is a Visiting Professor for System
Architecture and Integration at Keio University. Her teaching and research
interests include systems integration, systems architecture and design, business
process reengineering and rapid systems engineering. She has authored several
papers on these topics. She holds a PhD and an MS in Technology
Management from Stevens Institute of Technology.
1
Systems model introduction
Emerging technologies face several barriers to commercialisation. For instance, in the
quest to achieve energy independency and to reduce greenhouse gases (GHG) emissions
many technologies have been developed that are more efficient than others but most of
them have not been commercialised despite their technical feasibility or demonstrated
A systems model for commercialising emerging technology
443
potential. It is clear that a broader approach is needed to successfully commercialise these
types of emerging technology.
A systems life cycle model to commercialise emerging technologies is proposed
based on a review of the literature for the last 20 years (see sub-Section 2.1). Three major
domains are explored, technical, operational, and economic. The focus of this paper is on
the economic domain. An economic after tax analysis (ATA) model was developed to
explore the economics of a patented wave energy conversion device. The financial results
demonstrate that the technology meets standard business criteria. The sensitivity analysis
(SA) indicates that electricity revenue, cost of capital, and the total capital costs are the
dominant factors. The financial leverage analysis indicates that as loan amounts increase,
the economics significantly improve. Since capital intensive projects like this would be
financed this would add to the project’s economic attractiveness but would also add to the
project’s financial risk.
2
Review of literature
2.1 Key factors for commercialising new/emerging technology
Commercial development of renewable energy technology began since the early
1990s, but yet the amount of modern renewable energy only accounts for 1% of the
world’s energy demand (Pinkze and van den Busee, 2012). It is a clear indicator that
commercialising new and emerging renewable energy technologies is still a problem that
needs to be addressed.
A major obstacle to commercialising new technologies like renewable energy is the
length of time to scale the technology from pilot plants to commercial production. There
is a need to quickly commercialise beneficial technologies to abate GHG and alleviate the
increasing demand for energy (Johnson, 2009). The demand for renewable energy is
driving several alternative energy solutions, not all of which are carbon-free. Therefore, it
is critical to be able to develop and commercialise technologies quickly. To do this a
concurrent engineering approach must be appropriately designed and implemented; there
is evidence that its successful implementation reduces the technology cycle time and
time-to-market (Sharma, 2004).
One of the most dominant factors for technology commercialisation failure is the
unfavourable economic performance (Kimura, 2010; Patterson, 1992; Pinkze and van den
Busee, 2012). Moreover, the factors resulting in the failure of commercialisation and
diffusion are classified into five patterns:
1
unfavourable performance of the technology, such as low efficiency and lack of
reliability
2
unfavourable economic performance, i.e., high cost and long payback period
3
organisational changes, such as restructuring and shifts of business strategy
4
market changes, which refer to unexpected changes in market demand
5
regulatory changes, such as the tightening of environmental regulations
(Kimura, 2010).
Major impediments to achieving commercial viability of new technology were found in
the mid ‘80s during the adoption of compressed natural gas as an alternate transportation
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fuel in Canada (Flynn, 2002). The lack of supporting infrastructure and poor design of
promotional programmes, known as the marketing strategy, limited the acceptability and
adaptability of the emerging technology in the market.
On the other hand, commercialisation and diffusion in the market can be expedited
and successful if all of the key factors are considered and dealt with in a concurrent rather
than a sequential manner. Examining successful cases published in the literature, reveals
that most of the key factors can be identified as well as those driving the technology
to fail in the market. Table 1 summarises the key factors for success/failure in the
commercialisation/diffusion of new and emerging technologies for relevant studies
published over the last two decades. Such factors such as public R&D, long-term R&D
support by the government, a marketing and diffusion strategies to respond to and
influence market demand, investment subsidy, and combination of R&D and deployment
policy were the key factors to accelerate the commercialisation of energy-efficient
technologies in Japan during the ‘80s and ‘90s (Kimura, 2010).
Energy efficiency policy is a fundamental factor for successful commercialisation of
new technologies in the energy area and must be considered. Both developers and
government have to work together to allow technologies to be diffused and to allow
markets to accept them.
Government plays a crucial role in renewable energy commercialisation, while it is
essential for government to promote deployment of technologies that are already on the
market by regulations and financial incentives such as subsidies, it is also important to
stimulate research and development (R&D) to supply new, innovative energy efficient
technologies by publicly funded R&D (Geller et al., 2006). Subsidies not only reduced
the relative cost disadvantage of new technology against conventional technology, but
also increased market volumes and thus stimulated technology learning (Kimura, 2010).
Another key factor for successful commercialisation of new technology that has been
broadly discussed in the literature is market segmentation and strategy (Balachandra
et al., 2010). Commercialisation is the process whereby the technology comes to play a
useful role in society, ‘commercialisation’ is thus a quintessential ‘market’ concept, if the
price is too high, people will not buy the technology; if the price is too low, the
manufacturers will gain no benefit from selling it, and will stop (Patterson, 1992).
Therefore, knowing the market and defining a niche is a fundamental factor to address. In
new technology commercialisation there are a few users who put high value on
innovativeness or environmental friendliness of the technologies which at first are much
more expensive than conventional technologies. These are innovators or early adopters in
the diffusion process of the technology (Rogers, 2003), and form the initial market on
which the developers can expand their marketing activities. Market development based
on the feedback from market experience proved to be indispensable (Pinkze and van den
Busee, 2012).
It is possible to identify success/failure factors for some of the renewable energy
technologies that have made their way through commercialisation such as solar, both
thermal and photovoltaic (PV) and wind. The Luz International Limited (LUZ) is one of
the world’s most successful company in commercialising solar power plants for the
utility sector with 95% of the world’s solar generated electricity (Lotker, 1991). In a
study conducted on solar energy, the analysis suggests that it is uncertain whether all oil
and gas firms will abandon the commercialisation of solar PV technology completely, as
this depends to what extent they are able to generate profits (Pinkze and van den Busee,
2012). This study highlights the importance of economic feasibility in the process of
A systems model for commercialising emerging technology
445
commercialising an emerging technology. Investments in solar PV increased extensively
after 2000 due to increased cell efficiency (technology improvement), reduced capital
costs (economic feasibility), and favourable policy, leading to annual growth in
grid-connected solar capacity of 60% from 2002 onwards (REN21, 2008) whereas wind
had an average annual growth rate of 25% from 2002 to 2006 (REN21, 2008).
Understanding the success factors is an opportunity for technologies such as wave energy
which indirectly depend on wind currents to generate energy.
Though, solar technology has proven to be efficient there is still a lot of uncertainty
regarding its economic benefits. There are many solar plants out there but yet only few
are making money. It is a major treat for the environment because the oil industry is
leaving solar and positioning towards a ‘re-carbonisation’ of business activities.
Hydrogen or fuel cells (FC) technology on the other hand, has proved to be economically
feasible on certain applications such as bus public transportation (Bonilla and Merino,
2010). Although, it is currently in a pre-commercial phase of development with a lack of
cost and performance competitiveness, current high costs and the predicted gradual cost
reduction are likely to imply slow market acceptance (Hellman and van den Hoed, 2007).
Table 1 summaries the literature review for the key factors impacting the success or
failure of technology commercialisation or diffusion.
Table 1
Key factors for success/failure in commercialisation/diffusion of new and emerging
technologies
Author(s)
Year
Lotker
1991
Patterson
Flynn
1992
2002
Rogers
Sharma
Geller et al.
Hellman and
van den Hoed
Balachandra
et al.
2003
2004
2006
2007
Kimura
Pinkze and
van den Busee
Key factors for success/failure in
commercialisation/diffusion
Government tax policy, avoided cost energy
pricing, lack of incentives to utility owners,
lack of recognition to environmental benefits
Timing, cost
Lack of supporting infrastructure, marketing
strategy, subsidies
Market segmentation, cost
Concurrent engineering approach
Energy efficiency policy
Lack of cost and performance competitiveness
2010 Market strategy, market-based approaches,
private sector driven business model,
innovative regulatory, financing
2010 Public R&D, long-term R&D support
by the government, marketing and diffusion
strategies, investment subsidy, R&D and
deployment policy, technology performance,
economic performance, business strategy,
market changes, regulatory changes/policy
2012 Profit, economic feasibility
Domain
Economic, policy
Technical, economic
Operational,
economic, policy
Operational, economic
Policy
Economic, technical
Operational, economic
Technical, economic,
operational, policy
Economic
2.2 Systems perspective on commercialising new technology
A systems perspective provides the life-cycle approach to design, development,
implementation, maintenance, support and retirement of systems. A system life-cycle
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begins from its conception and ends in its retirement or disposal (Lang and Merino,
2002). Circulating fluidised-bed combustion (CFBC) for instance, is a textbook example
of how to commercialise a new energy technology and an example of a systems life cycle
approach, where every aspect of the technology was investigated, from fuel handling to
waste disposal (Patterson, 1992). Therefore, understanding the systems perspective to
commercialising a new technology is the first step (Blanchard, 2004; Parnaby, 1955).
Systems engineering has been defined in several different ways – each having its own
flavour. NASA views it as a robust approach to the design, creation, and operation of
systems. It is the management of technology that controls a total system life-cycle
process, which involves the definition, development, and deployment of a system that is
high quality, trustworthy, and cost-effective in meeting user needs (Sage and Armstrong,
2000). The Mil-Std defines it as the application of scientific and engineering efforts to:
1
transform an operational need into a description of system performance parameters
and a system configuration through the use of an iterative process of definition,
synthesis, analysis, design, test, and evaluation
2
integrate related technical parameters and ensure compatibility of all related,
functional, and programme interfaces in a manner that optimises the total system
definition and design
3
integrate reliability, maintainability, safety, survivability, human, and other such
factors into the total technical engineering effort to meet cost, schedule, and
technical performance objectives.
From an industry life cycle perspective, two main phases in industry evolution can be
identified: a formative period and a market expansion period. The formative period is
characterised by uncertainty in technologies, markets, and regulations, whereby a range
of competing technology designs exist. Market formation on the other hand is
characterised by growth in multiple niche markets for which the technology is superior
(Jacobsson and Bergek, 2004).
From the systems engineering perspective, some approaches and frameworks have
been proposed to minimise technology uncertainty and the product/technology
development time to allow market expansion. Reverse engineering (RE) is one of them
(Dereli et al., 2008), though renewable energy technology is emerging there are so many
of the shelf components involved for which RE and systems cost integration can be
leveraged.
The rapid systems engineering framework (Jain et al., 2011) and the systems
integration framework (SIF) are systems life cycle-based frameworks to reduce
development time and to increase technology operational effectiveness. SIF is an
end-to-end approach based on the premise that integration occurs in the life cycle of a
system, it is not a one-time activity (Jain et al., 2010).
An important step in technology commercialisation is to know when it is fully
developed and when the system is ready. Knowing this can minimise the time to market
and to accelerate its diffusion. This can be accomplished through the use of the
technology readiness level (TRL) scale and the systems readiness level (SRL) index.
TRL is a measure of maturity of an individual technology, with a view towards
operational use in a system context. SRL is a method for determining readiness of a
system in the systems engineering life cycle (Saucer et al., 2008).
A systems model for commercialising emerging technology
447
Through the use of a system life cycle approach all the domains/phases of a system
are integrated concurrently minimising time to market and enhancing technology
maturity. The main aim of this paper is therefore to provide a system-based model for the
commercialisation of new/emerging technology.
3
System model to commercialise new technology
3.1 Proposed systems model
The literature review and examples from the renewable energy area indicate
that a broader systems approach is needed to successfully commercialise emerging
technologies. Figure 1 illustrates a systems decision model which could be used to
examine the development of new technologies.
Figure 1
A system model to commercialise new technology
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O. Bonilla et al.
Based on the literature review it is clear that a company or investor needs to analyse all
the elements of the model to ensure success. This is not an easy task and will require
robust sub models for all the domains and project phases.
This paper will concentrate on the economic domain in the planning phase. A brief
overview of the various domains will be discussed. However, each domain is complex
and needs to be explored more fully. A more comprehensive explanation of the other
domains and phases will be explored in subsequent papers.
3.2 General system requirements for model
At least three major domains are indicated in the planning phase in Figure 1. They are
technical, economic and operational constraints. A key to all of these is the interaction
among tasks. Timing and processor/successor relationships need to be carefully
considered to develop a robust project plan.
Every business venture has three phases, namely, planning, implementing and
feedback/analysis together with feedback loops that ensure continuous process
improvement and learning. The learning model is essential for efficient operation and for
effective future development.
Once the technical, economic and operational constraints are considered feasible a
business plan is developed, funding is obtained and the project proceeds to the
implementation phase. Feasibility criteria should be established at the beginning of the
project and used a phase gates. In each case the feasibility is a function of acceptable risk.
The implementation phase consists of manufacturing/assembling the system and
installing it. Lastly, lessons learned from this enterprise need to be used to formulate
future developments.
Among the domains, unfavourable economics and unexpected market change seem to
be the major factors on the factors for limited diffusion, economics and market factors are
almost domain.
3.3 Technical domain
The venture starts with conceptual ideas and invention(s). Intellectual property (IP) is
usually sought to provide a barrier to entry from other competitors. Developing the IP
may require multiple patents and licensing from others.
Engineering design is normally aided by computer models (simulations, discrete
programmes, etc.). Design and testing are interrelated. As the design progress, models
and/or prototypes are used to provide feedback to the models and alter the design. A
concurrent engineering process has been shown to both reduce the manpower required as
well as the elapsed time to complete the design (Blanchard, 2004; Parnaby, 1955).
Another major constraint is the cost of the devise. As the engineering design
progresses from the models/calculations to prototypes the accuracies of cost should
increase. At each stage of technical development both the cost and risk needs to be
developed.
Lastly, when the risk is deemed acceptable the technical feasibility is achieved. Risk
criteria should be established at the beginning of the project and reviewed at various
stages of development. Go or no go decisions should be based on predetermined criteria
(Blanchard, 2004).
A systems model for commercialising emerging technology
449
At this point in the planning phase of the project the technical capabilities are
assessed in order to determine the technology’s feasibility.
Once the technical domain has successfully passed through all the steps a
decision can be made with respect to the technical domain. Successful outcomes from
the economic and operational constraints domains are necessary to proceed with
implementation/deployment.
3.4 Economic domain –ATA
The major emphasis of this model is on determining the economic feasibility of a new
technology, as previously found in several studies, the economic feasibility was
determined to be the major factor for technology commercialisation success. Figure 2
illustrates a typical after tax process used in industry to determine economic feasibility
(Lang and Merino, 2002).
Figure 2
General decision model to determine economic feasibility
Economic Feasibility Model – Business Cases
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O. Bonilla et al.
3.5 Operational domain
This domain includes a number of important operational constraints that new
technologies need to satisfy in their operational environment. These constraints are
extremely critical to their successful deployment and sustainability. Issues such as global
warming, security, reliability, maintainability and aesthetics provide constraints for
power generation projects. Risk determination of the technical, economic and operational
constraints requires detailed analysis.
4
Business case study for a wave energy harnessing device system:
economic feasibility
4.1 Introduction for business cases
World energy demand has steadily increased over the last 50 years. The slope of the
demand curve has increased due to rapid industrialisation in a number of countries such
as Brazil, Russia, India and China (BRIC countries) (Energy Information Administration,
2009). Exaggerated fossil fuel use has increased the amount of green house gases (GHG)
in the atmosphere resulting in global warming. Global warming has been discussed for
almost a decade with scientific and political opinions varying widely, particularly in the
USA. The United Nations Intergovernmental Panel on Climate Change report that states
“global warming is a serious issue and alternative to fossil fuels are required to reduce
GHG emissions” (Bernstein et al., 2007). There have been a wide variety of solutions
ranging from limiting demand (conservation) to producing energy with little or no carbon
footprint. Wave energy is one of the emerging technologies that could lead to electricity
production without using fossil fuels.
4.2 Use of after tax model – Figure 2 for business cases
Though every factor is important towards commercialisation, the economic domain is one
of most important and is usually explored first or in parallel with the technical domain.
See Appendix A for a more detailed description of the technology.
This paper concentrates on determining the economic feasibility of the wave energy
harnessing device (WEHD) using business case economics. There are many methods to
determine the economics of electricity produced from renewable and non-renewable
sources (Kammen and Pacca, 2004). The ATA method is widely used by utilities and
industrial companies (Lang and Merino, 2002) – see Figure 2.
An economic decision model was used to determine the economics. It required an
ATA framework with capital and cost estimated for the WEHD. This was not a static
model but was run numerous times to help refine the estimates and evaluation. In
addition, cost sub-models were developed that automatically updated the main model.
For example, as more refined cost estimates became available they were entered into the
cost sub model which updated the main model.
Another major advantage of the ATA approach with sub-models is that it helps
optimise engineering design as it is being developed. For instance, should additional
hardware capital be spent to increase the equipment life from seven years to ten years, the
ATA will produce the NPV or internal rate of return (IRR) or EUAC incremental
A systems model for commercialising emerging technology
451
difference. If the difference meets the economic criteria it should be pursued. This
incremental analysis can also be used to provide ‘targets’ or ‘goals’ which would guide
engineering design. These approaches are examples of a Concurrent Engineering
approach outlined in Figure 1.
Figure 2 is a description of a typical ATA decision process. ATA is necessary
because the government is a de facto ‘partner’ in capital decisions since depreciation
rates, tax rates, investment tax credits and capital gains taxes greatly influence the
attractiveness of capital expenditures.
In addition, future investors need to determine the rate of return [or other figure of
merit (FoM)] of their investment before committing to the project. This is true for private
as well as public companies.
4.3 Scenarios and alternatives trade-offs
The first step in determining the business case and economic feasibility is to develop
possible alternatives that are mutually exclusive. In this case, two alternatives were
explored including wave farms of 500 megawatts (MW) and 1,000 (MW) plate capacity.
Trade-off analysis is conducted of the different alternatives by analysing their ability to
support the required operational scenarios.
4.4 Cost estimation – capital-related – see Table 2
For new technologies estimating the capital required is the most difficult task. By
definition they do not have an established set of capital and operating costs. As indicated
in the technical domain, cost estimation is an evolutionary process. As the design
matures, the cost estimates become substantiated.
Table 2
WEHD capital cost estimates
Cost description
WEHD system
Cost estimate (k$)
500 MW
1,000 MW
1,528,649
2,780,416
Anchoring system hardware
12,320
22,400
Electrical distribution system
232,198
284,222
Installation cost and oil fill
41,500
80,000
1,814,668
3,167,038
Total capital cost
Note: The estimates are for 2009 in USD$.
For this case study estimates were based on discussions with vendors and industry subject
matter experts (SMEs). These estimates tend to be plus or minus 30%. To further refine
the estimates requires the following:
1
Detailed parts list for equipment including size of parts, number required and
material specification
2
Technology identification as commercial, emerging or new. Commercial suppliers
should be identified together with catalogued costs and features.
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O. Bonilla et al.
3
Determination of size impacts on part costs. Sizing techniques such as using
the Boston Consulting Group (BCG) curves and/or the Lang factor (Lang and
Merino, 2002) could be used to estimate the cost of a larger part.
4
Determination of economies of scale through the learning curve can be applied to
pricing multiple units.
Rules of thumb are used for engineering design, environmental studies and other capital
related costs. Installation plans and associated budgets provide the basis for these
estimates.
For this particular case, most of the capital expenditure is for commercial
off-the-shelf (COTS) parts already in use in a marine environment. Assembling these
items will require some engineering design. Overall, the range of cost estimates should be
relatively low.
4.5 Cost estimation – operating costs – see Table 3
Development of the facilities, installation plans and associated budgets are required to
estimate fixed and variable operating costs. Some rules of thumb for items such as
insurance and taxes are also used.
Table 3
WEHD operating cost
Cost description
Cost estimate (k$/yr)
500 MW
1,000 MW
Fixed costs
2,002
2,002
Maintenance costs
14,185
24,696
Other yearly fixed costs
33,192
55,386
Total operating yearly costs
49,380
82,084
Notes: The estimates are for 2009 in USD$. Variable costs were estimated to be zero
(or economically insignificant) because the case is a 24/7 engineering operation.
4.6 Revenue estimates – see Table 4
The key revenue estimate is the sales price of electricity. The actual electricity revenue
will depend upon the location, the utility involved and other factors. This business case
includes all the transmission lines and control equipment to deliver the energy to the
system so the sales price would be closer to retail than wholesale. With national
distribution of electricity it is difficult to estimate the location. The wave farm in this case
would probably be located off the Pacific or Atlantic coasts of the USA. Electricity costs
in the Northeast and California coastal areas are higher than in the middle of the USA.
Thus, the 10 cents per kWh is a conservative estimate.
A systems model for commercialising emerging technology
Table 4
453
Electricity costs in USA
Electricity cost
New England – all sectors – retail
Date
Amount (c$/kWh)
April 2009
12.24
USA – all sectors – retail
April 2009
9.30
Base case 500/1,000 MW
2011–2012
10.00
Note: Estimates are in cents USD$.
Source: Average retail price of electricity to ultimate customers by end – use
sector, by state, April 2009 and 2008. From the DOE/EIA Energy
Report (Energy Information Administration, 2009, Table 5.6.A)
4.7 ATA – economic criteria – see Table 5
The first step is to determine the minimum attractive rate of return (MARR) an electric
utility company would use to evaluate this case. The MARR reflects the opportunity cost
for the investor’s capital. Risk plays a role because wave energy investments will involve
more risk than others until the technologies have an operational track record. Since this
study focuses on the ATA an applicable tax rate needs to be estimated for the chosen time
horizon.
A time horizon needs to be determined that reflects the project life. The ATA starts
with a base case where the owner uses 100% of their own capital (e.g., full equity). Next
financial leverage is explored at various levels of loans and equity. What level of loans
and equity capital is chosen depends upon the availability of financing, risk of defaulting
and other facts.
Other criteria are the figures of merit (FoMs). Given that the ATA model is an Excel
spreadsheet it is relatively easy to report on more than one FoM. FoMs include the net
present value (NPV) and the equivalent uniform annual cost (EUAC). EUAC is also
known as the life cycle cost. Table 5 summarises the figures used in the case study.
Table 5
WEHD economic model assumptions
Assumptions
Units
500 MW
1,000 MW
MARR/hurdle rate/cost of capital
%/yr
12
12
Income tax rate
%/yr
35
35
Capital gains tax rate
%
20
20
Investment tax credit
%
0
0
Loan interest rate
%/yr
7
7
Growth rate-revenue
%/yr
3
3
Operating-inflation
%/yr
2
2
Project life
Years
20
20
The most significant assumption is the MARR, which greatly varies by investor and
capital projects.
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4.8 Evaluation of non-economic factors
This is a major task which involves environmental studies, siting analysis, and risk
analyses (technical, economic and operational constraints). While the scope of this paper
is limited to the economic feasibility, all these areas must be assessed before
commercialisation can occur. There are a number of multi-attribute tools which can be
used. Analytical hierarchy process (AHP) and utility analysis are two common techniques
(Lang and Merino, 2002).
4.9 Decision process – trade-offs based on sensitivity analysis – see Table 6
Trade-off analysis of the alternative technologies will be based on sensitivity analysis.
Such analysis will help determine the most sensitive attributes impacting the decision.
This helps in separating the ‘vital few’ from the ‘trivial many’. This is an aid in decision
making because it focuses the effort on the most important variables.
Table 6
WEHD sensitivity analysis results
Attribute used in the ATA model
Spread
500 MW
a
1,000 MW
Sen. ratio
Rank
1
6.46
1
30.68
2
5.88
2
+/– 20%
30.01
3
4.97
3
+ 20%
22.97
4
4.41
4
Total replacement capital cost 7 and 14 yr
+/– 20%
7.44
5
1.29
5
Operating costs
+/– 20%
4.7
6
0.74
6
Electricity costs
MARR – cost of capital
Total capital cost
MARR – cost of capital
Sen. ratio
Rank
+/– 20%
34.04
– 20%
b
a
Notes: The ratio provided by the change in parameter with respect to its initial value
(base case).
b
Rank is given by considering the most sensitive variable as number 1 and the less
sensitive as number 6.
Electricity costs, the cost of capital (MARR) and total capital costs are the most sensitive
economic factors in this case. These are not surprising since the WEHD is similar to a
hydroelectric plant from an economic perspective. It has high capital costs, low operating
costs and no fuel costs. As with hydroelectric projects, siting is important for
wave energy projects taking into consideration stakeholder preferences, government
regulations, seafloor composition and wave activity.
Figure 3 – spider plot, graphically illustrates the sensitivity analysis. Electricity, cost
of capital (MARR) and total capital costs have the steepest slopes although in opposite
directions. That is, for a given change of the parameter (electricity or capital) there is a
relatively large change in the FoM (NPV or IRR or EUAC). Similar changes in the
parameters (operating or replacement costs) do not have a relatively large change in the
FoMs.
The implication of this is that the economic feasibility of this technology is a function
of the power purchase agreement, whether the capital cost of the wave farm can be built
as estimated and the owner’s cost of capital or MARR.
A systems model for commercialising emerging technology
Figure 3
455
Spider plots for sensitivity analysis results (see online version for colours)
4.10 Multiple attribute analysis decision process
Next, a decision needs to be made whether an economic or non-economic analysis is to
be employed. If all the attributes can be monetised and converted into dollars, then the
standard ATA with a FoMs such as NPV or EUAC can be used to either maximise
benefits or minimise costs. If all the attributes cannot be monetised then some form of
non-economic analysis must be employed such as multiple attribute analysis (MAA).
MAA was not in the scope of this paper.
4.11 Final decision – FoMs – see Table 7
Lastly, a decision needs to be made to determine whether the analysis yields a mutually
exclusive alternative that meets the economic and/or non-economic criteria. If it does,
then a decision is made. If it does not, the process needs to be repeated starting with
step 1 (Figure 2). This process must continue until a mutually exclusive feasible solution
is found.
The following results in Table 7 are based on the ATA model developed for this case.
The methodology used in this model is similar to that used by major industrial companies
and utilities in the USA.
Table 7
WEHD economic model (ATA) results
FoM
Units
500 MW
1,000 MW
IRR
NPV
EUAC
%/yr.
k$
k$
12.47
60,459
8,094
14.77
637,230
85,312
The results from this case indicate an acceptable IRR, given the industry MARR used. It
should be noted, however, that this analysis is based on 100% equity from the investor.
For a project this size, it is almost certain that the owner(s) would borrow a substantial
portion of the capital cost. Typically, this could be 40% to 70% or more. The percent of
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O. Bonilla et al.
loan utilised are decided by the utility or joint venture and/or by government agencies.
Thus, it must be examined how borrowing a portion of the capital impacts the economics.
4.12 Financial leverage – see Table 8 and Figure 4
The following table provides the FoMs for the two scenarios.
As indicated in Table 8, the FoMs increase significantly as the amount of loans
increase. Since financial leverage (using loans instead of equity) is commonly used to
finance large energy projects this has the effect of improving the project’s rate of return.
This is caused by using relatively cheaper after tax loan dollars for relatively more
expensive equity dollars in the case where the after tax loan interest rate is less than the
after tax IRR.
Table 8
WEHD financial leverage
Loan
(%)
Equity
(%)
Base case 0%
60%
70%
80%
90%
100%
40%
30%
20%
10%
NPV
(k$)
500 MW
IRR
(%/yr)
EUAC
(k$)
NPV
(k$)
60,459
523,496
600,668
677,841
755,014
12.47
21.62
26.72
37.45
72.06
8,094
70,085
80,417
90,749
101,080
637,230
1,445,053
1,579,690
1,714,327
1,848,964
1,000 MW
IRR
(%/yr)
14.77
27.04
34.00
48.50
93.01
EUAC
(k$)
85,312
193,462
211,487
229,512
247,537
However, increasing the percentage of loans increases the financial risk of default. If the
project is not successful (technology does not work, demand for electricity declines, etc.),
the financial losses could be substantial and the probability of default increases. There are
a number of strategies to offset this risk including purchasing insurance to guarantee the
loan. While this would be expensive, the additional economic return from the investment
is more than adequate to cover insurance expenses.
The following graph illustrates how increasing the loan percentage increases the
project’s IRR.
Figure 4
Financial leverage results (see online version for colours)
A systems model for commercialising emerging technology
457
4.13 Comparison with other technologies – benchmarking
Although both scenarios met the economic criteria, there is still the question of how this
technology compares with other renewable resources like wind and with the prevailing
technology-coal. There is an enormous amount of literature comparing alternative
technologies to produce electricity (Kammen and Pacca, 2004). It is beyond the scope of
this paper to fully explore all the potential alternative technologies.
However, as an example, a comparison with a clean coal project by Duke Energy in
North Carolina indicates a $2.45 billion to-date expenditure for a planned 825 MW plant
(Downey, 2009). Since 2005, the price of this plant has increased by $400 M with the
output reduced by half resulting in costs that are four times the original estimate per watt
of output capacity (Duke Energy, 2005). The National Energy Technology Laboratory
(NETL, 2007) projections for US coal plant production nationwide are consistent with
Duke Energy’s original estimates of $2 billion for 1,600 MW output capacity. It is
anticipated that the economic and political drivers for the cost overruns will be pervasive
in similar projects.
This WEHD project cost of $3.1 billion for a 1,000 MW and $ 1.8 Billion for a
500 MW plant to be built in 2011–2013 is in the same ballpark. Given that most of the
hardware for this project is COTS will help mitigate cost overruns.
Thus, the capital cost for this WEHD wave farm is in the ‘ball park’ of similar coal
plants. With the trend towards coal sequestration the capital costs for coal plants will rise
significantly.
4.14 Risk analysis
Another factor impacting the project is risk. Note that the case study was based on
deterministic estimates. While the sensitivity analysis helped us decide the importance of
the various attributes it does not provide us with the probabilities of various risks.
There are many risks that need to be analysed before a final decision is made. There
are technical, economic and operating constraint risks. There are project risk in
implementation and control. Lastly there is a weather risk in this case due to high seas.
For each of this risk there is a risk mitigation strategy that needs to be developed. For
instance the technical risk can be migrated by scaled up and tested in actual
environments. Scaling up via prototype reduces the risk that the devise will fail in
operation (Blanchard, 2004).
Developing a comprehensive set of risk models is beyond the scope of this paper.
5
Conclusions
The ATA model with a series of sub models were an example of how a concurrent
engineering approach could be used to shorten the commercialisation time cycle. Two
scenarios were considered: 500 MW and 1,000 MW wave energy farms. The capital cost
estimates are relatively conservative because most of the equipment is COTS.
The IRR and other FoMs showed that the WEHD case met standard business criteria
for both the scenario considered. Sensitivity analysis indicated that electricity revenues,
total capital costs and the owner’s cost of capital (MARR) dominated the economics for
both cases.
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O. Bonilla et al.
The financial leverage analysis indicated that the project economics significantly
improves as the amount of loans increase. This is a positive result this type of capital
intensive project that would financed. However, as the percentage of loans increase so
does the financial risk. Insurance could offset this risk.
The ‘bottom line’ is that this project meets the normal financial criteria used in
evaluating business cases and meets the Figure 1 (Part B – item 5) economic feasibility.
The other two major domains (technical and operational constraints) in Figure 1 need
to be satisfied. The ‘technical feasibility’ involves a series of prototypes while the
‘operational constraints’ involves risk analysis for environmental, financial, etc.
Lastly, the ATA model can be used to help design the wave farm by providing
guidance on cost/benefit trade-offs.
6
Future research
Future research will include a risk analysis which evaluates the financial and other risks
such as weather related events. This research will include MAA.
The engineering systems aspect of this project will also be explored. Included here
would be the impact of redundancy and other improvements to achieve a higher level of
reliability, supportability and maintainability.
Additional economic analysis will include the impact of carbon credits and/or cap and
trade for this project. A major feature of using ATA models is the ability to test various
engineering scenarios. For instance should extra hardware capital be spent to provide a
ten-year life instead of the seven-year life in the model?
Since the economic feasibility is greatly impacted by the amount of financial leverage
these alternatives should be explored. There already exist many different financial
schemes to finance major infrastructure projects like power plants. The financial impact
of those schemes on their impact on the project risk needs to be explored. This also has
implications for proposals to establish a ‘Green Bank’ to spur renewable energy projects.
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Appendix A
Seahorse Power’s WEHD description
A.1
WEHD system
The patent-pending WEHD is a system as illustrated in Figure 5, converts the energy
from surface ocean waves into electricity by using a buoy-driven electrical generation
subsystem. The electricity will be delivered to shore via existing sea floor cable
technologies currently in use in the offshore wind industry in Europe. The heavy
mechanical components of the electrical generation subsystem are contained in a
submersible housing anchored to the ocean floor and do not reside on the surface. A cable
reel and flotation device enables the system to harness a large range of waves on the sea
surface. By using winches and anchors, the electrical generation subsystem can be
lowered to a safe depth during storms or can be raised near the surface for a shoaling
effect in calm seas. The minimum depth of the anchors will be determined by the historic
wave climate in the region of operation. This configuration protects the components of
the electrical generation subsystem from the effects of damaging surface waves, such as
those generated by storms and hurricanes.
Unlike existing wave energy conversion devices, the WEHD is designed to operate in
a greater range of wave heights by using synthetic lines and winch reels. The WEHD is
designed to be fully operational in waves over 30 meters in height, resulting in an
increased electrical output capacity relative to other known existing systems.
The limitations of existing wave energy conversion systems can be solved by the
WEHD. This invention includes variable depth, shoaling, storm avoidance, continuous
phase control, energy storage, and a ‘no shaft seal required’ between the sea water and
the generator shaft features. The WEHD allows for rapid deployment and recovery of the
electricity generating system which helps deployment crews avoid the dangers of high
energy wave regions. The WEHD incorporates a shoaling feature that uses a buoyant
platform to shoal incident waves. The shoaling capabilities concentrate wave energy and
are a function of platform dimensions, buoyancy, lift capacity and wave conditions.
Figure 5
WEHD (patent pending) system (see online version for colours)
Design specs:
•
•
•
•
•
•
•
Stable platform based on ocean-going barges
Variable depth platform
Buoyant platform when fully flooded
Hydraulic mooring and power take-off
Continuous phase control
Autonomous and remote controls
Low mass/inertia point absorber
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One economic advantage of the WEHD over other existing systems is its ability to shoal
waves in calm and moderate seas. This feature enables the WEHD to change the shape of
waves, making the waves steeper which enables the device to harness more energy from
increased buoy acceleration. This effect increases output capacity of the WEHD. The
advantages are two-fold. One, this device is not required to be located in extremely
high-energy wave climates where the probability survival is lower and deployment and
maintenance costs are higher. Two, this device is more economically efficient over
existing systems because it has the capacity to continue to harness energy from waves
during periods of low to moderate wave activity and store the harnessed energy at sea to
provide electricity for up to an hour during periods of flat seas.
A.2
WEHD concept of operation
The WEHD is an electricity generating device energised by water motion. The general
field is commonly referred to as hydrokinetics.
The WEHD utilises a winch and buoy system to harness wave energy. Synthetic lines
connect the surface buoy to the winch reels situated on a submerged platform. The energy
is extracted with the buoy motion, similar to the motion of a yo-yo. The winch reels
combined with a buoyant platform, provide a method for controlling platform depth. This
variable depth feature enables two other features, the shoaling feature and active storm
avoidance feature. In calm and moderate seas the platform can be raised near the surface
to shoal waves. In storms or periods of extremely large waves, the platform can be
lowered near the sea floor where water particle motion is reduced, thereby reducing the
kinematic and dynamic loads on the platform and anchor lines. The variable depth feature
is designed to be controlled by submersible winches with continuous response to
changing anchor line or buoy tether tension, thereby providing a continuous phase control
feature.
A.3
Scope of WEHD
The scope of this case study is a simulated wave farm based on the WEHD design that is
equivalent to the size of one large coal plant or nuclear power plant. The objective of
wave energy system development is to eventually replace carbon-based and nuclear
power projects. The calculations are estimated costs for a full-scale deployment after an
in-water pilot programme. Some of the WEHD system objectives are:
•
1 MW minimum plate capacity
•
minimise anchor loading
•
provide buoyant integrity
•
provide wave tuning capabilities
•
decouple energy extraction and electricity generation
•
provide energy storage capabilities.
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A.4
O. Bonilla et al.
Risk analysis
Another factor impacting the project is risk analysis. Deterministic, component-based
safety factors, based on extreme storm analysis are required to increase the probability
of a 20-year life cycle. High safety factors generally increase production costs while
reducing the risk of catastrophic failure. A key financial risk is rate of return on
investment due to the large amount of capital required to commission a facility.
A.5
Wave farm comparisons and sizing
Investors have multiple choices in the renewable energy sector including solar, wind, and
wave energy. Key factors for economic viability include the magnitude, concentration,
and availability of each resource as well as the cost of commissioning and operating
systems.
Ocean wave energy is approximately 1000 times more concentrated than wind energy
and wind energy is a concentrated form of solar energy (Ocean Energy Council, 2009).
Capacity factor, or the percentage of output per year based on plate capacity for wind
energy ranges from 25% to 30% and wave energy ranges from 50% to 90% depending on
location (Department of Business, Economic Development, and Tourism, 2002). The
WEHD design facilitates plate capacity in excess of 1 megawatt (1 MW) using less than
500 square meters of sea surface area in moderate and high-energy wave climates.