Case Study of Cape Wind:
Identifying Success and Failure Modes of Offshore Wind Projects
ARCHIVES
By
OFASC;VkOW
Pierre Dennery
JUN 24 2015
LIBRARIES
Master of Science in Management
LBRAR
HEC Paris, 2015
_ES
SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN MANAGEMENT STUDIES
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2015
2015 Pierre Dennery. All rights reserved.
The author hereby grants to MIT permission to reproduce
and to distribute publicly paper and electronic
copies of this thesis document in whole or in part
in any medium now known or hereafter created.
Signature of Author:
Signature redacted
MIT Sloan School of Management
May 8, 2015
Certified by:
Signature redacted
Henry Birdseye Weil
Senior Lecturer
Thesis Supervisor
Accepted by:
Signature redactedMichael A. Cusumano
SMR Distinguished Professor of Management
Program Director, M.S. in Management Studies Program
MIT Sloan School of Management
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Case Study of Cape Wind:
Identifying Success and Failure Modes of Offshore Wind Projects
By
Pierre Dennery
Submitted to MIT Sloan School of Management
on May 8, 2015 in Partial Fulfillment of the
requirements for the Degree of Master of Science in
Management Studies.
ABSTRACT
Cape Wind was supposed to become the first offshore wind farm in the United States. In
2015, more than 10 years after its inception, a single turbine has yet to be produced and the
project is at a dead end. Facing a strong local opposition, it has suffered numerous setbacks that
eventually led to huge delays affecting its timeline. Understanding what happened in this
particular project and what could have been done differently could help the industry go forward
with new plans to develop offshore wind in the United States. In this paper, we have built a
System Dynamics model to simulate the dynamics of support, opposition, financial certainty and
technology that can affect an offshore wind farm during its approval process. We show that
contrary to a common idea, the relatively lower environmental awareness fifteen years ago was
not a major cause for the to date failure of Cape Wind. Rather, it is the level of advocacy against
the project at its beginning that has the most impact on its overall timeline. Major efforts should
therefore be devoted to defuse the most vehement opponents right from the beginning, rather
than trying to convince more people to support it. We also show that changes in plans during the
approval process to increase its NPV can have a strong impact on the project timeline. Lastly,
contrary to our hypothesis, we see that a regulatory framework doesn't necessarily mean a faster
approval process.
Thesis Supervisor: Henry Birdseye Weil
Title: Senior Lecturer
3
[Page intentionally left blank]
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Acknowledgments
This thesis wouldn't have been possible without the support and inspiration of the many people I
have had the chance to meet here at MIT.
First, I would like to sincerely thank my thesis advisor Professor Henry Birdseye Weil for his
benevolent support throughout the year. Not only that, he has also been a great source of
inspiration during my research.
I would also like to appreciate Professor John Sterman for giving me guidance prior to the
project and Katherine Dykes for sharing her expertise on wind energy.
Lastly, my gratitude goes to MIT as an institution and a community for introducing me to the
fascinating environmental, economic and social issues of Energy Transition through its classes,
conferences, and club activities.
5
Table of Contents
T he case for offshore w ind ....................................................................................................................8
Early developm ents ........................................................................................................................................... 8
The benefits of w ind ........................................................................................................................................11
Risks and criticism ..........................................................................................................................................14
The benefits of offshore w ind ........................................................................................................................17
Latest and future innovation ........................................................................................................................17
T he C ape W ind Project ......................................................................................................................18
Birth of the project ..........................................................................................................................................18
Initial Plan .........................................................................................................................................................19
H urdles ..............................................................................................................................................................19
Current state .....................................................................................................................................................22
Identifying success and failure modes of large energy projects ........................................ 23
Lack of regulatory fram ew ork .....................................................................................................................23
Failure to secure key local support ..............................................................................................................24
Location .............................................................................................................................................................24
M odel and D iscussion ..........................................................................................................................25
C ausal Loop diagram .....................................................................................................................................25
D efining variables ............................................................................................................................................27
M odel overview ................................................................................................................................................32
Sim ulations ........................................................................................................................................................35
D iscussion ..........................................................................................................................................................43
C onclusion .............................................................................................................................................45
A ppendix: E quations ...........................................................................................................................47
B ibliography ..........................................................................................................................................50
6
Figures and Tables
Figure 1: Evolution of rotors sizes and power output from 1980 to 2010 ..........................................
9
Figure 2: Blade pitch control....................................................................................................................10
Figure 3: Market expansion self-reinforcement loops........................................................................11
Figure 4: Wind Resource Potential in the United States (Schwartz et al.)......................................
12
Figure 5: Life-cycle emissions of different energy technologies (Schlomer et al.)...........................12
Figure 6: US Deployment & Cost for Land-based Wind: 1980-2013 (US DOE 1)..........................14
Figure 7: Turbine power curve (Wan).................................................................................................15
Figure 8: Causal loop diagram of support and financial dynamics .................................................
26
Figure 9: TableA ........................................................................................................................................
27
Figure 10: View 1 - Dynamics of support..........................................................................................
32
Figure 11: View 2 - Dynamics of legislative process ..........................................................................
33
Figure 12: View 3 - Dynamics of technology and financing .............................................................
33
Figure 13: View 4 - Dashboard ................................................................................................................
34
Figure 14: "Base Case" Public Opinion...............................................................................................35
Figure 15: "Base Case" Lobbying ........................................................................................................
36
Figure 16: "Base Case" Financing............................................................................................................36
Figure 17: "Base Case" Permits timeline.............................................................................................37
Figure 18: "Base Case" NPV and Technology cost.............................................................................37
Figure 19: "No Tech Change" Scenario Key Parameters.................................................................
38
Figure 20: "No Tech Change" Scenario Permits Timeline ...............................................................
38
Figure 21: "Eco-friendly" Scenario Key Parameters ........................................................................
39
Figure 22: "Strong Advocacy" Scenario Permits Timeline ...............................................................
40
Figure 23: "Targeted Opposition Diffusion" Scenario Key Parameters ........................................
40
Figure 24: "Regulatory Framework" Scenario Permit Timeline......................................................42
7
The case for offshore wind
Early developments
The first electricity-generating wind turbines were designed concomitantly in Europe and the US
in the late 1800s'. Soon, farmers across the United States adopted the technology, motivated by
the lack of energy distribution systems. In the 1930s, wind turbines were a common sight in the
Midwest, as attested by Dorothea Lange's iconic pictures of the Dust Bowl. Until the 1970s,
most wind turbines remained very small with capacities of ranging from 10 to 100 kilowatts
(Kammer), due to wind energy lack of competitiveness versus cheap fossil fuels. Even though a
successful attempt was made to build the first megawatt turbine in 1941 with the Smith-Putnam
Turbine, it broke less than 2 months after inauguration and was never repaired (McCaull).
Troubled energy markets in the 1970s, nuclear incidents, as well as growing concerns for the
protection of the environment under Nixon led to a renewed interest for wind energy in Europe
and the US. In Denmark, the first 2 megawatts turbine was built in 19782. In the US, the federal
government - through the Department of Energy - began funding NASA research for improving
wind turbines efficiency. The program made considerable technological breakthroughs still in
use today and achieved huge scaling: the first 4 megawatts turbine was developed in 1982 and
held the record for 20 years (Linscott). In 1980, the World's first wind-farm project came out of
.
the ground in New Hampshire3
The 1980s and the 1990s saw further enhancement and up scaling of wind energy technology,
making it ever more affordable: larger rotors to expand the area swept by the blades and higher
towers to help reach areas of stronger and more constant wind (figure 1), improvement of blade
pitch control including hydraulic systems and slewing drives (figure 2), product diversification
1Energy.gov,
«History of wind energy ), n.d. Web. 29 Apr. 2015
http://energy.gov/eere/wind/history-wind-energy
2 Frei, M., «Wind energy: Opportunities and Challenges >, n.d. Web. 29 Apr. 2015
http://www.cas.umn.edu/assets/pdf/Wind%20Energy%200pportunities.pdf
3 UMass, «Wind Energy Center Alumni , n.d. Web. 29 Apr. 2015
http://www.umass.edu/windenergy/about/history/alumni
8
with the advent of hot and cold air turbines, and early development of offshore wind were some
of the factors contributing to this improvement. The common wind energy converter capacity
rose from less than 100 kilowatts on average in the 1980s to close to 1 megawatt at the end of the
1990s (Kammer).
Figure 1: Evolution of rotors sizes and power output from 1980 to 2010
140
120
-
0
100
0
2
A
A
600
80-
* Moss production
APrototypes
-
40
50kW
2001
1980
1985
1990
1995
2000
2 005
2010
Source: International Energy Agency (IEA)
9
Figure 2: Blade pitch control
Being able to adjust the pitch angle of a turbine broadens the range of wind speeds at which the
wind turbine can be operated. When the wind is low, a smallerpitch can be used to maximize the
energy produced. When the wind is strong, adjustingto a largerpitch allows the turbine to turn
without over speeding. This is referred to as 'furling". Above survival speed, a stalling system
will ensure that the turbine doesn'tget damaged.
Low
Pitch
High
Pitch
Full
Feathered
90
Beside technological breakthroughs, market trends also had a great impact on the development of
the industry in the late 90s. The sole expansion of the market created self-reinforcing loops
(Figure 3) primarily leading to a decrease in costs.
States such as California pioneered tax
incentives for renewables energy with tax credits of up to 55% (Kammer). Finally, progressive
grid integration offered more competitive returns for wind farm developers as it broadened the
demand for such energy.
10
Figure 3: Market expansion self-reinforcement loops
Suppliers
Suppliers competition
Cheaper supply
Costs
Incentive to develop
+
new projects
Development of
Wind Farn
-.--
Installed Capacity
+
Learning
Learning curve effect
CR)
ScalingP
Economics of scale
Manufacturing capacity
The benefits of wind
Wind is widely considered as one of the most promising renewable energy. Wind is ubiquitous,
and can be found almost everywhere in the United States, with varying levels of intensity (figure
4). It is one of the cleanest: a study from the Intergovernmental Panel on Climate Change
(Schlkmer et al.) estimates its lifecycle greenhouse gas emissions (including direct emissions,
infrastructure and supply chain emissions, biogenic C02 emissions and methane emissions) per
kWh to range from 7 g to 56 g of C02 eq/kWh for onshore wind and 8 g to 35 g for offshore
wind. The median estimate for onshore and offshore wind production ranges from 11 to 12 g of
C02 eq/kWh. This is lower than any other energy source (figure 5). Solar power median
estimates range from 27 g of C02 eq/kWh for concentrated Solar Power to 48 g of C02 eq/kWh
for photovoltaic. Hydropower lifecycle emissions are double that of wind while geothermal's are
more than three times higher. Nuclear is the only source of energy that can compare with 12 g of
C02 eq/kWh.
11
Figure 4: Wind Resource Potential in the United States (Schwartz et al.)
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Figure 5: Life-cycle emissions of different energy technologies (Schl6mer et al.)
Direct emssions
Options
Infrastructure &supply
Dtrect
ermssins
Min/Median/Max
Currently
Commercially
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12
Wind is also cheap relative to other renewable energies. Depending on the weighted average cost
of capital used for the calculations, low/median/high estimates for onshore wind vary from
35/59/120 USD 2010/MWh to 92/160/300 USD 2010/MWh. It is significantly higher for offshore
wind with estimates ranging from 80/120/180 USD 2010/MWh to 160/240/350 USD 2010/MWh but
still
lower
than
USD2010/MWh]
rooftop
photovoltaic
[74/150/180
USD20l0/MWh,
or concentrated solar power [110/150/220 USD20l0/MWh,
250/490/600
220/320/480
USD201O/MWh]. Accounting for a carbon tax of 100 USD 2010/tCO 2 eq, 10% WACC and a high
capacity utilization, onshore wind is cheaper than coal, combined cycle gas (51/84/160
USD 2010/MWh vs. 97/150/210 USD 2010/MWh and 69/120/200 USD 201o/MWh) and comparable to
nuclear (45/99/150 USD 201o/MWh). Without a carbon tax, offshore wind remains more
expensive than coal but only by a slight margin (110/170/250 USD 2010/MWh vs 97/150/210
USD 20 1o/MWh) (Schlimer et al.).
However, this is only a static vision of costs by energy sources. The cost of kWh from wind has
steadily decreased over the past 35 years (figure 6) from 55 cents/kWh in 1980 to 6 cents/kWh in
2012. The slowdown in prices decrease around 2002 followed by a rebound is due to the upward
fluctuations of oil prices that had an impact on installation and manufacturing cost for wind farm
projects. 2012 prices for kWh are comparable to 2002 prices for kWh though oil prices are more
than five times higher (1 10$/barrel in 2012 vs. 19$/barrel in 2002). This hints at large cost
savings over the period to compensate for this increase. According to a recent study by
investment bank Lazard (Lazard) on the US energy market, wind is now cheaper than any other
source of energy with an unsubsidized levelized cost of 37 to 81 USD/MWh of electricity
generated. With subsidies, the cost of wind energy ranges from 14 to 67 USD/MWh, compared
to 61 to 87 USD/MWh for Gas Combined Cycle, the cheapest conventional energy source in the
US.
13
Figure 6: US Deployment & Cost for Land-based Wind: 1980-2013 (US DOE 1)
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It is not yet clear today whether subsidies for renewable energies will be extended when they
expire, but wind seems to have reached a point when it no longer needs them to be cost-efficient
compared to conventional fossil fuels.
Risks and criticism
At the time Cape Wind was going through its approval process, the main argument against wind
energy was its cost. In the early 2000s, the technological breakthroughs and the cost saving from
operational efficiency were not yet sufficient for wind energy to compete with fossil fuels, let
alone offshore wind. Today, offshore wind remains significantly more expensive than onshore
wind and most fossil fuels. According to the Lazard's levelized costs of energy analysis
(Lazard), offshore energy is twice as expensive as the highest estimate for onshore wind (162
USD/MWh). This is comparable to Integrated Gas combined cycle and cheaper than diesel
generators and conventional gas only. The cost of capital is two to three times higher than
onshore wind (4,300 USD/MWh to 1,200-1,800 USD/MWh).
14
The second most common argument' against wind energy is its variability and unreliability.
Effectively, there are uncertainties regarding the generation of electricity by wind turbines: the
strength of the wind blowing on a given day and the energy generation from a given turbine.
Though we can predict quite accurately the amount of wind on a given day based on
meteorological observations, it does not make it a reliable resource, as it is not correlated with
the grid's load curve. Wind varies on day-to-day, month-to-month and year-to-year bases. Most
inconveniently, wind is highly volatile on shorter time frames. It can change direction or strength
within minutes. Because of the turbines capacity constraints, this has a great impact on the
overall energy generation. Low winds as well as very strong winds will cause the turbines to stop
working (figure 7). Changes in direction also lead to stark changes in efficiency. In order to
maintain a sufficient quantity of electricity input into the grid throughout a day or a month, it is
therefore required to keep a certain number of power generators that can ignite rapidly enough to
respond to a sudden shortage of wind. These generators are usually conventional fossil fuel
plants as nuclear plants are very expensive to shut down and hydropower isn't reactive enough
for this purpose. Opponents to wind energy therefore argue that it is neither economical nor
green because of its reliance on secondary fossil fuel plants.
Figure 7: Turbine power curve (Wan)
Bellow 3 m/sec, there is not enough windfor the turbine to produce energy. The output increases
from 0 kW at 3 m/sec to 1500 kW at 15 m/sec and then stays constant up to 22.5 m/sec. Above
22.5 m/sec, the turbine is shut down to avoid damagesfrom strong winds.
15
1600
1400
1200
1000
B00
0
600
400
200
0
5
10
15
20
25
30
Wind Speed at 814 (m/s)
As previously stated, wind is ubiquitous. However, states are unequally gifted with wind
resources, and the best spots are usually located far from large areas of population. In the US,
most of the wind is concentrated in the Midwest, far from both the East and the West Coast
(figure 4). This entails large investments in transmission lines to move the electricity to where it
is needed.
Lastly, public perception of wind energy remains tainted by various concerns. There are some
local environment concerns, as wind turbines need significant grid connection infrastructures that
can affect the local environment during their construction. Residents have complained about the
visual impact of the turbines, a growing concern proportional to that of the turbines size. They
have also reported health issues related to the noise produced by the rotation of the blades.
Combined together, these factors are believed to affect the property value of residents, which is
why the strongest opposition to wind farm projects is often local. From an environmental point
of view, wind turbines have caused bird deaths in the past, especially when they are located on
avian migration routes. Lastly, military officials have raised a concern about the possible
interference of turbines frequencies with airport radars (Zhang).
16
The benefits of offshore wind
Offshore wind, despite its higher cost, addresses some of the issues that are raised with onshore
wind. The best winds are to be found on coastal areas (Pacific, Atlantic and Great Lakes). Not
only are they stronger and steadier than those of the Midwest, but also they are closer to the
cities where electricity is consumed, given that nearly 40 percent of the US population lives in a
.
coastal county 4
Offshore wind also resolves most of the public perception problems linked with onshore wind.
They are far enough from the coastline to not be heard by residents, making it easier to scale up.
And they have little to no visual impact depending on their distance from the shore.
Latest andfuture innovation
While turbines design and electricity outputs are still following a learning curve, three secondary
innovations could have even greater impact on the cost and reliability of wind energy.
The first one is purely financial. The cost breakdown of wind farms indicates that maintenance
costs are relatively small compared to cost of debt and cost of equity (20% vs. 80%)5. Moreover,
cost of debt is substantially lower than cost of equity. Therefore, promoters try to raise as much
debt as possible. However, banks are weary of lending money for projects with very high
revenue volatility. Insurance policies - developed by start-ups such as Resurety 6 - that guarantee
operators a floor revenue when weather conditions are adverse, diminish the overall risk of
investments and make it possible to add debt during the construction phase, reducing the overall
cost of the project.
The development of smart grid technologies that collect instantaneous information on the
demand and production of electricity throughout the day would also prove crucial to increase the
value of wind energy, by reducing its downsides through better prediction and broader
' NOAA, «The U.S. Population living at the Coast >, n.d. Web. 29 Apr. 2015,
http://stateofthecoast.noaa.gov/population
5Milborrow (2013), «Turbine advances cut O&M costs , Wind Energy Monthly, Web. 29 Apr.
2015, http://www.windpowermonthly.com/article/1183992/turbine-advances-cut-o-m-costs
6Resurety, Web. 29 Apr. 2015, http//www.resurety.com
17
integration. In Denmark (Kempener et al.), smart grid technologies have made it possible for
wind to reach a 30% penetration rate, the world's highest.
Lastly, a lot of research efforts have been put in developing large batteries that would enable to
store energy when the wind is blowing and release it when needed. Boosted by the proliferation
of smartphones and electric cars more recently, lithium ion batteries have been envisaged to play
that role. But the raw material needed to make them remains expensive (Lazard). Researchers
have therefore been trying to develop batteries using other more abundant elements. At MIT,
professor Sadoway and his research team have been working on an aluminum-magnesium
battery capable of storing energy at just a fraction of today's cost. The first real scale tests for
these batteries are scheduled to be held in 20157.
The Cape Wind Project
Birth of the project
At the turning of the millennium, Jim Gordon - a Boston entrepreneur who had founded Energy
Management Inc. in the 1970s and had successfully developed the business thanks to its early
adoption of natural gas-fired power plants - decided to sell all his existing assets. He would use
the proceeds of the sale to finance what he viewed as a revolutionary project for New England
and the United States: building the first ever offshore wind farm in the United States off the
South coast of Cape Cod (Vietor).
Offshore wind had just celebrated its tenth birthday anniversary in July 2001 (Rock) and was
already widespread in Northern Europe. Denmark, Sweden and the Netherlands had completed
several projects. The British Wind Energy Association had negotiated a set of guidelines with the
Crown Estate for the future development of offshore farms (British Crown Estate). There was no
such regulatory framework in the US, but Jim was confident he would quickly obtain approval
and support. Massachusetts was a liberal state with an above average awareness for environmentrelated issues, and Cape Cod's taxpayers had been complaining about air pollution generated by
7 Chesto
(2014), "Cambridge company's battery may give grid a boost", Boston Globe, Web.
http://www.bostonglobe.com/business/2014/12/30/cambridge-based-battery-maker-charged-formanufacturing/Iz5kS7khBgBJ5 SolLSKHtI/story.html
18
coal and oil fueled power plants (Whitcomb et al.). In the summer, sanitary threshold were
regularly passed.
Initial Plan
The initial project for Cape Wind was very ambitious at the time. Located in the Nantucket
sound, off the south coast of Cape Cod near Mashpee, and north of Martha's Vineyard and
Nantucket Island, it included 150 Siemens 2.8 megawatt wind turbines producing up to 420
megawatts (nameplate capacity) and an average of about 160 megawatts8 . Had it been completed
before 2012, it would have been the largest offshore wind farm in the world. As a comparison,
Middelgrunden (Larsen), completed in 2000 in Denmark, had a nameplate capacity of only 40
megawatts, more than 10 times lower than Cape Wind. Still in Denmark, Horns Rev, which was
completed in 2002, set a new record with a nameplate capacity of 160 megawatts 9. Jim Gordon
expected Cape Wind to start generating power in 20049.
Hurdles
By no means had Jim Gordon anticipated the battles that were awaiting him. Affluent residents
of Cape Cod south shore and the islands quickly became aware of the project and objected to it.
The farm was close enough to the islands for it to be seen from the shore (Whitcomb et al.). They
feared it would ruin the views of Nantucket sound as well as hinder navigation in the area, which
was at the time a good spot for yachting. By doing so, it would decrease their properties value.
They also advanced historical arguments, stating that the "Nantucket Sound was a national
treasure worthy of protection" (Whitcomb et al.). As early as 2001, they created the Alliance to
Protect Nantucket Sound (The Alliance), comprising both Democrats such as Robert Kennedy
and famous Republican supporters such as industry mogul Charles Koch 0 . Its stated purpose
Wind (2001), «New England's EMI plans 420 MW Nantucket wind farm >, Web. 29
Apr. 2015, http://www.capewind.org/article/2001/10/31/579-new-englands-emi-plans-420-mwnantucket-wind-farm
9 Hornsrev, Web. 29 Apr. 2015, http://www.homsrev.dk/en
10 Seelye (2013), «Koch Brother Wages 12-Year Fight Over Wind Farm , NYT, Web. 2 Apr.
2015 http://www.nytimes.com/2013/10/23/us/koch-brother-wages-12-year-fight-over-windfarm.html
8 Cape
19
was to support the long-term preservation of Nantucket Sound". They would become Jim
Gordon's fiercest opponents, filling more than 25 lawsuits and lobbying against Cape Wind.
Because the farm was to be developed in federal waters, but at the same time needed near-shore
infrastructures, Cape Wind had to get approval from both federal and state and local
jurisdictions.
At the federal level, the project needed approval from the US Army Corps of Engineers
(USACE) (US DOE 2). A permit application was submitted right from the beginning in 2001.
The USACE requested that an environmental impact study be conducted prior to giving its
approval (US DOE 2). Starting in early 2002, data was collected and then discussed during
heated public reviews, often opposing supporters of both sides. In November 2004 (US DOE 2),
the USACE made available their draft Environmental Impact Statement (EIS). It seemed that
Cape Wind had just obtained approval at the federal level. But, due to a change of legislation
brought about by the 2005 Energy Policy Act and the amendments to the Outer Continental Shelf
Lands Act, the Department of the Interior - and no longer the USACE - would have authority to
issue permits and leases for this kind of project. Cape Wind would have to go through the whole
process again, waiting for the newly competent Minerals Management Service (MMS) to issue
its own EIS (US DOE 2). Being subject to its own rules - specifically the National
Environmental Policy Act - the MMS determined that further investigations should be made and
that an independent contractor should conduct the surveys.
Concomitantly, Cape Wind was seeking approval from state legislators for its near-shore
infrastructures. In May of 2005, the Massachusetts Energy Facility Siting Board (MEFSB)
approved the transmission lines route project (MESFB). The Alliance challenged the decision but
the Massachusetts Supreme Court upheld it in 200612. Though it secured approval from the
Massachusetts secretary of Energy and Environmental Affairs (MEPA) in March of 200713
" Save our Sound, Web. 29 Apr. 2015 http://www.saveoursound.org/about us/mission/
12 Cape Wind, n.d. «Litigation History of Cape Wind
Web. 29 Apr. 2015,
http://www.capewind.org/sites/default/files/downloads/Litigation%20History%20of'%/2OCape%2
OWind%2OMay%202%202014.pdf
13 Cape Wind, n.d. «Cape Wind Timeline , Web. 29 Apr. 2015
http://www.capewind.org/when/timeline
20
(contested by the Alliance and upheld in 2008), Cape Wind was then denied a Development of
regional Impact (DRI) approval by the Cape Cod Commission (CCC) in October of 2007
(MESFB), asking for further environmental study.
By that time, the Minerals Management Service had finally finished drafting its own EIS (the
second after the USACE's). Once made public, the draft received more than 42,000 comments
from opponents. Cape Wind would have to wait another year (January 2009) before the MMS
made public its final EIS. As the final hurdles were being removed, Jim Gordon's efforts finally
paid off at the State level in May 2009 when the MEFSB decided to overturn the CCC's decision
by issuing a super permit (MESFB). The Alliance appealed but the Massachusetts Supreme Court
upheld the decision in August 201013. During that period, opponents raised a new challenge
asking the Department of Interior if Nantucket Sound was eligible to become a national park
(11/18/09). The National Park Service ruled it out in January 2010 (US DOI).
Approval from the Federal Aviation Association came in May 201014. There were fears that the
wind turbines could interfere with the radio signals of a nearby military airbase but these
concerns were dismissed. However, final approval was only granted in August of 201215 as a
court of appeals for the District of Columbia had overruled the decision in October 2011
(Barnstablevs. FAA).
In the aftermath of the first approval by the Federal Aviation Association, the Alliance filed a
lawsuit on the basis of the Endangered Species Act. Environmental impact, including potential
risk for migrating birds linked to the wind turbines blades, was a major concern during the
approval process. The case would finally be settled four years later, with the DC Circuit
affirming the FAA No Hazard Determination
16
Seelye (2010), «Massachusetts: FAA clears Wind Farm , New York Times, Web. 29 Apr.
2015, http://www.nytimes.com/2010/05/18/us/18brfs-FAACLEARSWIN_BRF.html
15 FAA n.d. PressRelease, Web. 29
Apr. 2015
https://www.faa.gov/news/press _releases/news story.cfm?newsld= 13819
16 Brookes (2014), «Cape Wind wins long list of court decisions , Cape Cod Today,
Web. 29
Apr. 2015, http://www.capecodtoday.com/article/2014/03/15/24515-Cape-Wind-wins-long-listcourt-decisions
14
21
The lease was finally signed with Secretary of Interior Salazar in October 201017. In November,
the Massachusetts Department of Public Utilities approved the Power Purchase Agreements
made by Cape Wind with utility provider National Grid' 8 . In January 2011, the EPA gave its
final green light to the project after the USACE issued a Section 10 permit (EPA).
With the permitting process finally completed, the developers would be able to focus their efforts
on securing project finance. They would have to go through other legal battles though, as
opponents tried to contest the Power Purchase Agreements, arguing that they would raise
electricity prices for customers. The Massachusetts Supreme Court upheld these agreements in
December 201118 and May 201419.
Where Jim Gordon's original plans were to start generating electricity by 2004, Cape Wind
eventually had to wait 10 more years to be able to pass to the financing phase.
Current state
After years of litigation, the path for Cape Wind seemed to have been cleared. Jim Gordon had
signed contracts with Utility companies National Grid and Eversource, which would have
respectively bought 50%20 and 25% of Cape Wind's energy output. Expecting to raise the
necessary funds by end of 2014, the production of wind turbines was supposed to start early
2015. But, as Energy Management Inc. failed to raise the money by the contract's deadline, and
Cape Wind (2010), << First U.S. offshore wind farm lease is signed by Secretary Salazar, issued
to Cape Wind , Web. 29 Apr. 2015, http://www.capewind.org/article/2010/10/06/1090-first-usoffshore-wind-farm-lease-signed-secretary-salazar-issued-cape-wind
18 Reuters (2012), «Massachusetts OK's Cape Wind/NSTAR power purchase pact , Web. 29
Apr. 2015, http://www.reuters.com/article/2012/11/26/us-utilities-capewind-nstaridUSBRE8AP17P20121126
19 Cape Wind (2014), «Federal Judge Dismisses Lawsuit against Cape Wind , Web. 29 Apr.
2015, http://www.capewind.org/node/1748
20 National Grid, n.d. «National Grid and Cape Wind Sign Power Purchase Contract , Web. 29
Apr. 2015, https://www.nationalgridus.com/aboutus/a3-1_news2.asp?document=5163
17
22
subsequently terminated construction contracts, National Grid and Eversource unilaterally
terminated the contract.
Until further negotiations, the project is suspended. But Jim Gordon has already pledged to keep
on fighting, stating that the delays Cape Wind went through because of litigation were force
majeure and therefore not valid reasons for termination 22 . Late February, he organized a rally in
Boston and, with the help of a Cambridge's based organization 23 , gathered nearly 100,000
signatures for a petition asking Marcy Reed of National Grid to reinstate the contract.
Meanwhile, the first offshore wind farm has started construction in Rhode Island.
Because it went through so many obstacles, Cape Wind is a good example that can help us
identify success and failure modes of renewable energy projects. We hypothesize that lack of
regulatory framework, rallying national support at the expense of securing key local support, and
controversial choice of location were the main shortcomings in the project.
Identifying success and failure modes of large energy projects
Lack of regulatory framework
When looking at the litigation history of Cape Wind, it is striking to see the lack of regulatory
framework for these kinds of projects. In some sense, Cape Wind made a precedent for the
future. Two elements in particular have caught our attention: the fact that the competent
Abel (2015), «More doubt is cast on Cape Wind plan , Boston Globe, Web. 29 Apr. 2015
http://www.bostonglobe.com/metro/2015/01/24/cape-wind-terminates-additional-contractscasting-more-doubts-project-viability/voEIRKmjXziMT5HAoM41KN/story.html
22 Abel (2015), «Cape Wind's future called into question , Boston Globe, Web. 29 Apr. 2015
http://www.bostonglobe.com/metro/2015/01/08/legal-wrangling-horizon-for-cape-wind-aftermajor-utilities-pull-out/kIEXaT5x4lkfUplijpdtsL/story.html
23 Crimaldi (2015) « Cape Wind vows to move project forward , Boston Globe, Web 29 Apr.
2015 http://www.bostonglobe.com/metro/2015/03/01/cape-wind-vows-continue-workproject/aeFotet5lyA5BSKgHqfngK/story.html
24 Kuffner (2015) «R.I. officials mark start of first offshore wind farm in U.S. , Providence
Journal, Web. 29 Apr. 2015
http://www.providencejoumal.com/article/20150427/NEWS/150429334/13748http://www.provi
dencejournal.com/article/20150427/NEWS/150429334/13748
21
23
administrations to give authorization evolved throughout the project (the EIS was first drafted by
the USACE and ended having to be redone by the MMS) and that there was no public tender for
the attribution of the construction sites. The latter weakened the reputation of the project, giving
its opponents the opportunity to denounce cronyism and suspicious contracts. The former might
have been a source of delay for the approval process by giving additional time for opponents to
file lawsuits. Though this cannot be imputed to Jim Gordon, he might have derisked the project
by starting with a smaller scale wind farm, laying the regulatory foundations for a future
extension.
Failure to secure key local support
From the beginning of the project, it seems that Jim Gordon abandoned efforts to convince local
residents of Cape Wind south shores and the islands to rally his project. Instead, Cape Wind's
communication's strategy was more targeted at the general public. In August 2007 for example,
Cape Wind was featured in Jason Jones' daily show2 5 . The show was heavily pro-Cape Wind,
picturing its opponents as super-rich - not-in-my-backyard - WASP coveting their privileges. It
also made fun at the allegation of visual impact of the project. Though these efforts might have
helped raise awareness of the project on a national scale, we believe it did little more than
antagonize the existing sides. Another possible path would have been to secure local support
first; targeting key influencers with aligned political agenda and turning them into heralds for the
project. Most of the containment efforts against the project came from the people who would
have been most affected by the project. We hypothesize that the marginal benefit of convincing
one of them largely exceeded that of rallying support from Californian residents.
Location
The choice of location for the project was driven by a rigorous analysis of estimated costs,
revenues and environmental impacts of the project (DOE 2). Installing the wind turbines farther
from the shore was not a viable economic option, as it induced increased costs from installing
turbines in deeper grounds and longer expansive High Voltage Direct Current transmission lines.
Cape Wind, n.d., «Daily Show, Jason Jones 180 - Nantucket , Web. 29 Apr. 2015
http://www.capewind.org/video/daily-show-j ason-j ones- 180-nantucket
25
24
However, Jim Gordon assumed that he would encounter less resistance for his project in a liberal
state rather than in a more conservative state. This however was not necessarily true. Wind
energy has seen most of its development in conservative Midwest states, where large energy
production sites such as oil and shale gas rigs as well as refineries are commonly sighted in the
landscape. Southern Atlantic coal dependant coastal states could have been an easier first target.
Model and Discussion
Causal Loop diagram
The goal of our model is to identify the drivers of success or failure of gaining approval and
raising funds for a large offshore wind project. In order to do so, we started by building a simple
causal loop diagram to evidence the reinforcing loops that exist between public support and the
financing process of a large clean energy project (figure 8). It excludes the particular dynamics
of public support and the details of the financing process. We can identify two powerful
reinforcing loops: the "follow the tide" and "abandon ship" loops. As the project gathers ever
more regulatory support, investors have more financial certainty and start comitting capital to the
project. These investments are a signal that the project will eventually be completed and is
inevitable. To be on the winning side, some former opponents rally the project. Regulators then
consciously or inconsciously follow the tide of public opinion. "Abandon the ship" is the exact
opposite loop, where opposition manages to slow the regulatory process, leading to less financial
certainty for potential investors. Because of that, they withdraw from negotiations or make
public statements against investing in the project, sending a negative signal to the general public.
As the project future looms, former advocates start questioning it and support further wanes.
25
NEW--- L- - - - - - - -
- ---
-
-
-
- --
Figure 8: Causal loop diagram of support and financial dynamics
Support
Opposition
Follow the tide
Reguoaco
Abandon ship
sfp4r
PrOmohtiOnl 01f
renewable energies
+
crtaie
AdTcanical
Adasageou s
pa
Market
a
Technical concerns
/
Change of
t
techn o logy
Cheap
money
conditions
Financial certainty
Costs
Availability of
financing
Secondary but no less powerfull loops are the "Promotion of Renewable Energies", "Cheap
money" and "Technical concerns". As support grows for the project, so does regulatory support
like in the previous loops. This leads to more legislation in favor of renewable energies, such as
subsidies or obligation for grid operators to favor green energies over conventional ones. The
result of that is more advantegeous Power Purchase Agreements; therefore better expected
revenues and more financial certainty. This then feeds back into more support. More financial
certainty also leads to better availability and cost of financing, as more institutions are willing to
fund the project and therefore compete for it. By reducing the cost of capital, it then feeds back
into the financial certainty of the project thanks to increased Net Present Value. On the other
side, if technical certainty for the project decreases because of exposed misestimates or increased
constraints on the project, financial certainty decreases, fostering more opposition, and raising
new regulatory barriers that will further increase constraints on the project and ask for more
reviews its technical aspects. It is interesting to note that while the adoption of a new technology
for cost purposes increases the financial certainty of the project, it can also reduce it by forcing
the promoter to conduct new studies and impact estimates, triggering the technical uncertainty
loop.
26
This simple causal loop diagram gives us a sense of the dynamics around the project. In order to
be able to simulate different paths to securing approval and financing, we need to define input
variables and build a working System Dynamics model.
Defining variables
Output variables
Final Approval: a 0/1 variable indicating if the project has gained regulatory approval
Financial certainty: a variable indicating if the project is able to secure financing. Financing is
considered secured when Financial certainty equals 1.
We stop running the model when both variables reach 1. The final outcome is the Time it took
from Design to Construction. We want to test for a set of input variables to see which ones affect
Time of the project the most.
Input variables
Each of these 4 variables is set with an initial value and evolves during the simulation.
TableA, TableF: tables representing the marginal intensity of a 1% change in voicing opposition
or support
Figure 9: TableA
A very small minority of vehement opposition can suffice to generate a strong lobbying
movement. This is what we have tried to incorporateinto our model with a diminishing marginal
lobbying curve. With 1% "Voice Against", the intensity of the lobbying is 50% of its theoretic
maximum. With 2%, this increases to 75%, etc. Above 10%, we consider that additional
opponents have no impact on lobbying.
100%
75%
......
...
---- --------
-
-------...
50%
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
% of people "voicing against" the project
27
Init Strongly Against, Init Strongly in Favor: The initial proportion of residents actively
advocating against or for the project
Initial percentage in favor: The initial proportion of residents in favor of the project including
"Voice in favor", "In favor" and "Lean in favor" (see below). Initial percentage against is
equal to 100 - Initial percentage in favor. We start with 0 "In favor" and 0 "Against"
Legislation in place: a 0/1 variable indicating if the legislation is already in place. When equal
0, the three approval processes occur in sequence, indicating a trial and error approach. When
equal to 1, the legislator already knows the extent of the approvals needed and several processes
can start at the same time. The overall theoretical procedure time is the same in both cases
though (27 months, or 3 times 9 months)
Change technology: a 0/1 variable indicating if we choose to change technology after the
second one becomes more profitable.
Ramp end time against, Ramp end time for: the time it takes for opponents and advocates of
the project to reach 100% of their respective lobbying efficiency.
Computation variables
The next set of variables is variable that change during the simulation and are used to calculate
our output variables.
Voice against, Voice in favor: a dimensionless variable indicating the percentage of the local
population who advocate against or for the project.
Against, In favor: a dimensionless variable indicating the percentage of the local population
who have a strong opinion against or for the project, but who do not actively participate in the
debates.
Lean in Against, Lean in favor: a dimensionless variable indicating the percentage of the local
population without a definitive opinion against or for the project
Become against, become in favor: the rate at which people "leaning in favor" / "leaning
against" change to "leaning against" / "leaning in favor"
28
CA, CF: the rate at which people "leaning against" / "leaning in favor" change to "Against" / "In
favor"
CVA, CVF: the rate at which people "Against" or "In Favor" become advocates for their cause,
in percentage/Month. Both can be positive or negative.
IntensityA, IntensityF: a dimensionless value between 0 and 1 indicating the strength of public
opposition or public support for the project. IntensityA is 0 if nobody is strongly opposed to the
project and 1 if everybody is.
LobbyingA, LobbyingF: a dimensionless value between 0 and 1 indicating the overall strength
of lobbying against / in favor of the project.
Time remaining to get permit i: the number of months remaining to get permit i.
Procedure i slowing rate: the speed at which opposition manages to delay the approval process.
Adoption of new Technology: 3 extra months added at time t =50 to each procedure if we
decide to adopt the new technology.
Expected remaining time for approval: the theoretical remaining time for approval at a given
moment, taking into account the procedure slowing rate. Expressed in Months.
Net Present Value of project: a financial indicator of the value of the project, in USD.
Availability of financing: a dimensionless variable between 0 and 1 indicating the attractiveness
of the project. It is also called Financial certainty.
Investment cost: the investment cost for the wind farm, in USD. It is based on the desired
capacity and the technology adopted at time t.
EfficiencyA and EfficiencyF: a dimensionless variable indicating the level of organization of
each camp from 0 to 1 that affects the power of their lobbying efforts.
Settings variables
These are variables that we don't test during our simulation. We try to calibrate them to make the
model as close to reality as possible.
29
WACC:
the weighted average cost of capital -used as our discount factor in our NPV
calculation. We have set it to 0.009 per month for an annualized 11% (Kopp).
Change time: the average time in months it takes for someone leaning on one side to change to
the other side when lobbying is at its maximum. The stock of people leaning on one side is
depleted following a first order delay. We have set it to 6 months.
Change time strong: the average time in months it takes for someone "against" or "in favor"
one side to become and advocate when lobbying is at its maximum. We have set it to 12 months
SPT and SPT all: standard procedure time of one procedure and standard procedure time of the
whole approval process. "SPT" is 9 months when there is no regulatory framework and 27
months otherwise. "SPT all" is always 27 months.
Procedure i completion rate: the speed it takes for a procedure to be completed. As we have
defined the procedure as a number of months, this variable is dimensionless and equal to 1.
Cost of tech 1 and 2: we simulate a scenario with 2 available technologies. The first one is
cheaper than the other one in the beginning but its cost reduction potential is lower than the other
one. They are expressed in USD/MW. Tech 1 starts at 6,000,000 USD/MW and Tech 2 starts at
8,000,000 USD/MW.
Ti and T2 min cost: the minimum theoritical costs for technology 1 and technology 2. They are
expressed in USD/MW. 5,000,000 USD/MW and 4,000,000 USD/MW. (Lazard)
Cost reduc pot 1 and 2: the cost reduction potentials of technology 1 and 2, corresponding to
the difference between the cost of technology and the minimum cost of technology for each one.
They are expressed in USD/MW.
Speed 1 and 2: the speed of cost reduction for each technology.
T1 and T2 cost reduction: the cost reduction of each technology every month, determined by
the cost reduction potential and the speed of cost reduction. It follows a first order delay
function. Expressed in USD/MW/Month.
30
Desired Capacity: the desired actual capacity (by opposition to nameplate capacity) of the wind
farm, in MW. We set it to 160 MW, the desired capacity of Cape Wind.
Cost of MWh and MWMonth: the costs associated with operating and maintening 1MW of
power during one hour or one month.
Monthly OM: the costs associated with operating and maintening the wind farm.
Price of MWh and MWMonth: the price the wind farm can expect to sell its MWh or
MWMonth to the utility grid.
Subsidies per MWh or MWMonth: the additional revenues the farm can expect for 1 MWh or
MWMonth of power sold.
Monthly expected returns: the overall revenues the farm can expect during one month, in
USD/Month.
Base NPV: a base NPV to which the actual NPV is compared in order to give an indication of
the financial attractiveness of the project.
31
W_
Model overview
Our model is made of 4 different views. Equations for each variable can be found in appendix.
Figure 10: View I - Dynamics of support
* niy
Against
,Change
2t
Voice.Against
i
tableA
rCA
time Strong
t
LobbyingA
/
Lean Against
Ramp End Time For
Z EfficicyA
Initial per
B
cntage
in fav orin
Become again.t
aor
Ramp End Time Againkst
EfficiencvF
RD
Lean
In Favor
I
LobbyingF
tabeF
+
CFu
---
Lob
ingA
IntensityF
In Favor
Voice in Favor
Cie
Finacialcert nty
Cbhange time Strong>
32
Figure 11: View 2
Dynamics of legislative process
-
Proced. 2
rProcedure Io
in
g
s
slowing rate
Procedure 3
loing rate
te
Time remaining to
get pennit 3
ime reann to
gperritrI
Tiermiigt
*Final
Approval
Procdure 3
cop
ompletion rate
Procedure 2
otrt
rate
Procedure 1
rocomptetion
-
Icompetio rat
Figure 12: View 3 - Dynamics of technology and financing
Expected remaining
time for approval
Al>
Availability of
financiny
Financial certainty
Cost of NVh
Base NPV
cost reduc pot 1 4V-- tI min cost
speed
Cost of
MWMonth
I
Monthly OM costs
Cost of tech 1
ii cost reduction
a Prsent Value
of Project
+
N
htinvestment cost
Desired capacity --
WACC
Cost of tech 2
t2 cost reduction
Monthly
expected return.
Subsidies per
Price of
MWmonth
Subsidies per MWh
Price of MN%
speed 2
cost reduct pot 2 -
t2 min cost
33
II
ETm
Permits
t
l
12
"
Lobbying
a-
5
.5
0
0
0
Cu
0
,0
0
Intensity of lobbying
36
72
108
0
0
144
36
Time (Month)
p
lx .n.A: C
LN
nr.
tC
Cu
ntinA : nnrnt
IntnsjynJ
am
On
"
nt
Itt
t
JC
MWi--
0
36
72
108
144
Time (Month)
Fcam
Crt -L
n
Ti.
-me
(Monh)
Cur=
1"
ir------Lwm:c
ra
-----~-Apnecra
m
MlWl,
Climm.
20
36
72
108
Time (Month)
144
4M
450 M
3M
48
96
Time (Month)
Ne prrnn Valhne dnpr -n: (%r%
In'nvm a-t : C-n -----.
144
36 Time72 (Mont
108
144
Financial certainty
.5
2 M
0
0
Cost of technology
900 M
0
(o
Su xdispr
Curu
Decided
~
NPV
if
i
Fppavc' rmn-g rs lwpp,,At
Fral Appnv':
Curmia
s
0
V
Cfst
~ - ~~~
0
ic
aslat-A
~~-
72
108
Time (Month)
40
2
."
!R
~
36
1=4 rn
n N nnk tcurvea
Tt-u rcsnartn V) rz 'aruit 2: Curni'WZA
in rnin
60
u i
wl,
- ~~
144
Lean
9
t
72
108
Time (Month)
-- ~~
Voice
C
en
0 Month
dmn
0
-
A,
-
ap
C1
0
36
72
108
Time (Month)
CO ast 1-gh I : Cunrm
Ct nina C-h
2: OC-n-nt
0
144
0
Fitnncial cuainy
36
72
108
Time (Month)
Dw m
144
144
Simulations
Base case scenario: our base case scenario is not supposed to reflect the exact dynamics of the
actual project but tries to approximate them. In this scenario, we assume that advocates of the
project had a head start compared to opponents and managed to organize their lobbying structure
in 6 months, compared to 12 for opponents. We estimate a conservative first split of 2% "Voice
against", 48% "Lean Against", 48% "Lean In Favor" and 2% "Voice in favor". Legislation is not
in place but a change in technology occurs during the project.
Figure 14: "Base Case" Public Opinion
Voice
Lean
Decided
60
9
0
36
72
108
Time (Month)
144
0
36
108
72
Time (Month)
144
0
36
72
08
44
Evolution of public opinion: overall, as time goes on, opinions get more polarized. There is a
decrease in people leaning on one side and an increase in both decided and advocates. The rapid
increase in people leaning in favor of the project is related to the difference in time to organize
for both parties (6 months vs. 12 months). After that period, they both reach cruise mode and the
gap is reduced. It takes more than seven years for the population of residents voicing against the
project to start decreasing. The change in technology after month 50 gives a new boost to
opposition, as the financial certainty of the project is reduced due to new studies having to be
conducted.
35
Figure 15: "Base Case" Lobbying
Lobbying
Intensity of lobbying
0
0
0
36
72
108
Time (Month)
0
144
36
72
108
Time (Month)
144
Lobbying: Lobbying rapidly increases for both advocates and opponents in the beginning as
efficacy reaches 100% for each. It then stabilizes over the whole period, with a slight boost for
opponents when technology is changed. Both lobbying against and intensity of lobbying against
start to decline quickly after year 10, when few regulatory obstacles remain for the project to be
granted approval (follow the tide loop).
Figure 16: "Base Case" Financing
Financial certainty
S.5
36
0 S 36
72 2(M4108
Time (Month)
14
Financial cranty : Curent
Financial certainty: financial certainty progressively increases as the project is advancing
through its regulatory process and NPV of the project is decreasing from declining technology
costs. It increases faster in the first year because opposition lobbying is not yet in place to slow
the process. After month 50, financial certainty drops abruptly because of the added time the new
technology adds on the regulatory process. In the end, as the voicing opposition vanishes,
financial certainty increases more rapidly (follow the tide loop). It should be noted that we
haven't incorporated PPA in our model for simplicity purposes. Loosing the PPA is what
finished Cape Wind, but it is not represented here as financial certainty doesn't drop at the end.
36
Figure 17: "Base Case" Permits timeline
Permits
30
Month
I
dnn
O
Month,
0
dtnin
_____________L_____
0
T--e renA~g
Cr dnl
pe.-rm -nt
iL Aph
36
:-
108
72
Time (Month)
144
CurventM
I
Permits and approval: The overall process takes more than 14 years. Permit 1 is obtained faster
than Permit 2 as some of it is processed at a time when the opposition is not yet organized. The
technology change largely increases the overall delay. Permit 3 is obtained rapidly at the end as
the opposition is vanishing. There is a flexion point in the Process of obtaining permit 2: when
the proportion of people voicing against the project starts decreasing, lobbying against the
project decreases and Permit 2 processing speed starts increasing again. The marginal
importance of people stopping to voice against the project increases as the proportion diminishes
because of the intensity table.
Figure 18: "Base Case" NPV and Technology cost
NPV
Cost of technology
4M
900 M
2M
600 M
0
36
72
108
144
Time (Month)
Time (Month)
c
1e:ca'
NPV: the NPV of the project increases because of the declining cost of technology. When the
project leaders adopt the new technology, they make a significant gain in NPV When the project
gains approval, NPV stands around $850 million.
37
No technology change scenario: in this scenario, we only assume that we stick with technology
one throughout the project.
Figure 19: "No Tech Change" Scenario Key Parameters
Decided
Lean
Voice
50
60
20
25
0
0
0
36
108
0Time72 (Month)
36
0
144
144
108
72
Time (Month)0
80D M
4M
700 M
3 .M
Nv Pram
ue xrq
0
0
96
144
48
Time (Month)
36
108
72
Time (Month)
144
Cca a I cu
C-z-n-.
14
Financial certainty
2M
600 M
10
(Month)
Cost of technology
NPV
0
3
T1
(L
T4
47
366 M2
~Time
~W
____-
0
36
108
72
Time (Month)
144
Financial ce.ainy :Cami
Evolution of public opinion: compared to the previous scenario, the overall trends in public
opinion are similar but opposition decreases more rapidly. Around month 50, voicing opinion is
already declining and the absence of a boost from technology change accelerates its fall.
Similarly, lobbying against decreases well before and financial certainty reaches 100 just after 96
months. The NPV of the project at approval is significantly lower than in our base case scenario
though (~$ 750 M) because the cheaper technology has not been adopted.
Figure 20: "No Tech Change" Scenario Permits Timeline
Pennits
30
I
Month
dmnl
0
0
Month
dmnl
mit I
1:
Tile rrai
Te prmit
EX
e
d rA
Final Appr0v
..
108
72
Time (Month)
36
0
rrl:
144
Curent
Current
C
vr
:Currerl
38
Overall, permits are obtained almost twice as fast as in our base case scenario (around 8.5 years),
even though only a third of the theoretical time (9 months) is removed. The project is at a tipping
point around month 50, about to accelerate. In this case we let the acceleration take place
whereas we delay it in the base case.
Eco-friendly scenario: in this scenario, we start with a greater proportion of people leaning in
favor of the project (60% compared to 50% in the base case)
Figure 21: "Eco-friendly" Scenario Key Parameters
Permits
30
Month
dmn
0
0
Month
dmnI
7-,-N-
0
lxrnp'F:n, Lrrrt
i't
36
72
108
Time (Mornb)
144
ps1Crm\<
CU mma
Lean
K
0
0
36
108
72
Time (Month)
144
With a 20% gap between people originally leaning in favor of the project and people originally
leaning against the project, the overall duration of the project is only reduced by 20 months. As
we can see on this graph, that is because the original gap is rapidly closed because lobbying
against the project has a greater impact than lobbying in favor as the pool from which it can
move people is higher. This is the balancing loop represented in figure 8.
39
Strong advocacy scenario: in this scenario, we increase the proportion of people advocating for
the project by 25% from 2% to 2.5% in the beginning
Figure 22: "Strong Advocacy" Scenario Permits Timeline
Permits
30
Month
0
dmnI
0
Month
dmnI
144
108
72
36
0
Time (Monh)
Tim rmmz
i
T11e r
permit Iur:
.
n .
Firal A-prav,-:
-
-
-
-
-
-
-
Tme
k
permit 2 Cur
prmit 3 Curzew.
-emiras
n.g Imn r : n al : CarL --
Current
d:-n
Similarly, the overall duration of the project is only reduced by around 2 years. The reason
behind it is that the additional advocates have a much smaller marginal contribution to lobbying
than the base ones, and therefore don't impact public opinion as much.
Targeted opposition diffusion scenario: in this scenario, we decrease the proportion of people
advocating against the project by 25% from 2% to 1.5% in the beginning
Figure 23: "Targeted Opposition Diffusion" Scenario Key Parameters
Permits
30
Month
I
dmnl
0
0
Month
dml
0
EpI
r
r.
108
72
Time (Month)
144
I Currin1
prm
\
.
o
Timreaing ageFpemit I Currmn
Th. 'raermit
E
36
aCrirni,
~rimi ~..un~gtiM
40
Lobbying
Intensity of lobbying
0
0
36
0
0
72
108
Time (Month)
72
108
Time (Month)
44
Intenity . CUm t
latenuityY : CI> n
....
A..
:...
.
.
L
36
Voice
Lean
Decided
60
60
20
30
0
00
0
36
72
108
Time (MonhM
)
0
144
36
72
108
Time (Month)
144
Ti
Financial certainty
Cost of technology
NPV
900 M
4
4M
3M
S750 M
.5
2M
600 M
0
48
R6
Time (Mon.th)
144
0
36
72
108
144
Time (Month)
0
36
72
08
Time (Month)
44
The overall duration of the project is reduced by more than 8 years. Compared to the base case
scenario, intensity of lobbying against the project starts significantly lower (
25%). Compared
to the "more advocates scenario", the impact of the change is stronger because the marginal
contribution of advocates between 1.5% and 2% is greater than that of advocates between 2%
and 2.5%. This strong decrease in lobbying affects all other key variables, leading to faster
rallying of public opinion and greater financial certainty.
Regulatory framework scenario: in this scenario, we introduce a regulatory framework,
avoiding trial and error type of process. The technology changes after month 50.
The results of the simulation are counter-intuitive. Introducing a regulatory framework can
impact a project by increasing its overall timeline. Introducing a regulatory framework in our
base case scenario eventually leads to the project never completing.
41
haft--_
__ ---.
- -
-
--
-
-
__n
Figure 24: "Regulatory Framework" Scenario Permit Timeline
Pennits
30
I
Month
dmnl
0
0
Month
dmnI
in ge :1 .t permit I:Curnew Sund
mn,gt at parmit 2: Cur=et - - - -- - - ~t
re ing
emit
spe -: Curm t -ExI,oaa stieS ap val : Curt
TiM
e
Tm
Tim
Firal
144
108
72
Tune (Month)
36
0
:Lro
aCrn..mn
--
-
--
-
enh
--
The reason is simple: after the technology change, only one legislator is working on the permit
process when the legislative framework exists whereas two legislators (one for Permit 1 and one
for Permit 2) are working without framework. The completion rate is therefore slightly faster in
our base case scenario. This however doesn't necessarily mean that having a pre-existing
regulatory framework negatively impacts the project. More reasonably, the framework also
comes with an overall shortened approval process, which we haven't modeled.
PPA scenarios: in these scenarios, we simulate the effects of signing a power purchase
agreement with the utilities. Signing the PPA significantly decreases the WACC of the project
and therefore increases its NPV and financial certainty. However, if approval is not granted
within two years of signing the contract, the PPA can be revoked, leading to a higher increase in
WACC due to the bad publicity generated.
Figure 25: "PPA scenarios" Scenario Permit Timeline
PPA Signed in year 6,10 and 14
Permits
30
Month
30
30
Month
.1
dnnI
I dmn
0
MonthL
0
0 dmnl
48
144
96
Timeremig
0
0
l'matd ran
Fi
pr; v
Tvx r mim
7
ui ft
zie fon zppvnl: Cwrr
urn
h
-
W1=
ai
Tume r-mn
F',x7<ved re
Finml Ape
Month
dmnI
0 Month
Month
Time (Month)
I%
I
[
0 dmni
0
et rmn 2 Carren - mh
Permits
Permits
r.,- perrl 2 Curnnr
-
%tn\
;v prmit 2 Curenw - K
appwoal ; Cwrrr-. - Mvwh
iru: x ,-f
ur
dmni
96
144
48
Time (Month)
ri
0
Tore r
Tim
inga
re
1inal AppMvn
:
144
96
48
Time (Month)
miga
emit Curew
.pemit3, C.ram
Larrent
----
-
MI
rh
d
42
It appears from these simulations that there is a right time to sign Power Purchase Agreements:
doing so too early (first graph) can affect the project negatively as it exposes it to a greater risk
of failure from not getting approval on time. If that happens, then the project looses financial
credibility and this can be a burden for the remaining of the process. Signing the agreements late
avoids the risk of not getting approval within the 2 years interval, but it doesn't benefit the
overall progress of the project as everything is already settled. The right time to sign the contract
is somewhere between the two, where it leaves enough time to gain approval on time while also
significantly accelerating the process during this 2 years period.
Discussion
Improving the model
Our model has several shortcomings that need to be improved if we wanted to make it more
accurate:
-
Financial certainty strongly depends on Time remaining to get approval, meaning that
when Time remaining to get approval is zero, financial certainty is equal to 1. However,
the real Cape Wind project would not fit in our model, as financial certainty never
reached 1, even though it had gained all necessary regulatory approvals. In order to
improve the model, financial certainty could also be made a function of time. As time
goes by and the project doesn't come out of the ground, debt and equity provider can
become wary that the project is "cursed" and refuse to invest even though the project has
been approved.
-
Our model is sensitive to the assumptions we make for the additional PPA: the duration
of the offer as well as its effects on the WACC of the project. Longer offer periods and
steeper drops in WACC make it more favorable to sign the agreements early, whereas
short offer periods and smaller drops in WACC make it more favorable to sign the
agreements when the project is nearing its end.
-
The overall delays in our model are sensitive to our intensity tables. However, even
though the delays can be greatly affected when we change our assumptions on the
43
marginal lobbying intensity of a 1% increase in support or opposition, our conclusion
regarding the relative efficacy of project approaches remains the same.
Randomness should also be added in the project: in the public opinion variables as well
-
as for WACC (because it depends on interest rates) and for the expected price of MW.
Our intuition is that adding randomness would increase the duration of our simulation.
Interpreting our results
The first insight from our simulations is that changing technology is a trade off: doing so can
improve the NPV of the project significantly but it also delays its approval. When facing a strong
opposition, it can be advantageous to keep the same technology so as to not give a boost to
opposition. However, when opposition is weaker as in the "Targeted Opposition Diffusion"
scenario, the delay is very small (around one year) and the NPV gains are important (-$50
million).
Secondly, the best public relation's strategy is to target and try to reduce opposition rather than
choosing an eco-friendly environment or recruiting advocacy champions. Reducing opposition
can be done either by reducing opponents' aversion or by choosing a project site where the initial
opposition is already weak. This suggests that developing large renewable energy projects in
more conservative states could be easier than doing so in liberal states where residents are not
used to living next to large energy plants and are therefore more averse to changing their
immediate area. Also, shrinking the size of the project, and using it as a proof of concept for an
extension can reduce opponent's aversion quite significantly. This is what seems to be the
strategy of Deepwater in Rhode Island, where the first offshore wind farm in the USA will only
start with 5 turbines.
There is a right timing for signing Power Purchase Agreements: doing so too early exposes the
project to not obtaining permits on time and loosing those PPA after the offer period. Doing so at
the end of the project is a safe bet but doesn't help accelerate the approval project. Our
simulations suggest that the best time to sign the PPA is once the developers are certain that they
wi1
-4-ain
permits witAin the P-A
*he
er
peiod. T-is accelerates the end vf the project by
giving it more financial certainty towards the end while not exposing it to the risk of contract
44
termination. Of course this is purely theoretical and developers should evaluate their level of
certainty. Nevertheless the suggestion to not sign those contracts too early remains valid.
Lastly, having a regulatory framework in place can only be a time-saver if it goes with a
significant reduction in the overall process. Having one single entity approving the project
focuses opposition's attacks and can result in the deciding body being overwhelmed, which is
not the case when several deciding bodies are working at the same time.
Looking forward to Cape Wind
Cape Wind is stalled at the moment. But is it too late to turn it around? What would make it
possible?
Evidently, the termination of the Power Purchase Agreements was a major blow to Cape Wind
that abruptly ended its "follow the tide" reinforcing loop that seemed to inevitably lead to the
launch of the project. The tide has suddenly reversed and rapid changes are needed for the
project to survive: opponents are regaining strength and will likely undermine any new attempt
to revive Cape Wind. Only a series of events could potentially reverse the strong negative
reinforcing loop: major regulatory support for Wind Energy in the form of large subsidies
making the project attractive for both investors and Utilities, a sudden energy shock reversing the
downward trend of fossil fuel prices, as well as a psychological trigger for advocates of the
project such as a spill or a contamination scandal seem necessary.
Likely, the project will never be developed, at least in its current state. Meanwhile, Block Island
wind farm will serve as a test. If successful, it will benefit the rest of the industry by giving it
financial credentials and Rhode Island's Request for Proposal framework could be extended to
other states.
Conclusion
System Dynamics have been extensively used to better understand the dynamics of project
implementation after approval. In this paper, our goal was to better understand the dynamics of
pre-approval process for a large offshore wind project. Our model suggests that the key factors
that impact the overall duration of a project are opponents rather than proponents, and the
45
decision to adopt a new technology while going through the approval process. On the contrary,
the overall level of environmental awareness barely affects its timeline, suggesting that no
renewable project is too early for its time. Rather, it is the developers' duty to find places where
local opposition from residents is minimal; and we suggest these places could be found in more
conservative states rather than liberal states. Moreover, if their goal is to advance a "green"
agenda, massive projects will likely be more counterproductive as they crystallize opposition.
Starting with less ambitious projects can clear the path for larger extensions and be a more
profitable strategy in the end. During the process approval, developers are faced with a number
of decisions, including redesigning the project to incorporate new and cheaper technologies and
choosing when to sign Power Purchase Agreements. Changing technology is a trade off between
greater NPV and shorter approval process. Depending on the extra time and procedure cost that
can arise from changing technology, keeping the same technology throughout the project is often
the better strategy. As for the Power Purchase Agreements, we find that pushing to sign them
early in the process can ultimately hurt the project. It should be done when developers have
enough confidence that they will gain approval before the offer ends.
46
Appendix: Equations
Stocks
Lean Against: Become against-Become in Favor-CA ; Initial Value = 100-Initial percentage in favor-Init
Strongly Against
Against: CA-CVA ; Initial Value = 0
Voice Against: CVA ; Initial Value = Init Strongly Against
Lean in Favor: Become in Favor -Become in For -CF ; Initial Value = Initial percentage in favor-Init
Strongly in Favor
In Favor: CF - CVF ; Initial value =
0
Voice Against: CVF ; Initial Value = Init Strongly in Favor
Time remaining to get permit 1: Procedure 1 slowing rate-Procedure 1 completion rate ; Initial Value
SPT
Time remaining to get permit 2: Procedure 2 slowing rate-Procedure 2 completion rate ; Initial value
SPT
=
Time remaining to get permit 3: Procedure 3 slowing rate-Procedure 3 completion rate ; Initial value
SPT
=
Cost of tech 1: -tl cost reduction ; Initial value = 3,000,000
Cost of tech 2: -t2 cost reduction ; Initial value = 4,000,000
Variables
CVA: max(0,DELAY3( CA/5 , Change time Strong ))+Voice Against*MIN(0,0.5-Financial certainty)/5
CA: max(0,DELAY3( CA/5 , Change time Strong ))+Voice Against*MIN(0,0.5-Financial certainty)/5
Become Against: Lean In Favor*(LobbyingA/Change time)
CVF: max(0,DELAY3( CA/5 , Change time Strong ))+Voice Against*MIN(0,0.5-Financial certainty)/5
47
CF: max(O,DELAY3( CA/5 , Change time Strong ))+Voice Against*MIN(0,0.5-Financial certainty)/5
Become In Favor: Lean Against*(LobbyingF/Change time)
IntensityA: tableA(Voice Against)/100
EfficiencyA: IF THEN ELSE( Ramp End Time Against =0 , 0 , MIN (1,RAMP(1/Ramp End Time
Against, 0,Ramp End Time Against)))
LobbyingA: EfficiencyA*IntensityA
IntensityF: tableF(Voice in Favor)/100
EfficiencyF: IF THEN ELSE( Ramp End Time For =0 , 0 , MIN(1,RAMP(1/Ramp End Time For,
0,Ramp End Time For )))
LobbyingF: IF THEN ELSE( Ramp End Time For =0 , 0 , MIN(1,RAMP(1/Ramp End Time For,
0,Ramp End Time For )))
Procedure 1 slowing rate: IF THEN ELSE( Time remaining to get permit 1<=0 , 0 , LobbyingA* 1.1
)+Adoption of New Technology
,
Procedure 2 slowing rate: IF THEN ELSE( Time remaining to get permit 2<=0, 0, IF THEN ELSE(
Legislation in place= 1 , LobbyingA* 1.1 , IF THEN ELSE( Time remaining to get permit 1 <=0
LobbyingA*1.1 , IF THEN ELSE (Time>50:AND:Time remaining to get permit 1<=3,LobbyingA*1.1,0)
)))+Adoption of New Technology
Procedure 3 slowing rate: IF THEN ELSE( Time remaining to get permit 3<=O, 0, IF THEN ELSE(
Legislation in place= 1 , LobbyingA* 1.1 , IF THEN ELSE( Time remaining to get permit 2<=0,
LobbyingA* 1.1 , LobbyingA* 1.1,0) ))+Adoption of New Technology
Adoption of New Technology: Change technology*3*PULSE( 50, 1)
Procedure 1 completion rate: IF THEN ELSE(Time remaining to get permit 1<=0, 0,1)
Procedure 2 completion rate: IF THEN ELSE (Time remaining to get permit 2<=0,0,IF THEN ELSE(
Legislation in place= 1,1,IF THEN ELSE( Time remaining to get permit 1 <=0 , 1 , IF THEN ELSE
(Time>50:AND:Time remaining to get permit 1<=3,1,0) )))
Procedure 3 completion rate: IF THEN ELSE(Time remaining to get permit 3<=0,0,IF THEN ELSE(
Legislation in place= 1,1 ,IF THEN ELSE( Time remaining to get permit 2<=0 , 1, 0) ))
Expected remaining time for 2nproval: IF THEN ELSE(Legislation in place=1, max(0,Time remaining
to get permit 1),max(0,Time remaining to get permit 1)+ max(0
,Time remaining to get permit 2)+max(0,Time remaining to get permit 3))
48
Final Approval: IF THEN ELSE (Time remaining to get permit 3<=0, 1,0)
Cost reduc pot 1: max(0,Cost of tech 1-tl min cost)
Cost reduc pot 2: max(O,Cost of tech 2-t2 min cost)
T1 cost reduction: cost reduc pot 1*speed 1
T2 cost reduction: cost reduc pot 2*speed 2
Investment cost: IF THEN ELSE(Change technology=1,IF THEN ELSE(Time<=50,Cost of tech
1 *Desired capacity,Cost of tech 2*Desired capacity),Cost of tech 1 *Desired capacity)
Availability of financing: MIN(i,exp(ln(0.4)*(1+(Expected remaining time for approval/100-(SPT
All/i 00))/(SPT All/100))) *Net Present Value of Project/Base NPV)
Financial certainty: Availability of financing
Cost of MWMonth: 30*24*Cost of MWh
Monthly OM costs: Desired capacity*Cost of MWMonth
Price of MWMonth: 30*24*Price of MWh
Subsidies per MWMonth: Subsidies per MWh*30*24
Monthly Expected Returns: Desired capacity*(Price of MWmonth+Subsidies per MWMonth)
Net Present Value of Project: -Investment cost+(Monthly expected returns-Monthly OM costs)/WACC
49
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