A comparison study of offshore wind support structures with monopiles and jackets for U.S. waters

Abstract

U.S. experience in offshore wind is limited, and high costs are expected unless innovations are introduced in one or multiple aspects of the project, from the installed technology to the balance of system (BOS). The substructure is the main single component responsible for the BOS capital expenditure (CapEx) and thus one that, if improved, could yield significant levelized cost of energy (LCOE) savings. For projects in U.S. waters, multimember lattice structures (also known as jackets) can render required stiffness for transitional water depths at potentially lower costs than monopiles (MPs). In this study, we used a systems engineering approach to evaluate the LCOE of prototypical wind power plants at six locations along the eastern seaboard and the Gulf of Mexico for both types of support structures. Using a reference wind turbine and actual metocean conditions for the selected sites, we calculated loads for a parked and an operational situation, and we optimized the MP- and jacket-based support structures to minimize their overall mass. Using a suite of cost models, we then computed their associated LCOE. For all water depths, the MP-based configurations were heavier than their jacket counterparts, but the overall costs for the MPs were less than they were for jackets up to depths of slightly less than 30m. When the associated manufacturing and installation costs were included, jackets resulted in lower LCOE for depths greater than 40m. These results can be used by U.S. stakeholders to understand the potential for different technologies at different sites, but the methodology illustrated in this study can be further employed to analyze the effects of innovations and design choices throughout wind power plant systems.

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A comparison study of offshore wind support structures with monopiles and jackets for U.S.
waters
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2016 J. Phys.: Conf. Ser. 753 092003
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A comparison study of offshore wind support
structures with monopiles and jackets for U.S. waters
R Damiani1, K Dykes1, G Scott1
1NREL, 15013 Denver West Parkway, Golden, CO 80401, USA
E-mail: rick.damiani@nrel.gov
Abstract. U.S. experience in offshore wind is limited, and high costs are expected unless
innovations are introduced in one or multiple aspects of the project, from the installed technology
to the balance of system (BOS). The substructure is the main single component responsible for
the BOS capital expenditure (CapEx) and thus one that, if improved, could yield significant
levelized cost of energy (LCOE) savings. For projects in U.S. waters, multimember lattice
structures (also known as jackets) can render required stiffness for transitional water depths
at potentially lower costs than monopiles (MPs). In this study, we used a systems engineering
approach to evaluate the LCOE of prototypical wind power plants at six locations along the
eastern seaboard and the Gulf of Mexico for both types of support structures. Using a reference
wind turbine and actual metocean conditions for the selected sites, we calculated loads for
a parked and an operational situation, and we optimized the MP- and jacket-based support
structures to minimize their overall mass. Using a suite of cost models, we then computed their
associated LCOE. For all water depths, the MP-based configurations were heavier than their
jacket counterparts, but the overall costs for the MPs were less than they were for jackets up
to depths of slightly less than 30 m. When the associated manufacturing and installation costs
were included, jackets resulted in lower LCOE for depths greater than 40 m. These results can
be used by U.S. stakeholders to understand the potential for different technologies at different
sites, but the methodology illustrated in this study can be further employed to analyze the
effects of innovations and design choices throughout wind power plant systems.
1. Introduction
Offshore wind power promises to deliver an essential contribution to a clean, robust, and
diversified U.S. energy portfolio [1]. However, the lack of domestic experience with offshore
wind technology has contributed to considerable uncertainty in estimates of the potential cost
of domestic offshore wind energy, which remains high even in the most optimistic predictions.
Significant cost drivers lie in the BOS activities. The BOS for a wind plant includes all
capital expenditures except the wind turbines themselves. The key categories of BOS costs
include: the offshore support structure (in this study, the jacket or monopile costs of fabrication,
transportation, and installation); all the costs associated with the transport of the components
to the port for staging; staging costs; transport of components to the offshore wind site;
all installation costs and additional warranty, insurance, and financing costs. Experience
in Europe has shown that costs are raised by project management inefficiencies and supply
chain bottlenecks [2], as well as by technology limitations. Offshore wind turbines have
largely used marinized land-based machines mounted on oil and gas (O&G) industry derived
platforms. Innovations in turbine and support structure design have the potential to streamline
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Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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manufacturing and installation and to reduce material costs. According to [3], the substructure
and foundation are responsible for 14% of the total offshore wind power plant LCOE, and the
largest uncertainty in LCOE is attributable to its sensitivity to CapEx.
MPs and lattice structures (or jackets, a common term used for lattice substructures from
the O&G industry experience) are among the most popular substructures for offshore wind.
Ninety-one percent of the 2014 installations were MPs, whereas some 5% were jackets [4]. MPs
are easy to fabricate and to install in shallow waters (≤20m), but they require progressively
more material tonnage to guarantee modal performance in deeper waters, where installation
also becomes more expensive. Lattice substructures can deliver needed structural stiffness by
increasing the substructures footprint and by concentrating mass away from the neutral axis,
but this is at the expense of more laborious fabrication than that of the MPs.
In the United States, analyses have shown that economic wind development would occur
over transitional water depths (depths of 30–60 m and distances from shore of 5–50 nautical
miles), where the estimated capacity potential for Class 4 wind and above exceeds 600 GW
[5; 6]. Several studies, e.g., [7; 8], have shown that MPs are progressively unfeasible as projects
are sited in deeper waters and use larger turbine sizes (6 MW+). Yet, in Europe, new extra-large
and extra-extra-large MPs are being proposed and deployed in more challenging sites (depths
>30 m) and for larger turbines (ratings >6 MW) [9].
Although the support structure plays an important role in defining the system’s reliability
and performance characteristics, it also affects costs associated with the BOS as well as with the
operation and maintenance (O&M). The link between these costs and the main environmental
design drivers must be recognized and understood (see also [10]) for the industry to flourish in the
United States and to avoid mistakes observed in past experience. Selecting the proper support
structure is one of the most important questions to address for U.S. offshore wind development,
and it is one that must be resolved in a multidisciplinary context that encompasses both the
technical aspects of the structural design and the BOS aspects of logistics and installation costs.
In this study, we made use of an integrated modeling tool, the Wind-Plant Integrated
System Design & Engineering Model (WISDEM™) developed at the National Renewable Energy
Laboratory (NREL), to perform a system-level nonlinear optimization of wind power plants.
Using an integrated model with combined physics and cost modeling capabilities allows for
the direct exploration of trade-offs in the design of different subsystems. We compared LCOE
estimates for prototypical wind power plants composed of 100 5-MW turbines sited along the
Eastern Seaboard and in the Gulf of Mexico. For each of the six sites, the analysis was conducted
with both an MP and with a typical four-legged jacket as substructure configuration. The hub
height was fixed throughout the sites, but the tower geometry was allowed to change depending
on the structural requirements and the varying deck heights, which were identified based on
the local wave crest conditions. Preliminary designs of the support structures were achieved via
JacketSE and TowerSE (design tools within WISDEM) driven by a gradient-based optimization,
which had the objective to minimize the support structure mass.
For this analysis, several plant cost models were used for the nonturbine cost elements of the
LCOE. The LCOE analysis showed that jackets become more economical than MPs at sites in
waters deeper than 45 m.
Section 2 presents the primary metocean conditions for the sites analyzed, whereas the details
of the methods used and the results of the analysis are given in Section 3 and 4, respectively.
Conclusions and areas of future work are presented in Section 5.
2. Metocean data
The modeling tool required information on wind and wave conditions at each site to establish
wave loading, deck height, and other parameters. Six sites were chosen to represent a range of
water depths. Each site was at (or very near, as in the case of Site 6) the location of a National
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Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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Table 1. The six buoy sites analyzed(a).
Case No. Buoy ID Name Region Lon (deg) Lat (deg)
1 41013 ‘FRYING PAN SHOALS’ ‘atl’ -77.743 33.436
2 42035 ‘GALVESTON’ ‘gulf’ -94.413 29.232
3 44025 ‘LONG ISLAND ‘atl’ -73.164 40.251
4 41035 ‘ONSLOW BAY’ ‘atl’ -77.280 34.476
5 44008 ‘SE NANTUCKET’ ‘atl’ -69.247 40.502
6 42036 ‘W TAMPA’ ‘gulf’ -84.517 28.500
aFrom National Data Buoy Center (NDBC) http://www.ndbc.noaa.gov/ and U.S. Army
Corps of Engineers Wave Information Studies (WIS) http://wis.usace.army.mil/. Region:
’atl’: Atlantic Ocean, ’gulf’: Gulf of Mexico
Figure 1. Locations of NDBC buoys.
Oceanic and Atmospheric Administration buoy to provide an accurate measure of the metocean
climate at the site. Note that deck height was calculated for each location based on the wave
conditions and supplied as an input to the support structure optimizations. The selected sites
are identified in Table 1 and Fig. 1. The key metocean parameters are given in Table 2.
2.1. Calculated parameters
A few parameters had to be extrapolated or calculated from the buoy and WIS records, including
maximum and breaking wave heights, surge height, and maximum wave crests at different return
periods.
The height of a breaking wave at a site is calculated as 0.78 times the water depth.
Significant wave heights (Hs50 and Hs100) with a return period of 50 and 100 yr are calculated by
extrapolating from the largest wave events recorded at a site. If the breaking wave height at a
site is lower than the extrapolated wave height, then the breaking wave height is used. Maximum
wave heights (Hmx50 and Hmx100) are also estimated for each return period by multiplying the
respective significant wave height by 1.86 ([11]).
The deck heights of the substructures were calculated to clear the 1000-yr wave-crest heights,
accounting for a 1.5 m run-up 1. To determine the 1000-yr wave crests, highest astronomical
1Wave run-up is the maximum vertical extent of wave uprush on a beach or structure above the still water level.
[12]
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tide (H AT ), 1000-yr storm surge heights, and the 1000-yr maximum wave height had to be
obtained. The 1000-yr wave heights and the HAT s were extrapolated from the maximum wave
heights in the buoy records. Surge height is difficult to estimate at offshore sites. When data
from nearby coastal water level measurement stations were available, the published extreme
value distribution was used to compute the extreme surge height. These values can be regarded
as conservative (high) because coastal areas will usually have higher surge heights than open
ocean sites.
We used data from a NOAA report [13] 2, which includes the shape and scale parameters
of a Gumbel Extreme Value distribution fit to the data of each offshore station, to analyze two
or three stations close to each of our proposed wind power plants. Through the R library evd
[14], we computed the magnitudes of the 1000-yr surges.
These derived metocean parameters for the six sites are given in Table 2.
Table 2. Key metocean parameters for the six sites analyzed.
Case No. W S(a)Water
Depth Hs50(b)Hmx50(b)Tmx50(c)H AT (d)δ1000(d)Hmx1000(b)Deck
Height
m s−1m m m sec m m m m
1 9.74 23.50 10.82 18.33 13.34 1.26 1.25 18.33 13.20
2 8.06 12.80 7.24 9.98 10.91 0.47 6.00 9.98 13.00
3 9.33 40.80 9.48 17.63 12.48 0.33 2.50 23.26 16.00
4 9.52 9.70 10.46 7.57 13.11 0.83 0.90 7.57 7.00
5 9.26 65.80 12.15 22.60 14.13 0.79 1.54 28.31 18.00
6 7.58 50.60 7.63 14.19 11.20 0.84 1.50 17.81 12.70
aAnnual average wind speed adjusted to 90 meters above sea level (MASL) using shear exponent of
0.12.
b50-yr significant wave height (Hs50), 50-yr maximum wave height (Hmx50), and 1000-yr maximum
wave height (Hmx1000) are given as trough-to-peak differences in meters.
cPeak wave spectral period (Tmx50) is in seconds.
dHighest astronomical tide (H AT ), 1000-yr storm surge (δ1000) and deck height are given as MASL.
2.2. Wind data
Wind data for this study was licensed from AWS Truepower, Inc. (AWS) and consisted of
gridded data at a resolution of 200 m, with separate shape files for annual, monthly, and diurnal
distributions of wind speed and wind direction. Original wind data was at 100 MASL, but it
was scaled downward slightly to adjust to 90 MASL.
3. Analysis method
The analysis carried out in this study made use of various tools available within the WISDEM
software suite. WISDEM integrates a variety of models for the entire wind energy system,
including turbine and plant equipment, O&M, energy production, and cost modeling [15]. The
tool set allows for trade-off studies and guides the design of components as well as the overall
system toward a configuration that minimizes the LCOE through multidisciplinary optimization.
The main submodules used in this study include RotorSE, DriveSE, TowerSE, JacketSE [16],
PlantEnergySE, TurbineCostsSE, and PlantCostsSE. RotorSE, a module that can calculate loads
on rotor blades, has been described in detail in [17]. DriveSE [18] can calculate reaction loads
2The report is an analysis of data from 112 long-term stations from the National Water Level Observation
Network.
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at the interface with the tower, and together with RotorSE it was run a priori and produced the
ultimate limit state loads that were inputs to the support structure sizing tools, i.e., TowerSE
and JacketSE. The latter two modules, which were at the core of the optimization, are described
in more detail in Section 3.1. PlantEnergySE, TurbineCostsSE, and PlantCostsSE are briefly
described in Sections 3.2 and 3.3.
3.1. TowerSE and JacketSE
TowerSE and JacketSE are preliminary sizing tools for support structures including towers, MPs,
and jacket substructures. These tools are based on simplified physics and load case analyses and
do not claim to be sufficient to arrive at final design details, but they offer a rapid and versatile
way to analyze multiple effects of design choices and environmental conditions, which would be
much more resource intensive with aeroelastic and finite element analysis tools.
JacketSE and TowerSE aid the designer in the search for an optimal preliminary configuration
of the substructure and tower and for given metocean conditions, turbine loading, modal
performance targets, and design standards’ criteria. The two software programs are similar
in framework and share some load calculation routines. JacketSE includes the dimensioning of
the tower component and uses the same structural code checks as TowerSE. The main difference
lies in the treatment of the substructure, which in TowerSE is a continuation of the tower to the
seabed (the pile) with the addition of a tubular transition piece (TP) to allow for the connection
of the main pile to the tower. JacketSE solves for a multimember substructure (either three- or
four-legged lattice) with a more complicated TP at the top [16].
The tools can size outer diameters (ODs) and wall thicknesses (ts) for piles, legs, braces,
and tower; other design variables that may be optimized are batter angle, pile embedment, and
tower tapering height. The design parameters (fixed inputs to the tools) include: water depth,
deck and hub height, design wind speed, design wave height and period, and soil characteristics
(stratigraphy of undrained shear strength, friction angles, and specific weight). Loads from the
rotor nacelle assembly (RNA) can be input to the model either from other WISDEM modules
or directly from the user. The user must also provide acceptable ranges for the design variables
— for example, maximum tower diameter, minimum and maximum diameter-to-thickness ratios
(DT Rs) for the various members, and maximum allowed footprint at the seabed.
The common software framework primarily consists of the following submodules: geometry
definition; load calculation; soil-pile interaction; finite element model; and structural code
checks. A number of simplifications have been incorporated to allow for rapid analyses of
multiple configurations on a personal computer. As such, complex hydrodynamics and associated
variables (e.g., tidal range, marine growth, and member-to-member hydrodynamic interaction)
are ignored, and fatigue assessments are not carried out by default. Although these aspects
can very well drive the design of certain subcomponents and of the overall structure [19–21],
it is believed that the main structural and mass characteristics should still be captured by
the simplified models for the sake of preliminary design assessments and trade-off studies, and
with a level of accuracy limited to those goals. More details on the codes can be found at
https://github.com/WISDEM and [16].
Within WISDEM, these tools allow for the full gamut of component investigations to arrive
at a minimum LCOE wind turbine and power plant layout. For example, together with a turbine
rotor and blade model, JacketSE can produce a design that meets tower/substructure clearance
criteria while also meeting mass or cost targets. In this study, the tools were used in stand-alone
mode, wherein the preliminary design realizations for substructures, foundations, and towers
were based on minimizing the overall structural mass.
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Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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3.2. PlantEnergySE and AEP calculations
OpenWind Enterprise (https://www.awstruepower.com/products/software/openwind/) (OE)
is a wind power plant micrositing tool that has been incorporated into WISDEM via the PlantEn-
ergySE module. PlantEnergySE was used to model wind power plant energy output and costs.
For each of the prospective sites, the obtained wind data were used to create a wind resource
grid (WRG) file. A 10-by-10 array of NREL 5-MW reference turbines was placed on the wind
grid, and OE computed the expected annual energy production (AEP). Turbine spacing was
1090m or 8.72 rotor diameters in each direction.
At full capacity (100% capacity factor (CF)), the wind power plant would produce
5 MW * 8,766 h * 100 turbines = 4,383 GWh/yr.
Figure 2(a) shows the annual mean wind speed and the wind rose at 90 MASL for the site
pertinent to Case 1. Divisions within each directional sector show the fraction of wind speeds
less than 4, 8, 12 and 25 m/s (full sector). Figure 2(b) shows the variation in capacity factor
across the wind power plant, primarily due to turbine wake effects and sheltering.
(a) Wind input (b) Capacity factor
Figure 2. Wind resource and capacity factor for a hypothetical wind power plant located at
the site corresponding to Case 1.
3.3. PlantCostsSE
Overall costs for the turbine and plant were modeled, and the overall wind power plant LCOE
was calculated via Eq. (1):
LCOE =F R ×(T C C +BOS) + (1 −T R)×OP E x
AEP (1)
In this study, the effects of the designs on financing via financing rate (F R) were ignored and
assumed to be a constant 9.8% based on [3]. In addition, the turbine capital costs were taken
from reported industry averages [3] of $1,952/kW, of which 18% is attributed to the tower or a
split of U.S. $1,600/kW for the rotor-nacelle-assembly and U.S. $352/kW for the tower. This
study used a fixed turbine configuration (see Section 3.4), and Case 1, a monopile at a water
depth of 23.5 m, was taken as the baseline. Tower cost was then adjusted for each case based
on the tower mass relative to the Case 1 tower mass for the monopile configuration. Adding the
tower cost to the RNA cost gave the overall total turbine capital cost (T CC ) for each case.
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Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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Table 3. Baseline turbine parameters.
Parameter Value Unit
Rating 5 MW
Rotor diameter 125 m
Hub height 90 m
RNA mass 350 t
Unfactored peak thrust (DLC1/DLC2) 1.28e3/188 kN
Unfactored moment (DLC1/DLC2) 8.96e2/1.31e2 kN m
Nominal wind speed at max thrust (DLC1/DLC2) 30/70 m s−1
Target system first eigenfrequency 0.26 Hz
For the plant costs, two models that have been integrated into WISDEM were used. The
first model is an empirical model of wind plant BOS costs, which has been recently developed
by NREL [22]. Offshore BOS costs are determined based on input parameters that include
the turbine configuration (rotor diameter, hub height, and rated power), plant characteristics
(water depth, distance to shore, and meteorological-oceanographic data), and support structure
characteristics (dimensions and component masses). For this study, the turbine configuration is
static while each case is adapted for the particular plant and support structure characteristics.
The second model, which computes operational expenditure (OP E x) costs, uses a calculator
from the Energy Research Center of the Netherlands (ECN). The ECN O&M calculator processes
information that is similar to the BOS cost model input, along with metocean time-series
data [23]. Although the model does calculate maintenance costs for the support structure,
the included version does not distinguish between support structure types, so the operational
expenditures depend only on the case site and not on the substructure type. The setup for the
model was based on a study of integrated installation and O&M [24]. The full set of plant costs
for each case was then incorporated in the cost of energy equation, Eq. (1), to compare the
impact of different support structures designs at different types of wind power plant sites with
varied water depths.
3.4. Case study assumptions
The reference turbine used for this study was the NREL 5-MW turbine [25] with main parameters
given in Table 3. The target first natural frequencies, based on a soft-stiff design approach [26],
represent the modal performance requested for the various system layouts. Table 2 shows the
range of main environmental parameters that were considered; for simplicity, soil characteristics
were fixed throughout the various cases at an average stiffness soil profile (friction angles ≥
35 deg).
Because of the simplifications in the physics of the used software programs, and because of the
limited number of load cases considered, additional conservatism was provided by the employed
drag (cd) and added mass (cm) coefficients, the choice of a worst-case loading scenario, and
additional safety factors. Based on verification runs with other codes [16], the substructure cd
and cmvalues were doubled with respect to those recommended by [27]; and for the tower, the
wind cdwas set at 2 to account for transition piece drag; further, wave loads calculated on
the main jacket legs were multiplied by a factor of 4 to account for hydrodynamics effects on
secondary members of the substructure otherwise ignored.
Two characteristic load cases were investigated. One, similar to the International
Electrotechnical Commission (IEC) DLC 1.6 [28], assumes maximum turbine rotor thrust and
maximum wave load aligned along the base of the structure. The other load case, similar to the
IEC DLC 6.1 [28], assumes the machine idling during an extreme (50-yr) wind and wave event.
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Table 4. Design variables and constraints for the monopile optimizations.
Description Number of
Variables Constraints
Tower and monopile ODs 3
Tower, TP, and MP ts 4
Utilization against shell and global buckling 68
Utilization against strength 34
Eigenfrequency lower limit 1
Tower taper ratio (manufacturability) 1
Diameter-to-thickness ratio (manufacturability) 7
DT R (manufacturability) 7
The loads from the RNA were precalculated by RotorSE and DriveSE as was stated earlier.
Both TowerSE and JacketSE were run in stand-alone mode to obtain minimum overall
mass configurations of support structures based on MPs and jacket substructures, respectively,
including the mass of the tower, four-legged jacket, transition piece, and pile(s).
3.5. Optimizer parameters
The optimization made use of Sparse NOnlinear OPTimizer (SNOPT), a gradient-based, sparse
sequential quadratic programming method as implemented in Python [29]. The final accuracy
in the optimization and the feasibility tolerance were set at 10−3. For each support structure
type, the design variables and constraints are listed in Tables 4 and 5. Constraint functions were
based on structural integrity and manufacturability criteria (see also [16]).
4. Results
The overall support structure mass as computed by the optimizer is given in Table 6, where the
comparison between MP and jacket includes the steel of the piles.
The first key finding is that the mass of monopile-tower combination is greater than the
jacket-tower combination for all cases. For the MP in all cases, the frequency constraint proved
to be the binding constraint and pushed the structure to a more massive configuration with
diameters and thicknesses above the lower bounds of their respective, allotted ranges (with
the exception of tower-top thickness). At low water depths, overall structural mass is similar
among jackets and MPs, but for the deepest site, the support structure mass when using the
MPs is nearly double that computed for jacket-based systems. A verification step showed good
agreement between the optimization results and industry trends data for the MPs [30] (though
there is mass underprediction for the TP), as shown in Figure 3. In general, we expect the
MP mass to increase significantly with water depth; it is this increase in mass, along with the
associated difficulties in the installation, that cause a decrease in the attractiveness for the MPs
when moving to deeper water sites.
For the jacket substructure, the comparison to industry trends data [30] shows less agreement
than for the MP (see Figure 4). Very few data points are available for jackets from the industry
(four points on the graph), and it is striking that the data associated with the shallowest site
(at a water depth less than 10 m) indicate a mass comparable to that at a site three times as
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Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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Table 5. Design variables and constraints for the jacket optimizations.
Description Number of
Variables Constraints
Tower ODs and DT Rs4
Tower waist height 1
TP girder ODs and ts 2
TP deck width 1
Jacket batter 1
Jacket Leg, x-brace, mud-brace ODs, ts 6
Pile OD, t, length 3
Tower taper ratio (manufacturability) 1
Tower utilization against shell and global buckling 36
Tower utilization against strength 18
Tower and member DT Rs (manufacturability) 5
Batter range (manufacturability) 1
Maximum footprint 1
Minimum jacket brace angle 1
Jacket member additional structural criteria 10
Jacket member utilization 316
Jacket joint utilization 48
Pile length (manufacturability and axial capacity) 2
Eigenfrequency range 1
Table 6. Computed overall masses in tonnes for the support structures and their towers for
each design case.
Case No. Monopile Jacket
Total Mass Tower Mass Total Mass Tower Mass
1 1,113 324 906 214
2 915 315 850 221
3 1,457 302 965 183
4 814 353 794 225
5 2,160 330 1,183 170
6 1,928 277 1,026 198
deep. We speculate that factors other than normal reliability sizing contributed to a larger than
normal mass for the project in shallow water. Further, the industry data points at ∼25 m are for
The Science of Making Torque from Wind (TORQUE 2016) IOP Publishing
Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
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6-MW turbine installations, which are expected to be associated with higher loads than those
from a 5-MW turbine as assumed in this study. If these data points are scaled by the ratio of
the power ratings, they would align closer to the calculated jacket mass trend. The trend in the
piles’ mass might be affected by the same considerations, and, in addition, actual soil conditions
might have promoted the use of larger piles than those calculated in this study for a generic soil.
Figure 3. Comparison of optimization results for monopile and transition piece masses with
industry data trends.
Figure 4. Comparison of optimization results for jacket and pile masses with industry data
trends.
Although the mass of the monopile-tower support structure is greater than the jacket-tower
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support structure for all cases, the overall cost impact on LCOE of the support structures results
in the monopile being economically more favorable than the jacket for water depths up to 40 m.
Summaries of the calculated costs are given in Table 7 and Table 8 for the MP- and jacket-based
wind power plants, respectively.
Table 7. Computed AEP and costs for the various cases with MP support structures.
Case No. AEP T C C BOS O&M LCOE
GWh $M$B$M$/MWh
1 1,815.65 976 2.21 70.28 195.1
2 1,544.88 971 1.71 65.70 195.8
3 2,071.86 964 2.07 70.51 163.9
4 1,638.29 992 1.37 67.24 166.9
5 2,240.18 979 3.48 74.31 215.0
6 1,214.24 950 2.89 65.70 342.4
First, as noted in the tables, although the overall LCOE for plants with MP support structures
is cheaper in shallow water depth sites, at some point around ∼40 m, the jacket support structure
becomes the more cost-effective approach. For each case analyzed, Fig. 5 shows the structure
BOS difference and the overall LCOE difference when using the MP compared to the jacket
support structures. Fig. 5a shows that the crossover point from the calculated, linearized trends
in BOS costs occurs around a water depth of ∼35 m. Although MPs are much simpler to
manufacture, as they become larger for deeper waters, the overall material costs overtake the
manufacturing costs, and the overall MP BOS costs become greater than those for the jackets.
However, the costs for MP installation and assembly are still cheaper than those of the jacket
counterparts up to water depths of ∼50m. Beyond this depth, the extremely heavy MP would
require a different type of vessel for transportation and installation, which is reflected in a step
change in BOS cost. For this reason, and as shown in Fig. 5b, around ∼45 m, the jackets
become the cheaper option with respect to plant cost impacts and overall LCOE.
5. Conclusions
We proposed a case study to assess the economical applicability of MP and jackets as
substructure layouts for typical sites along the U.S. Eastern Seaboard and Gulf of Mexico. We
used tools available within WISDEM combined with a gradient-based optimizer. One-hundred-
turbine, 500-MW wind power plants were envisioned at six sites, where metocean conditions
were collected from reliable buoy data and numerical reanalysis. Results showed that the MP
is preferable to jackets up to water depths of approximately 40 m. The first U.S. offshore wind
Table 8. Computed AEP and costs for the various cases with jacket support structures.
Case No. AEP T C C BOS O&M LCOE
GWh $M$B$M$/MWh
1 1,815.65 916 2.44 70.3 204.6
2 1,544.88 920 1.90 65.7 204.6
3 2,071.86 900 2.18 70.5 166.0
4 1,638.29 922 1.59 67.2 175.2
5 2,240.18 892 3.50 74.3 212.1
6 1,214.24 908 2.81 65.7 332.3
The Science of Making Torque from Wind (TORQUE 2016) IOP Publishing
Journal of Physics: Conference Series 753 (2016) 092003 doi:10.1088/1742-6596/753/9/092003
11

(a) Comparison of MP to jacket support structure cost (b) Support structure influence on plant costs and
LCOE
Figure 5. BOS costs (a) and LCOE difference (b) of the MP- and jacket-based support
structures as a function of water depth.
power plant, which will use 6-MW turbines, is being built with jacket substructures, although
water depth is lower than 40 m. This emphasizes the fact that other factors may have played a
bigger role than pure economics in that case, such as the unavailability of large MP construction
and installation entities in New England. Nevertheless, the methods proposed in this study
can be used by U.S. stakeholders to evaluate various aspects of the project and their effects on
the LCOE, including metocean conditions, turbine characteristics, and technology innovations.
Future work will account for other DLCs and a full dynamic analysis of the turbine system,
which will include the effect of fatigue loading and lead to more accurate estimates of the costs.
Acknowledgments
This work was supported by the U.S. Department of Energy (DOE) under Contract No. DE-
AC36-08GO28308 with the National Renewable Energy Laboratory (NREL). Funding for the
work was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and
Water Power Technologies Office. We acknowledge the insight and feedback on this paper by
Aaron Smith and Ben Maples of NREL.
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