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


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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.
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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
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
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
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) and U.S. Army
Corps of Engineers Wave Information Studies (WIS) 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
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.
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
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
m s1m 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
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
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
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 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.
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
3.2. PlantEnergySE and AEP calculations
OpenWind Enterprise ( (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.
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
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 s1
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.
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
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 103. 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
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
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
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
Although the mass of the monopile-tower support structure is greater than the jacket-tower
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
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.
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.
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
(a) Comparison of MP to jacket support structure cost (b) Support structure influence on plant costs and
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.
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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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
... The increase in the size of a wind turbine would demand an increase in the size of the supporting monopile, triggering new manufacturing, transportation, installation and design challenges [4,5]. Alternative bottom-fixed solutions to the monopile structures can be sought by deploying support structures such as hybrid jacket-towers [6] or full space-frame structures [7,8,9]. Among these two solutions, the space-frame structure enables a larger margin for the optimization of its configuration and corresponding mass, generally leading to better design solutions. ...
The increasing size of wind turbines and the need to develop offshore wind farms in deeper waters trigger new research on alternative solutions to the monopile support structures. Among such solutions, the full lattice (or space-frame) structure concept is regaining interest in the offshore research community. To obtain reliable predictions of the mechanical behaviour of space-frame structures for offshore applications, one can make use of high-fidelity numerical models. However, it is often necessary to use fast and simple low-fidelity models to cope with eventual nonlinear dynamic simulations that call for computationally expensive sensitivity analysis or to interact in real time with an incoming data stream generated by an ongoing structural health monitoring campaign. Developing an accurate low-fidelity model for tapered full lattice structures, assembled through rigid welded connections, is not a straightforward task. In fact, most of the models in the literature are either derived for extremely slender, repetitive and regular structures, assembled through pin-like joints (e.g., truss structures for aerospace applications), or they can only capture the first natural frequency for structures that differ from the above description. Within this context, this study presents a low-fidelity model of a lattice structure for offshore wind applications. The proposed low-fidelity model consists of a sequence of regular Timoshenko beams, each of them characterized by homogenized mechanical and mass properties representative of the single bays of the reference space-frame structure. The homogenized elastic coefficients of the sequence of beams are then computed by means of two alternative procedures: a) via analytical expressions available in the literature and developed for regular and repetitive structures for aerospace applications, and assembled through pin-like joints; b) by means of an optimization procedure. The suggested methods to derive the homogenized elastic coefficients are then tested for both straight and tapered lattice structures. The prediction performance is evaluated in terms of estimation of the first five natural frequencies and mode shapes, response to dynamic loads, and ability to predict rotor-structure interaction phenomena. A parametric study is then performed to evaluate the potential and limitations of the proposed models. To bypass the optimization procedure (b), a data-driven approach is also proposed for the case of straight lattice structures.
... There is much attention for wind farm layout optimisation in practice and literature. Most of the research is done in selecting and creating the best optimisation algorithms [22] [19] [7] [5] [20] [15], wake models [1] [12] [8] [3] [11], and cost models [16] [6]. ...
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This is not yet another study into better modelling or optimiser selection for OWFLO. Instead, this study aims to provide insight into what performance can be expected from offshore wind farm layout optimisation(OWFLO) and to know when further optimising is not justifiable anymore. The study consists of three parts. All three parts make use of a referent. (The definition of the term’referent’ as used here is given in the paper.) The first part uses the referent to find and understand the characteristics of the OWFLO problem. Wind farms with 9, 25 and 64 turbines have been optimised 100 times with the referent. The results show a small spread in the performance of the found optimised layouts, indicating that many local optima exist with similar performances in an OWFLO problem. The second part compares performances from optimised layouts with 25 turbines resulting from optimisations with alternative implementation choices, evaluated by the referent model. The difference in performance resulting from the alternative optimisers indicates that improvement of a state-of-the-art optimiser is not expected to lead to much better results. The third part explores the need to improve the analysis by adding a phenomenon currently not considered in OWFLO. The influence of neighbouring wind farms(NBWFs) on layout optimisation without including atmospheric stability is investigated. It is evident that adding NBWFs for accurate energy yield assessments is necessary. However, for layout optimisation, the benefit of including NBWFs is not apparent.
... However, the development of new turbines with higher rated power in combination with the need for deeper water installations might be a catalyst for a technological leap toward jacket substructures. Damiani et al. (2016) calculated that for water depths deeper than 40-m 10 jackets promise lower costs than monopiles, considering six offshore sites along the U.S. Eastern Seaboard and the Gulf of Mexico. The break-even point or water depth, respectively, where the jacket technology becomes truly competitive, is however dependent on the costs of the vessels used to transport and install the structures. ...
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The main obstacles in preliminary design studies or optimization of jacket substructures for offshore wind turbines are high numerical expenses for structural code checks and simplistic cost assumptions. In order to create a basis for fast design evaluations, this work provides the following: firstly, a jacket model is proposed that covers topology and tube sizing with a limited set of design variables. Secondly, a cost model is proposed that goes beyond the simple and common mass-dependent approach. And thirdly, the issue of numerical efficiency is addressed by surrogate models both for fatigue and ultimate limit state code checks. In addition, this work shows an example utilizing all models. The outcome can be utilized for preliminary design studies and jacket optimization schemes and is suitable for scientific and industrial applications.
... The need for renewable energy and the decrease in the Levelized Cost of Energy (LCOE) of offshore wind has increased the global volume of installed and planned Offshore Wind Turbines (OWTs) over the past two decades [1,2]. The U.S. offshore wind sector, however, expects higher costs due to the lack of domestic experience with offshore wind technology [3,4]. The Balance of System (BOS) activities are the main cost drivers for a planned wind farm, and the cost of support structures are recognized as the dominant portion of the BOS Capital Expenditure (CapEx). ...
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The U.S. offshore wind industry can expect higher costs due to the lack of domestic experience with offshore wind technology. A key factor of the capital expenditure related to offshore wind farms is the cost of the support structures of offshore wind turbines. Therefore, improvements to the reliability of support structures under ultimate and fatigue loading conditions will help reduce the levelized cost of energy of offshore wind. This study presents a framework that accounts for the wind directionality by assuming a distinct and independent wind speed distribution per each wind direction and investigates its effect on the fatigue life of offshore wind turbine support structures. A monopile support structure in a potential wind site close to a National Oceanic and Atmospheric Administration buoy in the north-eastern US waters is used in this study. Fatigue damage assessment is performed for the normal operational condition of wind turbine, and the results are presented considering both cathodic protection and free corrosion conditions at the mudline level of the monopile. The location and extent of the predicted fatigue damages are found to vary due to accounting for the wind directionality.
Large numbers of offshore wind turbines in earthquake prone areas increase the scientific interest in analysing their seismic vulnerability. This study investigates the deformation and collapses susceptibility of a 10 MW jacket supported offshore wind turbine (OWT) considering different geometry of pile and jacket. A three-dimensional finite element model of the soil-pile-jacket-tower is developed in ABAQUS. A performance-based analysis is conducted to quantify the engineering demand parameters by choosing first mode spectral acceleration as the intensity measure for different pile length-to-diameter ratios and the base width of the jacket. The significance of considering higher modes and the vertical component of the earthquake motion on the dynamic response of offshore wind turbines is also discussed. Considerable amplification of acceleration is observed at the nacelle due to vertical excitation. In low to moderate earthquake shaking, jacket base width shows a marginal influence on the responses, while a significant impact is observed for strong earthquake shaking. Mudline rotation and acceleration response at the nacelle are the most critical parameters that govern the design under the serviceability limit state. Moreover, it is observed that initiation of the rocking mode of vibration for jacket-supported OWTs resting on a pile foundation is significantly less until its embedment length reduces considerably (3 to 5 m).
Offshore wind farm jacket structures are preferable in the deeper waters due to their flexible construction and cost-effectiveness. However, both wind tower and jacket structure are sensitive to cyclic/dynamic loading. With the rapid advancement in offshore wind farms globally, they are being constructed in the seismic regions of the world, such as offshore Taiwan, Japan, and South Korea. Therefore, there is an urgent need to understand the seismic performance of offshore wind turbines. This research investigates the behavior of an offshore wind turbine jacket structure with pile foundations subjected to earthquake loading using dynamic centrifuge testing. A jacket-equivalent wind tower model was developed and tested under the two-layered sandy soil with a dense bottom layer overlain by a loose top layer to capture the liquefaction effects under seismic loading. Using the centrifuge test data, it will be shown that with the build-up of excess pore pressure in the soil causing liquefaction under strong seismic loading, the offshore wind turbine (OWT) structure suffers severe settlements and rotations that exceed the prescribed limits. However, it was possible to restrict the settlements and rotations by having an alternative arrangement that places the pile cap on the mudline. These results highlight the need to consider vertical settlements induced by the liquefaction in addition to the restriction of the rotations at the mudline level of the foundation and the need for more research on innovative ways to mitigate liquefaction induced settlements.
The long-term dynamic behavior of pile under cyclic loads is important for the stability and safety characteristics of jacket supported offshore wind turbine. In this study, large-scale indoor model tests were carried out on the jacking installation and lateral loading of jacket foundation in sand. For the process of pile installation, it shows that the linear growth of pile tip resistance during installation can be divided into two stages, and its critical depth (the depth at which the growth rate of pile tip resistance begins to decrease significantly) is 11D (diameter). The jacking resistance of latter pile is significantly greater than that of former pile. Under static load, the maximum bending moment point of front row pile is higher than that of back row pile. The depth of bending moment reverse point is also 11D for front and back row piles. Under cyclic loads, the displacement at tower top undergoes two stages: rapid growth stage and slow growth stage. The friction resistance of back row pile increases first and then decreases along depth, showing an oblique “V” shape. The natural frequency varies with different load frequencies and cyclic load ratios. The evolution trend of system damping ratio and natural frequency is roughly opposite.
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Planning an offshore wind project is considered as a highly complex and multivariable task since it involves a large number of parameters, controversial objectives and constraints to be considered. During the pre-feasibility and pre-planning stages for offshore wind farm site-prospecting, the current manual and sequential design approaches are not always sufficient to guarantee optimal solutions because inherent interactions and trade-offs are most of the times disregarded. Most of the already existing wind energy design tools are specifically built either for onshore environments or for specific offshore activities; hence most of them ignore many relevant key design aspects extended in both space and time. In addition, with the rapid evolution of the Geographic Information Systems (GIS) during the last two decades, numerous research studies, spatial modelling and spatial optimization approaches in the field of the Renewable Energy Sources (RES) gained attention. Highlighting the promising results occurred, considering the planning and designing procedures of such projects, in the near future, geospatial technology with its numerous services and fields can effectively be utilized for timely analysis and future planning assessments. Considering the aforementioned challenges, this Ph.D. thesis proposes the development of a set of tools, as a Spatial Decision Support System (SDSS) entitled SpOWNED-Opt (Spatial Optimization for Offshore WiNd Energy Development), in order to model, map, evaluate and identify continuous space for future OWF siting, towards the mathematical programming approach, based on GIS data structures and algorithms. Thus, the proposed tool can be defined as a more integrated GIS-based framework for the pre-feasibility assessment as also for parts of the Front and Engineering Stage of the Design (FEED) for offshore wind farm site-prospecting procedures in the North and Central Aegean Sea in Greece. In particular, the SpOWNED-Opt approach proposes a multi-level methodological framework for integrating different spatial modelling tools separated at four stages of development. The first stage consists of all preparative steps considering data acquisition pre-processing along with the screening analysis module, based on the Maritime Spatial Planning (MSP) guidelines and the national legislative regulations. Vector and raster data 10 are used expressing existing potential conflicts among human activities combined with socio-economic and environmental factors affecting the selection procedures. The second stage is linked to the cost assessment modules for the capital, operation and maintenance and decommissioning expenses (CAPEX, O&M and DECEX) approximation. An extensive review of all sub-cost components is carried out in order to formulate analytical expressions embedded in the SDSS. Moreover, graph-based optimization techniques are applied, based on Least Cost Path (LCP) algorithms upon raster surfaces in order to extract distance-based costs (transmission lines, installation, decommissioning and O&M costs). The third stage focuses on the energy yield estimation and wind power output variability based on the UERRA Regional Reanalysis data. Different probabilistic models (Weibull, Burr Type II and XII, Gen. Gamma), reanalysis data errors quantification, wind speed intermittent characteristics and the second-order dependence structure are examined, analyzed and modelled in order to stochastically generate wind power output time series that are served as inputs to the last stage of the SDSS. The final module refers to a multi-objective integer non-linear programming (INLP) algorithm; as a unified framework that allows exploring in a rigorous and systematic mode numerous alternatives for offshore wind farm site-prospecting. The economic viability and the performance of the proposed wind farms are assessed along with the optimality of the different scenarios, from which the best ones are finally identified and mapped. The novelty of this research lies both on the integrated nature of the SDSS and on the models used in the spatial modelling field. A critical advantage of the SDSS is that it addresses existing gaps on OWFs siting and overall, in RES location-allocation issues, by: i) introducing a holistic, step-by-step, spatial modelling framework, ii) providing a long-term planning approach, iii) implemented in a user-friendly graphical user interface (GUI), giving the opportunity to national and local authorities and stakeholders to delineate systematic assessment strategies in order to succeed an effective and sustainable renewable energy sources penetration.
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Offshore wind turbines are currently considered as a reliable source of renewable energy. Pre-feasibility study includs calculation of preliminary dimensions of the offshore wind turbine structure used to be perform for preliminary costing to achieve at the commercial capacity of project. The main objective of study is to perform preliminary configuration for commercial viability and approximate size of the foundation pile structure. Design nomograms and equations are derived for preliminary design of monopile founded wind turbines situated at offshore of Gujarat. Parametric studies are carried out on various configurations of hollow monopile by changing water depths and properties of soil. A nonlinear static analysis of substructure is carried out considering aerodynamic and hydrodynamic forces for various structural and soil parameters. The design of sub structure wind turbine is based on API (American petroleum institute) standards. An example problem involving the design of foundations for The proposed area is located 23-40 km seaward side from the Pipavav port at Gulf of Khambhat off Gujarat coast. The site is easily accessible from the Pipavav and Jaffrabad Port, is taken to demonstrate the proposed calculation procedure. The data used for the calculations are obtained from publicly available sources.
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Efficient extraction of wind energy is a complex, multidisciplinary process. This paper examines common objectives used in wind turbine optimization problems. The focus is not on the specific optimized designs, but rather on understanding when certain objectives and constraints are necessary, and what their limitations are. Maximizing annual energy production, or even using sequential aero/structural optimization, is shown to be significantly suboptimal compared to using integrated aero/structural metrics. Minimizing the ratio of turbine mass to annual energy production can be effective for fixed rotor diameter designs, as long as the tower mass is estimated carefully. For variable-diameter designs, the predicted optimal diameter may be misleading. This is because the mass of the tower is a large fraction of the total turbine mass, but the cost of the tower is a much smaller fraction of overall turbine costs. Minimizing the cost of energy is a much better metric, though high fidelity in the cost modeling is as important as high fidelity in the physics modeling. Furthermore, deterministic cost of energy minimization can be inadequate, given the stochastic nature of the wind and various uncertainties associated with physical processes and model choices. Optimization in the presence of uncertainty is necessary to create robust turbine designs.
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For the purposes of offshore wind turbine support structure design it is important to understand the relative importance of the various external conditions applied to the structure in the design process. This paper presents results from integrated load calculations performed for an offshore wind turbine on a jacket support structure subject to combined wind and wave loading. A sensitivity analysis is performed on a number of design load case parameters for both fatigue and extreme conditions. Damage equivalent loads and ULS loads are derived using the GH Bladed software tool at a number of key locations on the jacket. Conclusions are drawn regarding the influence of these parameters from comparisons with the baseline load set.
With a few exceptions, all of the offshore wind turbines in existence have been installed on monopile foundations in shallow waters (depths < 50 m). However, monopiles become progressively uneconomical as turbines become larger (> 5 MW) and as water depths increase beyond 25-30 m, primarily because of modal requirements that force structural dimensions to grow onerously, even beyond current fabrication and installation capabilities. Space-frame structures (e.g., jackets), derived from the oil and gas industry, offer a lighter and yet stiff alternative to monopiles. Modeling of these structures within the turbine system dynamics, however, is resource intensive. Thus, although jacket foundations could potentially contribute to the offshore wind industry's quest for lower levelized cost of energy (LCOE), research is needed to support their basic design and analysis. Moreover, the choice of one support strategy in place of another has ramifications on the whole turbine system mechanics and costs. To address these challenges, the National Renewable Energy Laboratory (NREL) has developed the wind energy systems engineering initiative (SEI). The initiative aims to develop a toolset that integrates a variety of models for the entire wind energy system, including: turbine and plant equipment, operation and maintenance (O&M), and cost modeling. The toolset allows for trade-off studies and guides the design of components as well as overall systems towards a configuration that can achieve minimum overall LCOE through multidisciplinary analysis and optimization. This paper discusses one of the software modules in development—the jacket sizing tool (JST). This tool integrates with the remainder of the system's technical modules (e.g., the rotor, drivetrain, and turbine-to-turbine interaction module) and allows for preliminary design of a space-frame substructure, given turbine mass and load input at the tower top and environmental and soil conditions for the foundation. The JST can perform modal and static analyses while running design and code checks on frame members and joints per API RP 2A (WSD), AISC, GL, and IEC standards. Two case studies discuss the preliminary structural design of supports for a baseline 10-MW offshore wind turbine and the NREL 5-MW offshore reference turbine. Parametric trade-off studies conducted for these cases are discussed, including secondary considerations that are important for transportation and installation. Configurations optimized for minimum mass are presented in terms of main geometric parameters (such as batter, brace geometry, and tower layout) together with comparisons to results from a commercial finite element code. The examples intend to capture overall trends more than definitive figures on best designs, and in particular they shed light on the relative importance of tower-top mass and on the role played by the tower stiffness on the design of the entire support systems. Reducing tower-top mass by 15% may lead to a savings of about 10% of the overall mass for 10-MW rated systems. It is also shown that, for large turbines in deep waters, the tower and substructures should be designed simultaneously to achieve optimized configurations that lower the expected costs of materials. While land-based towers could be utilized offshore (in truncated, marinized versions) in cases of moderate hub heights, in cases where the hub heights are raised higher than on land, the use of land-based towers becomes either questionable or outright economically unfeasible. As a result, the JST can help the designer with important choices on main parameters and to assess the relative significance of various components, especially when combined with other SEI modules, that can track the effects on subsystems, the balance of plant, and O&M.
The development of an offshore wind resource database is one of the first steps necessary to understand the magnitude of the resource and to plan the distribution and development of future offshore wind power facilities. The U.S. Department of Energy supported the production of offshore wind resource maps and potential estimates for much of the United States. This presentation discusses NREL's 2010 offshore wind resources report; current U.S., regional, and state offshore maps; methodology for the wind mapping and validation; wind potential estimates; the Geographic Information Systems database; and future work and conclusions.
This paper assesses the potential for U.S. offshore wind to meet the energy needs of many coastal and Great Lakes states.
A poster presentation for AWEA's WindPower 2005 conference in Denver, Colorado, May 15-18, 2005 that provides an outline of the requirements for deepwater offshore wind technology development