Global levelised cost of electricity from offshore wind

Global levelised cost of electricity from offshore wind

Jonathan Boscha,b *, Iain Staffellc, Adam D. Hawkesb

a Grantham Institute – Climate Change and the Environment, London, UK, SW7 2AZb Department of Chemical Engineering, Imperial College London, London, UK, SW7 2AZc Centre for Environmental Policy, Imperial College London, London, UK, SW7 1NE

Abstract

There is strong agreement across the energy modelling community that wind energy will be a key route to mitigating carbon emissions in the electricity sector. This paper presents a Geospatial Information System methodology for estimating spatially-resolved levelised cost of electricity for offshore wind, globally. The principal spatial characteristics of capital costs are transmission distance (i.e. the distance to grid connection) and water depth, because of the disparate costs of turbine foundation technologies. High resolution capacity factors are estimated from a bottom-up estimation of global wind speeds calculated from several decades of wind speed data. A technology-rich description of fixed and floating foundation types allows the levelised cost of electricity to be calculated for 1×1 km grid cells, relative to location-specific annual energy production, and accounting for exclusion areas, array losses and turbine availability. These data can be used to assess the economically viable offshore wind energy potential, globally and on a country basis, and can serve as inputs to energy systems models.

Keywords: LCOE, levelised cost, offshore wind energy, energy potential, AEP, wind turbines

Highlights:

· Global 1x1 km country-by-country cost estimation for offshore wind power.

· A technology rich approach considering both fixed and floating structures.

· Bottom-up estimation of levelized cost, including country-specific financing costs.

· Results sensitive to annual energy production, capital cost and financing costs.

· Offshore wind costs continue to decline since recent estimates.

Running head: global Offshore WIND Levelised cost of electricty53

global Offshore WIND Levelised cost of electricty41

Nomenclature

AEPAnnual energy production

CAPEXCapital expenditure

EEZExclusive economic zone

GISGeographic information system

GWAGlobal Wind Atlas (DTU)

IAMIntegrated assessment model

LCOELevelised cost of electricity

MERRA-2Modern-Era Retrospective analysis for Research and Applications, V2

O&MOperations and maintenance

OPEXOperating expenditure

RDRotor diameter

VREVariable renewable energy

WACCWeighted average cost of capital

Introduction

Since the Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment Report (AR5) [1], a consensus has been reached by world governments that global average temperatures should not exceed 2 C above pre-industrial levels. Recognising new scientific understanding on the ecological and societal impacts of this level of warming [2] the Paris agreement [3] included a non-binding commitment to “pursue efforts” in limiting the temperature increase even further to 1.5 C. Most mitigation scenarios suggest significant deployment of variable renewable energy (VRE) is needed to reach this goal because of their decarbonisation potential [1, 4, 5]. Many Integrated Assessment Models (IAMs), which are the key analytical tools for interdisciplinary whole-system climate change mitigation analysis, can only build viable future global energy systems by drawing on these renewable, and even negative emissions, technologies [1].

Offshore wind energy has recently become an economically competitive VRE technology and a preferred development option because of its relatively low impact on terrestrial activities. Offshore wind farms can reach higher capacities, with taller and larger turbines and fewer constraints on size and noise pollution [6]. Higher capacity turbines have allowed capital costs to fall since fewer foundations and inter-array cables are needed to reach the same wind farm capacity [7]. Furthermore, since the offshore wind generation capacity in the United Kingdom (UK), the Netherlands, Denmark and other projects has grown to tens of gigawatts (GW) of capacity, experience in wind farm operation has led to a steady increase in the achieved net capacity factors as operation and maintenance (O&M) and other reliability issues have improved [8]. This trend, along with a fall in the cost of capital [9], has allowed offshore wind economics to improve significantly in recent years.

Researchers, analysts, policymakers and investors are interested in cost assessments of offshore wind technology to better plan, forecast and assess the potential of offshore wind energy. As with all energy supply and demand vectors (e.g. [10]) used in IAMs, approaches that apply cost variables across disparate geospatial domains and time scales, gives stakeholders a better understanding of the main variable sensitivities to maximise the cost reduction potential. Whereas fossil fuel power stations have a relatively high power density and are reliant on a predictable supply of fuel, offshore wind farm occupies a much larger area for an equivalent energy output, and is reliant on an intermittent and location-specific power source. In addition, the capital costs of offshore wind are dependent on site-specific conditions other than the wind: the distance to grid infrastructure, and hence the cost of transmission lines; the depth of the foundation and hence which foundation technology is suitable; and weather conditions which affect both the energy availability and the installation and maintenance strategies.

This paper describes a Geographic Information System (GIS) model to estimate the cost per unit of energy generated by potential offshore wind farms, globally. A levelised cost of electricity (LCOE) methodology combines high resolution annual energy production (AEP) potential, derived from a high-resolution wind speed model, with other spatially-resolved cost dependencies, including distance to coast, water depth, and technology-specific characteristics. The cost of finance in the form of the weighted average cost of capital (WACC) is also estimated for each country based on analysis of available financial data. The outputs of this LCOE model yield geospatially granular cost potentials for 157 countries with a viable offshore wind resource, and can be used to assess the economic potential, disaggregated by cost ranges and by water depth class (i.e. by type of foundation technology). The sensitivity of LCOE to input variables, and an analysis of technology improvements expected by the industry is also explored.

Offshore wind energy technology development

The UK leads the world in offshore wind development, with 5.8 GW installed [8, 11]. There are a further 8 GW in development in the UK and deployment is expected to reach 20-55 GW by 2050 [11]. Similarly, the Netherlands announced an Offshore Wind Energy Roadmap 2030 [12] to install 11.5 GW by 2030, while it currently has around 1 GW operating. France, Portugal, Norway, Japan and the USA all have smaller but growing offshore capacities. The USA currently has no operational offshore capacity but has over 15 GW of offshore projects in the development pipeline [13], and an estimated net technical potential of over 2,000 GW, with land-use and environmental exclusions included [14]. However, most offshore wind currently consists of near-shore, fixed-bottom foundation technologies. As these shallow water sites become exhausted, new projects will need to be developed in deeper water and further from shore.

Although conventional monopile foundation technologies are the simplest and cheapest solution in shallow water, they are only practical and economic up to water depths of approximately 40 m [15]. In transitional water depths (approximately 30 – 50 m) other fixed-bottom systems, such as jacket/lattice, tripod or gravity based structures are feasible [15, 16]. These systems are more structurally stable than monopiles at transitional depths and require a lower volume of steel for the equivalent structural characteristics. At depths greater than 50 m, only floating foundations are suitable for installation, but this allows, in general, access to areas with a strong wind resource away from the coast. The supplementary data to this paper surveys the main offshore foundation technologies, appraising their suitability for different water depths and offshore conditions.

A rapid increase in rotor diameters and hub heights are also improving offshore wind energy economics. Larger offshore turbines are feasible because there are fewer limits in transportation and installation than exist for onshore turbines [13], and fewer constraints on size and noise pollution [6]. Higher hub heights can access stronger wind speeds, and larger capacity turbines mean fewer foundations need to be installed per site for the same project capacity. Valpy [17] reports that, other variables being equal, an increase in turbine capacity from 4 to 8 MW could lead to a 10% reduction in LCOE [17]. Bloomberg (BNEF) analysis of industry data show that beyond 2020, there is the possibility of developers favouring 13-15 MW turbines offshore, and projects reaching upwards of 900 MW, with project CAPEX continuing to fall as a result.

In 2017, Final Investment Decisions (FID) for UK offshore projects were reaching a levelised cost of energy (LCOE) below £100/MWh (€112/MWh), four years earlier than expected [18]. Projects reached a weighted average strike price of £62.14/MWh (€70.88/MWh) for projects commencing in 2021/22, and £57.50/MWh (€65.59/MWh) for projects commencing in 2022/23, compared to £142/MWh (€163/MWh) in 2010/11. Danish and Dutch wind farms have yielded lower prices compared to the UK, with awarded strike prices of €63.90 /MWh and €54.50/MWh, respectively [19].

Cost analysis methodology

Many variables determine the optimal source of energy production. The cost of energy production is the main consideration, requiring a cost metric that considers the full lifecycle of the energy generator in question. The economic feasibility of power generation projects is typically determined using the levelised cost of electricity (LCOE), which indicates the minimum price of electricity, above which a return on capital can be obtained [20]. However, given there are specific project risks as well as generic technology risks, there is a gap between the LCOE and the costs to owner-operators in real electricity markets facing specific uncertainties. Measures such as the Breakeven Price, which represents the minimum electricity sale price required for financial viability under a set of external conditions, such as policies, tax and purchase contract structure, have also been carried out for offshore wind [21].

Comparison of different energy technologies is often difficult since LCOE calculations focus on the producer’s costs while additional costs to consumers resulting from policies or impacts on system integration are omitted. Thus for policy planning, the total costs to society might not be included [22]. A System Value approach is suggested which normalises total system cost reduction by the level of capacity installed for an increasing share of that given technology [23].

However, for a global cost model, the particularities of tax regimes, local policies, and details of asset ownership cannot be adequately introduced. IRENA’s LCOE definition excludes the impact of government incentives and financial support, system balancing costs, or the benefits of renewables, such as the merit order effect. This method aims to inform policy makers about the current trends in the relative costs and competitiveness of renewable technologies [24].

Cost assessment of offshore wind energy

Offshore wind energy cost assessments have been carried out by industry, governments, consultancy, and academic researchers in recent years. Most analyses have focussed on technology comparative studies [11, 16, 25], financial assessment methodologies [21, 26], cost reduction scenarios [17, 27-31], or country-specific studies [15, 32, 33]. These are typically constrained to a specific site or type of site. There have also been several global-scale offshore wind cost analyses by international organisations such as the IEA [34, 35], IRENA [36, 37] and EWEA [38], which survey and analyse the cost differences between regions and technological developments. Government department assessments also exist, e.g. USA [13], UK [39] and Australia [40]. In all except the studies focused on financial methodology, the approach is to produce an LCOE figure in terms of $/MWh (or another currency) that can be compared with current or proposed project specifications, or with future projected costs.

In Myhr et al. [25], the main contribution is the computation of a comprehensive list of cost components for several floating technologies, presenting a sensitivity analysis for each technology. This study is limited from a investment decision perspective however, because it takes a single set of input parameters from a reference wind farm (such as site depth and distance to shore) meaning the costs may not be applicable to sites with different characteristics or in different countries.

In cost reduction studies [17, 29, 30, 36], LCOE relies on technology learning curves, or forecasts, to arrive at a future cost. These can be limited by firstly the small cumulative capacity of offshore wind farms to model cost reductions against, and secondly, the limited number of site types, which define the distance to grid connection and water depth. Whereas several cost components may fall in cost over time, actual project costs might tend to increase as projects move into deeper and farther from shore waters, aspects that are not explicitly dealt with in [29, 30].

Furthermore, although analyses from international organisations [36, 38] bring a global perspective of cost comparisons, most studies do not explicitly deal with the cost of finance, which has a significant impact on LCOE because the wind industry is capital-intensive. This is demonstrated by Ebenhoch et al. [31], where a small percentage change in the weighted average cost of capital (WACC) had the largest impact on total project costs, behind capacity factor. In Rinne et al. [41], with modelled costs lower below 50 €/MWh (US$ 57/MWh), a change in the interest rate caused the second largest reduction in the wind energy potential when operating lifetime was assumed to be 20 years.

Geospatially-explicit cost assessment

The most similar studies to this one deal with geospatial variables explicitly. Cavazzi [42] and Hdidouan [43] conduct spatially-explicit analyses of the levelised cost of electricity for the United Kingdom (UK). The first focusses on the cost of the offshore energy potential of the UK, the second focusses on the effect on LCOE from weather and climate variance. In both cases, distance and depth dependent functions determine the foundation and transmission costs of wind farms, while other costs are collected from the literature. Cavazzi employs a more detailed cost function for foundation costs, using three types of foundation for shallow, transitional and deep water, with a non-linear dependence on water depth. Cavazzi also uses two transmission technologies with a different cost-distance dependency. Hdidouan’s analysis yields LCOEs that follow the direction of government projections but overestimates LCOEs in relation to the current strike prices. The validation of wind speeds and derived capacity factors is strength; however, validation is only carried out for near-to-shore wind farms, because at the time of writing, no deep water wind farms existed. Current wind turbine technology costs [17, 24], and recently agreed strike prices [19] would suggest actual LCOEs (as well as operator costs) are much lower than those produced in these studies. High values for installed capacity density lead to annual generation potential higher than turbine array efficiencies, calculated in the literature, allow for [44, 45].

McKenna et al. [46] produce cost potentials for Europe using suitable turbines based on the location-specific average wind speeds, and detailed land suitability factors for onshore wind energy. Turbine spacing for the rotor diameters of turbines is variable depending on wind regime. However, variable sensitivity is explored only for the aggregated spatial region. For example, The discount rate is varied for the whole continent even though the cost of capital available in each region or country varies significantly [47]. A more granular treatment of wind speeds would allow higher resolution derived capacity factors and aid in better technology choice, necessary for accurate wind turbine generation estimates.

Methodology

The methodology uses a bottom-up approach which characterises the capacity factor (CF) of offshore wind turbine operation by calculation of the energy content of the wind from high-resolution global wind speed data. Attention is given to the wake losses using a simple empirical model from the literature. The available offshore surface area is limited to reflect foundation technology constraints and competing surface uses. A Geographic Information Systems (GIS) approach allows the overlay of spatially coincident raster and vector data to calculate costs per grid square in relation to its energy production. The methodology can be summarised by the following steps, described in detail in section 2.1 (and visualised in the supplementary material).

1. Wind speed data calibration

NASA MERRA-2 wind speed data is bias-corrected and interpolated to a high spatial resolution using the DTU Global Wind Atlas (GWA).

2. Produce global capacity factors (CFs)

A CF is assigned to each grid cell by combining a geographically specific wind speed distribution with turbine power characteristics. Array efficiency is applied to the capacity factor to account for the wake effects of multiple turbines in close proximity.

3. Calculate turbine annual energy generation

Installed capacity is considered in each grid cell with respect to the suitability of the surface area and water depth constraints for current technologies. Availability due to operation and maintenance (O&M) constraints are factored. Finally, local generation potentials are summed from the product of capacity density and CF for each grid cell.

4. Economic assessment

The LCOE is obtained at every grid location considering the variability of CAPEX and OPEX due to distance to coast, water depth and the cost of finance. The LCOE also depends on the energy generation over the life of the project, and therefore takes as inputs, modelled capacity factors and an assumption on the life time of a wind farm project.

Energy generation estimation

A major component of the LCOE model is the annual energy production (AEP), which is assumed constant in every year over the lifetime of the project. AEP is dependent on three main factors:

1. the turbine technology used (section 2.1.1);

2. The installed capacity density (section 2.1.2); and

3. The derivation of capacity factors from wind speed data (section 2.1.3).

Turbine technology

The supplementary material provides a survey of current wind turbine models, relative to the number of farms in operation. Turbine models are ranked by power density, i.e. the rated power, divided by the rotor swept area (W/m2) to allocate their wind class. This approach leads to seven class II, and nine class III turbines.

Since a range of wind turbines is considered, only the most suitable class of turbine is assumed to be deployed in each grid cell. Class II turbines are assumed for average wind speeds ≥ 8.5 m/s, Class III for lower average wind speeds. The average wind speed for each grid cell is derived from the long-term average of the MERRA-2 global wind data.

An exemplary turbine is chosen to calculate the energy density of wind farms for each grid location in the analysis. In this study, the 8MW, 180m diameter Siemens AD 8-180 is chosen, with a hub height of 100m, since it represents the size of turbines being installed offshore today.

Capacity density

The capacity density of existing and planned projects in the United Kingdom is investigated in Figure 1 and the supplementary material. Array size and spacing is chosen to align with current industry practice. Thus, this study assumes an exemplary wind farm array of 10 x 10 turbines, with spacing of 10 rotor diameters (RD). This yields an installed density of 0.31 turbines per km2 of sea surface, and a capacity density of 2.47 MW/km2. An array efficiency of 88.55% is calculated for this layout [48]. Full specifications are given in the supplementary material.

Figure 1 Turbine spacing of United Kingdom Round 2 & 3 offshore wind farms. The average turbine spacing has increased in the second round, but the turbines also have higher nominal power rating. The average turbine density over all projects is 7.7 times the turbine rotor diameter.

Capacity factors

To yield realistic wind farm energy production estimates, the wind speeds for each grid location are converted to capacity factors (CFs), accounting for the wind speed distribution suitable for each grid location. In this study, 30 years of NASA MERRA-2 reanalysis [49] wind speed data are bias corrected using the DTU Global Wind Atlas [50] data set, since it has the best spatial accuracy available, although it only covers a 30 km of offshore envelope. The Renewables.ninja model [51] is then adapted to derive capacity factors directly from the wind speeds provided. In brief, the model defines a suitable wind speed distribution, which:

1. Acquires wind speeds at 2, 10 and 50 m above ground at each MERRA-2 grid point;

2. Extrapolates wind speeds to the desired hub height using a logarithm profile law with parameters estimated from a regression of known speeds and heights;

3. Applies a Gaussian filter to account for the distribution of wind speeds in any given hour, with parameters determined empirically;

4. Converts speeds to turbine power outputs using real wind turbine power curves.

In step 2, the wind speed is estimated as a function of height using the logarithmic relationship in equation (1).

(1)

where W is wind speed, h is height, d is the displacement height, A is the wind shear coefficient and z is the surface roughness. d, A and z are temporally- and spatially-varying parameters; d is sourced directly from MERRA-2, allowing A and z to be found from the regression of wind speeds, which are given at 2, 10 and 50 metres above ground, against height.

In step 3, the model applies a smoothing transform to account for there being a distribution of wind speeds within any given hour, and between the individual turbines of a geographically dispersed farm. A Gaussian filter is applied to the composite power curve of width σ, which is a function of wind speed (W):

(2)

The parameters in Equation (2) were determined empirically in [52] to give the best representation of historic output across Europe. The influence of this smoothing is shown in Figure 2, which plots the normalised capacity factors for two classes of turbine and that of the aggregated wind farm (class I is not included in this study).

Figure 2. Wind turbine power curves from class II and III turbines (left), from turbines listed in Table 5. And the influence of applying a wind speed distribution for an aggregated wind farm (right).

Because of the cubic response of the turbine power curve, and the wind speed distribution given above, the CF is approximated by relationship shown in Equation (3).

(3)

where is the rated power of the exemplary wind turbine, is the power output as a function of wind velocity, and is the wind speed distribution as a function of wind velocity.

Geospatial constraints

In order to calculate reasonable country-wide average LCOEs, some geographical constraints are imposed (see Table 1). Model outputs are firstly constrained to the exclusive economic zones (EEZs) of each country which extends 200 nautical miles (370 km) off the coast, unless this area intercepts with another country’s EEZ, in which case the mid-point between each country’s shoreline signifies the boundary. Secondly protected areas are excluded for possible development. These are obtained from the World Database on Protected Areas (WDPA) [53] and the World Marine Heritage Sites (WMHS) database [54].. Although some offshore activities have been developed in these areas (e.g. UK Round 3 offshore wind energy areas), it provides a reliable and consistent way to constrain the total allowable offshore area available to the model. Lastly, an approximate 1 km buffer zone around subsea cables is imposed using the global TeleGeography Submarine Cable map [55], which is up-to-date as of 2018.

Table 1. Geographical vector data sets for model constraints

Constraint

Data set

Suitability factor

Exclusive Economic Zones

EEZ union data set

0% (outside of zone)

Protected areas

World Marine Heritage sites

0%

World Database on Protected Areas

0%

Vicinity of subsea cables

TeleGeography Submarine Cable map

0%

Calculation of costs

The total costs of an offshore wind energy project are intricate, and only the main cost drivers are considered in detail in this study. A summary of the cost components with respect to when costs are incurred in the project timeline can be seen in Figure 3.

Figure 3. Summary breakdown of life cycle costs of a wind farm, showing that capital costs (CAPEX) and operating and maintenance costs (OPEX) are paid over different stages of the wind farm’s life. In reality, decommissioning costs (DECOM) are financed up-front as part of CAPEX, but in the time line of the wind farm appears at the end of life.

The levelised cost of electricity

This study uses the levelised cost of electricity (LCOE) to characterise the Dollar per megawatt-hour ($/MWh) cost of offshore wind over the lifetime of the capacity, and over the spatial extents of each country’s available EEZ area. LCOE is based on the equivalence of the sum of the discounted costs over the lifetime of the project, including payments to capital providers, and the sum of the discounted revenues accrued over the operating lifetime. In equation (4), the LCOE is derived such that the variable cost of production per unit of output is the same each year. CAPEX and OPEX are variable and are dependent on distance to the coast, , and water depth, in each grid square, .

(4)

Where:

· is the levelised cost of electricity at each grid location, i;

· are the capital costs in year t;

· is the fixed charge rate for country j (see equation (5));

· are the operating and maintenance costs in year t;

· is the annual energy generation in year t.

The fixed charge rate (FCR) is the fraction of capital costs that must be set aside each year to pay the cost of capital, including interest paid on debt and return on equity, so that is the effective annuity payment and is constant each year. In equation (5), the weighted average cost of capital (WACC) is used as the effective discount rate, which implies a weighting of the costs of different financing sources[40].

(5)

Cost of finance

Ondraczek et al. [47] is used as a basis for estimating the cost of borrowing in each country. Ondraczek used the rate of return for equity and the prime lending rate to estimate WACC, assuming a 70:30 debt:equity split in Kyoto Annex I countries, and a 50:50 split in others. Data were from the Clean Development Mechanism (CDM) [56], Dimson et al. [57] and the International Monetary Fund (IMF) [58], but are not specific to renewable energy projects. Despite the harmonised methodology across countries, the study presented some notable anomalies: A WACC of over 28% in Brazil due to a 45% interest rate for borrowing; and a higher WACC in Germany (9.2%) than in Libya, at a time when German interest and bond rates were hovering around zero. Three additional sources are therefore combined to create a broader estimate of the cost of capital for renewable energy projects:

1. the WACC for wind and solar projects in 31 European countries based on surveys of renewable energy developers for the DiaCore project [59];

2. the annual survey of discount rates in 41 countries from Fernandez [60];

3. the country risk premium (CRP) from 45 countries from Bloomberg (terminal).

As these sources only covered a subset of the countries in Ondraczek et al., each dataset was adjusted with a constant offset to deliver the same median across the common countries.

The left panel of Figure 4 depicts the level of agreement between sources. Some notable differences are highlighted, which appear to fit with recent geopolitical developments: worsening situations in Venezuela, Argentina and Greece, and improving prospects for renewable investment in Romania and Brazil. The right panel of Figure 4 visualises the combined WACC values used in this study, highlighting the world’s largest economies. WACC is lowest in Japan, Germany and the UK (where interest and bond rates are around zero). WACC is 5–7% in most developed countries, 8.5% in China, 12.5% in India, 13% in Russia and 14.8% in Brazil.

Figure 4. (left) A comparison between the cost of capital across different studies, highlighting countries with notable differences, and the correlation between sources (given as Pearson’s R value). (right) A cascade chart showing the average cost of capital, with bar widths proportional to each country’s gross domestic product (GDP).

Capital expenditure

Capital costs are calculated on a per-megawatt (1/MW) basis for each grid square, i (see equation (6)). Development costs () and turbine costs () are only dependent on wind farm capacity, and therefore depend only on the capacity density per grid square. Foundation costs () are dependent on water depth (), as described in section 2.3.3.1. Transmission costs () and installation costs () are dependent on distance () of each grid square centre to the nearest coast line. Decommissioning costs () are calculated as a proportion of installation costs (see section 2.3.5).

(6)

Foundation costs

The water depth of planned wind farm sites is an important consideration because the costs and capabilities of different technologies varies widely over a range of water depths. The supplementary material details a survey of foundation technologies conducted for this study. Several foundation technologies can technically be installed at a range of overlapping water depths, but the main determinant for foundation choice is cost. An upper depth limit of 1,000 m is imposed since current technologies in development are designed for a maximum depth of 800 m. In this study, foundation costs and cost parameters are combined from several sources (see Table 2), covering several foundation types. Adjustments are made to the costs in the sources to account for the following factors:

1. Steel costs were adjusted, where appropriate, to the price of steel in 2016-dollar prices (US$);

2. Where costs are only available for foundations of a smaller wind turbine (i.e. less than 8 MW), a scaling factor, extrapolated from [61], and considering structural requirements, is applied to yield a per-MW foundation cost.

Foundation costs are estimated for several discrete water depths for each technology. Cost data for 7 technologies are then regressed with respect to water depth using a suitable polynomial or linear function, leading to the choice of Monopiles, Jackets and the Tension Leg Buoys (TLB), which were the lowest cost technologies over water depths from 0 m to 1,000 m. Figure 5 shows the water depth-dependent per-MW foundation cost for the three chosen technologies. Table 2 summarises the three foundation depth categories according to transition depths as determined from the minimum cost curves in Figure 5.

Table 2. A description of the functional parameters used for each water depth categories and suitable foundation technologies, using a polynomial of the form ax2+bx+c. A cut off of 1,000 m is imposed to limit total potentials to current floating technology feasibility

Foundation type

Depth range (m)

Functional parameters

a

b

c

Monopile [61]

0-25

201

612.93

411,464

Jacket [62]

25-55

114.24

-2,270

531,738

TLB [25]

55-1000

0

773.85

680,651

Figure 5. Model of foundation costs as a function of water depth. Monopile and Jacket technologies are the cheapest option up to 55m and are regressed using a polynomial fit. Floating structures are more economical after 55m and have a linear fit with respect to water depth.

Installation costs

Installation costs are calculated per-MW of installed capacity using a cost methodology from Myhr et al. [25], with costs converted to 2016 US Dollars. Installation costs are specific for each foundation technology. For fixed-bottom foundations (monopile and jacket) the vessel types, duration of quay-side lifts, and operational weather window (OW) values are identical. The only difference is the substructure installation time, which is a third higher for jacket structures.

For floating foundations (TLB), additional vessel types are needed; Anchor Handling Tug and Supply (AHTS) for the floating out of floating foundations, and Platform Supply Vessels (PSV) for the transportation of turbines. For floating foundations, OWs for the installation phase are assumed lower since they assume farther from shore weather conditions, and more difficult lifting operations. The installation procedure follows the “2D” optimised lifting operation assumptions in Bjerkseter [63], which details the cost and OW of the TLB foundation. For all types of foundation technology, the difference in cost with respect to the distance from shore is reflected in the time for the vessels to reach the offshore installation and the daily costs for personnel. An itemised cost breakdown can be seen in the supplementary material.

Transmission costs

There are several determining factors for the cost of submarine transmission cables. The best technology choice is largely dependent on the costs of all transmission infrastructure required at different transmission distances, and the capacity rating of the connected project [64-66]. For large offshore projects with hundreds of MW capacity, Alternating Current (AC) transmission lines are only suitable up to a certain transmission distance, at which point electrical losses make it likely that high voltage direct current (HVDC) transmission will be cheaper. The exact “breakeven” distance depends on the MW capacity, the diameter and number of cables required, the cost and location of converter or compensation stations, and for HVDC, the type of converter technology chosen.

In this study, component costs are taken from Elliot et al. [65] for representative AC and HVDC solutions, assuming a 500-1000 MW wind farm, and including all the necessary switchgear, transformer and substation costs for each technology. HVDC transmission becomes the cheapest option at approximately 56 km distance under these conditions, and therefore transmission costs in this study follow the lowest cost technology option. Figure 6 shows the cost-transmission distance model.

Constant 3% electrical array losses are implemented in this study since different cable and converter station configurations have different loss characteristics, and are dependent on wind farm performance.

Figure 6. Model for transmission costs as a function of distance from coast with two technology options, High voltage Alternating Current (HDAC) and High Voltage Direct Current (HVDC)

Operation and maintenance costs

Estimates for operation and maintenance expenditures (OPEX) vary amongst literature sources (see Table 3) with Bloomberg onshore OPEX prices shown as a baseline [67]. The actual cost depends on several variables including the local cost of labour, vessel and port costs, as well as project site types and conditions, including the distance to shore. In the BVG data, the two extreme values represent a contrast between a shallow water (25 m) and close to shore (40 km) site, and a deeper water (35 m) and further from shore (125 km) site. The other sources show a plausible range of values but concentrate on expected values for monopile wind farms.

Table 3. Representative O&M costs available in the literature

Source

OPEX (2016$k/MW/year)

Bloomberg NEF [67]

30.6 (onshore)

BVG [39]

61 - 86

IRENA [36]

35 - 71

Ernst & Young [68]

61 - 100

In this study, a cost methodology is duplicated from Myhr et al. [25], where operating and maintenance costs depend on technology type and component costs are available from the installation cost assessment. The difference between fixed-bottom structures and floating foundations is: 1) the type and speed of the maintenance vessels, and 2) the operating conditions, with the operational weather window set to 75% for floating foundations since they are further from shore. For fixed foundation sites, only jack-up type vessels are needed with a daily rate of $158,270 (4.8 days per MW capacity installed). For floating foundations, an offshore crane vessel is needed with a daily rate of $331,860 (needed for 4.5 days per MW capacity installed). Figure 7 shows the per-MW O&M costs as a function of distance used in this study.

Decommissioning costs

Decommissioning costs are considered the opposite of the installation phase. They include all costs involved in returning the wind farm site to its original state, including the revenue obtained for the sale of scrap materials. Table 4 shows literature estimates for decommissioning, which taken together, suggest costs are between 1.2 and 2.5% of total project costs. Topham & McMillan [69] further suggest that decommissioning costs are in the range of 60-70% of installation costs, but there could also be cost benefits for floating foundations that do not require extensive underwater activities to remove monopiles. In this study, a conservative estimate of 70% of installation costs for fixed-bottom foundations and 60% for floating is used. These assumptions yield decommissioning cost of $60,000/MW for floating foundations and $163,000/MW for Jacket foundations for a site 50 km from shore.

Figure 7. Model for operating and maintenance costs (O&M) as a function of distance from the coast. Fixed and floating type foundations have different O&M regimes

Table 4. Decommissioning costs according to literature estimates

Source

DECOM (2016$k/MW)

% of full life cycle costs

UK government [70]

40

2-2.5

Topham and McMillan [69]

150-300

2-3

BVG [17]

N/A

1.2-1.8

Results and discussion

The levelised cost of electricity is presented for a selection of countries in the following sections. The following sections present cost-supply curves for the wind energy resource of several countries (section 3.1); a comparison to other studies (section 3.2); a sensitivity analysis to the main input parameters (section 3.3); and an assessment of cost reduction potentials (section 3.4). Figure 8 shows modelled LCOE output for European offshore areas. It can be intuited that the cheapest areas for development are in the northern regions of Europe which generally have the strongest wind resource. The most expensive areas are locations far from shore, in deep waters, or in the Mediterranean Sea which have the lowest wind speeds.

Figure 8. Levelised cost of electricity modelled for European offshore exclusive economic zones (EEZ), circumscribed in red.

Country results

An example of the country level results is seen in Figure 9, showing the energy and cost potential of Japan. It can be seen that a large area of the EEZ is excluded because of depth constraints, but there is still a large wind resource with a large swathe of energy densities reaching between 9,000 and 12,000 MWh/km2/yr of electricity production. The results published as supplementary data with this paper show that the average capacity factor within the available area exceeds 38%, with more than 1,300 GW of capacity potential. The average LCOE is 86 $/MWh, while the cheapest LCOEs (79 $/MWh) are located in the deepest available waters (> 55 m) which have floating (TLB) foundations.

Figure 9. (left) Energy generation density (MWh/km2/year) and (right) LCOE ($/MWh) for Japan, with protected areas and deep water (>1,000 m) excluded, Economic Exclusive Areas are circumscribed in red, while colourless areas are unsuitable for development.

Figure 10 shows LCOE curves for cumulative generation potential for 9 selected countries. Brazil and China have the largest potential with over 7,000 TWh/yr of electricity production potential. However, most of the available potential in these countries have LCOEs higher than that of Japan and the United Kingdom, which have the largest generation potentials below 100 $/MWh. South Africa, for example, generally has access to high capacity factors (43-51%), but has a large proportion of its EEZ in deep waters, and a relatively high WACC, leading to a relatively small viable resource and high average LCOEs.

Figure 10. Cost potential supply curves for a selection of a selection of 9 countries with high generation potential

Comparison to UK assessments

There are few comparable studies in the literature that generate offshore wind costs over a spatial domain. However, two studies, Hdidouan [43] and Cavazzi [42] derive LCOEs for the United Kingdom (UK). In Hdidouan, simulated LCOEs across the UK EEZ are compared to the available Contract for Difference (CfD) strike prices (2017) awarded to projects between Rounds 2-3 of the UK government CfD auctions. These produced a roughly comparable cost range for Round 2 projects, while over-shooting the Round 3 strike price by approximately 30 £/MWh.

In Figure 11, generation potential and simulated LCOEs are shown for the UK EEZ, including vector outlines of Rounds 1-3 wind farm sites. It can be seen that between rounds 1 to 3, the development areas have become larger and further from shore, and have therefore began to access higher energy density areas. However, simulated LCOEs in these areas are not the lowest available, due to transmission and foundation costs being higher. This is reflected in Figure 12, which shows the simulated LCOEs for each UK CfD Round site alongside those for the whole of the UK EEZ. Average LCOEs for Round 1 to 3 areas increase from 67.60 $/MWh to 73.20 $/MWh, while the CfD strike prices awarded to these wind farms start at over $100 higher and fall significantly in Round 3 to around 90 $/MWh (57.50 £/MWh). It should be noted that the simulation is for cutting-edge turbines with present-day financing costs, whereas the CfD auctions for Rounds 1 and 2 occurred in 2001 and 2003 respectively. The rate of decrease in CfD strike prices between rounds reflects the improvement in cost fundamentals of the offshore industry and also the improved cost of finance, the increase in capacity of turbines, and the expected lifetime performance of the wind farms. The trend suggests that although the simulated LCOEs here are significantly lower than current UK project strike prices, the fundamental LCOE of new projects is likely to be much more consistent, since the simulation is based on bigger turbines (8 MW), lower WACC (5.6%), and longer lifetimes (25 years), figures consistent with current industry developments.

Figure 11. Energy generation density of United Kingdom (UK) offshore area (MW/km2/year) and simulated LCOE (2016$/MWh) with 3 offshore development areas outlined. UK Contract for Difference bidding rounds: Round 1 (red), Round 2 (green) and Round 3 (grey)

Figure 12. Boxplots of the modelled LCOE ranges for the whole of the UK Exclusive Economic Zone (EEZ) and ranges for areas reserved for project auctioning rounds 1 – 3. These are compared to actual Contract for Difference bids awarded in each round and the projected value for Round 3 by the Department for Energy and Climate Change (2016)

Table 5 and Table 6 show model results for the UK with respect to depth (foundation) categories, and LCOE ranges, respectively. These can be compared with results generated in Cavazzi [42]. In general, Cavazzi produces a much higher cost for deep foundations (Table 5), and much lower generation potential at low LCOEs (Table 6), but these differences can be explained by the following factors:

1. Modelled capacity factors are similar, except for those calculated in the shallowest waters (51% vs. 44%). In this study, the GWA is used to interpolate MERRA-2 wind speeds to a high spatial accuracy, which accounts for wind speed-up effects of geographical features near to shore;

2. The mean CAPEX is similar apart from in deep waters. In both studies, a unique foundation cost model is implemented for different depths. However, floating foundations were based on TLP technology in Cavazzi, with a base cost of $ 4m. In this study, a detailed cost model was implemented, estimating the cost from detailed component analysis from [25];

3. Project lifetime is set to 20 years (Cavazzi), compared to 25 years;

4. The interest rate is set to 10% (Cavazzi), compared to 5.6% (WACC figure);

5. In general the energy generation potential is lower in this study. This is due to a much lower capacity density in this study (2.47 MW/km2 vs. 12.8 MW/km2) because of turbine spacing being limited to 10 RD and an array efficiency set to 88.55% following literature results [44]. Furthermore, Capacity potential was constrained in this study to areas less than 1,000 m depth, while there were no depth constraints in Cavazzi.

Table 5. Comparison of UK LCOE results to Cavazzi study [42], by depth category. Cavazzi cost figures converted to 2016 US Dollars with a rate of 1.585 US$:£. Energy potential in Cavazzi is GWh/year per turbine

Depth category (m)

Mean CF (%)

Capacity (GW)

Energy potential (TWh/yr)

Mean CAPEX ($m/MW)

Mean LCOE ($/MWh)

This study

<25

51

42.4

182

4.06

65.45

25-55

54

118

538

4.44

68.44

>55

59

890

4,430

4.49

69.50

Cavazzi

0-30

44

N/A

19.4 (GWh/yr/t)

4.08

201.70

30-60

52

N/A

22.8

4.53

183.97

>60

59

N/A

25.8

6.88

246.72

Table 6. Comparison of UK LCOE results with Cavazzi [42], by LCOE ranges (US$). Cavazzi cost figures converted to 2016 US Dollars with a rate of 1.585 US$:£

This study

< $75

$75 - 100

$100 - 125

$125 - 150

$150 - 175

$175 +

Capacity (GW)

815.9

234.3

0.220

0

0

0

Generation (TWh/yr)

3,981

1,171

1.070

0

0

0

Capacity Factor (%)

58

59

56

-

-

-

Cavazzi

-

-

-

< $159

$159 - 174

$174 +

Capacity (GW)

-

-

-

4.90

304.0

2,580

Generation (TWh/yr)

-

-

-

18.9.0

1,191

10,750

Capacity Factor (%)

44

45

46.3

Sensitivity analysis

The six parameters listed in Table 7 were systematically altered within a symmetric range to test the model sensitivity. Figure 13 shows the results of the sensitivity analysis for two countries: China and the United States of America (USA), which have average LCOEs of 120 and 116 $/MWh, respectively.

Table 7. Variables and the range they altered in the sensitivity analysis

Variable

Central assumption

Sensitivity range

Annual Electricity Production

variable

± 10%

Turbine availability

97%

± 5%

CAPEX

variable

± 10%

Lifetime

25 years

± 5 years

OPEX

variable

± 10%

WACC

4 – 33 (specific to country)

± 10%

Figure 13. Variable sensitivity for China (top) and the United States (bottom).

Although, there are differences between China and the USA with respect to their total electricity production resource (AEP: 7,030 TWh vs. 5,156 TWh) and their available WACC (8.5% vs. 6.5%), the average LCOE is similar. In both analyses, altering the AEP by ± 10% had the biggest impact, increasing or decreasing LCOE by 17 $/MWh (China) and 11 $/MWh (USA). LCOE was also quite sensitive to CAPEX with ± 10% causing a ± 10-11 $/MWh change in LCOE. Project lifetime caused a non-symmetric response on LCOE because of its nonlinear behaviour. A 5 year reduction in lifetime had a proportionally larger effect on LCOE than a 5 year increase to 25 years. This effect is pronounced for the USA with a 5 year reduction in project lifetime causing an 11 $/MWh increase in average LCOE, and this is despite the USA having a smaller compound WACC. For a 10% increase in WACC, average LCOEs increased the most in China (+12.5 $/MWh). Wind turbine availability also had a large influence on LCOE. A ± 5% change on a 97% availability factor caused ± 7 $/MWh change in LCOE. OPEX had the smallest impact.

Cost reduction potential

The cost reduction potential of average LCOEs was tested for four countries: China, India, Mexico, and the UK. Table 8 summarises the variables that are found in the literature to significantly influence LCOE, because of both technology improvements and financial risk improvement [17, 36, 71]. Cost reductions in this study assume the technological improvements in a 2025 scenario in the BVG innovation study [17, 39]. Annual energy production, CAPEX, project lifetime, and OPEX are all improved from a combination of factors, including the increase in turbine size, improvements in turbine operation and performance, and multiple innovations in turbine components. There are no predictions available for future WACC values, but [39] highlights the trend that some developers are currently achieving lower costs of capital using finance with up to 80% debt ratios, benefitting from lower central interest rates. In this analysis WACC is reduced by 10% from the baseline range of values which are already country-specific.

Table 8. Factors that could reduce levelised cost via technological improvements, and the changes from central values used for a sensitivity analysis

Factor

Central assumption

Change

AEP

variable

+ 13%

CAPEX

variable

- 18%

Lifetime

25 years

+ 5 years

OPEX

variable

- 20%

WACC

4 – 33 (country specific)

- 10%

Figure 14 shows the effect on country average LOCEs when each variable is systematically altered by the amount stated in Table 8. The Maximum reduction shows the cost reduction from the addition of all variables. The large reduction potential in OPEX has a relatively small impact on LCOE reduction potential, while an 18% reduction in CAPEX has the highest impact, for all countries included, of all forecast improvements. India offshore costs benefits the most from almost all cost reduction strategies, except for project lifetime for which Mexico benefits the most. A 10% reduction in WACC has a significant impact, but because each country starts from a different baseline, the UK benefits the least, and India benefits the most with almost 18 $/MWh reduction from its baseline LCOE of 226 $/MWh. With all variables aggregated, India benefits from a 34% reduction in LCOE, with a new average LCOE of 149 $/MWh. Mexico also benefits from cost reduction strategies with a 73 $/MWh (35%) total reduction in LCOE, from a baseline of a 209 $/MWh average.

Figure 14. LCOE reduction potential based on innovations from [39]

Conclusions

This study presents a geospatially-explicit cost (LCOE) model to assess offshore wind energy potential, allowing comparison of costs between countries, and also across country offshore areas. The use of technology data and geospatial marine characteristics has allowed the evaluation of the available wind generation capacity and generation potential against the levelised cost of electricity, and across the areas of all country exclusive economic zones (EEZs), globally.

Wind speed data from 30 years of NASA MERRA-2 reanalysis were improved in spatial accuracy by calibrating against the higher resolution DTU Global Wind Atlas to yield capacity factors in 1 km x 1 km resolution. This approach has allowed a highly granular assessment of wind turbine energy production potential, allowing a unique cost value to be assigned to each grid cell. Furthermore, a reliable wind farm array efficiency is applied to capacity factors, using an empirical model, to better represent the losses of a 10 x 10 array with 10 RD spacing. Each grid cell therefore has a capacity factor which represents the output of a wind farm of those characteristics.

LCOEs are developed using a technology-rich description of offshore wind technology costs. A “current innovations” approach leads to an assumption of an 8 MW turbine size and 100 m hub height. The capital costs are broken down into the main components and a cost model is built that allows costs to vary with respect to water depth and distance to shore. Transmission costs, installation costs, and OPEX vary with distance to shore, while foundation costs and installation costs are dependent on water depth. Three water depth categories are described which define foundation cost models based on the cheapest type of foundation available at every depth: Monopile (0-25 m), Jacket (25-55 m), and Tension Leg Buoy (55-1000 m).

Project finance is a major factor comparing project viability between countries. In this study, a country-specific weighted average cost of capital (WACC) is implemented by analysing WACC data from several sources and assuming different finance ratios are available for each economy type. This leads to unique LCOEs across countries.

A sensitivity analysis shows which variables have the biggest impact on the modelled LCOE. Annual energy production, CAPEX and WACC all have a significant impact on LCOE, and therefore improvements in data in those areas should be a priority in future work. OPEX reductions have the least influence on LCOE even though many studies show that significant opportunities are available to reduce OPEX. Reductions in the lifetime (25 years) have a proportionally larger effect than potential increases.

Offshore wind energy potentials data are available for a range of LCOE levels for every country with a viable offshore wind potential. Average LCOEs are also available for each depth category (see supplementary data). In comparison to country studies, the modelled LCOEs in this study were lower. However, the detailed review of current costs and, furthermore the recent development of floating offshore technologies suggest that actual costs in 2018 are lower than those found in current literature. Furthermore, the rapid reduction in actual strike prices in the UK market, as well as those in the Netherlands and Denmark, suggest that LCOEs are more in line with this assessment, given the up-to-date assumptions made in this study; namely 8 MW capacity turbines, 100 m hub heights, and lower project finance costs than usually assumed.

Acknowledgements

Dr Bosch was supported in this work by The Grantham Institute – Climate Change and the Environment. Dr Staffell was supported by the EPSRC under EP/N005996/1. Dr Hawkes was supported by NERC under NE/N01856/1.

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0%20%40%60%80%100%0102030Capacity factorWind speed (m/s)Class 2Class 3

0%20%40%60%80%100%0102030Capacity factorWind speed (m/s)Class 2Class 3

Development & consentingProduction & acquisitionProject managementLegal authorisationSurveysEngineeringContingenciesTurbinesFoundationPower transmission•Cables•Offshore substation•onshore substationMonitoring systemPortInstallation•Foundation•Wind turbine•Offshore elec. system•Onshore elec. systemCommissioningInsuranceInstallation & commissioningOperation & maintenanceOperation•Rental•Insurance•Transmission chargesMaintenance•Direct costs (corrective maintenance, proactive maintenance)•Indirect costs (port, vessels, labour)CAPEXOPEXDECOMEnd of lifeOffshore preparationVessel mobilisationDisassemblyFoundation removal

0%5%10%15%20%25%30%0%5%10%15%20%25%30%Country WACC (other sources)WACC the dogFernandez (R=0.68)Bloomberg (R=0.75)EcoFys Wind (R=0.41)GermanyVenezuelaBrazilGreeceRomaniaArgentina

JapanUKGermanyFranceCanadaUSItalySpainSouth KoreaAustraliaChinaMexicoIndiaRussiaBrazil0102030405060700%2%4%6%8%10%12%14%16%18%Weighted average cost of capital (WACC)Cumulative GDP ($ trillion)

020406080100120140160180200Foundation costs (2016$/MW)Water depth (m)Poly. (Monopile, 8 MW (Schaumann & Boker))Linear (Floating TLB, 8 MW (Myhr et al.))Poly. (Jacket, 8 MW (Collu et al.))

020406080100120140160180200O&M cost (2016$US/MW)Distance from coast (km)O&M fixedO&M floating