- Email: [email protected]
An Offshore Wind Energy Geographic Information System (OWE-GIS) for assessment of the UK's offshore wind energy potential
Renewable Energy 87 (2016) 212e228
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
An Offshore Wind Energy Geographic Information System (OWE-GIS) for assessment of the UK's offshore wind energy potential S. Cavazzi, A.G. Dutton* Energy Research Unit, Technology Department, Science & Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 December 2014 Received in revised form 27 August 2015 Accepted 8 September 2015 Available online xxx
An Offshore Wind Energy Geographic Information System (OWE-GIS) has been developed for the purpose of assessing the economically accessible offshore wind energy resource for the United Kingdom. The UK OWE-GIS estimates the costs of energy from an offshore wind farm taking account of the major capital components; development costs dependent on water depth and distance from nearest ports or grid connection points; the potential energy production dependent on annual average wind speed, potential array losses, and turbine availability; operations and maintenance costs; and financial parameters such as discount rate and project lifetime. A sensitivity analysis is presented to show the influence of discount rate, project lifetime, and assumptions about overall capital expenditure (CAPEX), availability, and annual mean wind speed. © 2015 Published by Elsevier Ltd.
Keywords: Offshore wind energy GIS Cost modelling LCOE
1. Introduction The wind energy resource varies considerably, on a global scale between climate zones, and on a local scale due to variations in terrain and surface roughness. The cost of exploitation of the raw wind resource depends largely on the magnitude of the resource and its distance from the point of energy end-use. In general, the energy available in the wind is greater and more stable offshore than on land at nearby sites [13] and [14]. In addition, there is perceived higher public acceptance for offshore developments and fewer constraints (e.g. due to visual intrusion or noise) on the choice of technology [18]. The European Union's Renewable Energy Directive in December 2008 [5] committed the EU to generate 20% of energy consumption from renewable sources by 2020. The UK was required to raise the proportion of energy that is produced from renewable sources from 1.5% in 2006 to 15% by 2020. Given the restricted scope for radical change in other sectors, such as heat and transport, within such a short time span, the electricity sector was identified as the leading candidate to make progress towards meeting this target. In outlining its approach for meeting the Renewables Directive, in its 2009 Renewable
* Corresponding author. E-mail address: [email protected] (A.G. Dutton). http://dx.doi.org/10.1016/j.renene.2015.09.021 0960-1481/© 2015 Published by Elsevier Ltd.
Energy Strategy (RES), the UK Government described a number of scenarios within which 30e35% of electricity generation could be produced from renewable sources by 2020 [20]. As a relatively mature renewable technology, wind power was expected to deliver most of this generation capacity, and the UK Government declared its intention to make ‘full use’ of the potential for offshore wind power in meeting the Renewables Directive [20]. In the RES's ‘lead scenario’ around 27 GW of wind power is required to be installed by 2020, roughly evenly split between onshore (c.14 GW) and offshore (c.13 GW). However, the RES also makes clear that the Government's ambitions for offshore deployment to 2020 are much greater than those in this lead scenario, with 20 GW described as ‘achievable’ and 33 GW as ‘feasible’ [20]. Supporting analysis commissioned by the UK Government suggested that 20e30 GW of combined onshore and offshore wind could feasibly be deployed by 2020 [29]. Other studies [30,36]; suggested that up to 34 GW of deployed offshore wind capacity may be achievable by 2030, but with only around half that capacity (14e18 GW) deployable by 2020. Leasing rights to the UK's offshore wind resource is managed by the Crown Estate. In 2010, the Crown Estate announced acceptance of up to 32 GW of offshore capacity in Round 3 of its offshore leasing programme, building on the 8 GW already accepted under Rounds 1 and 2. The Crown Estate selected its Round 3 lease sites [37] based on technological feasibility of development (essentially water depth < 60 m) and with regard to a set of constraints and
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
into question earlier assumptions, prevalent in the 1990s, that energy costs should fall monotonically with time. Thus, whereas in the period 2000e2004 the typical capital expenditure involved in building the Round 1 UK wind farms was in the range £1.2 m/MW to £1.5 m/MW, by 2010 reported costs were clustering around £3.0 m/MW. Part of this rise was due to later developments being driven to deeper water sites located further offshore and a general rise in costs across the energy sector, but the overall causes are complex and have been analysed in depth in the report Great Expectations [19]. The costs of recent large offshore wind energy projects have also increased due to the higher associated risks perceived by investors [23].
Nomenclature CAPEX EU FCR GIS LCOE OPEX OWE RES REZ TCE
213
capital expenditure European Union fixed charge rate geographical information system levelised cost of energy operations and maintenance expenditure offshore wind energy Renewable Energy Strategy Renewable Energy Zone The Crown Estate
2. Methodology
restrictions respecting existing uses of the sea and sea-bed. Underlying the Crown Estate's analysis was a database of all relevant factors which unfortunately was never made public. The study reported here is the first transparent review of the whole of the UK Renewable Energy Zone. The first comprehensive analysis of the European wind resource was the European Wind Atlas [38] published in 1989, but this did not include offshore wind. The first application of a geographic information system (GIS) to assess the northern European offshore wind resource occurred during the EC project Opti-OWECS [6]. The earliest analysis of world onshore and offshore wind resources at 80 m hub height from data alone was published by Ref. [3]. Later studies applying GIS to offshore wind resources have been published for the United States [28], China [21] & [22], Denmark [27], Japan [1] and the North Sea [34]. A common challenge faced by all these analyses has been to break down the cost of an offshore wind farm into fixed components, independent of geography, and variable components dependent on spatial location, usually either the distance from shore (or nearest port or grid connection point) or the depth of water at the candidate site. In both cases estimating suitable values is often subjective with successive studies borrowing from their predecessors and developing a consensus of the “right value” to use. The OWE-GIS developed in this research extends previous work in this area by tying together an economic database built using existing costs data with geographic properties calculated at each individual site for both CAPEX and OPEX components. It is well known that costs in the offshore wind energy sector are higher than expected at the current time [32]. A 2013 consultation conducted by the Department of Energy and Climate Change in the UK including generators, suppliers, consumer organisations and environmental groups has exposed that the assumed learning rates of the industry were not as rapid as expected and therefore the strike price for electricity under contract for difference had to be reduced less quickly [12]. Recent disturbances in commodity prices and financial markets have brought
2.1. The geographic information system (GIS) tool The development of offshore wind energy is related to optimal use of the available geographical areas in the sea. Although the wind energy output from a certain area will increase as technology improves, the amount of area that is economically feasible for wind power development will diminish as existing areas are exploited.
Fig. 1. Modelling approach.
Fig. 2. UK renewable energy zone (UK-REZ).
214
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Table 1 Offshore wind farm CAPEX breakdown [31].
Category
Cost
Development & Consent
4%
Turbine
Balance of Plant
Installation & Commissioning
Sub-Category environmental survey seabed survey met mast development services
Cost 0% 1% 0% 3%
3.2 £m/MW 0.01 £m/MW 0.02 £m/MW 0.1 £m/MW 0.01 £m/MW 0.09 £m/MW
Rotor
11%
0.35 £m/MW
Nacelle
22%
0.70 £m/MW
Tower foundations
6% 16%
0.19 £m/MW 0.51 £m/MW
Cables
5%
33%
1.1 £m/MW
26%
Development & Consent Turbine Foundations Transmission
0.16 £m/MW 1.2 £m/MW
37% offshore substations
7%
0.22 £m/MW
other electrical
3%
0.09 £m/MW
foundations cables turbines offshore substations
7% 9% 9% 1%
0.22 £m/MW 0.29 £m/MW 0.29 £m/MW 0.02 £m/MW
0.8 £m/MW
0.13 £m/MW 1.54 £m/MW 0.74 £m/MW 0.78 £m/MW
Furthermore, there is increasing competition from social, economic, and political interests for use of the seas. Finding the best possible areas on which to site turbines is vital to the economic feasibility of a project, and there is a need for a systematic tool for determining where these sites exist, and for what cost electricity can be produced. The tool described herein is an analytical site modelling tool capable of supporting preliminary selection of new turbine sites. The model has two principal components: an economic database, based on existing cost data and predicted future cost developments, and a Geographic Information System (GIS) component, which ties the cost data to the geographic properties unique to each site. These properties include, among others, wind speed, sea depth, slope of sea bottom, cable distance to shore and the distance to the nearest grid connection point. Additionally, areas that are currently not available for wind farm development due to social and political reasons, including visual impact, nature reserves, trade and shipping routes and military testing zones can be assessed in order to determine the total amount of potential wind resource that is being lost. If any of these zones contains a significant amount of area exceptionally suitable for wind development, the model could be used to assess the effects of compromises, such as provision of specific shipping lanes for ocean traffic.
The GIS developed for this research has been implemented within ArcGIS 10.1 (ESRI) enabling the automatic computation of various cost algorithms. The overall methodology from collection of basic data, through application of exclusions, to estimation of energy yield and cost is summarised in Fig. 1. Originally it was intended to include weighted restrictions for less important constraints, as in the original analysis by TCE [37], but this was abandoned as being open to a number of stakeholders interpretations. A schematic for the full model is provided as Appendix 1.
2.2. Study area e the UK Renewable Energy Zone The Renewable Energy Zone (REZ) was declared under section 84 of the Energy Act 2004 by the government of the United Kingdom (UK) as the area of the sea, beyond the United Kingdom's territorial sea, which may be exploited for energy production (Fig. 2). The UK has exclusive rights in this area with respect to production of energy from wave/tide or wind. For this analysis the entire UK REZ was included, divided into grid squares of 10 km 10 km thereby taking into account local conditions at a scale compatible with the available raw wind speed data and computational feasibility.
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
215
Table 2 Basic input parameters for the cost of foundations.
Monopile Jacket Floating
a
b
c
[days]install
0.0011 0.0008 0
0.0023 0.0154 0.0017
1.2253 1.5847 3.732
1 3 2
Number of turbines per visit Speed of installation vessel (km/h) Length of the working day (h) Material cost of steel (£/tonne) Cost of installation vessel (£m/day)
[n]per-visit [speed]jack-up [hours]working-day [cost]steel [cost]jack-up
5 20.0a 24 1800b 0.15
a
MPI Resolution. World Composite Stainless Steel Price (www.worldsteelprices.com) average monthly price in 2013 ¼ $2741/tonne @ average USD/GBP exchange rate of $1.56/ £¼. b
-
Existing wind farms IMO traffic separation schemes Active submarine cables Oil & Gas safety zones and infrastructure
2.4. Economic assessment e the Levelised Cost of Energy (LCOE) The concept of Levelised Cost of Energy (LCOE) is used as a technique for establishing the final cost of producing energy. The LCOE formula uses the total installed capital expenditure, annual maintenance cost and annual energy production to estimate a total production cost for energy, according to the formula [see, for example Feng et al. (2010)]: Fig. 3. UK REZ bathymetry classified into three categories: shallow (0e30 m); transitional (30e60 m) and deep (>60 m).
2.3. Exclusions Not all areas of the sea are suitable for the development of a wind farm as other marine activities might have policy protection and exclusivity. For the purpose of this research, exclusions have been applied largely in line with the Crown Estate GIS decision support system [37], so far as data could be independently source. The exclusions applied included: - Protected wrecks; - Anchorage areas - Aggregate dredging
LCOE ¼
ðCAPEX*FCR þ OPEXÞ Eannual
(1)
where CAPEX is the initial capital cost (£), OPEX is the annual operation and maintenance cost (£), Eannual is the annual energy production (MWh), and FCR is the annual fixed charge rate for n operational life (years) given by:
FCR ¼ h
r 1 ð1 þ rÞn
i
(2)
The LCOE is then the price at which electricity must be generated to break even over the lifetime of a project. This concept, paired with a GIS analysis comprises a powerful tool to assess the offshore wind resource of the chosen study area e the UK Renewable Energy Zone.
Fig. 4. Water depth (m) on the x axis v. foundation cost (£m/MW) on the y axis using data from Refs. [7,33]; steel price of $3200/tonne; exchange rate $1.6 ¼ £1.
216
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 5. Transmission cost comparison between AC (red lines) and DC (green lines) in function of distance. Thin lines represent the cost of the terminal plus the cables while thick lines account also for electricity losses. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Typically the LCOE is calculated over a 20e30 years lifetime and quoted in units of currency per megawatt-hour, for example, £/MWh. For this study, a lifetime of 20 years and an interest rate of 10% were assumed [8] and [10]). The investment costs comprise a variable component (foundations, grid connection and installation) depending on water depth and distance from shore and a fixed component including the turbine cost. OPEX is also spatially variable depending on the accessibility of the site from a suitable port and the energy production dependent on the climatic conditions of the site. An exemplar wind farm made of 256 turbines of 5 MW rated power for a total of 1.28 GW was used to evaluate the LCOE at each grid point within the UK REZ. Fig. 6. Exclusion areas.
2.5. Research question The OWE-GIS application estimates the economically accessible wind power resource within the UK REZ using spatial data of wind speed, water depth, natural & physical features, surface and subsurface structures, environmental protection measures and other socio-economic uses of the sea. The model evaluates distances to the nearest mainland electricity network connection and assesses cabling requirements within the wind farm and to shore. Consideration is given to appropriate foundation type based on
Table 3 Basic input parameters for the cost of connection.
Number of offshore platforms Cost per platform (£m) Number of cables to shore Cable cost (£m/km) Number of onshore convertor stations Cost per onshore convertor station (£m)
[n]offshore [cost]offshore [n]cables [cost]cable [n]onshore [cost]onshore
AC
DC
3 30.0 3 1.8 e e
2 110.0 2 0.9 2 65.0
Table 4 Basic input parameters for the cost of installation. Number of days on site to install each turbine Length (intra-array) cable (km) Intra-array cable cost (£m/km) Intra-array cable installation cost (£m/km) Number of turbines per visit Speed of installation vessel (km/h) Length of the working day (h) Cost of installation vessel (£m/day)
[days]install [cabling]length [cabling]cost [cable_instal]cost [n]per-visit [speed]jack-up [hours]working-day [cost]jack-up
2 350 0.15 0.1 5 20.0 24 0.25
bathymetry, seabed terrain and sediments characteristics. Distances to nearest ports with (potential) facilities for installation and to support ongoing operations and maintenance are evaluated and used as a basis for evaluating installation and ongoing operational costs. An overall cost of energy to the grid is developed including capital and installation costs and ongoing operations and maintenance. The overall aim of the research project was to provide a spatially explicit model, using GIS, capable of estimating the economically accessible offshore wind energy resources in the designated UK Renewable Energy Zone. 3. Component data 3.1. Capex Capital expenditures occur at the beginning of the project and involve the environmental surveys, seabed analysis, installation of a met mast to assess the wind conditions at the site, development and consents services, the turbine with all its components (nacelle, rotor, tower and blades), the foundations and all the electrical components utilised in energy transmission from the wind farm to the grid (submarine cables, offshore platform and substation).
Table 5 Basic input parameters for the cost of O&M. Fixed costs Variable costs Port fees
fixed_cost var_cost port_fees
18.0 £/MWh 6 £/100 km 3.0 £/MWh
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
217
Fig. 7. Exclusions plotted as proportion of gridded squares.
Table 1 shows the cost breakdown by component as estimated by the Renewables Advisory Board [31]. The costs are apportioned according to an overall estimate by Ernst & Young [17] that the CAPEX for an offshore wind farm in average North Sea conditions (80 km from shore and water depth of approximately 30 m) is 3.2 million £/MW. Ernst & Young [17] themselves estimated the turbine cost as 47% (£1.5 m/MW) somewhat more than [31]. In reality the presented costs change according to site conditions and this can be well represented in the GIS model. The following formula is used in the calculation of the investment costs:
3.1.1. Wind turbines Despite the cost of turbines being the largest single investment in the wind farm, actual cost data are not readily available as the industry is still in a highly competitive emerging phase. The cost of turbines assumed for this study is £1.5 m/MW in line with the previously presented Ernst & Young [17] estimate. This value has been increased by 50% in the case of floating turbines as these are still in a research and development phase with few prototypes to date.
Costturbines þ Costtransmission þ Costfoundation þ Costinstall þ Costconsent CAPEX ¼ 1 factordevelopment
where factordevelopment is a factor to account for project development, consent, and overall management, typically of the order of 5% of the overall CAPEX. (This factor can also be adjusted to explore sensitivity to the overall capital value of the project.) Each of the components is now described in detail.
(3)
3.1.2. Foundations Water depth and consistency of the seabed determine the choice of foundation and so far, there is no universal foundation type suitable for the range of water depth found around the UK (Fig. 3). In the offshore wind industry, monopile foundations, with a
218
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 8. Foundation costs (£m/MW).
market share of 75% in 2011 [15], are the most commonly used foundation type in shallow waters (below 30 m), followed by jacket foundations in transitional water conditions (30e60 m). Significant research and development are still necessary to develop standard and cost-efficient concepts for production at industrial scale. New concepts, e.g. floating foundations, are being tested for deep waters above 60 m, but currently there are no commercial floating wind turbines. A panel of industry and academic offshore wind experts within the UPWIND project [24] evaluated different types of foundations technology based on their suitability at different water depths. Monopiles emerged as the most suited in shallow waters (<30 m), while jackets were better in transitional waters (30 me60 m) with floating tension leg platforms (TLP) being the most likely candidate for deep water (>60 m). In this research cost algorithms have been developed for each principal foundation type using literature data [7,33,35]. The costs have three components: basic material and manufacturing cost
(dependent on local water depth), transportation cost to the wind farm site (dependent on distance from nearest deep water port), and installation cost (basically the number of days the jack-up barge is needed for each turbine foundation). The basic algorithm for material and manufacturing cost depends on mass of steel required for the foundation piece, which, in turn, depends on the water depth at the site. The minimal amount of published data noted above provides just sufficient data on cost v water depth to fit a set of quadratic expressions (Fig. 4) for the different technologies so that:
½CAPEXfoundation ¼ ax2 þ bx þ c þ ½daysinstall þ ½daystosite $½costjackup
(4)
where x ¼ [WATER_DEPTH] and (a,b,c) are constants obtained from the data fit and the journey time [days]to-site is given by:
o n . $½costjackup ½daystosite ¼ 2$ ð½NEAR_DIST=1000 Þ ½speedjackup $½hoursworkingday $½npervisit
(5)
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
219
Fig. 9. Transmission costs (£m/MW).
where [NEAR_DIST] is the distance (m) to the nearest deep water port, [speed]jack-up is the speed (km/h) of the installation vessel, [hours]working-day is the length of the working day, and [n]per-visit is the number of foundation pieces that can be carried per trip. The material cost data is boosted prior to data-fitting by a fixed mass transition piece for monopile and jacket designs, additional manufacturing cost factor for jackets (0.5) and floating (0.2) to reflect complexity of the structure, and mooring costs for foundations. Input data for the algorithm applied to the exemplar wind farm are shown in Table 2. Allowing for the greater cost of installation of jacket structures, the manufacturing cost data supports the use of monopile foundations up to 20e30 m water depth and implies that floating would be the technology of choice beyond 60 m water depth, although the technology is yet to be commercially demonstrated. 3.1.3. Grid connection Two main technologies compete to transmit power to the grid onshore: alternating current (AC) and direct current (DC). Currently, high voltage AC (HVAC) cables are used to link turbines to an offshore substation, with power clean-up at each turbine. HVAC cables are also used to transmit power to the onshore substation as current wind farms are relatively close to shore, within
the range 20e60 km [25]. The AC topology is currently the dominant technology for offshore wind transmission as it is a cost effective solution for near shore generation. However, as the distance increases, the charging current in the AC cable will tend towards the rated current limit of the cable and thereby limit transmission of real power. Capacitance is less of an issue with HVDC cables since the continuous charging current should be zero, neglecting self-discharge losses. Traditionally, HVAC has been the technology of choice due to its existing large scale developments and low development risks in comparison with a more recent technology such as HVDC. In addition the first offshore wind farms were mainly located in shallow waters close to shore. As development of offshore wind moves further offshore with longer cables, HVDC becomes a viable alternative due to the lower energy losses. While HVDC transmission line costs less than an AC line for the same transmission capacity, the terminal stations are more expensive due to the fact that they must perform the conversion from AC to DC and back again. Above a certain “break-even” distance, HVDC gives the lowest cost (Fig. 5). Wind turbines typically generate power at a voltage of up to 1 kV AC. A transformer located either in the nacelle or at the bottom of the tower steps the voltage up to either 11 kV or 33 kV. The power from the wind turbines is transmitted at to an offshore substation which increases the voltage for transmission to shore. In
220
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 10. Installation costs (£m/MW).
an HVAC transmission system, a typical transmission voltage to shore is 132 kV which can be connected directly to the electricity grid or stepped-up at an onshore substation if it is to be connected at 400 kV. Alternatively the AC voltage can be converted to DC by an HVDC converter on the offshore platform and then converted back to AC by a HVDC converter located onshore.
to carry out various optimisation strategies for multiple wind farms or to weigh cable redundancy against O&M strategy and risk of failure. For the current scenario, the assumptions are as shown in Table 3. The unit cost of connection per installed MW is then given by:
o. n ½capacitywindfarm ½CAPEXconnect ¼ ð½Cable_dist=1000Þ$½ncables $½costcable þ ½noffshore $½costoffshore þ ½nonshore $½costonshore (6)
For offshore wind farms, the generated power may be transmitted by conventional AC technologies or HVDC technologies depending on the distance to shore. Fig. 5 shows the relationship between grid connection distance and cost of connecting the exemplar wind farm to the grid onshore. According to the literature the most probable break-even distance is uncertain and this still remains subject for debate: Ackermann [2] states 55 km, while other authors go for higher values, up to 80 km [39]. For the exemplar wind farm, costs have been developed for both HVAC and HVDC connections. The costs have been evaluated depending on the number and complexity of offshore platforms and the number of cables to shore. There is the potential in future
The cabling distance [Cable_dist] is estimated from the closest grid substation of 275 kV or 400 kV in England (data from National Grid), Scotland (data from Scottish Power and Scottish and Southern Energy Power) and Northern Ireland (data from System Operator for Northern Ireland). There is considerable potential to extend the analysis to look at the effects of building new substations, assessing grid extension plans in the light of planned installations, and generally looking at network integration on a North Sea, UK and even pan-European scale. 3.1.4. Installation Offshore wind turbine installation is unconventional and
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
221
Fig. 11. OPEX (£/MWh).
challenging. New techniques and equipment have enabled successful installations worldwide, but the cost for installation is still high and scope for improvement is great. In this model the assumption of using a jack-up crane vessel for the installation of the
½CAPEXinstall ¼
n
onesite activities. The installation costs per MW installed capacity (Table 4) include the shipping of turbines to the site, their installation onsite, and the cost of cabling between them:
o ½daysinstall þ ½daystosite $½costjackup þ ½cablinglength $ ½cablingcost þ ½cable installcost
. ½capacitywindfarm
turbines at a cost of £250,000 per day has been made [9]. A simple
where the journey time [days]to-site is given by:
o n . $½costjackup ½daystosite ¼ 2$ ð½NEAR_DIST=1000Þ ½speedjackup $½hoursworkingday $½npervisit
cost algorithm has been created based on a variable cost due to the distance from port (Dist_Port) and a fixed component for the
(7)
(8)
where [NEAR_DIST] is the distance (m) to the nearest deep water port, [speed]jack-up is the speed (km/h) of the installation vessel,
222
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
[hours]working-day is the length of the working day, and [n]per-visit is the number of turbines that can be carried per trip. 3.2. OPEX Experience in offshore wind farm management shows that OPEX costs normally account for 25% of the cost of energy. Ernst & Young [17] estimated a cost for operation and maintenance of £24/ MWh in average North Sea conditions. In this research a cost algorithm depending on distance from port (Dist_Port) has been assumed including a fixed cost of maintenance of £18/MWh and £3/ MWh for port activities and licence fees:
ABP mer) was used to calculate the available energy in each cell grid. Using the power curve for the Supergen Wind 5 MW exemplar wind turbine (cut-in wind speed 4 ms1, rated wind power 5 MW at 11 ms1, cut-out wind speed 25 ms1), the nominal output of the turbine was calculated. Wind distribution (assuming a Weibull parameter of 2.0) was expressed as a fraction of time for each nominal wind speed and using an empirical power curve for the turbine to calculate the exact power output for each site with different mean wind speed. An availability of 95% was assumed and a wake array loss of 11% was detracted for each cell [4].
OPEX ¼ fixedcost þ portfees þ ðð½NEARDIST =1000Þ*varcost =100Þ (9) where [NEAR_DIST] is the distance to the nearest suitable port. The OPEX cost assumptions are then given in Table 5. Decommissioning costs apply at the end of the wind farm operating life. As there may be a residual value associated to the wind farm assets which could be sold or reused in the event of a repowering. In this research, it is assumed that this residual value equates to the decommissioning cost. Thus, the impact of decommissioning is neutral to the overall LCOE. 3.3. Energy production The average wind speed at 100 m (Renewable Energy Atlas,
4. Results and discussion 4.1. Exclusions The first step of the model was to calculate the suitable areas for offshore wind farm development as presented in Fig. 6. Overall 11.5% of the REZ is excluded from wind energy development, with the main exclusion areas being from the Oil & Gas industry in the north east and shipping protection in the English Channel. The grid square approach to calculation makes it difficult to apply the specific exclusions directly (in general they will only exclude part of a grid square) and so the data in Fig. 6 was reevaluated in terms of the proportional exclusion in each grid square (Fig. 7).
Fig. 12. Energy potential (MWh/km2/a).
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
4.2. GIS model 4.2.1. Base case results The developed model (Appendix 1) uses an iterative approach to calculate the different components of CAPEX, OPEX and energy potential for each grid square within the REZ. The spatial distribution of each cost components is presented in Fig. 8 to Fig. 14. Foundation costs are sensitive to water depth, which directly influences the type of technology chosen by the model (0e30 m monopile, 30e60 m jacket and >60 m floating). While costs of monopile and jacket foundations are based on empirical data from the literature, the cost of floating foundations is not yet well understood and characterised, so these costs should be treated with lower confidence. As presented in Fig. 8, the main cost driver is water depth with a lesser effect of distance from the nearest port for installation activities. Transmission costs are very sensitive to cable length due to onshore substation grid connection (Fig. 9). The cost ranges from around 0.2 £m/MW for areas within approximately 25 km from an onshore grid substation to above 1.0 £m/MW for sites as much as 500 km away from the grid. The model might underestimate these extreme cases as the data used in the cost algorithm creation did not including marine cables longer than 120 km so the technical feasibility and cost increase with distance above 200 km must be considered to have high uncertainty. Installation costs (Fig. 10) resemble the pattern previously
223
observed for the transmission cost with increases directly proportional to the distance from nearest port. In the majority of cases the nearest port is adjacent to a grid substation because industry complexes accessing that port facility for logistics also require available power for their industrial processes. Fig. 11 presents OPEX values ranging from 21 £/MWh, including both fixed operations and maintenance component (18 £/MWh) plus fixed port licences and fees (3 £/MWh) to 62 £/MWh for sites distant from port which also include a variable component to take into account the journey to site and time delays considering the challenging marine weather conditions. Apart from the initial capital and yearly maintenance cost a key variable in the assessment of offshore wind cost of energy is the potential energy production which must justify the initial high cost of investment in offshore wind. The purpose of an offshore wind farm is to convert kinetic energy from wind into electricity and the quality of the wind regime is a vital factor. The results from the energy production algorithm are presented in Fig. 12 showing, as expected, the highest energy potential in areas of open ocean off the coast of Scotland with the lowest energy yields close to shore where lower average wind speeds prevail. The energy potential in Fig. 12 can be adjusted for the excluded regions shown on Fig. 7 to produce a map of maximum available energy (Fig. 13). Finally, Fig. 14 presents the LCOE for the UK REZ where all the previously discussed components are combined to calculate the
Fig. 13. Energy available for exploitation (MWh/km2/a).
224
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 14. LCOE for the UK REZ.
Table 6 Average LCOE [£/MWh] for the three depth categories in UK-OWE-GIS. Water depth [m]
REZ [%]
Mean energy per turbine (MWh/a)
Capacity factor
Mean CAPEX (£m/MW)
Mean LCOE [£/MWh]
0e30 m 30e60 m >60 m
9.9% 14.9% 75.2%
19384 22790 25848
0.44 0.52 0.59
2.58 2.86 4.34
127.25 116.07 155.66
break-even value of electricity at which the investment in the wind farm is economically justified. Different patterns emerge from the analysis: - Sites close to shore with low cost of capital investment and intermediate average wind speed obtain the lowest LCOE in the entire UK REZ; - Sites close to shore with low potential energy production obtain, despite the low cost of capital investment high values of LCOE;
Table 7 Average LCOE [£/MWh] from UK-OWE-GIS compared with DECC [10]. Mean LCOE [£/MWh]
[10]
OWE GIS (2014)
Round 2 Round 3
113 120
105 114
Fig. 15. Sensitivity analysis of the OWE-GIS across the whole UK-REZ (green ¼ reduction of parameter; red ¼ increase of parameter). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
225
Fig. 16. Sensitivity analysis of the OWE-GIS in different water depth regions (green ¼ reduction of parameter; red ¼ increase of parameter). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
226
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
- Sites with the highest energy potential but furthest from grid distribution and in high water depths obtain high values of LCOE confirming the importance of cost over wind speed (but note the much greater uncertainty attached to costs for floating wind turbines e i.e. water depths above 60 m in Fig. 3). Table 6 summarises the average LCOE for the three depth bands and associated foundation types considered: 0e30 m (monopile), 30e60 m (jacket), and >60 m (floating). Surprisingly, the mean LCOE is higher in the shallower water band than the intermediate band, perhaps reflecting lower energy yields in regions closest to the coast. Comparing the potential LCOE for the Crown Estates's Round 2 and Round 3 sites the UK-OWE-GIS produces numbers (Table 7) broadly in line with the DECC [10] levelised cost estimates for projects starting in 2013. 4.2.2. Model sensitivity Six parameters were altered systematically to test model sensitivity (Fig. 15): -
interest rate (±2%), lifetime of the wind farm (±5 years), availability (±5%), CAPEX (±5%), average wind speed (±5%), bad weather factor (double installation times to represent bad weather)
The mean cost of energy across the whole UK Renewable Energy Zone was evaluated as 147 £/MWh. Sensitivity to interest rate was greatest, with 2% increase or decrease resulting in a change of ± 15 £/MWh. Shortening the lifetime to just 15 years had the next biggest effect, adding 13 £/MWh to the energy cost. Although 15 years is much shorter than the design life, it might be considered to show the cost risk of early failure of items or a decision to upgrade before 20 years. Lengthening the lifetime to 25 years gave a proportionately smaller advantage of 7 £/MWh, indicating a lower benefit to be gained from “sweating the assets”. The other parameters, CAPEX, availability and wind speed, showed similar variations of LCOE of approximately 5 £/MWh in the ranges considered, as far as averaged costs across the whole zone are concerned. Wind speed, however, has a much bigger effect on cost of energy in the near-shore, shallower waters than in further out, deeper waters (Fig. 16). Lower wind speeds have a proportionately bigger impact than higher wind speeds, adding weight to the argument in section 4.2.1 that the higher cost of energy in the 0e30 m zone compared with the 30e60 m zone is due to a higher proportion of sites being inshore with lower wind speeds. As might be expected, bad weather has highest impact in the 30e60 m zone where foundation work, in particular, is more complex. Clearly, the results are also sensitive to the many cost assumptions made in the model and presented in detail in sections 3.1e3.3. Cost information is scarce in the wind energy industry and it is also difficult to predict future technological advancements. For example,
there are potential cost reductions to be gained by standardising foundation types and research efforts on floating turbines could significantly decrease the cost of positioning turbines in deeper waters. The fast development of HVDC could lead to capital cost reductions and reduction in energy losses over long distance transmission. Increasing turbine size and power rating will further impact the installation cost. Overall, the cost of an emerging technology such as offshore wind, can be expected to reduce along a trajectory known as the ‘learning’ or ‘experience’ curve as more units are manufactured and installed. 4.3. UK offshore wind energy potential The discussion in section 4.2 has so far ignored constraints. To calculate the maximum offshore wind energy potential, it is necessary to take account of the “hard” constraints presented in section 4.1. In addition to these, there are a number of “soft” constraints presented by other potential uses of the sea, such as actual and potential environmental protection areas, heritage coastline, visual landscape, civil and military radars, etc., which have been ignored in this work but which will inevitably reduce the realisable potential. The economically accessible potential then depends only on the cost which the customer is willing to pay. This is a complex issue since the Levelised Cost of Energy (LCOE) presented here is different from the so-called “strike price” which the Government might be willing to contract for [see: [10e12]]. Table 8 shows the potential wind capacity available for a range of final LCOE, together with the percentage of the UK-REZ covered at each stage; essentially this is the data depicted in Fig. 14. This study finds that a total of up to 675 GW nameplate capacity might be available at a cost of less than 120 £/MWh, about the modelled cost of the Round 3 sites, or 1450 GW at less than 140 £/MWh. If 10% of this area were exploited then 150 GW could be installed at less than 140 £/MWh. The mean (calculated from predicted wind speed distribution) capacity factors in Table 8 are in the range 41e48%, substantially higher than the values of 38% (Round 2) and 39% (Round 3) assumed by DECC [10], which, in turn, were consistent with the 38% assumed by Ernst & Young [17]. These new modelled estimates agree better with published values by Energy Numbers [16] for Danish wind farms in the North Sea which quote 12-months rolling capacity factors of more than 50% for several wind farms. The higher capacity factors in the current study account for the lower mean values of LCOE compared with DECC [10]. 5. Conclusions The presented cost model can simulate major technical and economic aspects of an offshore wind farm and can be used in preliminary studies for offshore wind energy assessment. The combined use of cost data and site specific marine conditions has allowed the evaluation of the available wind energy capacity in UK territorial waters. This robust evidence based approach can be used
Table 8 Economically accessible wind energy potential in the UK-REZ. LCOE [£/MWh]
<100
100e110
110e120
120e130
130e140
140e150
150e160
Capacity [GW] Available capacity [GW] Proportion of UK-REZ [%] Energy [TWh] Capacity factor
7.2 4.9 0.2 18.915 0.44
400 304 (308) 8.8 (9.0) 1191 (1209) 0.45
418 367 (675) 9.2 (18.2) 1448 (2658) 0.45
147 133 (808) 3.2 (21.4) 480 (3138) 0.41
689 640 (1448) 15.2 (36.5) 2714 (5852) 0.48
951 880 (2328) 20.9 (57.4) 3736 (9588) 0.48
608 560 (2888) 13.4 (70.8) 2375 (11,964) 0.48
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
to either identify areas for future development or to assess existing Phase 3 development sites in a consistent manner. In addition, the tool allows testing policy or investment priorities with different future scenarios. The developed OWE-GIS tool could be applied to other areas of Northern Europe with similar wind profiles and bathymetric conditions to the UK. The research approach could also be used for other parts of the world as the proposed constants and equations offer enough flexibility to include different scenarios and might need only minor refinements. Since accurate cost information is hard to come by in the wind energy industry, this study has adopted similar cost models for CAPEX and OPEX to those prevailing in recent UK Government studies. The overall Levelised Cost of Energy (LCOE) estimates developed here are generally lower than those in recent compa-
227
constraints presented by other potential uses of the sea that will reduce the realisable energy potential of offshore wind. These constraints are difficult to quantify and will require the use of marine planning instruments to resolve potential conflicts ultimately achieving optimal site selection. Acknowledgements The Engineering and Physical Sciences Research Council (EPSRC) supported this work through grant no. EP/H018662/1. The work was carried out as part of the Supergen Wind Energy Technologies (Phase 2) Consortium. Appendix 1
Fig. A-1. UK OWE-GIS model structure in ArcGIS 10.1, incorporating input variables and code blocks.
rable studies [26] due in part to the fact that capacity factor has been estimated based on the predicted wind resource at each grid square, rather than assuming a single mean value across all sites. Sensitivity analysis has shown that, for such a capital intensive undertaking as an offshore wind farm, interest rate is a critical parameter in determining the cost of output energy. The cost of energy is less sensitive to project lifetime, provided that the design lifetime of 20 years can be achieved. Wind speed is particularly critical for shallow water sites, presumably because these tend to be closest to the shore with generally lower mean wind speeds. Acknowledging the approximate nature of some of the cost estimates, it is found that if only 10% of the accessible UK offshore wind resource were to be exploited then 150 GW of capacity could be installed at less than 140 £/MWh (or 67.5 GW at less than 120 £/MWh). It is worth noting that there is a number of “soft”
References [1] A. Abudureyimu, J. Hayashi, K. Nagasaka, Analyzing the economy of off-shore wind energy using GIS technique, APCBEE Procedia 1 (2012) 182e186. [2] T. Ackermann, Evaluation of electrical transmission concepts for large offshore wind farms, in: Copenhagen, Denmark, Proc. Copenhagen Offshore Wind Conference and Exhibition, 2005. [3] C.L. Archer, M.Z. Jacobson, Evaluation of global wind power, J. Geophys. Res. 110 (2005) D12110. [4] P. Argyle, Computational Fluid Dynamics Modelling of Wind Turbine Wake Losses in Large Offshore Wind Farms, Incorporating Atmospheric Stability, Doctoral Thesis, Loughborough University, 2015. [5] CEC, Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources (Renewable Energy Directive), s.l.: Commission of the European Communities, 2009. [6] T. Cockerill, et al., Combined technical and economic evaluation of the Northern European offshore wind resource, J. Wind Eng. Ind. Aerodyn. 89 (2001) 689e711.
228
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
[7] M. Collu, A.J. Kolios, A. Chahardehi, F. Brennan, A Comparison Between the Preliminary Design Studies of a Fixed and a Floating Support Structure for a 5 MW Offshore Wind Turbine in the North Sea. s.l., RINA, 2010, p. 63. Royal Institution of Naval Architects-Marine Renewable and Offshore Wind Energy Papers. [8] Crown Estate, Offshore Wind Cost Reduction Pathways Study, Crown Estate, 2012. [9] Y. Dalgic, I. Lazakis, O. Turan, Vessel charter rate estimation method for offshore windfarms O&M activities, in: International Maritime Association of Mediterranean IMAM 2013, 2013e10-14-2013-10-17, a Coruna, 2013. [10] DECC, Electricity Generation Costs 2013, s.l., DECC, 2013a. [11] DECC, Annex B: Strike Price Methodology, DECC, London, 2013b. [12] DECC, Investing in Renewable Technologies e CfD Contract Terms and Strike Prices, DECC, London, 2013c. [13] M.J. Dvorak, C.L. Archer, M.Z. Jacobson, California offshore wind energy potential, Renew. Energy 35 (2010) 1244e1254. [14] M.J. Dvorak, B.A. Corcoran, J.E. Ten Hoeve, N.G. McIntyre, M.Z. Jacobson, U.S. East Coast offshore wind energy resources and their relationship to peak-time electricity demand, Wind Energy 16 (2012) 977e997. [15] E.On, E. On Offshore Wind Energy Factbook, s.l.: Eon, 2012. [16] Energy Numbers, Capacity Factors at Danish Offshore Wind Farms, 2014 [Online] Available at: http://energynumbers.info/capacity-factors-at-danishoffshore-wind-farms [accessed 29.05. 14.]. [17] Ernst & Young, Cost of and Financial Support for Offshore Wind, Report for the Department of Energy and Climate Change, 27 April 2009, URN 09D/534, s.l.: s.n, 2009. [18] European Environment Agency, Europe's Onshore and Offshore Wind Energy Potential: an Assessment of Environmental and Economic Constraints, EEA Technical Report No. 6, European Environment Agency, Copenhagen, 2009. [19] P. Greenacre, R. Gross, P. Heptonstall, Great Expectations: the Cost of Offshore Wind in UK Waters e Understanding the Past and Projecting the Future, s.l., UK Energy Research Centre (UKERC), 2010. [20] HM Government, The UK Renewable Energy Strategy, TSO, London, 2009. [21] L. Hong, B. Moller, Offshore wind energy potential in China: under technical, spatial and economic constraints, Energy 36 (2011) 4482e4491. [22] L. Hong, B. Moller, Feasibility study of China's offshore wind target by 2020, Energy 48 (2012) 268e277. [23] International Energy Agency, Technology Roadmap Wind Energy (Technical
Report), 2013. [24] M. Kuhn, UPWIND WP4 Offshore support structures, s.l., in: EWEC 2007: UPWIND Workshop, 2007. [25] LCICG, Technology Innovation Needs Assessment (TINA): Offshore Wind Power e Summary Report, s.l., Low Carbon Innovation Coordination Group (LCICG), February 2012. [26] A. Levitt, W. Kempton, A. Smith, W. Musial, J. Jeremy Firestone, Pricing offshore wind power. University of Delaware, Energy Policy 39 (2011) 6408e6421. [27] B. Moller, L. Hong, R. Lonsing, F. Hvelplund, Evaluation of offshore wind resources by scale of development, Energy 48 (2012) 314e322. [28] W. Musial, B. Ram, Large-scale Offshore Wind Power in the United States, s.l., NREL, 2010. €yry, Compliance Costs for Meeting the 20% Renewable Energy Target in [29] Po €yry Consulting, Oxford, 2008. 2020, Po [30] Renewables Advisory Board, 2020 Vision e How the UK Can Meet its Target of 15% Renewable Energy, Renewables Advisory Board, London, 2008. [31] Renewables Advisory Board, Value Breakdown for the Offshore Wind Sector, s.l., RAB, 2010, p. 0365, 2010. [32] S. Rodrigues, C. Restrepo, E. Kontos, R. Teixeira Pinto, P. Bauer, Trends of offshore wind projects, Renew. Sustain. Energy Rev. 49 (2015) 1114e1135. [33] P. Schaumann, C. Boker, Can jackets and tripods compete with monopiles, in: Copenhagen, Proceedings of European Offshore Wind Conference, 2005. [34] C. Schillings, T. Wanderer, L. Cameron, J.T. van der Wal, J. Jacquemin, K. Veum, A decision support system for assessing offshore wind energy potential in the North Sea, Energy Policy 49 (2012) 541e551. [35] M. Seidel, Jacket substructures for the REpower 5M wind turbine. s.l., in: Published in: Conference Proceedings European Offshore Wind 2007. Berlin, 2007. [36] SKM, Growth Scenarios for UK Renewables Generation and Implications for Future Developments and Operation of Electricity Networks, SKM, Newcastle, 2008. [37] The Crown Estate, Round 3 Offshore Wind Site Selection at National and Project Levels, The Crown Estate, London, 2009. [38] I. Troen, E. Petersen, European Wind Atlas, Riso National Laboratory, Roskilde, Denmark, 1989. [39] B. Van Eeckhout, The Economic Value of VSC HVDC Compared to HVAC for Offshore Wind Farms, Master Thesis, Katholieke Universitet Leuven, 2008.
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
An Offshore Wind Energy Geographic Information System (OWE-GIS) for assessment of the UK's offshore wind energy potential S. Cavazzi, A.G. Dutton* Energy Research Unit, Technology Department, Science & Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 December 2014 Received in revised form 27 August 2015 Accepted 8 September 2015 Available online xxx
An Offshore Wind Energy Geographic Information System (OWE-GIS) has been developed for the purpose of assessing the economically accessible offshore wind energy resource for the United Kingdom. The UK OWE-GIS estimates the costs of energy from an offshore wind farm taking account of the major capital components; development costs dependent on water depth and distance from nearest ports or grid connection points; the potential energy production dependent on annual average wind speed, potential array losses, and turbine availability; operations and maintenance costs; and financial parameters such as discount rate and project lifetime. A sensitivity analysis is presented to show the influence of discount rate, project lifetime, and assumptions about overall capital expenditure (CAPEX), availability, and annual mean wind speed. © 2015 Published by Elsevier Ltd.
Keywords: Offshore wind energy GIS Cost modelling LCOE
1. Introduction The wind energy resource varies considerably, on a global scale between climate zones, and on a local scale due to variations in terrain and surface roughness. The cost of exploitation of the raw wind resource depends largely on the magnitude of the resource and its distance from the point of energy end-use. In general, the energy available in the wind is greater and more stable offshore than on land at nearby sites [13] and [14]. In addition, there is perceived higher public acceptance for offshore developments and fewer constraints (e.g. due to visual intrusion or noise) on the choice of technology [18]. The European Union's Renewable Energy Directive in December 2008 [5] committed the EU to generate 20% of energy consumption from renewable sources by 2020. The UK was required to raise the proportion of energy that is produced from renewable sources from 1.5% in 2006 to 15% by 2020. Given the restricted scope for radical change in other sectors, such as heat and transport, within such a short time span, the electricity sector was identified as the leading candidate to make progress towards meeting this target. In outlining its approach for meeting the Renewables Directive, in its 2009 Renewable
* Corresponding author. E-mail address: [email protected] (A.G. Dutton). http://dx.doi.org/10.1016/j.renene.2015.09.021 0960-1481/© 2015 Published by Elsevier Ltd.
Energy Strategy (RES), the UK Government described a number of scenarios within which 30e35% of electricity generation could be produced from renewable sources by 2020 [20]. As a relatively mature renewable technology, wind power was expected to deliver most of this generation capacity, and the UK Government declared its intention to make ‘full use’ of the potential for offshore wind power in meeting the Renewables Directive [20]. In the RES's ‘lead scenario’ around 27 GW of wind power is required to be installed by 2020, roughly evenly split between onshore (c.14 GW) and offshore (c.13 GW). However, the RES also makes clear that the Government's ambitions for offshore deployment to 2020 are much greater than those in this lead scenario, with 20 GW described as ‘achievable’ and 33 GW as ‘feasible’ [20]. Supporting analysis commissioned by the UK Government suggested that 20e30 GW of combined onshore and offshore wind could feasibly be deployed by 2020 [29]. Other studies [30,36]; suggested that up to 34 GW of deployed offshore wind capacity may be achievable by 2030, but with only around half that capacity (14e18 GW) deployable by 2020. Leasing rights to the UK's offshore wind resource is managed by the Crown Estate. In 2010, the Crown Estate announced acceptance of up to 32 GW of offshore capacity in Round 3 of its offshore leasing programme, building on the 8 GW already accepted under Rounds 1 and 2. The Crown Estate selected its Round 3 lease sites [37] based on technological feasibility of development (essentially water depth < 60 m) and with regard to a set of constraints and
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
into question earlier assumptions, prevalent in the 1990s, that energy costs should fall monotonically with time. Thus, whereas in the period 2000e2004 the typical capital expenditure involved in building the Round 1 UK wind farms was in the range £1.2 m/MW to £1.5 m/MW, by 2010 reported costs were clustering around £3.0 m/MW. Part of this rise was due to later developments being driven to deeper water sites located further offshore and a general rise in costs across the energy sector, but the overall causes are complex and have been analysed in depth in the report Great Expectations [19]. The costs of recent large offshore wind energy projects have also increased due to the higher associated risks perceived by investors [23].
Nomenclature CAPEX EU FCR GIS LCOE OPEX OWE RES REZ TCE
213
capital expenditure European Union fixed charge rate geographical information system levelised cost of energy operations and maintenance expenditure offshore wind energy Renewable Energy Strategy Renewable Energy Zone The Crown Estate
2. Methodology
restrictions respecting existing uses of the sea and sea-bed. Underlying the Crown Estate's analysis was a database of all relevant factors which unfortunately was never made public. The study reported here is the first transparent review of the whole of the UK Renewable Energy Zone. The first comprehensive analysis of the European wind resource was the European Wind Atlas [38] published in 1989, but this did not include offshore wind. The first application of a geographic information system (GIS) to assess the northern European offshore wind resource occurred during the EC project Opti-OWECS [6]. The earliest analysis of world onshore and offshore wind resources at 80 m hub height from data alone was published by Ref. [3]. Later studies applying GIS to offshore wind resources have been published for the United States [28], China [21] & [22], Denmark [27], Japan [1] and the North Sea [34]. A common challenge faced by all these analyses has been to break down the cost of an offshore wind farm into fixed components, independent of geography, and variable components dependent on spatial location, usually either the distance from shore (or nearest port or grid connection point) or the depth of water at the candidate site. In both cases estimating suitable values is often subjective with successive studies borrowing from their predecessors and developing a consensus of the “right value” to use. The OWE-GIS developed in this research extends previous work in this area by tying together an economic database built using existing costs data with geographic properties calculated at each individual site for both CAPEX and OPEX components. It is well known that costs in the offshore wind energy sector are higher than expected at the current time [32]. A 2013 consultation conducted by the Department of Energy and Climate Change in the UK including generators, suppliers, consumer organisations and environmental groups has exposed that the assumed learning rates of the industry were not as rapid as expected and therefore the strike price for electricity under contract for difference had to be reduced less quickly [12]. Recent disturbances in commodity prices and financial markets have brought
2.1. The geographic information system (GIS) tool The development of offshore wind energy is related to optimal use of the available geographical areas in the sea. Although the wind energy output from a certain area will increase as technology improves, the amount of area that is economically feasible for wind power development will diminish as existing areas are exploited.
Fig. 1. Modelling approach.
Fig. 2. UK renewable energy zone (UK-REZ).
214
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Table 1 Offshore wind farm CAPEX breakdown [31].
Category
Cost
Development & Consent
4%
Turbine
Balance of Plant
Installation & Commissioning
Sub-Category environmental survey seabed survey met mast development services
Cost 0% 1% 0% 3%
3.2 £m/MW 0.01 £m/MW 0.02 £m/MW 0.1 £m/MW 0.01 £m/MW 0.09 £m/MW
Rotor
11%
0.35 £m/MW
Nacelle
22%
0.70 £m/MW
Tower foundations
6% 16%
0.19 £m/MW 0.51 £m/MW
Cables
5%
33%
1.1 £m/MW
26%
Development & Consent Turbine Foundations Transmission
0.16 £m/MW 1.2 £m/MW
37% offshore substations
7%
0.22 £m/MW
other electrical
3%
0.09 £m/MW
foundations cables turbines offshore substations
7% 9% 9% 1%
0.22 £m/MW 0.29 £m/MW 0.29 £m/MW 0.02 £m/MW
0.8 £m/MW
0.13 £m/MW 1.54 £m/MW 0.74 £m/MW 0.78 £m/MW
Furthermore, there is increasing competition from social, economic, and political interests for use of the seas. Finding the best possible areas on which to site turbines is vital to the economic feasibility of a project, and there is a need for a systematic tool for determining where these sites exist, and for what cost electricity can be produced. The tool described herein is an analytical site modelling tool capable of supporting preliminary selection of new turbine sites. The model has two principal components: an economic database, based on existing cost data and predicted future cost developments, and a Geographic Information System (GIS) component, which ties the cost data to the geographic properties unique to each site. These properties include, among others, wind speed, sea depth, slope of sea bottom, cable distance to shore and the distance to the nearest grid connection point. Additionally, areas that are currently not available for wind farm development due to social and political reasons, including visual impact, nature reserves, trade and shipping routes and military testing zones can be assessed in order to determine the total amount of potential wind resource that is being lost. If any of these zones contains a significant amount of area exceptionally suitable for wind development, the model could be used to assess the effects of compromises, such as provision of specific shipping lanes for ocean traffic.
The GIS developed for this research has been implemented within ArcGIS 10.1 (ESRI) enabling the automatic computation of various cost algorithms. The overall methodology from collection of basic data, through application of exclusions, to estimation of energy yield and cost is summarised in Fig. 1. Originally it was intended to include weighted restrictions for less important constraints, as in the original analysis by TCE [37], but this was abandoned as being open to a number of stakeholders interpretations. A schematic for the full model is provided as Appendix 1.
2.2. Study area e the UK Renewable Energy Zone The Renewable Energy Zone (REZ) was declared under section 84 of the Energy Act 2004 by the government of the United Kingdom (UK) as the area of the sea, beyond the United Kingdom's territorial sea, which may be exploited for energy production (Fig. 2). The UK has exclusive rights in this area with respect to production of energy from wave/tide or wind. For this analysis the entire UK REZ was included, divided into grid squares of 10 km 10 km thereby taking into account local conditions at a scale compatible with the available raw wind speed data and computational feasibility.
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
215
Table 2 Basic input parameters for the cost of foundations.
Monopile Jacket Floating
a
b
c
[days]install
0.0011 0.0008 0
0.0023 0.0154 0.0017
1.2253 1.5847 3.732
1 3 2
Number of turbines per visit Speed of installation vessel (km/h) Length of the working day (h) Material cost of steel (£/tonne) Cost of installation vessel (£m/day)
[n]per-visit [speed]jack-up [hours]working-day [cost]steel [cost]jack-up
5 20.0a 24 1800b 0.15
a
MPI Resolution. World Composite Stainless Steel Price (www.worldsteelprices.com) average monthly price in 2013 ¼ $2741/tonne @ average USD/GBP exchange rate of $1.56/ £¼. b
-
Existing wind farms IMO traffic separation schemes Active submarine cables Oil & Gas safety zones and infrastructure
2.4. Economic assessment e the Levelised Cost of Energy (LCOE) The concept of Levelised Cost of Energy (LCOE) is used as a technique for establishing the final cost of producing energy. The LCOE formula uses the total installed capital expenditure, annual maintenance cost and annual energy production to estimate a total production cost for energy, according to the formula [see, for example Feng et al. (2010)]: Fig. 3. UK REZ bathymetry classified into three categories: shallow (0e30 m); transitional (30e60 m) and deep (>60 m).
2.3. Exclusions Not all areas of the sea are suitable for the development of a wind farm as other marine activities might have policy protection and exclusivity. For the purpose of this research, exclusions have been applied largely in line with the Crown Estate GIS decision support system [37], so far as data could be independently source. The exclusions applied included: - Protected wrecks; - Anchorage areas - Aggregate dredging
LCOE ¼
ðCAPEX*FCR þ OPEXÞ Eannual
(1)
where CAPEX is the initial capital cost (£), OPEX is the annual operation and maintenance cost (£), Eannual is the annual energy production (MWh), and FCR is the annual fixed charge rate for n operational life (years) given by:
FCR ¼ h
r 1 ð1 þ rÞn
i
(2)
The LCOE is then the price at which electricity must be generated to break even over the lifetime of a project. This concept, paired with a GIS analysis comprises a powerful tool to assess the offshore wind resource of the chosen study area e the UK Renewable Energy Zone.
Fig. 4. Water depth (m) on the x axis v. foundation cost (£m/MW) on the y axis using data from Refs. [7,33]; steel price of $3200/tonne; exchange rate $1.6 ¼ £1.
216
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 5. Transmission cost comparison between AC (red lines) and DC (green lines) in function of distance. Thin lines represent the cost of the terminal plus the cables while thick lines account also for electricity losses. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Typically the LCOE is calculated over a 20e30 years lifetime and quoted in units of currency per megawatt-hour, for example, £/MWh. For this study, a lifetime of 20 years and an interest rate of 10% were assumed [8] and [10]). The investment costs comprise a variable component (foundations, grid connection and installation) depending on water depth and distance from shore and a fixed component including the turbine cost. OPEX is also spatially variable depending on the accessibility of the site from a suitable port and the energy production dependent on the climatic conditions of the site. An exemplar wind farm made of 256 turbines of 5 MW rated power for a total of 1.28 GW was used to evaluate the LCOE at each grid point within the UK REZ. Fig. 6. Exclusion areas.
2.5. Research question The OWE-GIS application estimates the economically accessible wind power resource within the UK REZ using spatial data of wind speed, water depth, natural & physical features, surface and subsurface structures, environmental protection measures and other socio-economic uses of the sea. The model evaluates distances to the nearest mainland electricity network connection and assesses cabling requirements within the wind farm and to shore. Consideration is given to appropriate foundation type based on
Table 3 Basic input parameters for the cost of connection.
Number of offshore platforms Cost per platform (£m) Number of cables to shore Cable cost (£m/km) Number of onshore convertor stations Cost per onshore convertor station (£m)
[n]offshore [cost]offshore [n]cables [cost]cable [n]onshore [cost]onshore
AC
DC
3 30.0 3 1.8 e e
2 110.0 2 0.9 2 65.0
Table 4 Basic input parameters for the cost of installation. Number of days on site to install each turbine Length (intra-array) cable (km) Intra-array cable cost (£m/km) Intra-array cable installation cost (£m/km) Number of turbines per visit Speed of installation vessel (km/h) Length of the working day (h) Cost of installation vessel (£m/day)
[days]install [cabling]length [cabling]cost [cable_instal]cost [n]per-visit [speed]jack-up [hours]working-day [cost]jack-up
2 350 0.15 0.1 5 20.0 24 0.25
bathymetry, seabed terrain and sediments characteristics. Distances to nearest ports with (potential) facilities for installation and to support ongoing operations and maintenance are evaluated and used as a basis for evaluating installation and ongoing operational costs. An overall cost of energy to the grid is developed including capital and installation costs and ongoing operations and maintenance. The overall aim of the research project was to provide a spatially explicit model, using GIS, capable of estimating the economically accessible offshore wind energy resources in the designated UK Renewable Energy Zone. 3. Component data 3.1. Capex Capital expenditures occur at the beginning of the project and involve the environmental surveys, seabed analysis, installation of a met mast to assess the wind conditions at the site, development and consents services, the turbine with all its components (nacelle, rotor, tower and blades), the foundations and all the electrical components utilised in energy transmission from the wind farm to the grid (submarine cables, offshore platform and substation).
Table 5 Basic input parameters for the cost of O&M. Fixed costs Variable costs Port fees
fixed_cost var_cost port_fees
18.0 £/MWh 6 £/100 km 3.0 £/MWh
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
217
Fig. 7. Exclusions plotted as proportion of gridded squares.
Table 1 shows the cost breakdown by component as estimated by the Renewables Advisory Board [31]. The costs are apportioned according to an overall estimate by Ernst & Young [17] that the CAPEX for an offshore wind farm in average North Sea conditions (80 km from shore and water depth of approximately 30 m) is 3.2 million £/MW. Ernst & Young [17] themselves estimated the turbine cost as 47% (£1.5 m/MW) somewhat more than [31]. In reality the presented costs change according to site conditions and this can be well represented in the GIS model. The following formula is used in the calculation of the investment costs:
3.1.1. Wind turbines Despite the cost of turbines being the largest single investment in the wind farm, actual cost data are not readily available as the industry is still in a highly competitive emerging phase. The cost of turbines assumed for this study is £1.5 m/MW in line with the previously presented Ernst & Young [17] estimate. This value has been increased by 50% in the case of floating turbines as these are still in a research and development phase with few prototypes to date.
Costturbines þ Costtransmission þ Costfoundation þ Costinstall þ Costconsent CAPEX ¼ 1 factordevelopment
where factordevelopment is a factor to account for project development, consent, and overall management, typically of the order of 5% of the overall CAPEX. (This factor can also be adjusted to explore sensitivity to the overall capital value of the project.) Each of the components is now described in detail.
(3)
3.1.2. Foundations Water depth and consistency of the seabed determine the choice of foundation and so far, there is no universal foundation type suitable for the range of water depth found around the UK (Fig. 3). In the offshore wind industry, monopile foundations, with a
218
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 8. Foundation costs (£m/MW).
market share of 75% in 2011 [15], are the most commonly used foundation type in shallow waters (below 30 m), followed by jacket foundations in transitional water conditions (30e60 m). Significant research and development are still necessary to develop standard and cost-efficient concepts for production at industrial scale. New concepts, e.g. floating foundations, are being tested for deep waters above 60 m, but currently there are no commercial floating wind turbines. A panel of industry and academic offshore wind experts within the UPWIND project [24] evaluated different types of foundations technology based on their suitability at different water depths. Monopiles emerged as the most suited in shallow waters (<30 m), while jackets were better in transitional waters (30 me60 m) with floating tension leg platforms (TLP) being the most likely candidate for deep water (>60 m). In this research cost algorithms have been developed for each principal foundation type using literature data [7,33,35]. The costs have three components: basic material and manufacturing cost
(dependent on local water depth), transportation cost to the wind farm site (dependent on distance from nearest deep water port), and installation cost (basically the number of days the jack-up barge is needed for each turbine foundation). The basic algorithm for material and manufacturing cost depends on mass of steel required for the foundation piece, which, in turn, depends on the water depth at the site. The minimal amount of published data noted above provides just sufficient data on cost v water depth to fit a set of quadratic expressions (Fig. 4) for the different technologies so that:
½CAPEXfoundation ¼ ax2 þ bx þ c þ ½daysinstall þ ½daystosite $½costjackup
(4)
where x ¼ [WATER_DEPTH] and (a,b,c) are constants obtained from the data fit and the journey time [days]to-site is given by:
o n . $½costjackup ½daystosite ¼ 2$ ð½NEAR_DIST=1000 Þ ½speedjackup $½hoursworkingday $½npervisit
(5)
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
219
Fig. 9. Transmission costs (£m/MW).
where [NEAR_DIST] is the distance (m) to the nearest deep water port, [speed]jack-up is the speed (km/h) of the installation vessel, [hours]working-day is the length of the working day, and [n]per-visit is the number of foundation pieces that can be carried per trip. The material cost data is boosted prior to data-fitting by a fixed mass transition piece for monopile and jacket designs, additional manufacturing cost factor for jackets (0.5) and floating (0.2) to reflect complexity of the structure, and mooring costs for foundations. Input data for the algorithm applied to the exemplar wind farm are shown in Table 2. Allowing for the greater cost of installation of jacket structures, the manufacturing cost data supports the use of monopile foundations up to 20e30 m water depth and implies that floating would be the technology of choice beyond 60 m water depth, although the technology is yet to be commercially demonstrated. 3.1.3. Grid connection Two main technologies compete to transmit power to the grid onshore: alternating current (AC) and direct current (DC). Currently, high voltage AC (HVAC) cables are used to link turbines to an offshore substation, with power clean-up at each turbine. HVAC cables are also used to transmit power to the onshore substation as current wind farms are relatively close to shore, within
the range 20e60 km [25]. The AC topology is currently the dominant technology for offshore wind transmission as it is a cost effective solution for near shore generation. However, as the distance increases, the charging current in the AC cable will tend towards the rated current limit of the cable and thereby limit transmission of real power. Capacitance is less of an issue with HVDC cables since the continuous charging current should be zero, neglecting self-discharge losses. Traditionally, HVAC has been the technology of choice due to its existing large scale developments and low development risks in comparison with a more recent technology such as HVDC. In addition the first offshore wind farms were mainly located in shallow waters close to shore. As development of offshore wind moves further offshore with longer cables, HVDC becomes a viable alternative due to the lower energy losses. While HVDC transmission line costs less than an AC line for the same transmission capacity, the terminal stations are more expensive due to the fact that they must perform the conversion from AC to DC and back again. Above a certain “break-even” distance, HVDC gives the lowest cost (Fig. 5). Wind turbines typically generate power at a voltage of up to 1 kV AC. A transformer located either in the nacelle or at the bottom of the tower steps the voltage up to either 11 kV or 33 kV. The power from the wind turbines is transmitted at to an offshore substation which increases the voltage for transmission to shore. In
220
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 10. Installation costs (£m/MW).
an HVAC transmission system, a typical transmission voltage to shore is 132 kV which can be connected directly to the electricity grid or stepped-up at an onshore substation if it is to be connected at 400 kV. Alternatively the AC voltage can be converted to DC by an HVDC converter on the offshore platform and then converted back to AC by a HVDC converter located onshore.
to carry out various optimisation strategies for multiple wind farms or to weigh cable redundancy against O&M strategy and risk of failure. For the current scenario, the assumptions are as shown in Table 3. The unit cost of connection per installed MW is then given by:
o. n ½capacitywindfarm ½CAPEXconnect ¼ ð½Cable_dist=1000Þ$½ncables $½costcable þ ½noffshore $½costoffshore þ ½nonshore $½costonshore (6)
For offshore wind farms, the generated power may be transmitted by conventional AC technologies or HVDC technologies depending on the distance to shore. Fig. 5 shows the relationship between grid connection distance and cost of connecting the exemplar wind farm to the grid onshore. According to the literature the most probable break-even distance is uncertain and this still remains subject for debate: Ackermann [2] states 55 km, while other authors go for higher values, up to 80 km [39]. For the exemplar wind farm, costs have been developed for both HVAC and HVDC connections. The costs have been evaluated depending on the number and complexity of offshore platforms and the number of cables to shore. There is the potential in future
The cabling distance [Cable_dist] is estimated from the closest grid substation of 275 kV or 400 kV in England (data from National Grid), Scotland (data from Scottish Power and Scottish and Southern Energy Power) and Northern Ireland (data from System Operator for Northern Ireland). There is considerable potential to extend the analysis to look at the effects of building new substations, assessing grid extension plans in the light of planned installations, and generally looking at network integration on a North Sea, UK and even pan-European scale. 3.1.4. Installation Offshore wind turbine installation is unconventional and
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
221
Fig. 11. OPEX (£/MWh).
challenging. New techniques and equipment have enabled successful installations worldwide, but the cost for installation is still high and scope for improvement is great. In this model the assumption of using a jack-up crane vessel for the installation of the
½CAPEXinstall ¼
n
onesite activities. The installation costs per MW installed capacity (Table 4) include the shipping of turbines to the site, their installation onsite, and the cost of cabling between them:
o ½daysinstall þ ½daystosite $½costjackup þ ½cablinglength $ ½cablingcost þ ½cable installcost
. ½capacitywindfarm
turbines at a cost of £250,000 per day has been made [9]. A simple
where the journey time [days]to-site is given by:
o n . $½costjackup ½daystosite ¼ 2$ ð½NEAR_DIST=1000Þ ½speedjackup $½hoursworkingday $½npervisit
cost algorithm has been created based on a variable cost due to the distance from port (Dist_Port) and a fixed component for the
(7)
(8)
where [NEAR_DIST] is the distance (m) to the nearest deep water port, [speed]jack-up is the speed (km/h) of the installation vessel,
222
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
[hours]working-day is the length of the working day, and [n]per-visit is the number of turbines that can be carried per trip. 3.2. OPEX Experience in offshore wind farm management shows that OPEX costs normally account for 25% of the cost of energy. Ernst & Young [17] estimated a cost for operation and maintenance of £24/ MWh in average North Sea conditions. In this research a cost algorithm depending on distance from port (Dist_Port) has been assumed including a fixed cost of maintenance of £18/MWh and £3/ MWh for port activities and licence fees:
ABP mer) was used to calculate the available energy in each cell grid. Using the power curve for the Supergen Wind 5 MW exemplar wind turbine (cut-in wind speed 4 ms1, rated wind power 5 MW at 11 ms1, cut-out wind speed 25 ms1), the nominal output of the turbine was calculated. Wind distribution (assuming a Weibull parameter of 2.0) was expressed as a fraction of time for each nominal wind speed and using an empirical power curve for the turbine to calculate the exact power output for each site with different mean wind speed. An availability of 95% was assumed and a wake array loss of 11% was detracted for each cell [4].
OPEX ¼ fixedcost þ portfees þ ðð½NEARDIST =1000Þ*varcost =100Þ (9) where [NEAR_DIST] is the distance to the nearest suitable port. The OPEX cost assumptions are then given in Table 5. Decommissioning costs apply at the end of the wind farm operating life. As there may be a residual value associated to the wind farm assets which could be sold or reused in the event of a repowering. In this research, it is assumed that this residual value equates to the decommissioning cost. Thus, the impact of decommissioning is neutral to the overall LCOE. 3.3. Energy production The average wind speed at 100 m (Renewable Energy Atlas,
4. Results and discussion 4.1. Exclusions The first step of the model was to calculate the suitable areas for offshore wind farm development as presented in Fig. 6. Overall 11.5% of the REZ is excluded from wind energy development, with the main exclusion areas being from the Oil & Gas industry in the north east and shipping protection in the English Channel. The grid square approach to calculation makes it difficult to apply the specific exclusions directly (in general they will only exclude part of a grid square) and so the data in Fig. 6 was reevaluated in terms of the proportional exclusion in each grid square (Fig. 7).
Fig. 12. Energy potential (MWh/km2/a).
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
4.2. GIS model 4.2.1. Base case results The developed model (Appendix 1) uses an iterative approach to calculate the different components of CAPEX, OPEX and energy potential for each grid square within the REZ. The spatial distribution of each cost components is presented in Fig. 8 to Fig. 14. Foundation costs are sensitive to water depth, which directly influences the type of technology chosen by the model (0e30 m monopile, 30e60 m jacket and >60 m floating). While costs of monopile and jacket foundations are based on empirical data from the literature, the cost of floating foundations is not yet well understood and characterised, so these costs should be treated with lower confidence. As presented in Fig. 8, the main cost driver is water depth with a lesser effect of distance from the nearest port for installation activities. Transmission costs are very sensitive to cable length due to onshore substation grid connection (Fig. 9). The cost ranges from around 0.2 £m/MW for areas within approximately 25 km from an onshore grid substation to above 1.0 £m/MW for sites as much as 500 km away from the grid. The model might underestimate these extreme cases as the data used in the cost algorithm creation did not including marine cables longer than 120 km so the technical feasibility and cost increase with distance above 200 km must be considered to have high uncertainty. Installation costs (Fig. 10) resemble the pattern previously
223
observed for the transmission cost with increases directly proportional to the distance from nearest port. In the majority of cases the nearest port is adjacent to a grid substation because industry complexes accessing that port facility for logistics also require available power for their industrial processes. Fig. 11 presents OPEX values ranging from 21 £/MWh, including both fixed operations and maintenance component (18 £/MWh) plus fixed port licences and fees (3 £/MWh) to 62 £/MWh for sites distant from port which also include a variable component to take into account the journey to site and time delays considering the challenging marine weather conditions. Apart from the initial capital and yearly maintenance cost a key variable in the assessment of offshore wind cost of energy is the potential energy production which must justify the initial high cost of investment in offshore wind. The purpose of an offshore wind farm is to convert kinetic energy from wind into electricity and the quality of the wind regime is a vital factor. The results from the energy production algorithm are presented in Fig. 12 showing, as expected, the highest energy potential in areas of open ocean off the coast of Scotland with the lowest energy yields close to shore where lower average wind speeds prevail. The energy potential in Fig. 12 can be adjusted for the excluded regions shown on Fig. 7 to produce a map of maximum available energy (Fig. 13). Finally, Fig. 14 presents the LCOE for the UK REZ where all the previously discussed components are combined to calculate the
Fig. 13. Energy available for exploitation (MWh/km2/a).
224
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
Fig. 14. LCOE for the UK REZ.
Table 6 Average LCOE [£/MWh] for the three depth categories in UK-OWE-GIS. Water depth [m]
REZ [%]
Mean energy per turbine (MWh/a)
Capacity factor
Mean CAPEX (£m/MW)
Mean LCOE [£/MWh]
0e30 m 30e60 m >60 m
9.9% 14.9% 75.2%
19384 22790 25848
0.44 0.52 0.59
2.58 2.86 4.34
127.25 116.07 155.66
break-even value of electricity at which the investment in the wind farm is economically justified. Different patterns emerge from the analysis: - Sites close to shore with low cost of capital investment and intermediate average wind speed obtain the lowest LCOE in the entire UK REZ; - Sites close to shore with low potential energy production obtain, despite the low cost of capital investment high values of LCOE;
Table 7 Average LCOE [£/MWh] from UK-OWE-GIS compared with DECC [10]. Mean LCOE [£/MWh]
[10]
OWE GIS (2014)
Round 2 Round 3
113 120
105 114
Fig. 15. Sensitivity analysis of the OWE-GIS across the whole UK-REZ (green ¼ reduction of parameter; red ¼ increase of parameter). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
225
Fig. 16. Sensitivity analysis of the OWE-GIS in different water depth regions (green ¼ reduction of parameter; red ¼ increase of parameter). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
226
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
- Sites with the highest energy potential but furthest from grid distribution and in high water depths obtain high values of LCOE confirming the importance of cost over wind speed (but note the much greater uncertainty attached to costs for floating wind turbines e i.e. water depths above 60 m in Fig. 3). Table 6 summarises the average LCOE for the three depth bands and associated foundation types considered: 0e30 m (monopile), 30e60 m (jacket), and >60 m (floating). Surprisingly, the mean LCOE is higher in the shallower water band than the intermediate band, perhaps reflecting lower energy yields in regions closest to the coast. Comparing the potential LCOE for the Crown Estates's Round 2 and Round 3 sites the UK-OWE-GIS produces numbers (Table 7) broadly in line with the DECC [10] levelised cost estimates for projects starting in 2013. 4.2.2. Model sensitivity Six parameters were altered systematically to test model sensitivity (Fig. 15): -
interest rate (±2%), lifetime of the wind farm (±5 years), availability (±5%), CAPEX (±5%), average wind speed (±5%), bad weather factor (double installation times to represent bad weather)
The mean cost of energy across the whole UK Renewable Energy Zone was evaluated as 147 £/MWh. Sensitivity to interest rate was greatest, with 2% increase or decrease resulting in a change of ± 15 £/MWh. Shortening the lifetime to just 15 years had the next biggest effect, adding 13 £/MWh to the energy cost. Although 15 years is much shorter than the design life, it might be considered to show the cost risk of early failure of items or a decision to upgrade before 20 years. Lengthening the lifetime to 25 years gave a proportionately smaller advantage of 7 £/MWh, indicating a lower benefit to be gained from “sweating the assets”. The other parameters, CAPEX, availability and wind speed, showed similar variations of LCOE of approximately 5 £/MWh in the ranges considered, as far as averaged costs across the whole zone are concerned. Wind speed, however, has a much bigger effect on cost of energy in the near-shore, shallower waters than in further out, deeper waters (Fig. 16). Lower wind speeds have a proportionately bigger impact than higher wind speeds, adding weight to the argument in section 4.2.1 that the higher cost of energy in the 0e30 m zone compared with the 30e60 m zone is due to a higher proportion of sites being inshore with lower wind speeds. As might be expected, bad weather has highest impact in the 30e60 m zone where foundation work, in particular, is more complex. Clearly, the results are also sensitive to the many cost assumptions made in the model and presented in detail in sections 3.1e3.3. Cost information is scarce in the wind energy industry and it is also difficult to predict future technological advancements. For example,
there are potential cost reductions to be gained by standardising foundation types and research efforts on floating turbines could significantly decrease the cost of positioning turbines in deeper waters. The fast development of HVDC could lead to capital cost reductions and reduction in energy losses over long distance transmission. Increasing turbine size and power rating will further impact the installation cost. Overall, the cost of an emerging technology such as offshore wind, can be expected to reduce along a trajectory known as the ‘learning’ or ‘experience’ curve as more units are manufactured and installed. 4.3. UK offshore wind energy potential The discussion in section 4.2 has so far ignored constraints. To calculate the maximum offshore wind energy potential, it is necessary to take account of the “hard” constraints presented in section 4.1. In addition to these, there are a number of “soft” constraints presented by other potential uses of the sea, such as actual and potential environmental protection areas, heritage coastline, visual landscape, civil and military radars, etc., which have been ignored in this work but which will inevitably reduce the realisable potential. The economically accessible potential then depends only on the cost which the customer is willing to pay. This is a complex issue since the Levelised Cost of Energy (LCOE) presented here is different from the so-called “strike price” which the Government might be willing to contract for [see: [10e12]]. Table 8 shows the potential wind capacity available for a range of final LCOE, together with the percentage of the UK-REZ covered at each stage; essentially this is the data depicted in Fig. 14. This study finds that a total of up to 675 GW nameplate capacity might be available at a cost of less than 120 £/MWh, about the modelled cost of the Round 3 sites, or 1450 GW at less than 140 £/MWh. If 10% of this area were exploited then 150 GW could be installed at less than 140 £/MWh. The mean (calculated from predicted wind speed distribution) capacity factors in Table 8 are in the range 41e48%, substantially higher than the values of 38% (Round 2) and 39% (Round 3) assumed by DECC [10], which, in turn, were consistent with the 38% assumed by Ernst & Young [17]. These new modelled estimates agree better with published values by Energy Numbers [16] for Danish wind farms in the North Sea which quote 12-months rolling capacity factors of more than 50% for several wind farms. The higher capacity factors in the current study account for the lower mean values of LCOE compared with DECC [10]. 5. Conclusions The presented cost model can simulate major technical and economic aspects of an offshore wind farm and can be used in preliminary studies for offshore wind energy assessment. The combined use of cost data and site specific marine conditions has allowed the evaluation of the available wind energy capacity in UK territorial waters. This robust evidence based approach can be used
Table 8 Economically accessible wind energy potential in the UK-REZ. LCOE [£/MWh]
<100
100e110
110e120
120e130
130e140
140e150
150e160
Capacity [GW] Available capacity [GW] Proportion of UK-REZ [%] Energy [TWh] Capacity factor
7.2 4.9 0.2 18.915 0.44
400 304 (308) 8.8 (9.0) 1191 (1209) 0.45
418 367 (675) 9.2 (18.2) 1448 (2658) 0.45
147 133 (808) 3.2 (21.4) 480 (3138) 0.41
689 640 (1448) 15.2 (36.5) 2714 (5852) 0.48
951 880 (2328) 20.9 (57.4) 3736 (9588) 0.48
608 560 (2888) 13.4 (70.8) 2375 (11,964) 0.48
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
to either identify areas for future development or to assess existing Phase 3 development sites in a consistent manner. In addition, the tool allows testing policy or investment priorities with different future scenarios. The developed OWE-GIS tool could be applied to other areas of Northern Europe with similar wind profiles and bathymetric conditions to the UK. The research approach could also be used for other parts of the world as the proposed constants and equations offer enough flexibility to include different scenarios and might need only minor refinements. Since accurate cost information is hard to come by in the wind energy industry, this study has adopted similar cost models for CAPEX and OPEX to those prevailing in recent UK Government studies. The overall Levelised Cost of Energy (LCOE) estimates developed here are generally lower than those in recent compa-
227
constraints presented by other potential uses of the sea that will reduce the realisable energy potential of offshore wind. These constraints are difficult to quantify and will require the use of marine planning instruments to resolve potential conflicts ultimately achieving optimal site selection. Acknowledgements The Engineering and Physical Sciences Research Council (EPSRC) supported this work through grant no. EP/H018662/1. The work was carried out as part of the Supergen Wind Energy Technologies (Phase 2) Consortium. Appendix 1
Fig. A-1. UK OWE-GIS model structure in ArcGIS 10.1, incorporating input variables and code blocks.
rable studies [26] due in part to the fact that capacity factor has been estimated based on the predicted wind resource at each grid square, rather than assuming a single mean value across all sites. Sensitivity analysis has shown that, for such a capital intensive undertaking as an offshore wind farm, interest rate is a critical parameter in determining the cost of output energy. The cost of energy is less sensitive to project lifetime, provided that the design lifetime of 20 years can be achieved. Wind speed is particularly critical for shallow water sites, presumably because these tend to be closest to the shore with generally lower mean wind speeds. Acknowledging the approximate nature of some of the cost estimates, it is found that if only 10% of the accessible UK offshore wind resource were to be exploited then 150 GW of capacity could be installed at less than 140 £/MWh (or 67.5 GW at less than 120 £/MWh). It is worth noting that there is a number of “soft”
References [1] A. Abudureyimu, J. Hayashi, K. Nagasaka, Analyzing the economy of off-shore wind energy using GIS technique, APCBEE Procedia 1 (2012) 182e186. [2] T. Ackermann, Evaluation of electrical transmission concepts for large offshore wind farms, in: Copenhagen, Denmark, Proc. Copenhagen Offshore Wind Conference and Exhibition, 2005. [3] C.L. Archer, M.Z. Jacobson, Evaluation of global wind power, J. Geophys. Res. 110 (2005) D12110. [4] P. Argyle, Computational Fluid Dynamics Modelling of Wind Turbine Wake Losses in Large Offshore Wind Farms, Incorporating Atmospheric Stability, Doctoral Thesis, Loughborough University, 2015. [5] CEC, Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources (Renewable Energy Directive), s.l.: Commission of the European Communities, 2009. [6] T. Cockerill, et al., Combined technical and economic evaluation of the Northern European offshore wind resource, J. Wind Eng. Ind. Aerodyn. 89 (2001) 689e711.
228
S. Cavazzi, A.G. Dutton / Renewable Energy 87 (2016) 212e228
[7] M. Collu, A.J. Kolios, A. Chahardehi, F. Brennan, A Comparison Between the Preliminary Design Studies of a Fixed and a Floating Support Structure for a 5 MW Offshore Wind Turbine in the North Sea. s.l., RINA, 2010, p. 63. Royal Institution of Naval Architects-Marine Renewable and Offshore Wind Energy Papers. [8] Crown Estate, Offshore Wind Cost Reduction Pathways Study, Crown Estate, 2012. [9] Y. Dalgic, I. Lazakis, O. Turan, Vessel charter rate estimation method for offshore windfarms O&M activities, in: International Maritime Association of Mediterranean IMAM 2013, 2013e10-14-2013-10-17, a Coruna, 2013. [10] DECC, Electricity Generation Costs 2013, s.l., DECC, 2013a. [11] DECC, Annex B: Strike Price Methodology, DECC, London, 2013b. [12] DECC, Investing in Renewable Technologies e CfD Contract Terms and Strike Prices, DECC, London, 2013c. [13] M.J. Dvorak, C.L. Archer, M.Z. Jacobson, California offshore wind energy potential, Renew. Energy 35 (2010) 1244e1254. [14] M.J. Dvorak, B.A. Corcoran, J.E. Ten Hoeve, N.G. McIntyre, M.Z. Jacobson, U.S. East Coast offshore wind energy resources and their relationship to peak-time electricity demand, Wind Energy 16 (2012) 977e997. [15] E.On, E. On Offshore Wind Energy Factbook, s.l.: Eon, 2012. [16] Energy Numbers, Capacity Factors at Danish Offshore Wind Farms, 2014 [Online] Available at: http://energynumbers.info/capacity-factors-at-danishoffshore-wind-farms [accessed 29.05. 14.]. [17] Ernst & Young, Cost of and Financial Support for Offshore Wind, Report for the Department of Energy and Climate Change, 27 April 2009, URN 09D/534, s.l.: s.n, 2009. [18] European Environment Agency, Europe's Onshore and Offshore Wind Energy Potential: an Assessment of Environmental and Economic Constraints, EEA Technical Report No. 6, European Environment Agency, Copenhagen, 2009. [19] P. Greenacre, R. Gross, P. Heptonstall, Great Expectations: the Cost of Offshore Wind in UK Waters e Understanding the Past and Projecting the Future, s.l., UK Energy Research Centre (UKERC), 2010. [20] HM Government, The UK Renewable Energy Strategy, TSO, London, 2009. [21] L. Hong, B. Moller, Offshore wind energy potential in China: under technical, spatial and economic constraints, Energy 36 (2011) 4482e4491. [22] L. Hong, B. Moller, Feasibility study of China's offshore wind target by 2020, Energy 48 (2012) 268e277. [23] International Energy Agency, Technology Roadmap Wind Energy (Technical
Report), 2013. [24] M. Kuhn, UPWIND WP4 Offshore support structures, s.l., in: EWEC 2007: UPWIND Workshop, 2007. [25] LCICG, Technology Innovation Needs Assessment (TINA): Offshore Wind Power e Summary Report, s.l., Low Carbon Innovation Coordination Group (LCICG), February 2012. [26] A. Levitt, W. Kempton, A. Smith, W. Musial, J. Jeremy Firestone, Pricing offshore wind power. University of Delaware, Energy Policy 39 (2011) 6408e6421. [27] B. Moller, L. Hong, R. Lonsing, F. Hvelplund, Evaluation of offshore wind resources by scale of development, Energy 48 (2012) 314e322. [28] W. Musial, B. Ram, Large-scale Offshore Wind Power in the United States, s.l., NREL, 2010. €yry, Compliance Costs for Meeting the 20% Renewable Energy Target in [29] Po €yry Consulting, Oxford, 2008. 2020, Po [30] Renewables Advisory Board, 2020 Vision e How the UK Can Meet its Target of 15% Renewable Energy, Renewables Advisory Board, London, 2008. [31] Renewables Advisory Board, Value Breakdown for the Offshore Wind Sector, s.l., RAB, 2010, p. 0365, 2010. [32] S. Rodrigues, C. Restrepo, E. Kontos, R. Teixeira Pinto, P. Bauer, Trends of offshore wind projects, Renew. Sustain. Energy Rev. 49 (2015) 1114e1135. [33] P. Schaumann, C. Boker, Can jackets and tripods compete with monopiles, in: Copenhagen, Proceedings of European Offshore Wind Conference, 2005. [34] C. Schillings, T. Wanderer, L. Cameron, J.T. van der Wal, J. Jacquemin, K. Veum, A decision support system for assessing offshore wind energy potential in the North Sea, Energy Policy 49 (2012) 541e551. [35] M. Seidel, Jacket substructures for the REpower 5M wind turbine. s.l., in: Published in: Conference Proceedings European Offshore Wind 2007. Berlin, 2007. [36] SKM, Growth Scenarios for UK Renewables Generation and Implications for Future Developments and Operation of Electricity Networks, SKM, Newcastle, 2008. [37] The Crown Estate, Round 3 Offshore Wind Site Selection at National and Project Levels, The Crown Estate, London, 2009. [38] I. Troen, E. Petersen, European Wind Atlas, Riso National Laboratory, Roskilde, Denmark, 1989. [39] B. Van Eeckhout, The Economic Value of VSC HVDC Compared to HVAC for Offshore Wind Farms, Master Thesis, Katholieke Universitet Leuven, 2008.