Spatial planning to estimate the offshore wind energy potential in coastal regions and islands. Practical case: The Canary Islands

Accepted Manuscript Spatial planning to estimate the offshore wind energy potential in coastal regions and islands. Practical case: the Canary Islands

Julieta Schallenberg-Rodríguez, Nuria García Montesdeoca PII:

S0360-5442(17)31789-9

DOI:

10.1016/j.energy.2017.10.084

Reference:

EGY 11728

To appear in:

Energy

Received Date:

05 March 2017

Revised Date:

28 September 2017

Accepted Date:

18 October 2017

Please cite this article as: Julieta Schallenberg-Rodríguez, Nuria García Montesdeoca, Spatial planning to estimate the offshore wind energy potential in coastal regions and islands. Practical case: the Canary Islands, Energy (2017), doi: 10.1016/j.energy.2017.10.084

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ACCEPTED MANUSCRIPT Highlights -

Novel methodology to determine off-shore wind potential for small regions and islands based on spatial planning and GIS.

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Inclusion of techno-economic constrains.

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Economical assessment to determine the marginal generation cost of wind energy.

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Practical case: application to the Canary Islands.

ACCEPTED MANUSCRIPT

Spatial planning to estimate the offshore wind energy potential in coastal regions and islands. Practical case: the Canary Islands Julieta Schallenberg-Rodríguez Nuria García Montesdeoca Universidad de Las Palmas de Gran Canaria, Phone (+34) 928451936, [email protected]

Abstract The Canary Islands, as many islands and coastal regions, are characterized by no conventional energy sources (but renewable resources, mainly wind and solar), by a high population density and land scarcity. Taking into account this context, it is crucial to determine the offshore wind energy potential as a first step for the energy planning. For this purpose, a methodology adapted to islands’ and coastal regions’ requirements has been developed. The methodology is based on GIS (Geographical Information Systems), and takes into account technical, economic and spatial constrains. Wind turbines (bottom-fixed or floating according to the bathymetry) are placed within the resulting suitable areas, quantifying also the energy production and its cost. The economic analysis includes the calculation of the LCOE (Levelized Cost Of Energy), including integration costs, and the resulting resource cost curves. The methodology has been applied to a practical case, the Canary Islands. Results show that the electricity produced by offshore wind farms exceeds the yearly electricity demand. Moreover, the offshore wind energy cost is lower than the current electricity cost. The analysis provides further useful indicators such as percentage of suitable areas, surface covered by wind turbines, array density of turbines and marginal offshore wind energy cost. Keywords Offshore wind energy; potential; GIS; Canary Islands 1

Introduction.............................................................................................................................................................2

2

Methodology...........................................................................................................................................................4

3 Suitable marine areas ..............................................................................................................................................5 3.1 Spatial Restrictions ........................................................................................................................................................................5 3.2 Techno-economic constraints ...................................................................................................................................................8 3.2.1 Bathymetry ...................................................................................................................................................8 3.2.2 Minimum average wind speed......................................................................................................................8 3.3 Suitable areas for wind production.........................................................................................................................................9 4 4.1 4.2

Wind farm configuration and turbines placement ..................................................................................................9 Wind farm configuration: wake effect ...................................................................................................................................9 Turbines placement ....................................................................................................................................................................10

5 5.1 5.2 5.3

Wind energy production .......................................................................................................................................11 Wind data.........................................................................................................................................................................................11 Determination of the wind production...............................................................................................................................11 Variability of the wind production .......................................................................................................................................12

6 6.1

Wind production cost............................................................................................................................................12 Determination of the LCOE ......................................................................................................................................................12

1

ACCEPTED MANUSCRIPT 6.2

Integration costs ...........................................................................................................................................................................15

7 7.1 7.2 7.3 7.4 7.5 7.6 7.7

Analysis of results.................................................................................................................................................16 Suitable areas.................................................................................................................................................................................16 Array density of turbines ..........................................................................................................................................................18 Offshore wind power and production.................................................................................................................................19 Offshore wind energy cost........................................................................................................................................................20 Economically viable offshore wind energy production versus electricity demand ........................................22 Marginal offshore wind energy production cost ............................................................................................................23 Surface covered by offshore wind energy and comparison to onshore ...............................................................24

8

Future research lines .............................................................................................................................................24

9

Conclusions...........................................................................................................................................................24

1

Introduction

The Canary Islands (Spain) is an archipelago similar to many other archipelagos and touristic coastal regions in terms of energy dependency, population density and land scarcity. 98% of its primary energy consumption is based on imported oil. In terms of electricity, this percentage was 92% in 2016 [1]. The Canary Islands, as the majority of the archipelagos, have no conventional energy sources, but plenty renewable resources, mainly wind and solar energy. For any archipelago located far away from mainland, like the Canary Islands, it is of high importance to increase the level of energy self-sufficiency. This can only be done by deploying renewable energy sources (RES), since the distance (and depth) to mainland makes underwater electrical connection economically unviable. The only existing underwater cable is of limited power and lies between the islands of Lanzarote and Fuerteventura. The seven islands are powered using six autonomous electrical grids. Renewable energies are autochthonous energy sources, thus they can contribute to reduce the energy dependency, diversify the energy resources and encourage regional development. All these facts gain special importance in isolated regions like the Canary Islands. Total installed electrical power in the Canary Islands at the end of 2016 was 2768 MW, 11.5% of which was from renewable energies, but in production terms this percentage falls to just 8%. The RES installed in the Canary Islands are mainly wind and solar photovoltaic, 153 MW and 166 MW respectively in 2016 [1]. For the year 2025, the Energy Strategy of the Canary Islands establishes that 45% of the installed electricity power should be RES, mainly wind and solar [2]. However, available land is scarce in the Archipelago. The total surface of the Archipelago is 7447 km2 and over 40% of this surface is protected. Moreover, the available area to install onshore wind energy is just 12.5% of the territory [5]. The islands’ population was 2.1 million in 2016 and nearly 14 million tourists visited the archipelago that year [3]. These figures lead to an average population density of roughly 570 people/km2. Therefore, it is understandable that land availability is an issue on the islands. There is a strong territorial pressure on the islands, finding available land for renewable facilities is difficult since they compete with urban, rural, agriculture and tourist developments. Considering both issues, land scarcity and renewable objectives, it is crucial to determine the offshore wind energy potential as a first step for the energy planning. For this purpose, a methodology based on GIS (Geographical Information Systems) that includes spatial, technical and economic constrains has been developed. This methodology focuses in island and small coastal regions and it is meant to be a valuable tool to be applied worldwide to determine the offshore wind energy potential in islands and small coastal regions.

2

ACCEPTED MANUSCRIPT Methodologies to determine the onshore wind potential based on spatial planning using GIS have been vastly described in the literature [4-14]. However, literature on offshore wind energy potential using GIS is scarce. Some references have been found but the targeted geographical areas were too big in comparison to the island/regional dimension needed for this paper. The scope of the papers reviewed was either very big countries like the U.S. or China [15-17] or countries like Denmark [18,19] or big states like California [20]. Therefore, the spatial planning proposed in these articles targeted big areas, which is not adequate to accurately determine the offshore wind energy potential in islands and small regions. The evaluation of offshore wind energy potential using GIS allows the quantification of the potential wind energy production and, at the same time, it allows to locate where this production takes place. This is vital to determine the resource cost, which depends on several factors but mainly on the average wind speed and the bathymetry (which is the key parameter to select bottom-fixed or floating turbines), but also to take into account any spatial constraint as natural protected area, maritime routes, military areas, etc. This methodology is also key for the planning phase since other parameters could be taken into account, which are also relevant to determine the final cost, e.g. distance to shore (which determines the length of the underwater cables), type of seabed (which affects the foundation cost, among others), availability of near onshore power substations, etc. This paper includes an economic assessment that determines the LCOE of offshore wind energy. Integration costs have also been taken into account to calculate the total wind energy cost. Finally, offshore wind energy costs are compared to the current electricity cost to determine the offshore wind energy potential that is economically viable in comparison to the current electricity cost. The paper is structured in 8 sections. Section 2 summarizes the methodology developed. Section 3 deals with the spatial restrictions and the techno-economic constraints, whose implementation leads to the resulting suitable areas. Section 4 addresses the placement of the wind turbines, considering the wake effect and all the restrictions described in section 3. Section 5 deals with the calculation of the wind energy production. Section 6 is devoted to the economic assessment: LCOE (Levelized Cost Of Energy), integration costs, cost-resource curves and marginal offshore wind energy cost. Section 7 analyses the results of the case study, which is the Canary Islands, providing new indicators that can be useful for future researches. Section 8 concludes.

2

Methodology

There are two main factors influencing the offshore wind energy potential: wind regime and available marine areas. The first step to estimate the offshore wind energy potential is to establish the marine areas that are suitable for the installation of offshore wind farms. This is done as follows: 1. Determination of exclusion areas (spatial constrains). 2. Determination of techno-economic constraints: minimum wind speed (annual average values) and bathymetry. 3. Implementation of all constrains in a GIS software package. One of the advantages of this methodology is that all results can be implemented in the GIS tool to elaborate the required maps, pinpointing areas where offshore wind energy is suitable [21]. The next step is to calculate the wind energy production. This is done as follows: 1. Determination of the wind farm configuration. 2. Placement of the wind turbines within the suitable marine areas using the selected configuration. 3. Calculation of the electricity produced by each wind turbine.

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ACCEPTED MANUSCRIPT Finally, the wind energy cost can be calculated as well as the resulting resource cost curves. The offshore wind energy cost can then be compared to the current electricity cost. The methodology developed can be summarized in 4 steps as shown in figure 1.

Figure 1. Methodology in 4 steps

3 3.1

Suitable marine areas Spatial Restrictions

Spatial restrictions have been identified according to the literature review and Governmental regulations (as the one that regulates safety areas around airports [22,23]). Each restriction identified includes a buffer area. Buffer areas are the areas surrounding the constraint and they are also considered part of the exclusion area. Restrictions for offshore wind energy development have already been analyzed by some authors [15-19]. Some of them considered also buffer areas [16,19,24] while others did not [18]. Table 1 summarizes the restrictions found in the literature as well as their buffer areas.

4

ACCEPTED MANUSCRIPT Table 1: Review of wind offshore spatial restrictions and buffer areas Restricted areas

Source

Buffer area

Extern boundary: -

50 nautical miles from shore

[15]

-

Exclusive economic zone (EEZ)

[16]

Intern boundary: -

12 miles from the coast (mainly to allow sand extraction)

[23]

-

5 – 15 km from the coast

[18]

-

8 km

[16]

Protected areas: I.

II.

Marine Reserves

NATURA 2000

Fishing grounds Military exclusion areas Main shipping routes

Oil and gas platforms Underwater cables and pipelines

[17]

-

[18]

1 – 2 km

[19]

0.5 – 1 km

[17,23]

-

[16]

3 km

[19]

1 – 2 km

[19]

1 – 2 km

[16,23]

-

[23]

2 nautical miles

[16]

1 – 3 km

[17]

-

[16,23]

500 m

[17]

-

[17]

-

[16]

500 m

Table 2 summarizes the restrictions considered in this study, as well as their buffer areas. Table 2: Off-shore spatial restrictions and buffer areas Restricted areas

Buffer area

Extern boundary: Territorial waters: 12 miles around each island Bathymetry restriction (see next section): 500 m Intern boundary (visibility restriction): 1 km from the coast Protected areas: 1. 2. 3. 4.

Marine Reserves Biosphere Reserves NATURA 2000: Special Protection Areas (SPAs) for birds & Special Areas of Conservation (SACs) Natural Protected Areas

Harbors (commercial, sport and fishing)

1000 m

500 m

5

ACCEPTED MANUSCRIPT Fish farms

500 m

Fishing grounds

500 m

Military exclusion areas

500 m

Shipping routes

500 m

Airports

Trunk-conical area at the end of both sides of the airstrip, 3500 m long, 10º aperture angle and 4000 m diameter [22,23]

In comparison to table 1, table 2 shows similarities but also some differences. The first constraint selected in this study was the extern ocean boundary, which are legally the territorial waters of the Canary Islands. But the bathymetry, which is a technical constraint, has been more restrictive than the territorial waters. A maximum bathymetry of 500 m has been selected, being the territorial waters more deep around all islands. Therefore, the practical extern boundary limitation was really based on the maximum bathymetry allowed rather than the territorial waters. Other differences are the inclusion of three new categories in table 2, namely airports, fish factories and harbors. The reason to include airports as a restriction is because approximation areas are completely restricted and these areas are relatively important in comparison to the surface of the islands. Fish factories and harbors represent also relatively significant areas and must be considered. On the other hand, two categories included in table 1 have not been considered in this study, namely “oil and gas platform” and “underwater cables and pipelines”, because there are neither oil and gas platforms, nor pipelines within the territorial waters of the Canary Islands. On the other hand, the visibility constraint is less restrictive in this study, mainly because the seabed around the islands is very deep and a constraint that would impose long distances to shore is not viable. Once all restrictions and their buffer areas are defined, as shown in table 2, they are implemented in the GIS. The GIS software packages utilized in this study was ArcGIS version 9 from the Environmental Systems Research Institute (ESRI) and the open source GIS software package Quantum-GIS, version 2.18.2, called Las Palmas. Each territorial constraint (including its buffer area) is one layer that represents marine areas where wind turbines cannot be installed. Figure 2 shows the territorial waters of the Canary Islands, which represent the legal extern boundary in this study. Figure 3 shows two maps of Gran Canaria island, the left side shows the island including all spatial restrictions listed in table 2 and the right side shows the same restrictions but adding also the buffer areas.

6

ACCEPTED MANUSCRIPT Figure 3 shows that the biggest exclusion area corresponds to the marine SAC (Special Areas of Conservation), which are part of the NATURA 2000 network. This restriction is the biggest in all islands except for Lanzarote, where the biggest restriction corresponds to the marine reserve followed by the marine SAC. Environmental restrictions have thoroughly been taken into account and they represent the main exclusion area. All these environmental restrictions try to minimize mammals and birds’ affections. Birds’ affection due to wind energy has been broadly studied; the impact during the operational phase seems not to be significant [25].

Figure 2. Territorial waters of the Canary Islands

Figure 3. Spatial restrictions, island of Gran Canaria, without buffer (left side) and with buffer (right side)

7

ACCEPTED MANUSCRIPT 3.2 3.2.1

Techno-economic constraints Bathymetry

Some authors [15-17] establish a maximum distance to shore as a restriction. The reasoning behind is that long distances to shore imply longer underwater cables and, therefore, more expensive connections. The seabed around Canary Islands is very deep. The Canary Islands are volcanic islands, which means that they were originated by volcanic eruptions. Therefore, very profound depths are reached relatively near to shore. The depth between islands can reach 3000 m. For this reason, in this study, the restriction selected has been the maximum depth instead maximum distance to shore. A maximum depth of 500 m has been selected. Figure 4 shows the 500 m bathymetry around each island. This bathymetry represents a more restrictive condition than the territorial waters. Therefore, it establishes the new extern boundary for this study, as mentioned also in the previous section. The maximum depth of 500 m is high in comparison to other studies and current technology status. But this study wants to serve as a planning instrument, considering long-term energy planning (until 2050 and beyond), when deep offshore wind energy should be a reality. Anyhow, the study provides results for depths below 50 m (bottom-fixed turbines) and for depths between 50 and 500 m (floating turbines). Some authors, e.g. [16], have considered only bottom-fixed turbines in their studies and, therefore, have limited the maximum depth to 50 m. Other authors have considered more profound depths, like 60 m [18] or 200 m [20].

Figure 4. Bathymetry of -500 m

3.2.2

Minimum wind speed

Another constraint is the local wind condition. Sites where the average annual wind speed is below the range 6 – 6.5 m/s at 80 m (the hub height) were not taken into consideration for this research, since the economic feasibility was considered to be too low. Other authors have considered slightly different values; e.g. [15,20] chose 7 m/s at the hub height (80 m), [17] selected 6.4 m/s at 90 m height. The selection of the minimum average wind speed affects the economic part of the study. The lower the average wind speed selected, the higher the wind energy cost. Wind turbines that exhibit a high LCOE (Levelized Cost Of Energy) will eventually be excluded from the economic potential due to its high cost (in this study the marginal cost considered is the current electricity cost). The way to implement the minimum wind speed restriction is to include the geo-referenced average wind speed at the hub height as a layer in the GIS tool and include a restriction to exclude all areas where the mean wind speed is lower than the selected value.

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ACCEPTED MANUSCRIPT This restriction of minimum wind speed affected the suitable areas in Tenerife (northwest part of the island), La Gomera (the whole east part of the island) and, to a lesser extend, the Lanzarote-Fuerteventura system (a small portion in the west part of the islands) and a small area in the south-east part of La Palma island. In the other islands, the required wind speed did not really affect the suitable areas, since the average wind speed was always higher than 6 m/s.

3.3

Suitable areas for wind energy production

Once all these constraints have been implemented in the GIS, the resulting maps show the areas where the wind farms can be located in each island. Figure 5 shows the suitable areas in Tenerife island (right side) in comparison to the spatial restrictions as per table 2 (left side). The suitable areas are the result of adding the bathymetry and the wind speed restrictions to the spatial ones.

Figure 5. Tenerife: spatial restrictions (left side) and suitable areas (right side)

4 4.1

Wind farm configuration and turbines placement Wind farm configuration: wake effect

The next step is to place the wind turbines in the suitable areas. The array efficiency (ar) is proportional to the inter-turbine and inter-row spacing, which are, in turn, function of the rotor diameter [26]. Wind turbines reduce the wind speed downwind the rotor. If wind turbines are located too closely, they interfere with each other, reducing the energy output of those downwind. The array efficiency is the actual energy output of clustered turbines compared to the one that would be produced without interference [27]. Thus, wake effect determines the array efficiency. Array losses can be reduced by optimizing the layout of the wind farm. In the Canary Islands the predominant winds are the trade winds, which are known for being the steadiest wind system in the lower atmosphere [28]. This suggests that wind turbines downwind, but specially crosswind, could be placed closer, keeping a high array efficiency. A micro-siting study of each wind farm, that would optimize the wind farm layout, is out of question for a general study at regional scale as this one. Therefore, a common spacing downand crosswind have been chosen for all wind farms. This simplified solution is the feasible one for regional studies [26]. Therefore, there is still room for increasing the wind production calculated in this study.

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ACCEPTED MANUSCRIPT Wind farm configuration can be chosen attending to several criteria [26]. The criterion selected in this study is the “minimization of the wind energy cost”, seeking for array efficiencies close to 100%. The wind farm configuration chosen is: downwind distance of 12 diameters (D) and crosswind distance of 4D (feasible thanks to the steady behavior of the trade winds). These distances are indeed big but this configuration seeks for the minimization of the wake effect. Therefore, the estimated array losses are negligible. Array efficiencies of 100% are theoretical, since they cannot be reached in clustered wind turbines, but this configuration leads to very high array efficiency, approaching 100% [26]. Other studies of offshore wind energy potential have chosen different configurations, e.g. [16] selected a square matrix of 8D, while [20] selected a configuration of 4D x 7D. A detailed description of wind farm configurations, wake affect and array efficiency can be found in [26].

4.2

Turbines placement

Once the suitable areas are identified and the wind farm configuration is chosen (12D x 4D), the next step is to select the wind turbine. Then the inter- and intra-array distances can be calculated since they are directly proportional to the rotor diameter. The wind turbine selected for this study is the G128-5.0 MW, hub height at 80 meters and rotor diameter of 128 m. This means that the distance among wind turbines is 1536 m downwind and 512 m crosswind. Finally, wind turbines are placed in the suitable areas, according to the resulting matrix form. Figure 6 shows the resulting location of each wind turbine in La Gomera island. The points represent each wind turbine that can be installed according to the distance requirements. The darker points represent the bottom-fixed wind turbines while the white points represent the floating wind turbines.

Figure 6. Location of each wind turbine in La Gomera, bottom-fixed (darker points) and floating (white points)

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ACCEPTED MANUSCRIPT 5 5.1

Wind energy production Wind data

As mentioned before, the predominant winds in the Canary Islands are the trade winds; winds from the northeast direction known for being very constant. There are several sources of offshore wind data for the Canary Islands. Moreover, an offshore wind map was developed by [29]. This wind map provides data at a grid scale of 3 x 3 km and includes the UTM coordinates, their corresponding wind speed (m/s), predominant wind direction and k shape factor of the Weibull distribution for each grid cell at an altitude of 40, 60 and 80 meters. This map is the one that has been utilized for this study.

5.2

Determination of the wind production

According to the approach followed in this paper, the energy production has been calculated as a function of the characteristic power curve and the wind distribution, namely mean wind speed and the shape factor of the Weibull distribution, k. The annual energy production is calculated according to equation 1 [26].

Eq. 1 where: P(vj): power output for the velocity j (kW), values from the power curve; s: width of the velocity interval selected (s =v); F(vj-s/2): cumulative wind distribution for the velocity j-s/2 (m/s); F(vj+s/2): cumulative wind distribution for the velocity j+s/2 The annual energy production for any location (x,y) at the hub height is calculated from equation 2 [26].

Eq.2 where: k: shape factor of the Weibull distribution at the hub height vE: cut-in wind speed vA: cut-off wind speed v: mean speed at the hub height (m/s)

(x): gamma function

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ACCEPTED MANUSCRIPT 5.3

Variability of the wind production

The previous section provides results on the yearly energy production but it does not provide any information on the variability of this wind energy. The trade winds, which dominates in the Canary Islands, have a strong seasonal behavior, being much more strong in summer than in winter. Figure 7 shows the monthly variation of the wind measured during 1998 in one met tower located in the North of Gran Canaria. The seasonal behavior is very clear. This monthly variation leads to an increase in the storage need, reserve requirements, ramping and balancing costs, among others. In short, the daily and seasonal behavior leads to an increase in the integration costs. Integration costs are discussed in the next section and have been included in the calculation of the wind energy cost.

Figure 7. Monthly variation of the wind resource

6 6.1

Wind production cost Determination of the LCOE

The most representative indicator to calculate the cost of the wind electricity production is the LCOE (Levelized Cost Of Energy). The cost components that have to be estimated to calculate the LCOE are the capital investment cost (CAPEX), the operation and exploitation cost (OPEX) and the decommissioning cost. Investment and operation costs are estimated separately for bottom-fixed and floating turbines. Bottom-fixed turbines can be installed in waters whose maximum depth is 50 m and floating turbines in waters deeper than 50 m, which leads to big differences in their costs. The annualized wind energy cost (€/kWh), LCOE, can be calculated from equation 3. LCOEx,y =

a × CAPEX + OPEX

Eq. 3

heq(x,y)

where: LCOEx,y: LCOE of wind electricity (€/kWh) in location x,y r

a: annuity factor, calculated from: a = 1 ‒ (1 + r) ‒ LT

12

ACCEPTED MANUSCRIPT CAPEX: capital investment cost (€/kW) r: interest rate (6%) LT: lifetime (30 years) OPEX: yearly operation and exploitation cost (€/kW·a) heq(x,y): annual equivalent hours in location x,y (h/a) heq(x,y) = Ex,y/WTpower

Eq. 4

where: Ex,y: annual energy production in location (x,y) calculated from equation 2 (MWh per year) WTpower: rated power of the selected wind turbine (MW) The interest rate for 30 years has been set at 6% since this study is rather conservative. Nowadays, lower rates, even of 4%, are negotiable. An interest rate of 4% would lead to an average decrease in the LCOE of 14.5%. Nonetheless, a literature review reveals a variety of values used in the energy sector. [67] suggested a value between 4% and 6%, [68] a value of 5% for the Euro-area and the USA, while other authors have implemented much higher rates in their research, like 10% [9] and 9% [65]. Estimation of the CAPEX CAPEX for bottom-fixed turbines The cost tendency in the last years for bottom-fixed turbines has been characterized by higher capital costs than expected. The average cost of offshore wind farms has been over 3000 €/kW [30,31]. The cost of bottom-fixed turbines can vary significantly depending, among others, on the type of foundation (monopile, gravity-based or jacket), the distance to shore, the water depth and the size of the wind farm. Table 3 shows a summary of the CAPEX of bottom-fixed wind farms located in relatively deep waters, as the Canary Islands. Table 3. CAPEX of bottom-fixed turbines

Monopile

3000

Water depth (m) 30

Jacket

3750

30

Gravity-based

3000

Jacket

4500

40

115

400

Jacket

3641

30 - 42

55

714

Gravity-based

3843

30 - 40

6

42

Foundation type

Cost (€/kW)

Distance to shore (km) 200

Installed capacity (MW) 500

200

500 5

Status

Source

Theoretical Calculations Theoretical Calculations Preconstruction Operating since 2015 Preconstruction Under construction

[28] [28] Demo project Elican [32,33] Global Tech I wind farm [33] East Anglia 1 wind farm [33] Blyth demonstrator array 2 [33]

Jacket structures are better suited for intermediate water depths, deeper than 25 meters, than monopiles [15,16,19,34]. Lately, some interesting developments have also taken place in gravity-based foundations, like the project Elican from Esteyco, which is a telescopic gravitybased foundation whose demo project is being developed in Gran Canaria. This foundation can

13

ACCEPTED MANUSCRIPT be installed in water depths from 18 to 55 meters [32], which makes it suitable for the majority of the available areas in the Canary Islands. The CAPEX utilized in this study for bottom-fixed turbines is 3750 €/kW. CAPEX for floating turbines For waters deeper than 50 m, floating turbines have to be installed [15,16,19,34]. There are different models of floating turbines, most of them in a pre-commercial stage. Table 4 shows a summary of the CAPEX of floating turbines that have already been fully developed and tested in real sea conditions. Table 4. CAPEX of floating turbines

Hywind (spar floater)

3750

Water depth (m) 200

Windfloat (semisubmersible platform) Hywind 2 (spar floater)

4600

200

200

500

Decommissioned

7700

95 -120

25

30

Under construction

Windfloat 2 (semisubmersible platform)

5000

100

20

25

Authorized

Fukusima Demo2 (Semi submersible platform and advanced spar)

4350

125

20

12

Under construction

Project and foundation type

Cost (€/kW)

Distance to shore (km) 200

Installed capacity (MW) 500

Status

Source

Decommissioned

Theoretical Calculations [28] Theoretical Calculations [28] Hywind 2 (Scotland Pilot Park) [33] East Anglia 1 wind farm [33] Fukusima Demo2 wind farm [33]

The CAPEX utilized in this study for floating turbines is 4600 €/kW. The estimated CAPEX for both, bottom-fixed and floating structures, includes the cost of the export cables to shore (HVDC cables), the offshore substation and the onshore substation to transform from DC to AC [33] . Estimation of the OPEX Information on offshore wind farms OPerating EXpenses (OPEX) is even scarcer in the literature than on investment cost. Moreover, most of the information is referred to bottom-fixed turbines and references for floating turbines are really limited. The cost tendency shows that O&M costs are much higher in offshore wind farms than in onshore. Table 5 shows a summary of the OPEX of offshore wind farms found in the literature.

14

ACCEPTED MANUSCRIPT Table 5. OPEX in offshore wind farms

Bottom-fixed turbines Floating turbines

115 €/kW

OPEX per year as percentage of the CAPEX 3%

131 €/kW

3%

Bottom-fixed turbines (monopile) Bottom-fixed turbines

80 €/kW

2.6%

60 £/kW (around 70 €/kW)

2%

Foundation type

OPEX per year (€/kW)

Comments

Source

Includes operation and insurance costs Includes operation and insurance costs. Major cost increase in comparison to bottom-fixed: vessel for unplanned maintenance Monopile wind farms in Norway (2010)

[28]

UK (2010)

[31]

[28]

[30]

In this study, the OPEX has been estimated in 3% of the investment cost (the highest considered in the reviewed studies). This results in yearly OPEX amounts of 138 and 112.5 €/kW for the floating and bottom-fixed turbines, respectively. Estimation of the decommission cost The cost of the decommission process has to be compared to the incomes due to the sale of steel scrap. Steel scrap prices were analyzed from 2000 to 2013, resulting in an estimate of 323.4 €/ton in 2013 and a linearized increase of 17.4 €/year [34]. Thus, some of the steel intensive structures may have a negative decommissioning cost [34]. Therefore, in this study the decommissioning cost has not been considered in the LCOE calculation since it has been estimated that the incomes from the steel scrap sales should balance out the decommissioning cost.

6.2

Integration costs

Integration costs are mainly cause by the variability of intermittent renewable energies, like wind and solar energy. Integration costs should provide the answer to the following question: to what extent the system operating costs are increased by the variability of the renewables [35]. Variability of renewables, even if correctly forecasted, results in increased regulation and ramping of the conventional system. Large amounts of intermittent renewables typically result in more reserves and additional start-up units while, at the same time, units are forced to operate at less favorable points on their power curves. Additionally, depending on the site and the local grid, reinforcements to the existing grid may also be needed [36,37]. According to [38], integration costs can be categorized as follows: balancing costs, grid-related costs and profile costs. Integration costs depend on the power system and the penetration level of intermittent renewables. Integration costs can be negative at low (<10%) penetration; they increase with the level of penetration and are typically smaller in hydro than in thermal systems [35,38] . Balancing costs are the costs caused by the deviation from the day-ahead generation schedules. These costs are relatively small. Moreover, variable renewable energies can also supply balancing services [38]. Grid-related costs are mainly caused by grid extensions and reinforcements. Profile costs are related to the impact of the timing of the generation. Wind energy can produce large amounts of electricity when the demand is low, in other words, when the electricity prices are low. Additionally, ramping constraints require thermal plants to run at part load. Nonetheless, ramping requirements are easily met by all power systems except for small island systems, and, anyway, they do not add a significant cost.

15

ACCEPTED MANUSCRIPT When the penetration level of renewables increases, the utilization rate of thermal capacity decreases, which increases the specific capital cost of thermal plants. In conventional (thermal) systems, with high variable renewable energy shares, the utilization rate can be more than half of all integration costs. In this regard, the largest integration cost component is the reduction of the utilization of the capital cost. This last cost component has usually not been considered as an integration cost in the literature [38]. Considering all mentioned cost components, the estimated wind integration costs is 25 to 35 €/MWh [38]. This cost is much higher than those estimated in other studies reviewed, mainly because other studies did not consider the reduction of the utilization of the capital cost. In this article, integration costs are estimated in 30 €/MWh, according to the ones calculated by [38], which are, by large, the highest from all the reviewed articles. This integration cost is added to the LCOE of each wind turbine to calculate its global cost. These global costs are represented in cost-resource curves. Cost-resource curves are used to determine the amount of energy that can be provided at a certain cost level (further explanation in [26]). The reference cost level selected is the current electricity cost. Figure 8 shows a summary of the whole process to calculate the global cost.

Figure 8. Flowchart of the cost calculation

7 7.1

Analysis of results Suitable areas

Table 6 shows the suitable area for the installation of offshore wind energy per island in comparison to the marine area whose maximum bathymetry is 500 m.

16

ACCEPTED MANUSCRIPT Table 6. Percentage suitable area versus total area (bathymetry > -500 m) Island El Hierro La Palma La Gomera Tenerife Gran Canaria Lanzarote– Fuerteventura Total Canary Islands

Suitable area (km2) 26.5 72.5 571 400 721 2159

Total area (km2) 71 256 802 814 1511 4138

Percentage

3950

7592

52%

37% 28% 71% 49% 48% 52%

Another interesting indicator could be the percentage of suitable area but in comparison to the territorial waters instead of to the maximum bathymetry. The condition of maximum bathymetry is much more restrictive than the one of territorial waters. Therefore, the indicator “suitable area versus maximum bathymetry” is considered to be more representative. Nevertheless, table 7 shows the indicator of suitable area versus territorial waters and also the percentage of territorial waters that have a bathymetry higher than 500 m. The percentage of suitable area versus territorial waters is much lower than the one compared to maximum bathymetry. The reason is that only a small portion of the territorial waters are above 500 m. As shown in table 7, in El Hierro this percentage is even as low as 2%, while for the system Lanzarote-Fuerteventura this percentage is nearly 40%. This speaks for the differences in the oceanic platform; while some of the islands barely have oceanic platform (like El Hierro), others do have oceanic platform, being the waters surrounding the island less profound. Thus, it is important to know the characteristics of the oceanic platform of the targeted area to select the indicators adequately. The percentage of territorial waters above 500 m is, in average, ca. 24%. Thus, less than one quarter of the territorial waters are exploitable considering only the bathymetry restriction. That is why it is very important to define the total area adequately; otherwise indicators of suitable area can change considerably. Table 7. Percentage of suitable areas versus territorial waters Suitable areas (km2)

Territorial waters (km2)

El Hierro La Palma

26.5 72.5

La Gomera-Tenerife

971

Island

3274 4099

Percentage of territorial waters above 500 m 2.2% 6.2%

Percentage of suitable area vs territorial waters 0.8% 1.8%

9054

17.8%

10.7%

Gran Canaria

721

5020

30.1%

14.4%

Lanzarote–Fuerteventura

2159

10 454

39.6%

20.7%

Total Canary Islands

3950

31 901

23.8%

12.4%

Figure 9 shows the final suitable area (darker marine areas) in comparison to the total area above -500 m for La Palma island.

17

ACCEPTED MANUSCRIPT

Figure 9. La Palma: suitable area versus total area (bathymetry > -500 m) The percentage of available area compared to the total area is an indicator than can be used in future studies, especially in small/medium coastal regions and archipelagos where the surrounding waters are not shallow. Table 8 shows the percentage of suitable area estimated in other studies. As shown in table 8, restricted areas are fewer in deeper waters. Table 8. Percentage suitable areas versus territorial waters Studied area

California

China

Suitable areas vs available area (%) 65% 90%

Water depth (m)

Comments

0 – 40 50 – 100

64% 93% 97% 98%

0 – 20 20 –50 50 – 100 > 100

[20] used this research to establish an exclusionary factor of 33% for their study in California In average, they estimated in 8.7% the exclusion area due to spatial constraints in the EEZ (Exclusive Economic Zone) of China

Source

[33]

[16]

The resulting figures shown in table 8 are much less restrictive than the ones in this study, which conclude that only about 50% of the area around the Canary Islands is free of constrains (considering depths until 500 m). This is explained by the high percentage of protected area around the Canary Islands. This indicator of 50% suitable area should be a valid indicator for similar Archipelagos where the percentage of protected area is important.

7.2

Array density of turbines

After locating all wind turbines on the available sites, the array density of turbines can be calculated. The array density of turbines (power divided by area) can be calculated as a function of the suitable area (the resulting area after implementing all restrictions) or as a function of the total considered area. This last approach is the most common one (e.g. [16,17]). In this study, the total area is the one whose maximum bathymetry is -500 m. Results are shown in table 9.

18

ACCEPTED MANUSCRIPT Table 9: Array density of turbines by island Power that can be installed (MW)

Suitable area (km2)

Array density of turbines as per suitable area (MW/km2)

Total area (km2) (above -500 m)

El Hierro

240

26.5

9

71

Array density of turbines as per total area (MW/km2) 3.4

La Palma

420

72.4

6

256

1.6

La Gomera

2310

571

4

802

2.9

Tenerife

3270

400

8

814

4.0

Gran Canaria

13 335

721

18.5

1511

8.8

Fuerteventura - Lanzarote

37 650

2159

17.5

4138

9.1

Total

57 225

3950

14.5

7592

7.5

Island

Very often the wind energy production is calculated as the product of the array density of turbines by the suitable surface. While there are many references for onshore studies, references for offshore array density are scarce, e.g. 5 MW/km2 [15,17], which is also a common figure for onshore wind farms. These values found in the literature differ from those calculated in this study, being higher in this case. In the case of the Canary Islands, the resulting average array density of turbines referred to the suitable area is around 14.5 MW/km2; but varies enormously from island to island, increasing from a minimum of 4 MW/km2 in La Gomera island to a maximum of 18.5 MW/km2 in Gran Canaria. Regarding the most common indicator, average array density of turbines referred to the total area, this figure is 7.5 MW/km2 (varying also enormously from 1.6 MW/km2 to 8.8 MW/km2). This last figure of 7.5 MW/km2 is closer to the one found in the literature. These array density figures are also higher than those calculated for wind onshore in the Canary Islands (0.6 MW/km2 –considering the total surface– and 5 MW/km2 –considering only the suitable surface–) [5]. The main reason behind is that spatial restrictions are higher onshore than offshore.

7.3

Offshore wind power and production

Table 10 shows the number of turbines (bottom-fixed and floating) that could theoretically be installed around each island. The 4th column shows the resulting offshore wind power, calculated as the number of turbines by the nominal power of the selected wind turbine (5 MW). Last column shows the total power currently installed in the Canary Islands (conventional and renewable sources), for comparison. Results show that the offshore wind power that could be installed is nearly 20 times higher than the current power installed. Table 10: Wind turbines and power that can be installed in each island Island

Number of wind turbines that can be installed Depth ≤ 50 m Depth > 50 m

Corresponding offshore wind power (MW)

Current installed power (MW) –for comparison–

El Hierro

0

48

240

15

La Palma

3

81

420

118

La Gomera

16

446

2310

23

Tenerife

150

504

3270

1270

Gran Canaria

243

2424

13 335

1150

19

ACCEPTED MANUSCRIPT Fuerteventura Lanzarote

1568

5962

Total

11 445

37 650

465

57 225

3041

Table 11 shows the potential offshore wind energy production per island (calculations have been done using equation 2). Input data are: the power curve of the G128-5.0 MW model from Gamesa, the wind speed and the k factor of the Weibull distribution at 80 m height in each cell where a turbine is placed. To provide an order of magnitude, the potential production is compared to the electricity demand in 2015 (last row). Results show that the potential production of wind energy is ca. 24 times higher than the current electricity demand. Table 11: Potential offshore wind energy production (GWh/a) Island

Lanzarote – Fuerteventura

Gran Canaria

Wind energy production (GWh/a) Depth ≤ 50 m

25 540

Depth > 50 m

Tenerife

La Gomera

La Palma

El Hierro

Total Canary Islands

3923

2296

298

44

0

32 101

103 811

41 146

7926

7877

1431

709

162 900

Total

129 351

45 069

10 222

8175

1475

709

195 001

Electricity demand (2015)

1404

3176

3109

63

236

40

8028

The wind energy production in table 11 has been calculated without considering any losses. Some authors have considered 10% losses a realistic assumption, including transmission losses, downtime for maintenance, technical failure, etc. [16,39] . Other authors considered 15% losses [17]. Anyway, the potential offshore wind energy production is so high in comparison to the demand, that this 10% generation reduction practically does not affect the analysis of results.

7.4

Offshore wind energy cost

The LCOE has been calculated for each turbine as per equation 3. As an example, table 12 shows the production cost of bottom-fixed turbines in La Gomera island. The last two columns show the LCOE and the total cost, which is the LCOE plus the integration cost, estimated in 30 €/MWh, as explained in the previous section. The total cost, LCOE plus integration cost, is the one used in the rest of the section. Cost resource curves, which has been calculated using total costs, show that the amount of wind energy produced at lower cost than the current electricity cost is higher than the current electricity demand. Table 12: LCOE and total cost of bottom-fixed turbines in La Gomera UTM Y

V80 (m/s)

heq

Production (MWh/a)

LCOE (c€/kWh)

LCOE + integration cost (c€/kWh)

267620

3115469

8.53

4056

20278

9.5

12.5

269457

3119726

7.74

3644

18220

10.6

13.6

267936

3117057

7.84

3721

18607

10.3

13.3

268381

3116804

7.84

3721

18607

10.3

13.3

268826

3116550

7.84

3721

18607

10.3

13.3

UTM X

20

ACCEPTED MANUSCRIPT 269270

3116297

7.84

3721

18607

10.3

13.3

268697

3118392

7.84

3721

18607

10.3

13.3

269141

3118138

7.84

3721

18607

10.3

13.3

269586

3117885

7.84

3721

18607

10.3

13.3

267749

3113628

7.66

3653

18265

10.5

13.5

268194

3113374

7.66

3653

18265

10.5

13.5

268065

3115216

7.66

3653

18265

10.5

13.5

268510

3114962

7.66

3653

18265

10.5

13.5

268954

3114709

7.66

3653

18265

10.5

13.5

267878

3111786

7.92

3781

18904

10.2

13.2

268323

3111533

7.92

3781

18904

10.2

13.2

In this study, 28 cost resource curves have been deployed. The archipelago consist of six independent electricity systems; only two of the islands are interconnected (LanzaroteFuerteventura). Moreover, for each electricity system and for the whole region, four resource curves have been developed: two for bottom-fixed turbines (cost versus production and cost versus power), and another two for floating turbines. The reference electricity cost is different for each electricity system (each electricity system has its own electricity production cost). The energy cost per production unit (e.g. each diesel unit, combined-cycle, etc.) has been defined by the government by royal decree [35]. The production cost of each generation unit has two components: fixed cost (depreciation cost) and the variable cost, both defined in [35]. Since the cost in isolated electricity systems is much higher than in mainland, the Government pays the electricity generators according to this Royal Decree [35]. Table 13 shows the electricity cost per electricity system and the mean weighted cost for the whole Canary Islands in 2014, that was 19.11 c€/kWh. The same year the average electricity cost in Spain (mainland) was 5.7 c€/kWh. Thus, the electricity cost in the Canary Islands was nearly 3.5 times more expensive than in Spain (mainland) in 2014. Table 13: Electricity cost per island (2014) Island/Electricity system

Electricity cost (c€/kWh)

El Hierro

26.32 [40]

La Palma

19.39 [40]

La Gomera

22.09 [40]

Tenerife

18.44 [40]

Gran Canaria

19.07 [40]

Fuerteventura & Lanzarote

20.28 [40]

Canary Islands (mean weighted value)

19.11

Spain (mainland) for comparison

5.7 [1]

21

ACCEPTED MANUSCRIPT As an example, figures 10 shows the cost resource curves for floating wind turbines in La Palma; cost versus production (left side) and cost versus power (right side). As shown in figure 10, most of the floating wind production cost is located below the current electricity cost. The same behavior is found in the other islands, except in El Hierro, whose floating wind production cost is always lower than the current electricity cost. In the case of bottom-fixed turbines, the wind energy cost in all the islands is always lower than the current electricity cost.

Figure 10. Cost resource curves for floating wind turbines in La Palma: cost versus production (left side) and versus power (right side).

7.5

Economically viable offshore wind energy production versus electricity demand

Table 14 shows the comparison between the economically viable offshore wind energy production and the electricity demand per island. The offshore wind energy potential that is considered economically viable is the one whose energy cost is lower than the current electricity cost. Table 14. Economically viable offshore wind energy potential versus demand Economically viable offshore wind energy potential Island

Current electricity cost (c€/kWh)

Wind power (MW) Depth > 50 m 240

Wind production (GWh)

El Hierro

26.32

Depth ≤ 50 m 0

240

Depth ≤ 50 m 0

709

40

La Palma

19.39

5

370

375

17

1338

1355

236

La Gomera

22.09

80

2230

2310

298

7877

8175

63

Tenerife

18.44

750

1620

2370

2296

5318

7614

3109

Gran Canaria Fuerteventura Lanzarote Total Canary Islands

19.07

1185

8415

9600

3866

31 730

35 596

3176

20.28

7840

28 385

36 225

25 540

99 999

125 539

1404

9860

41 020

51 017

32 017

146 262

178 988

8028

Total

Depth > 50 m 709

Demand 2015 (GWh)

Total

Results show that the economically viable wind production is much higher that the demand. Moreover, the potential wind production is also higher than the demand in each island. In regional terms, the wind energy production is more than 22 times higher than the demand: potential wind production 179 TWh/a versus a regional electricity demand of ca. 8 TWh in

22

ACCEPTED MANUSCRIPT 2015. In terms of power, the figures are also very high, in regional terms more than 12 times higher: wind potential power of more than 50 GW versus a regional installed power of 3 GW. An analysis of the results shown in table 14 leads to the following conclusions. The economically viable offshore wind energy generation is higher than the electricity demand in each island. Thus, theoretically, offshore wind energy could supply the demand in each island at a lower cost than the current electricity system. This is just a theoretical statement without considering technical issues like the grid stability. Nonetheless, the estimated wind energy cost includes an integration cost of 30 €/MWh, which takes into account the costs coming from the integration of variable renewables into the grid, like storage, grid reinforcement, additional reserve units, etc. A detailed analysis that distinguishes bottom-fixed from floating turbines provides further information. Four islands (three electricity systems) could theoretically cover their demand by installing only bottom-fixed wind farms. Another island could cover more than 70% of its demand by installing only bottom-fixed wind farms, always at a lower cost than current electricity cost. The remaining two islands should cover their demand mainly with floating wind farms, to remain competitive in terms of cost.

7.6

Marginal offshore wind energy production cost

An interesting indicator would be to estimate how much offshore wind power should be installed in each island to cover the demand, distinguishing also between bottom-fixed and floating wind turbines. This information, together with the corresponding marginal cost, can be extracted from the resource curves. The marginal cost is the energy cost of the last wind turbine whose accumulated energy production equals the energy demand; where the energy cost is the LCOE plus the integration costs, estimated in 30 €/MWh. Table 15 shows that offshore wind energy could produce the electricity demanded in each island at a lower cost that the current electricity cost. The potential savings ranges from 9 to 40%, depending on the island. The regional average savings would be 23%. Table 15. Marginal offshore wind energy: power and cost Wind power (MW)

Wind production (GWh)

Marginal cost (c€/kWh)

Island

Current electricity cost (c€/kWh)

Depth ≤ 50 m

Depth > 50 m

Total

Depth ≤ 50 m

Depth > 50 m

Total

Depth ≤ 50 m

Depth > 50 m

Total (weighted cost)

El Hierro

26.32

-

15

15

-

48

48

-

17.9

17.9

La Palma

19.39

5

55

60

17

232

249

15.0

14.2

14.2

27%

La Gomera

22.09

20

-

20

77

-

77

13.3

-

13.3

40%

63

Tenerife

18.44

750

275

1025

2296

945

3241

16.7

16.9

16.7

9%

3109

Gran Canaria Fuerteventura Lanzarote Total Canary Islands

19.07

705

395

1100

2170

1252

3422

13.1

13.6

13.3

30%

3176

20.28

370

-

375

1438

-

1420

13.2

-

13.2

19.11

1850

725

2575

5980

2429

8457

7.7

14.67

Cost Save (%)

Demand 2015 (GWh) 40

32%

236

1404

35% 23%

8028

Surface covered by offshore wind energy and comparison to onshore

An interesting indicator is the percentage of surface that would be occupied by offshore wind energy if the whole wind potential would be installed. The area occupied by one machine, 23

ACCEPTED MANUSCRIPT considering the total area affected by the wake effect, is 12Dx4D. The average occupied area per machine is 0.35 km2. Nonetheless, within these areas many activities can be developed, they are not exclusive, such as aquaculture, wave energy deployment, ancillary services for ships, etc. Therefore, the figure of total area occupied by one wind turbine is quite conservative. If the whole available area would be filled in with wind turbines, the total occupied surface would be ca. 3950 km2, representing around half of the considered area. This would mean that the electricity production would be around 195 TWh/a, nearly 25 times higher than the electricity needed, since the electricity demand in the Canary Islands was 8 TWh in 2015 (this comparison is done purely in terms of potential production, without considering energy for storage, etc.). Table 15 shows that 515 machines, 5 MW each, (370 bottom-fixed and 145 floating turbines) would produce the same amount of energy as the one demanded annually. The total area occupied by those wind turbines would be around 180 km2. An interesting analysis is to compare these results to the onshore ones. In the Canary Islands, 1350 wind turbines (2 MW each) would produce the same amount of energy than the one demanded annually [5]. The area occupied by those wind turbines would be around 500 m2. Even accounting for the difference in the wind turbine size used for each study (2 MW for onshore versus 5 MW for offshore), results show that less wind turbines and surface are needed for offshore than for onshore developments. The main reason behind is that wind conditions are better offshore than onshore (higher wind speed, less turbulence, etc.). In terms of cost, offshore more than doubles the wind onshore cost.

8

Future research lines

This study represents the first step of the energy planning. It aims at establishing if there is enough potential (and its production cost) to know if it is worthy to go to the next steps. A study of wind penetration is key to know how much wind energy can be integrated in each island’s grid. For this purpose, a grid stability study will be needed. Moreover, different scenarios should also be considered to increase the wind penetration, like storage (e.g. hydro-pumping), grid reinforcements, loads management (e.g. desalination, pumping, electric vehicles), etc. All these studies have to be developed for six independent electrical systems, some of which have over 1000 MW installed power. These additional studies could determine the current feasible wind penetration level and future scenarios with higher penetration levels, considering not only wind energy but also complementarity with other RES-E. In this study a flat rate for the integration cost has been considered: 30 €/MWh. Additionally, further detailed studies should be deployed to estimate the electricity system cost in scenarios with high RES-E penetration. These assessments should include grid reinforcement costs, integration costs (storage, load management, etc.), other energy sources costs (other RES-E and conventional back-up systems), among others. All these studies should be part of a thorough energy planning.

9

Conclusions

The first parameters to be defined to determine the wind potential of a region are the extern and intern boundaries. A natural extern boundary are the territorial waters. The intern boundary selected has been 1 km from shore. The techno-economic constraints considered have been: minimum wind speed (6 – 6.5 m/s at the hub height) and maximum bathymetry (-500 m). This bathymetry restriction represents a much more restrictive condition than the territorial waters, establishing the new effective extern boundary. In fact, the bathymetry restriction represents by far the most important constraint in terms of percentage of area excluded (less than one quarter of the territorial waters are above -500 m). The spatial restrictions represent also an important constraint. They include all protected areas but also other areas like harbors, fish farms, fishing grounds, military exclusion areas, main shipping routes and airports. From all these categories,

24

ACCEPTED MANUSCRIPT protected areas represent by far the most restrictive constraint. Within the protected areas, the most restrictive one is the “Special Areas of Conservation” (one of the Natura 2000 restrictions). Once the suitable areas are determined, wind turbines are placed. Bottom-fixed turbines are placed in waters less profound than 50 m and floating turbines in waters between 50 and 500 m deep. The result, after applying all mentioned restrictions and a wind farm configuration of 12Dx4D, is that around 50% of the total area above -500 m can be used to produce wind energy. This area represents ca. 12.5% of the territorial waters. The resulting array density of turbines is 7.5 MW/km2 (considering the total area above -500 m) and 14.5 MW/km2 (considering only the suitable area). These figures are higher than the ones calculated for wind onshore in the Canary Islands (0.6 MW/km2 –considering the total surface– and 5 MW/km2 –considering only the suitable surface–). The main reason behind is that spatial restrictions are higher onshore than offshore. Once all wind turbines are placed, the energy production and the LCOE of each turbine are calculated. The economically viable wind production is considered the one whose energy cost (including integrating cost) is lower than the current electricity cost. The electricity cost is different for each electricity system (only two of the islands are interconnected). The integration costs take into account the costs derived from the integration of variable renewables into the grid, like storage, grid reinforcement, additional reserve units, etc. and it has been estimated in 30 €/MWh. The conclusion is that the economically viable offshore wind energy is much higher than the electricity demand in the region and, even more important, than the demand in each island. Even if the Archipelago barely has oceanic platform, four islands could theoretically cover their whole demand by installing only bottom-fixed wind farms and another one could cover around 70% of its demand. The remaining two islands should cover their demand mainly using floating wind farms. The offshore wind energy that could be placed in the suitable areas is 57 GW; being the current installed power in the Canary Islands 3 GW (90% conventional power). The economically viable offshore wind production is also much higher that the demand, 179 TWh/a versus a regional electricity demand of ca. 8 TWh in 2015. However, a more interesting economic parameter is at what marginal cost could offshore wind energy produce the same amount of energy as the one demanded in each island. Results show that the marginal cost of offshore wind production, including integration costs, is much lower than the current electricity cost in all islands (percentages of savings from 9% to 40%). The weighted average saving for the region would be 23%. Results show that 515 machines (5 MW each), 370 bottom-fixed and 145 floating turbines, would produce the same amount of energy than the one demanded annually. The total area occupied by those wind turbines would be around 180 km2. An interesting analysis is to compare these results to the onshore ones. In terms of marginal cost, the offshore marginal cost more than doubles the marginal onshore cost. However, 1350 on-shore wind turbines (2 MW each) would be needed to produce the same amount of energy than the one demanded in 2015. The area occupied by those wind turbines would represent 500 km2. Results show that less wind turbines and surface (nearly 1/3) are needed for offshore than for onshore developments. The main reason behind is that wind conditions are better offshore than onshore. Acknowledgment This research has been co-funded by FEDER funds, INTERREG MAC 2014-2020 programme, ENERMAC project (MAC/1.1a/117)

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