Offshore wind farms development: Generation of relative site suitability indexes

France EEZ

Assumptions & computations details

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Context This document explains assumptions made and computations performed to identify the most suitable offshore sites for wind farms development in France EEZ. This study is based on a previous work, available here: https://offshorewindexplorer.files.wordpress.com/2017/09/mthesis-quantitative-offshore-site-selection-methodology.pdf

The preliminary step to this study, e.g a spatial analysis excluding already existing offshore activities has been performed here: • Offshore Atlantic Ocean: https://offshorewindexplorer.org/2017/10/04/analysis-of-the-atlantic-area-of-france-eez/ • Offshore Mediterranean Sea: https://offshorewindexplorer.org/2017/10/16/spatial-analysis-mediterranean-area-of-france-

eez/

The results of this study are available on offshorewindexplorer.org : http://offshorewindexplorer.org/2017/11/14/offshore-site-selection-suitability-of-the-atlantic-area-of-france-eez/

About the suitability index generation The methodology consists in applying cost functions to several geo-physical parameters, in order to derive quantitative suitability indexes over a region, as opposed to another commonly used method, which is weighting various parameters (technical, non-technical and environmental), function of their importance, generating qualitative indexes, dependent on experts’s perceptions. Those suitability maps have been generated by applying: • Costs on distance to/from suitable ports, where turbines are loaded and transported on site, • Costs related to depth, impacting directly mooring lines length or bottom-fixed foundations, • Cost of export cables burial (through various seabed substrates), from land electrical stations to offshore sites. Then the costs are divided by the squared expected annual energy production (AEP) all over the region.

Note: The costs could have been divided by the mean wind speeds, but mean wind speed does not reflect properly a wind climate. Indeed, several locations may have the same mean wind speed, this does not imply that those locations have the same probability density function (PDF). And, finally, different PDF lead to different energy output for a given turbine.

The scenario used for the study is the following: 504MW wind farm, composed of 63 – 8MW market turbines.

Note: The resulting indexes for both technologies are computed independently, and can’t be compared as-is.

Depth Range Turbine transport Foundation / Station keeping system

Bottom-Fixed turbines

0 to 50m Turbines transported by a crane vessel (Capacity: 4 turbines)

Jacket, costs are function of depth in M�/MW

Floating turbines

50 to 300m Turbines towed by Tugboats (Capacity: 1 turbine) 3 catenary mooring lines

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Cost Assumptions Vessels and Jacket foundation

Mooring chains for the floating platforms

Based on the following scenario: • 3 catenary lines, • Stud link chain grade R4, • Maximum breaking load (MBL): 5000 kN.

The metric cost of the chain can be derived using the following formulas:

𝑀𝐵𝐿 = 0.0274 ∙ 𝑑2 ∙ (44 − 0.08 ∙ 𝑑) [𝑘𝑁]

𝐶𝑜𝑠𝑡(𝑑) = 0.05688 ∙ 𝑑2 [�/𝑚]

𝑤= 0.1875∙𝑑2 [𝑁/𝑚]

𝑑: chain diameter in mm,

𝑤: wet weight.

Being function of the depth, the necessary mooring line length is assessed considering a static inextensible cable approximation using :

%

The metric cost of chain used in the computations is then 269.63 �/m.

Export cable burial costs

The costs associated to the burial of export cable are derived from the trenching speed in the seabed substrate and the cable laying vessel daily rate.

Using the following formula:

% [�/m]

Cost / Cost Function Comments

Cable Laying Vessel 85000� / day (24h) Speed depends on the cable burial technique

Crane Vessel 120000� / day (24h) Can transport 4x8MW turbines, speed : 6kts

Tugboat 3000� / day (24h) Can tow one floating turbine (floating platform + turbine), speed : 6kts

Jacket foundation 0.00096 ∙ d2 − 0.01848 ∙ d + 1.90164 [M�/MW]

d: depth in meters

lmin = depth ×2 × MBL

w × depth− 1

Substrate Trenching speed Trenching technique Burial cost per meter

Rock 2 m/min Cutting 29.51 �/m

Other 18 m/min Ploughing 3.27 �/m

CLVDailyRateTrenchingSpeed × 1440

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Transport/Installation Vessels

Used to compute costs of transportation of a turbine from a suitable port to any offshore location.

Annual Energy Production • The annual energy production over the studied offshore area has been derived based on the power curve of an MHI Vestas

8MW turbine. • The wind data come from the wave watch III model (all data and wind bins are available here : https://

offshorewindexplorer.org/2017/09/12/north-atlantic-wind-climate-data/). • Considered hub height is 107m above mean sea level (MSL).

Suitability index generation Scenario : 63 x 8MW turbines for a 504 MW wind farm For any offshore location (cell size : 200mx200m), costs can be derived: • Cost of transportation of one turbine, • Cost of one foundation, or 3 mooring lines, depending on

depth, • Cost of export cable burial from the closest electrical

substation.

The two first items represent costs associated to one turbine only, and it makes no sense to add the costs related to only one turbine with the export cable burial cost. The cost for the entire wind farm is necessary to be able to get consistent overall costs. Hence, each cell is assigned the average cost of its surrounding area, using a focal statistics tool, over a circle of area 63 x16D2 (total farm area). 16D2 being the area needed per turbine, D being the rotor diameter. This computation is performed on each and every cell.

More information about focal statistics : http://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/how-focal-statistics-works.htm

It is then possible to sum all the costs. The results should be interpreted as follows : Each cell C has been assigned the cost related to 63 turbines (the entire wind farm) and export cable burial (to reach this location from the closest electrical substation), for a circular area (63 x16D2), centered on C.

Costs have to be balanced with the annual energy output to derive the suitability index:

%

The AEP has been squared in order to get more contrast between regions with high AEP and low AEP. And finally normalised:

%

Technology Vessel type Nb of turbines Cruise speed Cost per km per turbine

Bottom Fixed Crane vessel 4 turbines 6 kts 112 �/km/turbine

Floating Tugboat 1 turbine 6 kts 11.26 �/km/turbine

In dex =CostsAEP2

Suitabilit yIn dex =In dex

min(In dex)offshorewindexplorer.org

Final interpretation Because of the use of the focal statistics tool, the suitability must be interpreted as follows: the value of a cell represents the suitability of the site defined by a circle the area of a wind farm (63 x16D2), centered on this same cell.

High values of suitability index mean that areas are relatively less suitable. A less suitable area does not mean that it must be discarded, it just means that the deployment of a wind farm in this area will be either more cost intensive or with lower resources. Ultimately, developers will improve their installation and maintenance strategies, turbines manufacturers their equipment’s efficiency, driving costs down, we may expect those areas to be eventually used at some point.

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Note: Caution with default color gradient display The color gradient display in GIS softwares (at least ArcGIS) must be used carefully, and shouldn’t be interpreted as-is. Using the default color gradient induces a more positive perception of the suitability of a studied area. As an example

Compared with a quantile display, here with only 10 quantiles : In this case, we observe a clear shift in areas suitability, from suitable (greenish) to less suitable (reddish). So, it is important to

select properly the way values are displayed.

References All references can be found in the following document: https://offshorewindexplorer.files.wordpress.com/2017/09/mthesis-quantitative-offshore-site-selection-methodology.pdf Most of the costing information have been taken from the DTOcean project (http://www.dtocean.eu).

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