Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm

ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Data in Brief xxx (xxxx) xxx Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Complete Dataset to b e use d as a workb ench to evaluate the profitability of an offshore wind farm Angel G. Gonzalez-Rodriguez a , b , ∗, Javier Serrano-Gonzalez a , b , Q1 Q2 Manuel Burgos-Payan a , b , Jesus Riquelme-Santos a , b a University of Jaen, Spain b University of Seville, Spain a r t i c l e i n f o Article history: Received 23 March 2022 Revised 8 June 2022 Accepted 14 June 2022 Available online xxx Keywords: Component costs Cable characteristics CAPEX Wind data Bathymetry a b s t r a c t The presented data collection has been used in the paper Multi-objective optimization of a uniformly distributed offshore wind farm considering both economic factors and visual impact , but can be used for a realistic evaluation of the annual energy production of an offshore wind farm and/or the calculation of the project investment cost. It contains realistic wind data, a bathymetric map, the definition of the coast shoreline and forbidden zones, as well as the acquisition and installation cost for the most important components influencing the investment and operation costs. ©2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Specifications table 1 DOI of original article: 10.1016/j.seta.2022.102148 ∗Corresponding author.: E-mail address: [email protected] (A.G. Gonzalez-Rodriguez). https://doi.org/10.1016/j.dib.2022.108396 2352-3409/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 2 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Table 2 Values for probability, and Weibull parameters (scale factor A at 62 m and shape factor WeibK) for every sector. N NNE NEE E EES ESS S SSW SWW W WWN WNN freq (%) 3.8 4.3 5.5 8.3 8.7 6.7 8.4 10.5 11.4 12.2 13.9 6.1 Weib A (m/s) 8.71 9.36 9.29 10.27 10.89 10.49 10.94 11.23 11.93 11.94 12.17 10.31 Weib K 2.08 2.22 2.41 2.37 2.51 2.75 2.61 2.51 2.33 2.35 2.58 2.01 Subject Environmental Engineering. Specific subject area Technical and economic evaluation of an offshore wind farm Type of data Table Map Wind rose How the data were acquired Bathymetry obtained from globalwindatlas.info and https://www.researchgate.net/publication/348571118 _ A _ Minimalistic _ Predictio Wind data obtained from a from a real square lattice mast erected in 1999. It has four measurement levels, although only those f Characteristics and prices of electrical components from reports, articles, thesis, and catalogues [1] Power and Thrust curve for Vestas V80 from [2] Macro-economic data and energy price recovered from [3] , and originally obtained from [4] [5] [6] [7] Data format Raw: copy of existing tables for wind data and power curve Analyzed: Economic data have been obtained from different countries and years, and have been converted into euros at 2017 Captured: From geographical maps. Description of data collection In order to convert prices and costs from different countries and years, obtained data from manufacturers or existing projects we A customized application captured the coordinates from the map of Figure 2 , and create a set of arrays containing the coordinate Data source location •Institution: Vattenfall •Horns Rev 1: •Denmark: •Latitude: 55 ◦29’ 9.5”; Longitude: 7 ◦50’ 23.9”; (423974, 6151447) - (429431, 6147543) Depth: -10 m; Distance from shore: 18 k Data accessibility •Repository name: Mendeley Data [8] •Data identification number: DOI:10.17632/btzfbjh49b.1 •Direct URL to data: https://data.mendeley.com/datasets/btzfbjh49b/1 Related research article For an article which has been accepted and is in press: Angel G. Gonzalez-Rodriguez, Javier Serrano-Gonzalez, Manuel Burgos-Pa Value of the Data 2 • These data are useful as a complete set of values for the evaluation of technical, economic 3 or environmental issues in a real offshore wind farm. Specifically, bathymetric and wind data 4 are related to Horns Rev I. Often, searching for coherent values related to the site, or to 5 the costs and characteristics of offshore wind farm components is a tedious and hard task 6 required prior to test any algorithm or method. By using these data, researchers can focus on 7 developing their ideas. 8 • Researcher working in the areas of layout optimization, macro-siting, electrical infrastructure 9 design, noise reduction, visual impact. 10 • Results obtained after using these data can be compared the obtained values with those ob- 11 tained from e.g. [4] or [3] 12 Data Description 13 Table 2 presents the wind rose (frequency for every wind direction) as well as the mean of 14 the Weibull parameters for every wind direction at Horns Rev. Figure 1 represents this table, and 15 specifies the probability that corresponds to certain speed intervals. In this figure, only values 16 between cut-in speed and cut-out speed are represented. 17 Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx 3 ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Fig. 1. Wind rose obtained from [2] . Only values between cut-in speed and cut-out speed are represented Table 3 Power and Thrust curve for Vestas V80 Wind speed (m/s) 1 2 3 4 5 6 7 8 9 10 11 12 13 Power (kW) 0 0 0 66 154 282 460 696 996 1341 1661 1866 1958 Thrust coef 0 0 0 0.818 0.806 0.804 0.81 0.81 0.807 0.793 0.739 0.709 0.409 Wind speed (m/s) 14 15 16 17 18 19 20 21 22 23 24 25 Power (kW) 1988 1997 1999 20 0 0 20 0 0 20 0 0 20 0 0 20 0 0 20 0 0 20 0 0 20 0 0 20 0 0 Thrust coef 0.314 0.249 0.202 0.17 0.14 0.119 0.102 0.088 0.077 0.067 0.06 0.05 Table 3 shows the power and thrust curve for a wind turbine model Vestas V80. 18 Table 4 presents required data to calculate the yearly cash flow obtained by selling the pro- 19 duced energy, after subtracting the operation and maintenance costs. 20 Table 5 contains the main costs in an offshore wind farm, which are the acquisition and 21 installation of turbines and foundations. 22 Table 6 contains the cost of secondary non-electrical components necessary to calculate the 23 investment cost. Table 7 lists price and characteristics for different model of medium-voltage 24 cables, to be used for connecting turbines in a row. 25 Table 8 lists price and characteristics for high-voltage cables with different capacities, to be 26 used as transmission cables, both offshore and onshore. 27 Table 9 presents the remaining components of the electrical infrastructure. 28 Figure 2 represents the map site, including depth curves (D1, D2, D3, D4, D5 and D6), for- 29 bidden zones (F1, F2, F3), concession area (C1), and coast shoreline. It also includes possible 30 locations for observers in order to evaluate the visual or noise impact. 31 Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 4 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Table 4 Items affecting the yearly cash flow Concept Cost O&M Costs 15 €/MWh Increase 5% per year Surface and insurances included in O&M Price of energy 130 €/MWh Increase 0% per year Availability 95% Life Time 20 years Interest rate 9.40% Inflation 1.5 % Table 5 Main costs affecting the investment Concept Cost Turbines Acquisition 765 k €/MW Installation 405 k €/MW Foundations Reference price 450 €/MW at 15 m depth, Zone 1 Increase +2 % per metre depth +30 % for zone 2 +60 % for zone 3 Vessels mob demob 430 k Table 6 Secondary non electrical items affecting the investment and decommissioning Concept Cost Design and management 95 kMW SCADA 50 k €/turbine Decommission 120 k €/MW Tables 10 , 11 , and 12 list the sequence of points defining the depth curves, forbid- 32 den/concession areas, and coast shoreline, respectively, which are visualized in Figure 2 . 33 The Excel file in [8] has several sheets with these data: 34 eographic. Size of the workspace, number of sectors for the wind rose, roughness height, reference 35 height, the wind rose, and value of Weibull parameters for each sector. 36 Economic. Type of currency, interest rate, inflation, life time, energy price, availability, decommissioning 37 cost, SCADA cost and O&M costs. 38 Algorithm. Typical values for a genetic or evolutive algorithm. 39 Turbine. Rated capacity, diameter, rotor height, price, installation cost, power curve, thrust curve. 40 undations. Mobilitation/demobilitation cost, foundation cost, cost increment as a function of the depth 41 and the load-bearing capacity. 42 ical_Data. All data related to cables and electrical infrastructure. 43 Depths. Depth curves obtained from the bathymetric charts. 44 Soil. Curves defining the different types of soil as a function of the load-bearing capacity. 45 orb_Conc. Curves defining the forbidden zones (e.g. too close to the coast or extraction areas) as well 46 as the concession areas. 47 Coast. Curve defining the coast shoreline. 48 Impact. Sensitive positions where impact can be measured, as well as the observation height. 49 Experimental design, materials and methods 50 Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx 5 ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Table 7 Acquisition cost of inner array cables. Cross area Fixed losses Variable losses I max Price mm 2 W/m W/A 2 m A €/m A95 0 7.14E-4 380 128 A150 6 4.35E-4 430 192 A400 24 1.92E-4 680 321 A630 34 1.23E-4 780 481 A800 50 0.86E-4 900 506 B95 0 8.33E-4 260 384 B150 6 5E-4 360 417 B400 8 1.72E-4 640 514 B630 10 1.11E-4 790 535 B800 12 0.86E-4 900 616 Additional cable length for connections: 40 m/turbine Table 8 Acquisition cost of export and HV onshore cable Export cable Onshore cable Voltage Section Var.Loss Capac. Cost Capac. Cost ( kV ) ( mm 2 ) W/A 2 m (MVA) ( €/m) (MVA) ( €/m) 220 500 6E-5 250 843 273 233 220 630 5E-5 273 946 297 266 220 800 4E-5 295 1061 314 299 220 10 0 0 3E-5 314 1214 348 367 Table 9 Electrical items affecting the investment Concept Cost Acq. MV cables see Tab. 7 Installation 120 €/m Acq. export cables see Tab. 8 Installation 170 €/m Acq. onshore cables see Tab. 8 Inst. onshore cables 400 €/m Offshore substation 76 k €/MW Offshore trafo 19 k €/MW Vessels mob demob 430 k € Reactive Compens. 128 kMVA Onshore substation 49 k €/MW Onshore trafo 11 k €/MW Conn. to grid 200 k €/MW Shoreline 1.65 M € OWF Power factor 0.85 The economic and technical data have been obtained from a deep review of technical reports, 51 articles, and thesis. The source of this information is in [1] . 52 Wind data regarding Horns Rev site has been obtained from [2] . This data were obtained 53 from a real square lattice mast erected in 1999 at Horns Rev. It had four measurement levels, 54 although only those from 62 meters (the highest one) have been used. Since this is not the tower 55 height, it is necessary to adjust the scale parameter A from the measurement height ( z ref = 56 62 m ) to the hub height ( z hub = 70 m ) due to the wind shear effect. The relationship between 57 scale factors, and in general between wind speeds, at different heights is given by 58 A = A ref ln (z hub ) −ln (z 0 ) ln (z ref ) −ln (z 0 ) (1) Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 6 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] Fig. 2. Site map for Horns Rev I with depth curves (Dx), forbidden zones (Fx), concession area (C1), coast shoreline (S1) and observer positions (ox). Obtained from [8] . Table 10 Sequence of points defining the depth curves. Coordinates given in percentage (%) Depth 15 Symbol in map: D1 (2.0, 50.5)-(4.7, 47.5)-(7.9, 36.3)-(18.7, 23.4)-(19.6, 7.1)-(23.5, 2.0)-(2.0, 2.0)-(2.0, 50.5) Depth 15 Symbol in map: D2 (17.9, 96.7)-(18.9, 82.0)-(22.5, 71.8)-(27.7, 67.0)-(27.2, 61.2)-(19.7, 70.6)-(15.8, 78.4)-(15.6, 93.1)-(17.9, 96.7) Depth 15 Symbol in map: D3 (33.0, 59.9)-(37.5, 53.3)-(43.1, 49.2)-(41.8, 44.2)-(38.9, 49.7)-(34.3, 49.7)-(30.5, 55.3)-(30.1, 59.9)-(33.0, 59.9) Depth 5 Symbol in map: D4 (47.3, 99.8)-(43.2, 83.8)-(43.6, 68.3)-(44.5, 64.7)-(49.4, 65.7)-(57.0, 55.8)-(71.9, 14.0)-(73.4, 0.3) -(0.3, 0.3)-(0.3, 99.8)-(47.3, 99.8) Depth 10 Symbol in map: D5 (1.1, 70.1)-(4.3, 73.4)-(5.9, 67.3)-(12.5, 58.6)-(12.1, 44.7)-(18.3, 27.7)-(23.5, 26.9)-(30.1, 30.2)-(38.9, 39.1) -(38.0, 46.7)-(30.7, 47.2)-(26.2, 54.6)-(23.8, 60.7)-(14.4, 72.8)-(8.4, 83.0)-(1.1, 84.5)-(1.1, 70.1) Depth 10 Symbol in map: D6 (35.7, 99.0)-(29.0, 88.6)-(24.2, 93.7)-(20.2, 90.9)-(27.4, 72.3)-(36.1, 60.4)-(43.6, 53.3)-(46.7, 53.0)-(57.0, 24.4) -(61.4, 17.0)-(62.6, 1.0)-(1.0, 1.0)-(1.0, 99.0)-(35.7, 99.0) Table 11 Sequence of points defining the forbidden zones and the concession area. Coordinates given in percentage (%) Forbidden Symbol in map: F1 (33.0, 49.0)-(39.0, 49.0)-(39.0, 41.0)-(33.0, 41.0)-(33.0, 49.0) Forbidden Symbol in map: F2 (15.0, 35.0)-(15.0, 23.0)-(19.0, 21.0)-(36.0, 21.0)-(36.0, 31.0)-(38.0, 31.0)-(38.0, 37.0) -(27.0, 37.0)-(25.0, 31.0)-(20.0, 31.0)-(15.0, 35.0) Forbidden Symbol in map: F3 (9.0, 43.0)-(9.0, 35.0)-(15.0, 35.0)-(15.0, 43.0) Concession Symbol in map: C1 (1.1, 70.1)-(4.3, 73.4)-(5.9, 67.3)-(12.5, 58.6)-(12.1, 44.7)-(18.3, 27.7)-(23.5, 26.9)-(30.1, 30.2)-(38.9, 39.1) -(38.0, 46.7)-(30.7, 47.2)-(26.2, 54.6)-(23.8, 60.7)-(14.4, 72.8)-(8.4, 83.0)-(1.1, 84.5)-(1.1, 77.2)-(1.1, 70.1) Table 12 Sequence of points defining the coast shoreline. Coordinates given in percentage (%) Coast Symbol in map: S1 (100.0, 100.0)-(100.0, 0.0)-(79.6, 0.0)-(74.3, 18.3)-(72.7, 23.6)-(73.0, 28.9)-(80.8, 32.2) -(82.4, 18.0)-(84.4, 21.3)-(85.7, 17.0)-(87.3, 4.6)-(86.0, 0.0)-(100.0, 0.0)-(100.0, 27.4) -(86.6, 27.2) -(71.6, 71.8)-(65.5, 63.7)-(70.5, 48.5)-(70.5, 41.4)-(68.0, 41.1)-(59.1, 61.4) -(51.4, 71.8) -(47.8, 72.8)-(45.4, 78.7)-(49.1, 100.0) Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx 7 ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] being z 0 the roughness length for the terrain. Its usual value taken for offshore sites is 0.005, 59 which is also consistent with the wind profiles presented in [2] . 60 The power and thrust curve for Vestas V80 has been obtained from [2] . 61 Bathymetry has been obtained from globalwindatlas.info and [9] . 62 Macro-economic data and energy price recovered from [3] , and originally obtained from 63 [4] [5] [6] [7] 64 Declaration of Competing Interest 65 The authors declare that they have no known competing financial interests or personal rela- 66 tionships that could have appeared to influence the work reported in this paper. 67 Data Availability 68 Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm 69 (Mendeley Data) 70 CRediT Author Statement 71 Angel G. Gonzalez-Rodriguez : Conceptualization, Methodology, Software, Validation, Inves- 72 tigation, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft; 73 Javier Serrano-Gonzalez : Validation, Data curation, Investigation, Writing –review & editing; 74 Manuel Burgos-Payan : Project administration, Funding acquisition, Writing –review & editing; 75 Jesus Riquelme-Santos : Formal analysis, Supervision, Funding acquisition, Writing –review & 76 editing. 77 Acknowledgment 78 This work was supported by CERVERA research program of CDTI under the research Project 79 HySGrid+ (CER-20191019). 80 Appendix A. Input data to the algorithm 81 Wind data have been obtained from [2] . The measurement height is 62 m, and roughness 82 height is 0.005. 83 Ethics statements 84 The authors comply with the ethical guidelines contained in Data in Brief’s Guide for Authors . 85 This work did not involve human subjects. 86 This work did not involve animal experiments. 87 This work did not involve data collected from social media platforms. 88 References 89 [1] A.G. Gonzalez-Rodriguez, Review of offshore wind farm cost components, Energy Sustain. Dev. 37 (2017) 10–19, 90 doi: 10.1016/j.esd.2016.12.001 . 91 [2] A. Sommer, Wind resources at Horns Rev, Tech-wise A/S (2002) 1–69. https://www.yumpu.com/en/document/view/ 92 4469663/wind-resources-at-horns-rev 93 [3] A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez, M. Burgos-Payan, J.M. Riquelme-Santos, Realistic optimization of 94 parallelogram-shaped offshore wind farms considering continuously distributed wind resources, Energies 14 (10) 95 (2021), doi: 10.3390/en14102895 . 96 Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396 8 A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al. / Data in Brief xxx (xxxx) xxx ARTICLE IN PRESS JID: DIB [mUS1Ga; June 23, 2022;9:54 ] [4] G. Gerdes, T. Albrecht, S. Zeelenberg, Case Study: European Offshore Wind Farms - A Survey for the Analysis of 97 the Experiences and Lessons Learnt by Developers of Offshore Wind Farms, Technical Report, Deutsche Wind- 98 Guard GmbH, 2005. https://tethys.pnnl.gov/sites/default/files/publications/A _ Survey _ for _ the _ Analysis _ by _ Developers _ 99 of _ Offshore _ Wind _ Farms.pdf 100 [5] J.A. Voormolen, H.M. Junginger, W.G. van Sark, Unravelling historical cost developments of offshore wind energy in 101 Europe, Energ. Policy 88 (2016) (2016) 435–4 4 4, doi: 10.1016/j.enpol.2015.10.047 . 102 [6] R. Green, N. Vasilakos, The economics of offshore wind, Energ. Policy 39 (2) (2011) 496–502, doi: 10.1016/j.enpol. 103 2010.10.011 . 104 [7] Douglas-Westwood, Offshore wind assessment for Norway, Technical Report, The Research Council of Norway, 2010. 105 [8] Gonzalez, A. (2021). Input data for optimization. Mendeley Data, V1. 10.17632/btzfbjh49b.1 106 [9] J.N. Sørensen, G.C. Larsen, A minimalistic prediction model to determine energy production and costs of offshore 107 wind farms, Energies 14 (2) (2021), doi: 10.3390/en14020448 . 108 Please cite this article as: A.G. Gonzalez-Rodriguez, J. Serrano-Gonzalez and M. Burgos-Payan et al., Complete Dataset to be used as a workbench to evaluate the profitability of an offshore wind farm, Data in Brief, https://doi.org/10.1016/ j.dib.2022.108396