Your browser does not support JavaScript!

Optimisation of offshore wind farm inter-array collection system

Optimisation of offshore wind farm inter-array collection system

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents an automatic tool developed for the optimisation of the layout of offshore inter-array cable systems. Employing stochastic approaches, this tool can quickly find a near-optimum cable connectivity solution based on one of the criteria including capital expenditure (CAPEX), operational expenditure (OPEX), or their combination considering the net present value over the project lifespan. The seabed geo-tech constraints are considered to minimise cable routes across seabed areas where challenging installation conditions may exist, and to avoid cable routes across the seabed areas where the installation is impossible. The tool employs advanced identification of suitable locations of multiple offshore substations for large wind farms. This optimisation tool is coded in Python 2.7 and scripted IPSA + is used as the load flow calculation engine for power loss calculation. This tool has been applied to 4 GW offshore wind projects developed in European and Asian waters and it is demonstrated that the tool automates the design for the inter-array cable system layout and delivers measurable overall project efficiency gains. Comparisons were made between radial design and branched design; it is shown that the branched design can achieve better cost savings than the radial design.


    1. 1)
      • 4. UK government: ‘Offshore wind industrial strategy-business and government action’, 2013.
    2. 2)
      • 28. Walling, R.A., Ruddy, T.: ‘Economic optimisation of offshore windfarm substations and collection systems’. Vth Int. Workshop on Large-Scale Integration of Wind Power, Glasgow, 2005.
    3. 3)
      • 15. Hou, P., Hu, W.H., Chen, C., et al: ‘Overall optimisation for offshore wind farm electrical system’, Wind Energy, 2016, 20, (6), doi: 10.1002/we.
    4. 4)
      • 1. Department of Energy and Climate Change: UK, ‘UK renewable energy roadmap update 2012’, 2012.
    5. 5)
      • 21. Wikipedia.: ‘Dijkstra's_algorithm’, Available at:'s_algorithm.
    6. 6)
      • 23. An O(n2 log n) Algorithm for Computing Visibility Graphs’: Smith College, US, Available at:∼streinu/Teaching/Courses/274/Spring98/Projects/Philip/fp/algVisibility.htm.
    7. 7)
      • 6. Marge, T., Lumbreras, S., Ramos, A., et al: ‘Integrated offshore wind farm design: optimizing micrositing and cable layout simultaneously’, Journal Paper Draft for Wind Energy, Available at:
    8. 8)
      • 26. Sannino, H.E.A.: ‘Reliability of collection grids for large offshore wind parks’. 9th PMAPS, Stockholm, 2006.
    9. 9)
      • 14. Hou, P., Hu, W.H., Chen, Z.: ‘Optimisation for offshore wind farm cable connection layout using adaptive particle swarm optimisation minimum spanning tree method’, IET Renew. Power Gener., 2016, 10, (5), pp. 694702.
    10. 10)
      • 10. Gonzalez-Longatt, F.M., Wall, P., Regulski, P., et al: ‘Optimal electric network design for a large offshore wind farm based on a modified genetic algorithm approach’, IEEE Syst. J., 2012, 6, (1), pp. 164172.
    11. 11)
      • 22. de Berg, M., Cheong, O., van Kreveld, M., et al (Eds.): ‘Visibility graphs’, in ‘Computational geometry-algorithms and applications’ (Springer-Verlag, Berlin Heidelberg, Germany, 2008, 3rd Edn.), pp. 326330.
    12. 12)
      • 19. Arthur, D., Vassilvitskii, S.: ‘K-means + +: the advantages of careful seeding’. SODA ‘07 Proc. of the 18th Annual ACM-SIAM Symp. on Discrete Algorithms, Philadelphia, 2007.
    13. 13)
      • 2. US Energy Information Administration: ‘Levelized cost of new generation resources in the annual energy outlook 2011’, 2011.
    14. 14)
      • 18. Jenkins, A.M., Scutariu, M., Smith, K.S.: ‘Offshore wind farm inter-array cable layout’. PowerTech (POWERTECH), 2013 IEEE, Grenoble, 2013.
    15. 15)
      • 29. Twidell, J., Weir, T.: ‘Renewable energy resources’ (Taylor & Frances, Oxford, UK, 2006, 2nd edn.), pp. 299304.
    16. 16)
      • 17. Gong, X., Kuenzel, S., Pal, B.C.: ‘Optimal wind farm cabling’, IEEE Trans. Sustain. Energy, 2018, 9, (3), pp. 11261136.
    17. 17)
      • 7. Valverde, P., Sarmento, A., Alves, M.: ‘Offshore wind farm layout optimization-state of the art’, J. Ocean Wind Energy, 2014, 1, (1), pp. 2329.
    18. 18)
      • 20. Schubert.: ‘Same-size k-means variation, tutorial of ELKI-environment for developing KDD applications supported by index structures’, Ludwig-Maximilians Universitat Munchen, Available at:
    19. 19)
      • 27. Centre for Sustainable Electricity and Distributed Generation: ‘Cost benefit methodology for optimal design of offshore transmission systems’, 2008.
    20. 20)
      • 16. Rodrigues, S., Restrepo, C., Katsouris, G., et al: ‘A multi-objective optimization framework for offshore wind farm layouts and electric infrastructures’, Energies, 2016, 9, p.216.
    21. 21)
      • 12. Pillai, A.C., Chick, J., Johanning, L., et al: ‘Offshore wind farm electrical cable layout optimization’, Eng. Optim., 2015, 47, (12), pp. 16891708.
    22. 22)
      • 13. Fischetti, M., Pisinger, D.: ‘Optimizing wind farm cable routing considering power losses’, Eur. J. Oper. Res., 2018, 270, (3), pp. 917930.
    23. 23)
      • 30. Scutariu, M.: ‘Techno-economical optioneering of offshore wind farms electrical systems’. IEEE Power Tech Conf., Lausanne, 2007.
    24. 24)
      • 25. IPSA1.6.9: Power system analysis software, TNEI,
    25. 25)
      • 3. Low Carbon Innovation Coordination Group: ‘Technology innovation needs assessment-offshore wind power summary report’, 2012.
    26. 26)
      • 9. Svendsen, H.G.: ‘Planning tool for clustering and optimised grid connection of offshore wind farms’, Energy Proc., 2013, 35, pp. 297306.
    27. 27)
      • 24. Graham's scanning’: Kent State University. Available at:∼rmuhamma/Compgeometry/MyCG/ConvexHull/GrahamScan/grahamScan.htm.
    28. 28)
      • 8. Lindahl, M., Fink Bagger, N.C., Stidsen, T., et al: ‘Optiarray from dong energy’. Proc. of Wind Integration Workshop, London, UK, 2013.
    29. 29)
      • 5. Pillai, A.C., Chick, J., Johanning, L.: ‘Optimisation of offshore wind farms using a genetic algorithm’, Int. J. Offshore Polar Eng., 2016, 26, (3), pp. 225234.
    30. 30)
      • 11. Bauer, J., Lysgaard, J.: ‘The offshore wind farm array cable layout problem: a planar open vehicle routing problem’, J. Oper. Res. Soc., 2015, 66, (3), p. 360.

Related content

This is a required field
Please enter a valid email address