A risk-based approach to layout implementation of WEC array by addressing accidental constraints

  • Ray John McCarthy
  • Ehsan Arzaghi
  • Mohammad Mahdi Abaei
  • Rouzbeh AbbassiEmail author
  • Vikram Garaniya
  • Irene Penesis
Research Article


Wave energy converters (WEC) are reaching a pinnacle in their prototype phase. World leaders in the energy sector are looking for renewable energy sources to replace the expiry of fossil-fuel energy capacity. For WECs to become a viable solution to the fossil-fuel challenges, there is a need to have a deeper understanding of the associated costs and the operational impacts of this technology. This study investigates the relationship between these two characteristics and finds an improved implementation strategy by developing a dynamic risk-based methodology. The methodology developed from this study will aid WEC technology to move towards a commercialised state by implementing an array or farm of WEC devices. Bayesian network (BN) is adopted to analyse the probability of a collision accident within the farm as well as the likelihood of meeting the desired level of power production. The BN is later extended to an influence diagram (ID) for selecting the optimum configuration of the WEC farm. The ID assists in decision-making based on the investigated probabilities, required capital investment, and economic impact of the accident scenario. To demonstrate the application of the developed method, a case study is adopted including three decision alternatives, each representing a farm with different layouts of point absorber WECs. The performance of the facility is assessed under real-life offshore environmental conditions. The developed methodology assists in finding the WEC layout which minimises the economic risk of an array implementation and also increases the reliability of these structures.


Wave energy converter Influence diagram Bayesian network Power production Renewable energy 



  1. Abaei MM, Arzaghi E, Abbassi R, Garaniya V, Penesis I (2017) Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications. Renew Energy 102:341–348CrossRefGoogle Scholar
  2. Abaei MM, Arzaghi E, Abbassi R, Garaniya V, Chai S, Khan F (2018a) A robust risk assessment methodology for safety analysis of marine structures under storm conditions. Ocean Eng 156:167–178CrossRefGoogle Scholar
  3. Abaei MM, Abbassi R, Garaniya V, Chai C (2018b) Reliability assessment of marine floating structures using Bayesian network. Appl Ocean Res 76:51–60CrossRefGoogle Scholar
  4. Abaei MM, Arzaghi E, Abbassi R, Garaniya V, Javanmardi M, Chai S (2018c) Dynamic reliability assessment of ship grounding using Bayesian inference. Ocean Eng 159:47–55CrossRefGoogle Scholar
  5. Ambühl S (2015) Reliability of wave energy converters. Department of Civil Engineering, Aalborg University, Aalborg, p 96Google Scholar
  6. Ambühl S, Kofoed JP, Sørensen JD (2014) Stochastic modeling of long-term and extreme value estimation of wind and sea conditions for probabilistic reliability assessments of wave energy devices. Ocean Eng 89:243–255CrossRefGoogle Scholar
  7. Ambühl S, Ferri F, Kofoed JP, Sorensen JD (2015) Fatigue reliability and calibration of fatigue design factors of wave energy converters. Int J Mar Energy 10:17–38CrossRefGoogle Scholar
  8. Arzaghi E, Abaei MM, Abbassi R, Garaniya V, Chin C, Khan F (2017) Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring. Eng Fail Anal 79:928–939CrossRefGoogle Scholar
  9. Arzaghi E, Abaei MM, Abbassi R, Garaniya V, Binns J, Chin C, Khan F (2018) A hierarchical Bayesian approach to modelling fate and transport of oil released from subsea pipelines. Process Saf Environ Prot 118:307–315CrossRefGoogle Scholar
  10. Babarit A (2013) On the park effect in arrays of oscillating wave energy converters. Renew Energy 58:68–78CrossRefGoogle Scholar
  11. Babarit A, Hals J, Muliawan M, Kurniawan A, Moan T, Krokstad J (2012) Numerical benchmarking study of a selection of wave energy converters. Renew Energy 41:44–63CrossRefGoogle Scholar
  12. BahooToroody A, Abaei MM, Arzaghi E, BahooToroody F, De Carlo F, Abbassi R (2019) Multi-level optimization of maintenance plan for natural gas system exposed to deterioration process. J Hazard Mater 362:412–423CrossRefGoogle Scholar
  13. Baksh A, Abbassi R, Garaniya V, Khan F (2016) A network based approach to envisage potential accidents in offshore process facilities. Process Saf Prog 36(2):178–191CrossRefGoogle Scholar
  14. Bhandari J, Arzaghi E, Abbassi R, Garaniya V, Khan F (2016) Dynamic risk-based maintenance for offshore processing facility. Process Saf Prog 35(4):399–406CrossRefGoogle Scholar
  15. Boyle G (1997) Renewable energy: power for a sustainable future. vol 2. Taylor & FrancisGoogle Scholar
  16. Bozzi S, Giassi M, Miguel AM, Antonini A, Bizzozero F, Gruosso G, Archetti R, Passoni G (2017) Wave energy farm design in real wave climates: the Italian offshore. Energy 122:378–389CrossRefGoogle Scholar
  17. British Petroleum (BP) (2014) BP Energy Outlook 2035. Accessed 15 Sept 2017
  18. Brown AC (2009) Towards reliable and survivable ocean wave energy converters. Oregon State University, CorvallisGoogle Scholar
  19. Cobb G (2015) Cardno contributes to award-winning wave energy project. Accessed 15 Sept 2017
  20. Cruz J (2007) Ocean wave energy: current status and future perspectives. Green energy and technology. Springer Science and Business Media, New YorkGoogle Scholar
  21. Clément A, McCullen P, Falcão A, Fiorentino A, Gardner F, Hammarlund K, Lemonis G, Lewis T, Nielsen K, Petroncini S (2002) Wave energy in Europe: current status and perspectives. Renew Sustain Energy Rev 6(5):405–431CrossRefGoogle Scholar
  22. Dastan Diznab MA, Mohajernassab S, Seif MS, Tabeshpour MR, Mehdigholi H (2014) Assessment of offshore structures under extreme wave conditions by modified endurance wave analysis. Mar Struct 39:50–69CrossRefGoogle Scholar
  23. Drew B, Plumner AR, Sahinkaya MN (2009) A review of wave energy converter technology. University of Bath, BathCrossRefGoogle Scholar
  24. Edenhofer O, Pichs-Madruga R, Sokona Y, Seyboth K, Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S (2011) IPCC special report on renewable energy sources and climate change mitigation. Prepared By Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  25. Eleye-Datubo A, Wall A, Saajedi A, Wang J (2006) Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Anal 26(3):695–721CrossRefGoogle Scholar
  26. Ewing FJ, Thies PR, Waldron B, Shek J, Wilkinson M (eds) (2017) Reliability prediction for offshore renewable energy: data driven insights. In: ASME 2017 36th international conference on ocean, offshore and arctic engineering. American Society of Mechanical EngineersGoogle Scholar
  27. Göteman M, Engström J, Eriksson M, Isberg J (2015) Optimizing wave energy parks with over 1000 interacting point-absorbers using an approximate analytical method. Int J Mar Energy 10:113–126CrossRefGoogle Scholar
  28. Harris RE, Johanning L, Wolfram J (2004) Mooring systems for wave energy converters: a review of design issues and choices. Marec 2004Google Scholar
  29. Hemer M, Penesis I, McInnes K, Manasseh R, Pitman T (2016) Catching the waves: it’s time for Australia to embrace ocean renewable energy. Accessed 15 Sept 2017
  30. Hodge CW, Bateman W, Yuan Z, Thies PR, Bruce T (eds) (2018) Coupled modelling of a non-linear wave energy converter and hydraulic PTO. In: The 28th international ocean and polar engineering conference. International Society of Offshore and Polar EngineersGoogle Scholar
  31. Hovland J (2010) Design through understanding for the reliability and survivability of wave energy converters, in school of mechanical, industrial, and manufacturing engineering. Oregon State University, CorvallisGoogle Scholar
  32. Karimirad M, Moan T (2013) Stochastic dynamic response analysis of a tension leg spar-type offshore wind turbine. Wind Energy 16(6):953–973CrossRefGoogle Scholar
  33. Khakzad N, Khan F, Amyotte P (2011) Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab Eng Syst Saf 96(8):925–932CrossRefGoogle Scholar
  34. Leoni L, BahooToroody A, De Carlo F, Paltrinieri N (2019) Developing a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Network. J Loss Prev Process Ind 57:17–24CrossRefGoogle Scholar
  35. Ltd O (2006) Orcaflex Manual, UK. Accessed 15 Mar 2017
  36. Margheritini L, Vicinanza D, Frigaard P (2009) SSG wave energy converter: design, reliability and hydraulic performance of an innovative overtopping device. Renew Energy 34(5):1371–1380CrossRefGoogle Scholar
  37. Mueller M, Wallace R (2008) Enabling science and technology for marine renewable energy. Energy Policy 36(12):4376–4382CrossRefGoogle Scholar
  38. Paasch R (2010) Towards a definition and metric for the survivability of ocean wave energy converters. School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, CorvallisGoogle Scholar
  39. Rinaldi G, Portillo J, Khalid F, Henriques J, Thies P, Gato L et al (2018a) Multivariate analysis of the reliability, availability, and maintainability characterizations of a Spar-Buoy wave energy converter farm. J Ocean Eng Mar Energy 4(3):199–215CrossRefGoogle Scholar
  40. Rinaldi G, Pillai AC, Thies PR, Johanning L (eds) (2018b) Verification and benchmarking methodology for O&M planning and optimization tools in the offshore renewable energy sector. In: ASME 2018 37th international conference on ocean, offshore and arctic engineering. American Society of Mechanical EngineersGoogle Scholar
  41. Shoghi R, Tabeshpour M (2014) An approximate method for the surge response of the tension leg platform. J Mar Sci Appl 13(1):99–104CrossRefGoogle Scholar
  42. Singh J (2014) A fast approach coupling boundary element method and plane wave approximation for wave interaction analysis in sparse arrays of wave energy converters. Ocean Eng 85:12–20CrossRefGoogle Scholar
  43. So R, Simmons A, Brekken T, Ruehl K, Michelen C (2015) Development of PTO-Sim: a power performance module for the open-source wave energy converter code WEC-Sim. OMAE 2015Google Scholar
  44. Sørensen JD (2004) Structural reliability theory and risk analysis. Institute of Building Technology and Structural Engineering, AalborgGoogle Scholar
  45. Strathclyde (2015) T.U.o. Cost estimations. Cited 2017
  46. Wolfram J (2006) On assessing the reliability and availability of marine energy converters: the problems of a new technology. Proc Inst Mech Eng Part O J Risk Reliab 220(1):55–68CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Centre of Maritime Engineering and Hydrodynamics, Australian Maritime College (AMC)University of TasmaniaLauncestonAustralia
  2. 2.Wind Energy Section, Faculty of Aerospace EngineeringDelft University of TechnologyDelftThe Netherlands
  3. 3.Renewable Energy Group, College of Engineering, Mathematics and Physical SciencesUniversity of ExeterCornwallUK
  4. 4.School of Engineering, Faculty of Science and EngineeringMacquarie UniversitySydneyAustralia

Personalised recommendations