A Markovian approach to power generation capacity assessment of floating wave energy converters

Ehsan Arzaghi, Mohammad Mahdi Abaei, Rouzbeh Abbassi, Malgorzata O'Reilly, Vikram Garaniya, Irene Penesis

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The significant cost required for implementation of WEC sites and the uncertainty associated with their performance, due to the randomness of the marine environment, can bring critical challenges to the industry. This paper presents a probabilistic methodology for predicting the long-term power generation of WECs. The developed method can be used by the operators and designers to optimize the performance of WECs by improving the design or in selecting optimum site locations. A Markov Chain model is constructed to estimate the stationary distribution of output power based on the results of hydrodynamic
analyses on a point absorber WEC. To illustrate the application of the method, the performance of a point absorber is assessed in three locations in the south of Tasmania by considering their actual long-term sea state data. It is observed that location 3 provides the highest potential for energy extraction with a mean value for absorbed power of approximately 0:54 MW, while the value for locations 1 and 2 is 0:33 MW and 0:43 MW respectively. The model estimated that location 3 has the capacity to satisfy industry requirement with probability 0.72, assuming that the production goal is to generate at least 0:5 MW power.
Original languageEnglish
Pages (from-to)2736-2743
Number of pages8
JournalRenewable Energy
Publication statusPublished - Feb 2020


  • Renewable energy
  • Power generation
  • Wave energy converter
  • Markov chain
  • Probabilistic modelling


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