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

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

Abstract

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 0.54MW, while the value for locations 1 and 2 is 0.33MW and 0.43MW 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 0.5MW power.

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  • Arzaghi, Ehsan & Abaei, Mohammad Mahdi & Abbassi, Rouzbeh & O'Reilly, Malgorzata & Garaniya, Vikram & Penesis, Irene, 2020. "A Markovian approach to power generation capacity assessment of floating wave energy converters," Renewable Energy, Elsevier, vol. 146(C), pages 2736-2743.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2736-2743
    DOI: 10.1016/j.renene.2019.08.099
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    References listed on IDEAS

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    Cited by:

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    2. Dillon, Trent & Maurer, Benjamin & Lawson, Michael & Polagye, Brian, 2024. "Forecast-based stochastic optimization for a load powered by wave energy," Renewable Energy, Elsevier, vol. 226(C).

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