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

Arzaghi, E, Abaei, MM, Abbassi, R, O'Reilly, M ORCID: 0000-0003-3898-3957, Garaniya, V ORCID: 0000-0002-0090-147X and Penesis, I ORCID: 0000-0003-4570-6034 2019 , 'A Markovian approach to power generation capacity assessment of floating wave energy converters' , Renewable Energy, vol. 146 , pp. 2736-2743 , doi: 10.1016/j.renene.2019.08.099.

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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 longterm 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.

Item Type: Article
Authors/Creators:Arzaghi, E and Abaei, MM and Abbassi, R and O'Reilly, M and Garaniya, V and Penesis, I
Keywords: renewable energy, power generation, wave energy converter, markov chain, probabilistic modelling
Journal or Publication Title: Renewable Energy
Publisher: Elsevier
ISSN: 0960-1481
DOI / ID Number: 10.1016/j.renene.2019.08.099
Copyright Information:

© 2019 Elsevier Ltd. All rights reserved

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