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

Journal article (2020)

Authors

E. Arzaghi Wind Energy - Aerospace Engineering
M.M. Abaei Ship Design, Production and Operations - Mechanical, Maritime and Materials Engineering
Rouzbeh Abbassi Macquarie University
Malgorzata O'Reilly University of Tasmania
Vikram Garaniya University of Tasmania
Irene Penesis University of Tasmania
Research Group
Ship Design, Production and Operations (Mechanical, Maritime and Materials Engineering) (TU Delft)
Markov chain Renewable energy Probabilistic modelling Power generation Wave energy converter
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Ship Design, Production and Operations

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.