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Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications

Abaei, MM, Arzaghi, E, Abbassi, R ORCID: 0000-0002-9230-6175, Garaniya, V ORCID: 0000-0002-0090-147X and Penesis, I ORCID: 0000-0003-4570-6034 2017 , 'Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications' , Renewable Energy, vol. 102, no. B , pp. 341-348 , doi: 10.1016/j.renene.2016.10.054.

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Research and development of alternative energy resources such as wave energy has always attracted significant attention due to their abundant and sustainable nature. The uncertainties associated with the marine environment and the significant costs required for implementation of Wave Energy Converters (WECs) require a sound decision making methodology. This paper presents a novel risk-based methodology for selecting sites for WEC installation to minimize the overall economic risk. It provides WEC developers, investors, governments and policy makers a methodology for evaluating influencing parameters for potential site locations whilst also optimizing wave energy extraction. A Bayesian network is developed to model the probabilistic influencing parameters and then it is extended to an influence diagram for estimating the expected utility of installing the WEC equipment in a selected location. To demonstrate the application of the developed methodology, three sites in the south coast of Tasmania are considered. Based on actual sea state data, the optimum location for installing WEC equipment is determined as location 2 and the economic risk associated with energy extraction is minimized by suggesting a specific wave height (HS = 5 m) as a design criteria.

Item Type: Article
Authors/Creators:Abaei, MM and Arzaghi, E and Abbassi, R and Garaniya, V and Penesis, I
Keywords: decision making, risk, renewable energy, wave energy converter, Bayesian network, influence diagram, expected utility
Journal or Publication Title: Renewable Energy
Publisher: Pergamon-Elsevier Science Ltd
ISSN: 0960-1481
DOI / ID Number: 10.1016/j.renene.2016.10.054
Copyright Information:

© 2016 Elsevier Ltd.

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