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As for any novel technology, the need to consider, identify and formulate performance requirements and related assessment criteria has been an important subject in the development of Wave Energy Converters (WECs). These allow the characterisation of each technology through techno-economic indicators, which in turn allow comparisons between different technologies, and an assessment of alternative solutions throughout all the development stages. Such assessment is ideally carried out through the application of metrics, which should comply with several attributes, such as being objective, quantitative, specific, measurable, repeatable, and independent. In the present work, more than 50 metrics to monitor the development of WECs are compiled, explained, and discussed. These metrics are divided in the following evaluation areas: 1) Performance; 2) Reliability; 3) Survivability; 4) Techno-economics. In addition, the important evaluation area of Environmental Impact is briefly discussed concerning the need for common metrics. The compilation summarised in this paper and its discussion aim to provide a practical reference source concerning metrics for WEC development, which is currently unavailable in the published literature in terms of broadness and condensed presentation. Such compilation includes multiple formulations from the wave energy sector and other relatable industries (e.g. wind energy) that are typically diluted among specialist literature, standards, guidelines and recommendations, scientific papers, and project reports. The paper is concluded with a reflection of any salient gaps that are not addressed by current metrics, in a context of accelerating the development of WEC technologies.
Europe government, Wave Energy, Ocean energy, Technology Development, Assessment Criteria, Sustainability, Evaluation Areas, Artificial Intelligence, Gap Analysis, Machine learning, Metrics, Converter
Europe government, Wave Energy, Ocean energy, Technology Development, Assessment Criteria, Sustainability, Evaluation Areas, Artificial Intelligence, Gap Analysis, Machine learning, Metrics, Converter
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