New tool for optimization of wave and tidal plants presented

Illustration (Image: flickr/Jessica Nicole)

A paper proposing a novel computational tool for the reliability-based assessment of wave, tidal and offshore wind farms was published yesterday in the Journal of Marine Science and Engineering

The paper presents the new tool to identify typical decision problems and to shed light on the complexity of optimising a wave power farm. Nevertheless, the tool is exclusively dedicated to marine renewables and adaptable to a range of technologies.

The proposed tool was used to investigate productivity and availability of a wave energy farm during 10 years of operational life. A number of optimization possibilities to improve productivity, namely vessel choice, maintenance regime, failure rate and component redundancy, were then explored in order to assess their effectiveness. The paper quantifies the yield increase and provides a practical approach to evaluate the effectiveness of strategic and operational decision options.

Results, in terms of the variations in productivity and availability of the farm, are analysed and discussed within the paper, with conclusions highlighting the importance of reliability-centred simulations that consider the specific decision parameters throughout the operational life to find suitable solutions that increase the productivity and reduce the running cost for offshore farms.

The computational tool used belongs to the class of Monte Carlo models, which repeatedly perform a non-deterministic calculation in order to obtain the most probable result. The Monte Carlo method coupled with the computational simulation uses a pseudorandom number generator (PRNG) to generate numbers uniformly distributed between zero and one. The implemented tool uses this methodology and the related reliability data to perform the energetic and economic characterization of offshore farms, simulating the failures that limit their availability, and consequently, productivity, the paper reads.

The authors are Giovanni Rinaldi, Philipp R. Thies, and Lars Johanning of College of Engineering, Mathematics and Physical Sciences at the University of Exeter, and Richard Walker from Mojo Maritime.

The work in the paper has been conducted within the multinational Initial Training Network (ITN) OceaNET, funded under the PEOPLE Programme (Marie Curie Actions) of European Union’s FP7, with Mojo Maritime providing access to Mermaid to support, and for integration with, the research.

 

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