A coupled Monte Carlo - Evolutionary Algorithm approach to optimise offshore renewables O&M

This is the author accepted manuscript. The final version is available from EWTEC via the link in this record.Improving the reliability and survivability of wave and tidal energy converters, whilst minimising the perceived risks and reducing the deployment costs, are recognised as key priorities to further develop the marine energy market. Computational decision-making models for offshore renewables have demonstrated to be valuable tools in order to provide support in these strategic areas. In this paper, the authors propose an integrated approach of Monte Carlo simulation and Evolutionary Algorithms to address these challenges. A time-domain method based on the Monte Carlo technique, with specific consideration of marine renewable energy requirements, is used for the assessment of the devices and the characterization of the offshore farms. This permits the obtainment of energy predictions and indications on the reliability, availability, maintainability and profitability of the farm. A multi-objective search, by means of a specifically designed Genetic Algorithm, is then used to determine the ideal variation of inputs set for the improvement of the results. Suitable objective functions aiming at the minimization of the maintenance costs and the maximization of the reliability are considered for this purpose. The outcomes obtainable for the assessment of an offshore farm, as well as suggested practices for the optimisation of the Operation and Maintenance (O&M) procedures, are introduced and discussed. Results on the ideal trade-off solutions between conflicting objectives are presented.The work in this paper has been conducted within the multinational Initial Training Network (ITN) OceaNET, funded under the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n° 607656. Mojo Maritime (JFMS) have provided access to Mermaid to support, and for integration with, this research

Rising Powers and Foreign Policy Revisionism: Understanding BRICS Identity and Behavior through Time

In Rising Powers and Foreign Policy Revisionism, Cameron Thies and Mark Nieman examine the identity and behavior of the BRICS (Brazil, Russia, India, China, and South Africa) over time in light of academic and policymaker concerns that rising powers may become more aggressive and conflict-prone. The authors develop a theoretical framework that encapsulates pressures for revisionism through the mechanism of competition and pressures for accommodation and assimilation through the mechanism of socialization. The identity and behavior of the BRICS should be a product of the push and pull of these two forces as mediated by their domestic foreign policy processes.State identity is investigated qualitatively through the use of role theory and the identification of national role conceptions. Both economic and militarized conflict behavior are examined using Bayesian change-point modeling, which identifies structural breaks in time series data, revealing potential wholesale revision of foreign policy. Using this innovative approach to show that the behavior of rising powers is governed not simply by the structural dynamics of power but also by the roles that these rising powers define for themselves, they assert that this process will likely lead to a much more evolutionary approach to foreign policy and will not necessarily generate international conflict.https://lib.dr.iastate.edu/pols_books/1000/thumbnail.jp

On the Analysis of a Wave Energy Farm with Focus on Maintenance Operations

Wave energy has a promising technical potential that could contribute to the future energy mix. However, costs related to the deployment of wave energy converters (WECs) are still high compared to other technologies. In order to reduce these costs, two principle options are available, a reduction in cost and an increase in productivity. This paper presents a reliability-based computational tool to identify typical decision problems and to shed light on the complexity of optimising a wave power farm. The proposed tool is 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, are 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. Conclusions highlight 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 work in this 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. Mojo Maritime have provided access to Mermaid to support, and for integration with, this research

A decision support model to optimise the operation and maintenance strategies of an offshore renewable energy farm

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In order to accelerate the access into the energy market for ocean renewables, the operation and maintenance (O & M) costs for these technologies must be reduced. In this paper a reliability-based simulation tool for the optimization of the management of an offshore renewable energy (ORE) farm is presented. The proposed tool takes into account the reliability data of the simulated devices and estimations on the energy produced to create a series of results in terms of availability and maintainability of the farm. The information produced supports operational and strategic decision making regarding the O & M for offshore farms. A case study simulating a conceptual tidal energy project, consisting of an array of two tidal turbines located off the north coast of Scotland, is presented to show some of the results achievable with this model. The proposed methodology, although ado pted for a tidal farm here, is generally applicable to other kinds of ORE farms.This research has been conducted within the multinational Initial Training Network (ITN) OceaNET, funded under the People Programme (Marie Curie Actions) of the European Union's Seventh Framework ProgrammeFP7/2007-2013/under REA grant agreement no 607656. Mojo Maritime have provided access to Mermaid to support, and for integration with, this research

Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms

The first author was funded by the Marie Curie Actions of the European Union’s Seventh Framework Programme FP7/2007- 2013/ under REA grant agreement number 607656 (OceaNet project) and by the industrial partner James Fisher Marine Services Ltd. Mojo Maritime (JFMS group) have provided access to Mermaid to support, and for integration with, this research. This work is also funded by the EPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]This is the author accepted manuscriptThis paper explores the use of genetic algorithms to optimize the operation and maintenance (O&M) assets of an offshore wind farm. Three different methods are implemented in order to demonstrate the approach. The optimization problem simultaneously considers both the reliability characteristics of the offshore wind turbines and the composition of the maintenance fleet, seeking to identify the optimal configurations for the strategic assets. These are evaluated in order to minimize the operating costs of the offshore farm while maximizing both its reliability and availability. The considerations used for the application of genetic algorithms as an effective way to support the assets management are described, and a case study to show the applicability of the approach is presented. The variation of the economic performance indicators as a consequence of the optimization procedure are discussed, and the implementation of this method in a wider computational framework for the O&M assets improvement introduced.European CommissionMojo Ocean Dynamics Ltd. T/A Mojo Maritime LtdEPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER

Effects of wheat and oat-based whole grain foods on serum lipoprotein size and distribution in overweight middle aged people : a randomised controlled trial

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