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

Experimental modelling of a parachute-type tidal energy converter

European Regional Development Fund (ERDF

The O&M driven design of a multi-row platform tidal project

This is the author accepted manuscript. The final version is available from EWTEC via the link in this recordA number of tidal projects with different design, sizes and conceptual approaches, have been developed in recent years. While the technology has proven to be effective in converting tidal streams into electric energy, the economic viability is still far from being achieved due to unforeseen complications following the installation of the devices. In this paper, the authors provide an overview of the major challenges tidal energy developers should consider in order to design a viable tidal energy device. In addition, based on past field experiences, the typical issues encountered by offshore contractors during the deployment of one or an array of devices are presented. Therefore, paying special attention to the operational requirements of the devices, the solutions to these offshore challenges are proposed. Hence, a novel tidal concept is presented, using lifecycle O&M costs as a top driver for the development of the device. Subsequently, the iterative improvement of the project is achieved by means of a verified and calibrated integrated framework, based on Monte Carlo simulation and evolutionary algorithms, in order to support the decision-making process and management of the assets. Thus, the pivotal role of computational tools to improve the profitability of the project while ensuring satisfactory levels of availability and reliability is highlighted, and the potential for cost reduction in the design of a tidal energy project, in order to achieve financial viability, is shown.European Regional Development Fund (ERDF

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

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

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

Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review

This is the final version. Available on open access from MDPI via the DOI in this record. Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.Engineering and Physical Sciences Research Council (EPSRC)European Union Horizon 202

Assessment of extreme and metocean conditions in the Maldives for OTEC applications

This is the final version. Available on open access from Wiley via the DOI in this recordThe Maldives is a group of tropical atolls, considered globally to be one of the most desirable holiday destinations. There is an urgent requirement to decrease their dependency on fossil fuels that are currently the main source of energy, and a number of renewable energy alternatives are being evaluated. Among these, due to the favorable oceanographic and bathymetric conditions, ocean thermal energy conversion (OTEC) systems represent a viable opportunity for clean and reliable power. However, the stresses the OTEC platform will need to endure during adverse environmental conditions are not well defined. The magnitude of these stresses will then have a direct influence on the design of the OTEC device. In order to overcome this uncertainty, this paper uses hindcast data sets from global weather and ocean models to assess the metocean conditions of the Maldives, with particular reference to extreme conditions. After selecting a suitable location for the deployment of the devices, return values calculated using the peaks‐over‐threshold (POT) methodology are estimated for wind, waves, and currents. The 100‐year return value for the significant wave height is found to be 4.5 m, with a joint occurrence of energy periods between 7.5 and 8.5 seconds, whereas the 100‐year return wind has a velocity of 17.8 m/s and the 100‐year return current of 1.9 m/s. The directionality of these extreme events is also considered, showing the southern and western sub‐quadrants as the prevailing sources, which provides essential information for positioning of the platform. Additional evaluations of tropical revolving storms (TRS) and variations in temperature and salinity patterns are also provided over a 1500‐m water column; temperature varies by approximately 24°C, and salinity by around 2 ppt, showing the suitability of OTEC platforms in the Maldives. This work is therefore of interest to offshore renewable energy stakeholders interested in developing a project in the Maldives or those conducting an analogous analysis in other locations.European Regional Development Fund (ERDF

Multivariate analysis of the reliability, availability, and maintainability characterizations of a Spar–Buoy wave energy converter farm

This is the final version of the article. Available from Springer via the DOI in this record.Quantitative reliability, availability, and maintainability (RAM) assessments are of fundamental importance at the early design stages, as well as planning and operation of marine renewable energy systems. This paper presents an RAM framework adaptable to different offshore renewable technologies, conceived to provide support in the choice of the device components and subsequent planning of the O&M strategies. A case study, characterizing a pilot farm of oscillating water column (OWC) wave energy converters (WECs), is illustrated together with the method used to obtain reliable estimate of its key performance indicators (KPIs). Based on a fixed feed-in-tariff for the project, economic figures are estimated, showing a direct relationship with the availability of the farm and the cost of maintenance interventions. Consequently, the probability distributions of the most relevant output variables are presented, and the mutual correlations between them investigated using principal components analysis (PCA) with the aim of discovering the relationships influencing the performance of the offshore farm. In this way, the contributions of the individual factors on the profitability of the project are quantified, and generic guidelines to support the decision-making process are derived. It is shown how this type of analysis provides important insights not only to ocean energy farm operators after the deployment of the devices, but also to device developers at the early design stage of wave energy concepts.The first and second authors were partially 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). The fourth author was funded by FCT researcher grant No. IF/01457/2014. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 654444 (OPERA Project) and from the FCT project PTDC/MAR-TEC/0914/2014

A Computational Tool for the Pro-Active Management of Offshore Farms

Operation and Maintenance (O&M) of offshore farms have been highlighted as one of the major contributors to the final cost of energy. Therefore, lower the costs related to such aspect is vital in order to speed up their access into the market. Several decision-making tools have been developed in different areas in the last decades. Unfortunately, many of these suffer a degree of approximation due to the lack of either reliable input data or capability to assess specific offshore tasks. In this work the authors address this problem developing a tool for the assessment of the optimal O&M procedures for offshore renewable energy farms. This uses Monte Carlo simulation, which permits to establish probability of exceedance and confidence intervals on the results obtained, to characterize and optimize the management of the farm. The model is expressly orientated towards offshore devices, and aims to reduce the assumptions generally needed in RAM (Reliability, Availability, Maintainability) analysis. Modelling possibilities offered by the implemented tool, as well as suggested practices for the optimisation of the management of offshore farms, are illustrated and discussed through the paper.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