An integrated operation and maintenance framework for offshore renewable energy

Offshore renewable devices hold a large potential as renewable energy sources, but their deployment costs are still too high compared to those of other technologies. Operation and maintenance, as well as management of the assets, are main contributors to the overall costs of the projects, and decision-support tools in this area are required to decrease the final cost of energy.\\ In this thesis a complete characterisation and optimisation framework for the operation, maintenance and assets management of an offshore renewable farm is presented. The methodology uses known approaches, based on Monte Carlo simulation for the characterisation of the key performance indicators of the offshore renewable farm, and genetic algorithms as a search heuristic for the proposal of improved strategies. These methods, coupled in an integrated framework, constitute a novel and valuable tool to support the decision-making process in this area. The methods developed consider multiple aspects for the accurate description of the problem, including considerations on the reliability of the devices and limitations on the offshore operations dictated by the properties of the maintenance assets. Mechanisms and constraints that influence the maintenance procedures are considered and used to determine the optimal strategy. The models are flexible over a range of offshore renewable technologies, and adaptable to different offshore farm sizes and layouts, as well as maintenance assets and configurations of the devices. The approaches presented demonstrate the potential for cost reduction in the operation and maintenance strategy selection, and highlight the importance of computational tools to improve the profitability of a project while ensuring that satisfactory levels of availability and reliability are preserved. Three case studies to show the benefits of application of such methodologies, as well as the validity of their implementation, are provided. Areas for further development are identified, and suggestions to improve the effectiveness of decision-making tools for the assets management of offshore renewable technologies are provided.European CommissionMojo Ocean Dynamics Ltd. T/A Mojo Maritime Lt

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

Techno-economic optimisation of offshore wind farms based on life cycle cost analysis on the UK

In order to reduce the cost of energy per MWh in wind energy sector and support investment decisions, an optimisation methodology is developed and applied on Round 3 offshore zones, which are specific sites released by the Crown Estate for offshore wind farm deployments, and for each zone individually in the UK. The 8-objective optimisation problem includes five techno-economic Life Cycle Cost factors that are directly linked to the physical aspects of each location, where three different wind farm layouts and four types of turbines are considered. Optimal trade-offs are revealed by using NSGA II and sensitivity analysis is conducted for deeper insight for both industrial and policy-making purposes. Four optimum solutions were discovered in the range between £1.6 and £1.8 billion; the areas of Seagreen Alpha, East Anglia One and Hornsea Project One. The highly complex nature of the decision variables and their interdependencies were revealed, where the combinations of site-layout and site-turbine size captured above 20% of total Sobol indices in total cost. The proposed framework could also be applied to other sectors in order to increase investment confidence

The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

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

Wave Energy Converter Array Optimization: A Genetic Algorithm Approach and Minimum Separation Distance Study

With the need to integrate renewable energy sources into the current energy portfolio and the proximity of power consumers to ocean coastlines, it is important to evaluate marine energy systems, specifically wave energy converters (WECs), as potential solutions for meeting electricity needs. The ability to model these systems computationally is vital to their eventual deployment. The power development, economics, grid integration requirements, operations and maintenance requirements, and ecological impacts must be understood before these devices are physically installed. However, the research area of WEC array optimization is young, and the few available results of previously implemented optimization methods are preliminary. The purpose of this work is to introduce a new WEC array optimization framework to explore systems-level concerns, specifically WEC layout and device spacing. A genetic algorithm approach that utilizes an analytical hydrodynamic model and includes an array cost model is presented, and the resulting optimal layouts for a preliminary test case are discussed. This initial work is integral in providing an understanding of device layout and spacing and is a foundational starting point for subsequent and more advanced WEC array optimization research

A parametric study of wave energy converter layouts in real wave models

Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction