A mixed-method optimisation and simulation framework for supporting logistical decisions during offshore wind farm installations

With a typical investment of in excess of £100 million for each project, the installation phase of offshore wind farms is an area where substantial cost-reductions can be achieved; however, to-date there have been relatively few studies exploring this. In this paper, we develop a mixed-method framework which exploits the complementary strengths of two decision-support methods: discrete-event simulation and robust optimisation. The simulation component allows developers to estimate the impact of user-defined asset selections on the likely cost and duration of the full or partial completion of the installation process. The optimisation component provides developers with an installation schedule that is robust to changes in operation durations due to weather uncertainties. The combined framework provides a decision-support tool which enhances the individual capability of both models by feedback channels between the two, and provides a mechanism to address current OWF installation projects. The combined tool, verified and validated by external experts, was applied to an installation case study to illustrate the application of the combined approach. An installation schedule was identified which accounted for seasonal uncertainties and optimised the ordering of activities

Evaluation of the Impact of Weather-Related Limitations on the Installation of Offshore Wind Turbine Towers

Weather conditions have a significant impact on the installation of offshore wind turbines. The rules for installation set clear limits. These limits are usually based on estimations of various experts and not on real assumptions and measurements on-site. When wind speeds and wave heights are too high, work cannot be carried out, and this leads to delays and additional costs. Therefore, we have carried out a measurement campaign during the installation of rotor blades to investigate to which extent the limits can be adjusted by using a tuned mass damper. The results from the measurement campaign—specifically empirically derived significant wave height limits—are used in a discrete event simulation. This study simulates delays resulting from weather conditions. Based on this, the total installation costs are considered. The results of the measurement campaign show that a safe installation with the use of a damper is possible at wave heights of up to 1.6 m. With the discrete event simulation, it is possible to prove that 17.9% can be saved for the costs of the installation vessel. In addition, the wind farm could be erected 32 days faster. Thus, it can be stated that the use of a tuned mass damper simplifies the installation from a technical point of view and is economical

On using simulation to model the installation process logistics for an offshore wind farm

The development of offshore wind farms (OWFs) in Europe is progressing to sites which are characteristically further from shore, in deeper waters, and of larger scale than previous sites. A consequence of moving further offshore is that installation operations are subject to harsher weather conditions, resulting in increased uncertainty in relation to the cost and duration of any operations. Assessing the comparative risks associated with different installation scenarios and identifying the best course of action is therefore a crucial problem for decision makers. Motivated by collaboration with industry partners, we present a detailed definition of the OWF installation process logistics problem, where aspects of fleet sizing, composition, and vessel scheduling are present. This article illustrates the use of simulation models to improve the understanding of the risks associated with logistical installation decisions. The developed tool employs a realistic model of the installation operations and enables the effect of any logistical decision to be investigated. A case study of an offshore wind farm installation project is presented in order to explore the impact of key logistical decisions on the cost and duration of the installation, and demonstrates that savings of up to 50% can be achieved through vessel optimization

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

Metocean risk analysis in offshore wind installation

Marine operations play a pivotal role throughout all phases of an offshore wind farm’s life cycle. In particular, uncertainties associated with offshore installation can extend construction schedules and increase the capital expenditure (CAPEX) required for a given project. Installation costs typically account for approximately 30% of the overall CAPEX. Therefore an understanding of the potential risks to these operations using simulation methods, can support planning decisions and reduce the costs of future projects. This research reviews the risks deriving from marine operations with an appreciation of the current standards in metocean risk management. It is intended that the analysis and benchmarking of existing tools, simulation methods and software to review metocean risks, will support and inform technical decisions prior to the construction of offshore wind projects in EDF Energy. By applying and testing the current state of the art in metocean risk analysis, this supports the estimation of risk profiles for marine operations. Several time series simulation techniques are adopted, expanded and tested to provide reliable metocean risk estimates. This has included the development of a comparative vessel risk methodology by adopting EDF’s existing probabilistic simulation tool ‘ECUME I’. The results provide a quantification of installation vessel performance and the structured method can be used to identify and benchmark offshore wind installation risk for developers or contractors. A commercially available simulation package ‘Mermaid’ was used to assess a range of marine operations for two planned offshore wind projects from EDF Energy’s portfolio: 1) Blyth Offshore Demonstrator and 2) Fecamp. The documentation of both analyses presents two different modelling approaches and supportive metrics such as percentage increase against baseline schedules, highlight the project phases with the greatest risk and where EDF Energy should prepare suitable mitigations or contingencies. A metocean weather modelling methodology has been investigated by applying and extending an existing Markov Switching Autoregressive (MS-AR) toolbox to produce stochastic wind speed and significant wave height time series. This model is analysed for inclusion in a next generation marine risk planning software tool and it is identified that the overall methodology produces similar weather window and workability outcomes compared to observed time series. Furthermore, an analysis of different marine operations, each with different metocean limits, revealed that the methodology can enhance the resolution of the risk profile, leading to improved estimates at intermediate percentiles. Each of the presented modelling approaches and simulation methods have limitations and a discussion of their impact is presented, offering recommendations for future analyses. It is intended that the methods analysed in this work will provide a useful reference for future metocean risk assessments in the offshore wind industry. These approaches have supported both academic and commercial practices, where project specific metocean risk assessments were used directly in project planning and the investigation of a MS-AR metocean modelling method has demonstrated the suitability of this approach for inclusion in a holistic simulation environment

Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry.

With the increasing number of wind turbines approaching their end of life, there has to be a decommissioning strategy in place as the removal of these assets is not as direct as reverse installation. Offshore asset decommissioning involves technical, financial, operational, safety, policy, and environmental considerations on handling offshore marine assets at their end-of-life, with phases from the planning to site clean-up and monitoring. Offshore decommissioning activities cost significantly more than onshore; thus, adequate financial and safety provisions are essential, and more research required in this area. Decommissioning projects have hitherto been performed on a small scale, but with large-scale aging structures, they must be optimised for lowered costs and risks. In terms of planning, execution and costs, there have been significant cost overruns on decommissioning projects, which are not profit-generating projects. These forecasted large-scale decommissioning activities also have associated risks. Although risk management is a well-researched area, there is limited literature on offshore wind decommissioning risk management. This research thus, applies risk management methods and strategies to develop a robust decommissioning risk framework. In addition, to improve decommissioning processes and technologies, there is a need to develop new protocols for decommissioning. This research identifies potentials for computational simulations and automations that need to be developed to identify and manage the highest cost and risk-drivers. This study seeks to close the research gap in understanding how to decrease decommissioning costs and risks. This research addresses potential opportunities in cost and risk estimation research, impact analysis and reduction frameworks that can be adapted to decommissioning activities specific to the offshore wind industry.Shafiee, Mahmood (Associate)PhD in Energy and Powe