A lifecycle financial analysis model for offshore wind farms (original) (raw)
Related papers
Journal of Physics: Conference Series, 2018
The offshore wind sector has achieved significant cost reductions in recent years. However, there is still work to be done to maintain and surpass these savings across current and future farms. There is increased competition to reduce costs within the industry itself. Additional challenges are foreseen at future sites located further from shore, in harsher conditions and deeper waters. Larger turbines and projects also mean new equipment, logistics and maintenance requirements. Moreover, farms are approaching the decommissioning phase where there is little experience. Modelling is a safe and cost-effective way to evaluate and optimise operations. However, there is a lack of comprehensive decision-support tools, detailed enough to provide insight into the effects of technological innovations and novel strategies. To address the gap, the EU FP7 LEANWIND project developed a suite of state-of-the-art logistics optimisation and financial simulation models. They can assess a farm scenario in detail at every stage of the project lifecycle and supply-chain, identifying potential cost reductions and more efficient strategies. This paper introduces the models including: an overview of their scope and capabilities; how they can be applied; and the potential end users.
Energy Procedia, 2016
This paper investigates three decision problems with potential to optimize operation and maintenance and logistics strategies for offshore wind farms: the timing of predetermined jack-up vessel campaigns; selection of crew transfer vessel fleet; and timing of annual services. These problems are compared both in terms of potential cost reduction and the stochastic variability and associated uncertainty of the outcome. Predetermined jack-up vessel campaigns appear to have a high cost reduction potential but also a higher stochastic variability than the other decision problems. The paper also demonstrates the benefits and difficulties of considering problems together rather than solving them in isolation.
Development of a Maintenance Option Model to Optimize Offshore Wind Farm O&M
The prediction and optimization of maintenance activities provides a significant opportunity for offshore wind farms operation and maintenance (O&M) cost reduction. This paper introduces the concept of predictive maintenance options applied to offshore wind farms managed via power purchase agreements (PPAs). For a single turbine, a predictive maintenance option is created by the incorporation of Health Monitoring (HM) or Prognostics and Health Management (PHM) into subsystems such that a remaining useful life (RUL) is predicted as the subsystem’s health degrades. The option is exercised when predictive maintenance is performed (based on the RUL) before the subsystem or turbine failures. The concept has been extended to offshore wind farms managed under a PPA with multiple turbines indicating RULs. The time-history paths of cost avoidance and cumulative revenue are simulated with the inclusion of uncertainties in wind and the forecasted RULs. Using a simulation-based real options analysis (ROA) that analyzes a series of “European” options and all possible predictive maintenance opportunities, the optimum maintenance opportunity that maximizes the value of the predictive maintenance option can be determined. The cumulative revenue and cost avoidance for a single turbine depends on the operational state of the other turbines and the amount of energy that the farm is required to deliver. The optimum predictive maintenance opportunity for the turbines in a farm subjecting to a PPA is different from subjecting to an “as-delivered” contract, and also different from the optimum opportunities for the individual turbines managed in isolation.
Return on Investment Modeling to Support Cost Avoidance Business Cases for Wind Farm O&M
Accurate life-cycle costing is a key enabler for wind farm operation and maintenance (O&M) optimization. Research has shown that maintenance, for both onand offshore installations, is not optimized and that significant opportunities exist for reducing the total cost for maintenance activities and the cost due to production losses, especially for large wind farms. This paper describes a stochastic model for detailed lifecycle cost analysis and associated return on investment (ROI) analysis that can be used to assess offshore wind farm O&M management alternatives and technologies. A case study that examines the return on investment for the implementation of prognostics and health management (PHM) systems on a wind turbine is presented
A combined supply chain optimisation model for the installation phase of offshore wind projects
International Journal of Production Research, 2017
This paper proposes a combined model for port selection and supply chain optimisation for the installation phase of an offshore wind farm. Two strategic models are proposed where the first model, based on Analytical Hierarchy Process (AHP), aims to select the most suitable installation port. The second model is developed using Integer Linear Programming (ILP) in order to determine the optimal transportation schedule of the components from suppliers to the chosen installation port. The proposed models are evaluated for the West Gabbard (UK) offshore wind farm located in southern part of the North Sea. According to the computational results, the AHP model chooses port of Oostende, Belgium as the most suitable installation port for this offshore wind farm whereas the proposed supply chain model shows that the total transportation cost makes up 9% of total supply chain cost.
Optimization of Operation & Maintenance for offshore wind farms modelling
Offshore wind technology is one of the most upcoming source of energy, and it could potentially replace parts of the fossil fuelled power production. However, offshore wind turbine technology is also associated with harsher weather conditions. Indeed, it experiences more challenging wind and wave conditions, which in turn limits the vessels capabilities to access the wind farms. Additionally, with the constant rise of power utilization, improvements in the Operation Maintenance (O&M) planning are crucial for the development of large isolated offshore wind farms. Improvements in the planning of the O&M for offshore wind farms could lead to considerable reduction in costs. For this reason, the interest of this research paper is the investigation of the most cost effective approach to offshore turbine maintenance strategies. This objective is achieved by implementing a simulation approach that includes a climate conditions analysis, an operation analysis, a failure evaluation and a simulation of the repairs. This paper points out how different O&M strategies can influence the sustainability of a wind farm.
Development of a Combined Operational and Strategic Decision Support Model for Offshore Wind
This paper presents the development of a combined operational and strategic decision support model for offshore wind operations. The purpose of the model is to allow developers and operators to explore various expected operating scenarios over the project lifetime in order to determine optimal operating strategies and associated risks. The required operational knowledge for the model is specified and the chosen methodology is described. The operational model has been established in the MATLAB environment in order to simulate operating costs and lost revenue, based on wind farm specification, operational climate and operating strategy. The outputs from this model are then used as the input to decision support analysis by establishing Bayesian Belief Networks and decision trees at various stages throughout the project life time. An illustrative case study, which demonstrates the capability and benefits of the modeling approach, is presented through the examination of different failure rates and alternative electricity price scenarios.
Operations and maintenance optimisation for a 100 MW wave energy farm in Ireland
International Marine Energy Journal
Marine operations that are required for the development and service of offshore wave energy farms represent a significant proportion of the total project costs. These operations can be optimised through design and innovation to improve the LCOE of the project. This paper presents an analysis of marine operations in offshore renewable energy projects and ows the importance of early, detailed analysis and optimisation of these activities. The analysis uses general-purpose techno-economic analysis software developed by Wave Venture. The software provides an integrated engineering and financial simulation specifically designed for the needs of offshore renewable energy technology. A 100 MW wave energy farm, made up of 250 CorPower devices, off the west coast of Ireland is defined and analysed to demonstrate the capabilities of the techno-economic analysis incorporating a marine operations logistics model. The results demonstrate the strength of integrated logistics and finance software ...
Sensitivity analysis of offshore wind farm operation and maintenance cost and availability
Renewable Energy, 2016
Operation and Maintenance (O&M) costs are estimated to account for 14%e30% of total Offshore Wind Farm (OWF) project lifecycle expenditure according to a range of studies. In this respect, identifying factors affecting operational costs and availability are vital for wind farm operators to achieve the most profitable decisions. Many OWFs are built in stages and the important factors may not be consistent for the different phases. To address this issue, three OWF case studies are defined to represent two phases and a complete project. An initial qualitative screening sensitivity analysis was conducted to identify the most important factors of O&M affecting operating cost and availability. The study concluded that the important factors for total O&M cost were access and repair costs along with failure rates for both minor and major repairs. For time-based availability, the important factors identified were those related to the length of time conducting the maintenance tasks, i.e. the operation duration and the working day length. It was found that the two stages had similar results, but these were different compared to the complete project. In this case, the results provide valuable information to OWF operators during the project development and decision making process.
Ocean Engineering, 2017
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 optimisation 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 adopted for a tidal farm here, is generally applicable to other kinds of ORE farms.