Mathematical tools for improving the understanding of uncertainty in offshore turbine operation and maintenance

8a001390-601d-43ee-81c0-e7d8b320b1a9

Closed

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£1,221,025

March 31, 2011

March 31, 2014

The UK is planning to make massive investments in offshore wind farms which will result in several fleets of similar wind turbines being installed around the UK coastline. The economic case for these wind turbines assumes a very high technical availability, which means simply that the turbines have to be working and ready to generate electricity for nearly all of the time. Not achieving this availability could well result in large economic losses. Unfortunately there is relatively little operational experience of offshore systems on which to base the estimates used. The systems may turn out to behave in unexpected ways by failing earlier than expected, or by proving more difficult to maintain. Even well-known systems can behave differently when used in new environments, which is why reliability databases often indicate ranges of failure behaviour rather than single number estimates. Availability is difficult to model because, in addition to the unknown impact of different environments, there is often a period of adjustment in which operators and manufacturers adapt their processes and systems to the new situation, leading to the potential for availability growth. However, with a new fleet of turbines there is also an aging process as they all grow older together which could lead to lower availability. The economic case for offshore systems depends a lot on whether high enough availability can be achieved, particularly in the early years of operation which are important for paying back the investment costs. This project looks at the degree of uncertainty there is in availability estimates for offshore wind turbines. This uncertainty is not one that averages out when there are a large number of turbines, because it has a systematic affect across all the turbines in a wind farm and therefore leads to corresponding uncertainty in the overall availability across the wind farm. This type of uncertainty is often called state-of-knowledge uncertainty and only gets reduced by collecting data over the longer term. Even if we are not yet able to collect operational data, we can still gain an understanding of the sources of state-of-knowledge uncertainty. Mathematical models can help us understand how different sources of uncertainty affect the uncertainty about availability, and to find out which ones we should be most concerned about. That, in turn, will help researchers to focus their energies on resolving the issues that ultimately have the biggest impact.In this project, operations researchers will work together with engineers and other researchers in the renewables sector, in order to build credible mathematical models to help answer these questions. Doing that requires the development of new mathematics, particularly in the way we represent how uncertainties are affected by different environmental and engineering aspects. It requires us to find better ways of getting information from experts into a form that we can use in the mathematical models, and it also requires us to find new ways of running the models on a computer.

Potential Impact:
The main stakeholders impacted by this research are: energy policy-makers; organisations in the off-shore wind energy supply chain (e.g. design, manufacture, operation, maintenance); the professional (and to a very limited extent overlapping) energy and reliability, maintainability & availability communities of practitioners and researchers; the university, and other OR, researchers and students. The policy-makers and businesses will primarily benefit economically because we will provide them with methods which can be converted to tools through our networks with knowledge exchange organisations (e.g. ECN, Risk Consortium, International Standards, Energy Partnerships). Consequently we should advise policy-makers and support businesses to make cost-effective decision-making about the early life maintenance of off-shore wind farms that will ultimately benefit the UK. As a result of this there will be benefits beyond the businesses to their shareholders (which in many cases are the general public, through our pension funds). The professional and research communities, both within the university and beyond, will benefit by the knowledge generated about the modelling of availability growth. This is an important practical problem affecting many sectors but one which neglected despite the large portfolio of reliability growth models which only address part, and increasingly the smallest part, of the problem. Availability growth modelling should impact other industries beyond energy through our communications with practising engineers at conferences, seminars and through professional societies. For example, involving a company from the aerospace and defence sector in a supporting role, because of their relevant technology experience in a different environment, provides a route for impact. The developments in mathematical modelling achieved in, for example, failure intensity models, uncertainty and sensitivity analysis, will contribute to the international presence of UK OR research through prestigious publication. The knowledge and skills of the researchers and their students, both research and instructional, will be developed through this collaboration between OR modellers and engineers through access to important problems for projects as well as awareness of different perspectives on analysis.