Palisade’s risk analysis software @RISK is being used by energy research organisation, ECN, to determine whether offshore windfarms are financially viable from an operation and maintenance perspective. In particular, the turbines are subjected to more severe weather conditions than those faced by their onshore equivalents which, coupled with their location, makes repair and maintenance time-consuming and expensive. Before progressing with a project, operators want to understand the cost implications of the long downtimes that are currently unavoidable in the industry.

Background
ECN is an independent research organisation based in the Netherlands that provides in-depth knowledge and technology to enable the transition to sustainable energy management. Its focus is on energy conservation, sustainable energy and the efficient and clean use of fossil fuels, which sees it undertake solar, wind and biomass projects.

One of the very few institutes to carry out R&D in the field of offshore operation and maintenance (O&M), ECN began to develop the ECN O&M Tool for offshore wind turbines in 2003/4. Offshore wind farms were still rare, which resulted in a lot of uncertainly about costings. At the same time, as they began to be more of a necessity, efforts increased to make them more financially viable.

The financial risk of long downtimes
Potential operators wanted to understand whether offshore wind farms were financially viable. A key issue was the more adverse weather conditions such as higher wind speeds and the increased risk of being struck by lightning faced by offshore operators. These had financial implications in terms of repair and downtime, which needed to be factored in to any calculations.

For example, if an onshore turbine fails, it is likely to be out of action for one or two days, as its location means that it is relatively straightforward to inspect and repair, even if this requires a crane. In contrast, a failed offshore turbine requires an inspection by a crew in suitable vessels. There are very few crane ships in operation, due to the prohibitive costs, so if it is decided one is required, there may be a six-month wait while it crosses the world. In addition to the opportunity cost of this downtime, the operator can be charged up to €150,000 for the crane ship to travel to the wind farm and day rates may be in the same order of magnitude.

Further delays – with knock-on effects on costs – can occur if sea conditions are too rough for the inspection crew to travel to the failed turbine. Good weather conditions are also required to repair the turbine as high winds can damage the new component.

Determining cost uncertainties with @RISK
Since the early days of offshore wind farms, there has been a significant amount of development work resulting in alternative ways of operating. For example, assessing damage to a turbine may be more effective using a helicopter rather than a boat, in which case the wind farm operator needs to look at contracts with helicopter companies.

ECN works with its clients doing ‘what if’ scenarios to determine the most feasible (ie cost-effective) method of maintaining their wind farms. Having selected the option that works best, ECN then undertakes probabilistic analysis with an @RISK model to determine the cost uncertainties of this method.

@RISK determines uncertainties in downtime and O&M costs
A key parameter (input to the @RISK model) is the failure frequencies of the turbines, based on the reliability of the components. In most cases, the wind farm operator considers 15-20 wind turbine components (with each having a different failure frequency and repair strategy) although on some occasions, the ECN O&M Tool will handle as many as 40. 

Field data is generally used to determine the failure frequencies of components, but the nascent nature of the industry means there is a shortage of historical information available. Even when data is available, the failure rates used in the cost modelling tool are subject to uncertainties. These include the prices of crane ships and access vessels (which may vary per season and even from day-to-day), the cost of spare parts, and the electricity price, as well as the lead times of spares and vessels.

@RISK enables informed decision-making
Feeding the input data into the @RISK model results in Cumulative Density Functions (CDF).  This distribution determines the uncertainty associated with the downtime and maintenance costs of an offshore wind farm in order that a project developer can make an informed decision, firstly as to whether to proceed with the project and then, if this affirmative, the best way to do so.  Measuring the uncertainty also helps to make the project more viable in terms of financing - banks don’t like risk, in particular when it is unknown.

Another result from the @RISK model is that the Tornado diagram shows which input has the most effect on the output. It might indicate, for example, that the mobilisation of large vessels for repairs and maintenance is the most significant factor in the cost uncertainty. Armed with this fact, the operator can negotiate a contract with the boat provider that sees it pay a higher fixed fee on an ongoing basis, rather than paying on an ad hoc basis, which is potentially more costly in the long term.

The @RISK model has also been useful for determining the effect that labour cost has on a project – an issue that often causes a great deal of discussion. Using @RISK ECN has shown that, contrary to popular belief, this does not have a major impact on the end repair costings.  @RISK can therefore redefine what is initially considered to be costly.

@RISK improves component management
In addition, @RISK provides operators with a tool to help them manage the components that have higher failure rates, and therefore a major impact on the profitability of the wind farm. Using specific calculations from the @RISK model on the effect of a failure, a developer can negotiate more favourable warranty agreements with the manufacturer of the component.

@RISK enables straightforward ROI calculations
“Risk and uncertainty are an inherent part of any business project, but they are increased further in ‘new’ industries such as alternative energy,” explains Luc Rademakers, senior manager, wind energy systems development at ECN.  “However, the @RISK model can handle vast amounts of data and present it in a way that is easy to understand, thereby making it straightforward to quantify the risk associated with a venture. Wind farm developers – and their potential investors – can therefore make informed decisions about whether an offshore operation will offer a good return on investment.”

» @RISK
» ECN