The ongoing debate about the feasibility of offshore windfarms as a renewable energy source continues to generate discussion and headlines.
A key issue for potential operators is that offshore windfarms face more adverse weather conditions, such as higher wind speeds and the increased risk of being struck by lightning, than their onshore counterparts. In addition, a failed offshore turbine may take several months to repair, rather than a few days. The financial implications of repair and downtime must therefore be factored in to any calculations related to the feasibility of the operation.
This is made more difficult due to the inherent level of uncertainty involved. For example, the nascent nature of the industry means there is a shortage of historical data on the failure frequencies of the turbines. Even information that is available is subject to uncertainties – such as 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.
However, using @RISK (Palisade's Microsoft Excel software add-in for risk analysis using Monte Carlo simulation) for probabilistic analysis, the Energy Research Centre of the Netherlands has developed an innovative approach to determine whether offshore wind farms are financially viable from an operations and maintenance perspective. The @RISK risk simulation software model produces a distribution that determines the uncertainty associated with the downtime and maintenance costs of an offshore wind farm. This enables a project developer to make an informed decision, firstly as to whether to proceed with the project and then, if this is affirmative, the best way to do so.
Measuring the uncertainty also helps to make the project more viable in terms of financing. With the help of @RISK, wind farm developers – and their potential investors – can make informed decisions about whether an offshore operation will offer a good return on investment.