Siemens Wind Power is a world-leading supplier of record-breaking and highly efficient Wind Turbine Generators. The pace of their innovation, however is making planning component supply, assembly and installation a challenge. With goal posts constantly shifting as new turbines come on line, previous projects become less of a useful a guide for the next. The need is to look forward, not back.
The value of a forward view of installation is highlighted by fact that typically 70% of 25 year wind farm lifetime costs can be attributed to the construction phase. “
Robin approached Aaron McDougall, Director of Houlder’s Advanced Analysis Group. Aaron’s group consists of a cross-section of industry experts, the best and brightest technical talent available in the UK, specialising in modelling and simulation of all types of marine operations. “Whatever the task, installing a wind turbine, drilling for oil or building a structure, it becomes incredibly challenging offshore. We formed our group to reduce the impact of weather delays and unpredictability. We use advanced tools to increase confidence, cut downtime, reduces risks and stop project cost overruns.”
James Russell, Chief Hydrodynamicist for Houlder’s Advanced Analysis Group, relished the challenge. The statistical solution he conceived can virtually model the entire process of installing a wind farm from components leaving the factory to final installation and commissioning of the assembled WTG offshore. James explains “We’ve gone into granular detail. Robin had to know how his plan would pan out in the real world, in real time. We mapped the entire project as process gates, with each unit representing a real-world process step. For example, how long it takes to sail the WTG components from Denmark to Northern Ireland, how long it takes for them to be loaded / offloaded from the vessel, what happens at the pre-assembly site, etc.
The software tool created by James allowed Houlder to first test Robin’s original plan, then identify bottlenecks, failure points and opportunities for improvement. This is of particular importance for weather-sensitive operations such as crane lifting and marine transport operations. James continues “We took the last 10 years of hindcast weather data for the site, and applied it to the model. It identified equipment utilisations, downtime durations and ultimately a percentage probability of completing the whole operation in the target time.”
Houlder provided Siemens Wind Power with a 3D-visualisation of the entire process. Aaron McDougall describes the outcome “If you are trying to get buy-in to your plan and really engage a team in what’s happening, you have to have more than a spreadsheet with a number at the bottom. Our software output is in 4 dimensions – the most critical of this being time. Anyone can watch the animation on screen and instantly understand what is going on, how any high risk or costly points can be avoided and what an optimised result might look like.”
He continues; “With Siemens, we have widened the benefit of statistical simulation from risk and financial management alone to include project delivery teams. It allows front end designers and engineers to have a tested, proven forward view. Given that installation tasking and resources are changing so fast and there is only so much developers can learn from past performance, we have provided a tool that answers.. what if?”
The software represents a continuation of the successful relationship between Houlder and Siemens Wind Power. Both are committed to championing innovation to secure the future of renewable energy in the UK.
Robin Odlum, Project Manager of Siemens Wind Power Walney Extension, asked marine consultancy Houlder for help. Their innovative modelling software de-risks the logistics share of installation expenditure and identifies potential savings. Robin explains “Unpredicted delay, for any reason, is incredibly costly. We therefore can’t rely on estimations, however carefully made, to achieve our aggressive schedules and budgets. We need a high degree of confidence in our logistics planning, which is why we turned to statistical modelling.