It consisted of 30 turbines of 3MW in a water depth of 22 metres, sited 29 kilometres from shore.
Compare that with, say, the Global Tech 1 wind project currently under construction in the German North Sea. When completed next year it will feature 80 turbines of 5MW in water depths of 65 metres, 110 kilometres from the coastline. That is more than four times the power generation in waters three times as deep and nearly four times the distance from shore than the current average, ample evidence that offshore wind farms are growing exponentially in size, cost and complexity.
The advantages of far-shore, deep-water wind farms — stronger, more constant winds and fewer restrictions in terms of their environmental and visual impact — are widely recognised. But the challenges to constructing and maintaining them remain formidable, not least because the number of projects under parallel development, together with competition from the oil and gas industries, will place considerable pressure in terms of skilled workers, facilities and equipment on the supply chain, especially in Europe.
Choosing the right type of foundation to support bigger and more complex wind turbines in harsher sea and weather conditions and differing seabed characteristics is crucial if developers are to harness the wind energy in these regions safely and cost effectively. But a great deal more research and field-testing is still required before planned and consented projects reach fruition. We are, for example, a very long way from knowing how a 7MW turbine on a semi-submersible platform will stand up to decades of service in the typhoon-prone South Pacific. Computer modelling can only take us so far in that regard.
This special report focuses on three approaches to the problems. We look at the latest developments in two relatively straightforward answers, extra large (XL) monopiles and spaceframe or jacket structures, and we examine the case for the cutting-edge solutions offered by floating foundations.
Shaun Campbell is a contributor for Windpower Monthly