Wave energy resource assessment

The use of satellite altimeter data for spatial mapping of the wave resource is examined.<br/>A new algorithm for estimating wave period from altimeter data is developed and<br/>validated, which enables estimates of wave energy converter (WEC) power to be<br/>derived. Maps of the long-term mean WEC power from altimeter data are of a higher<br/>spatial resolution than is available from global wave model data. They can be used for<br/>identifying promising wave energy locations along particular stretches of coastline,<br/>before a detailed study using nearshore models is undertaken.<br/><br/>The accuracy of estimates of WEC power from wave model data is considered. Without<br/>calibration estimates of the mean WEC power from model data can be biased of the<br/>order of 10-20%. The calibration of wave model data is complicated by non-linear<br/>dependence of model parameters on multiple factors, and seasonal and interannual<br/>changes in biases. After calibration the accuracy in the estimate of the historic power<br/>production at a site is of the order of 5%, but the changing biases make it difficult to<br/>specify the accuracy more precisely.<br/><br/>The accuracy of predictions of the future energy yield from a WEC is limited by the<br/>accuracy of the historic data and the variability in the resource. The variability in 5, 10<br/>and 20 year mean power levels is studied for an area in the north of Scotland, and<br/>shown to be greater than if annual power anomalies were uncorrelated noise. The<br/>sensitivity of WEC power production to climate change is also examined, and it is<br/>shown that the change in wave climate over the life time of a wave farm is likely to be<br/>small in comparison to the natural level of variability. It is shown that despite the<br/>uncertainty related to variability in the wave climate, improvements in the accuracy of<br/>historic data will improve the accuracy of predictions of future WEC yield.<br/><br/>The topic of extreme wave analysis is also considered. A comparison of estimators for<br/>the generalised Pareto distribution (GPD) is presented. It is recommended that the<br/>Likelihood-Moment estimator should be used in preference to other estimators for the<br/>GPD. The use of seasonal models for extremes is also considered. In contrast to<br/>assertions made in previous studies, it is demonstrated that non-seasonal models have a<br/>lower bias and variance than models which analyse the data in separate seasons.

Name dc:type.qualificationname

Ph.D.

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of Southampton

Year dc:date.issued

2009

Author dc:creator

• Mackay, Edward B.L.

Advisor dc:contributor.advisor

• Bahaj, A.S.