Presentations

Abstract

Floating LIDAR Systems (FLS) are the industry standard for collecting metocean measurements globally. However, their application is constrained by historically conservative measurement uncertainties associated with the wind speed data used for wind resource and energy yield assessments. These uncertainties, often exceeding 3%, primarily arise from comparing FLS to cup anemometers during performance verifications and then conservatively assigning classification uncertainty. Despite the increasing credibility and wide-spread use of FLS, these persistent high levels of uncertainty continue to impact project uncertainty budgets and financing options. To address this issue, ERM initiated a Joint Industry Project (JIP) and co-authored with Copenhagen Offshore Partners a study of the FLS measurement uncertainty from actual wind measurements collected across the Dutch North Sea. ERM has sourced a set of nine FLS wind measurement datasets collected approximately 80 km from the coast of the Republic of Korea, to carry out the next phase of the JIP. The datasets are generally within 10 km to 30 km of each other, which is a very significant volume of offshore measurements in a small region.  Additionally, there is some concurrency of the datasets allowing a direct comparison of the FLS measurements on a 10-minute basis, which is consistent with the resolution used for performance verification tests conducted with masts.  Our aim is to present findings at WindEurope Tech 2025 can be used by analysts to support applying measurement uncertainty levels that more accurately reflect the (e.g., less than 3%). Although this study is focused on Korea, coupled with the earlier Dutch North Sea results, it will demonstrate whether variations in measurement performance exist across FLS technologies, geographies, and metocean conditions.  ERM will present a reliable uncertainty metric for FLS by showing two approaches for assessing measurement uncertainty using the Korean data.  1.    Reconciliation Approach: Use of long-term corrected wind speed estimations against mesoscale maps in a consistent manner as was adopted for analysing the Dutch North Sea measurements.  2.    Analysis of Variance: Use of aligned datasets, with standardised measurement periods, to perform inter-device correlation analysis with high-quality data pairs. The pooled variance is computed to determine overall variability followed by sensitivity analysis to test the robustness of the uncertainty estimates under various scenarios. Statistical significance testing is conducted to evaluate whether the data pairs show a variation that is statistically significant and also to investigate any conditions affecting the variation. Our work will show that by leveraging a comprehensive dataset and an evidence-based approach that a more realistic measurement uncertainty for FLS derived from actual offshore measurement campaign datasets. We encourage analysts to consider these findings when assessing FLS uncertainty as industry adoption of a lower and more accurate uncertainty value will ultimately benefit project development timelines and financing costs.