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Comparison of Weather Window Statistics and Time Series Based Methods Considering Risk Measures

: Lübsen, Julia; Wolken-Möhlmann, Gerrit

Volltext ()

Institute of Physics -IOP-, London:
EERA DeepWind 2020, 17th Deep Sea Offshore Wind R&D Conference : 15 - 17 January 2020, Radisson Blu Royal Garden Hotel, Trondheim, Norway
Bristol: IOP Publishing, 2020 (Journal of physics. Conference series 1669)
Art. 012003, 9 S.
Deep Sea Offshore Wind R&D Conference (DeepWind) <17, 2020, Trondheim>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IWES ()

Offshore projects, like the installation of offshore wind farms, consist of a number of different and often weather dependent activities. These tasks and their relations are defined in project schedules, which have to be assessed for weather effects before the project realization. Here, often Weather Window Statistics are used to calculate probable weather delay times using relative frequencies of weather windows. Another possible approach is the Weather Time Series Scheduling (WaTSS) method which combines the given project schedule, weather restrictions and historical weather time series data for several decades. The aim of this paper is a comparison of the two approaches especially when it comes to risk analysis for project schedules using percentile values or related risk measures. The same ERA5 model data is used both as basis for the Weather Window Statistics and the WaTSS method. We calculate weather downtimes for each task, as well as for the total project, and apply the risk measures Value at Risk (VaR), Conditional Value at Risk (cVaR) and Upper Partial Moments (UPM). This is followed by an analysis of the results from Weather Window Statistics and the WaTSS method considering their applicability and consistence. We obtain that risk measurement for project schedules under use of Weather Window Statistics is not always practicable because certain risk values tend to overestimation. WaTSS method provides project adjusted modeling and leads to more realistic risk measurement for complex project schedules.