Offshore infrastructure planning using a vine copula approach for environmental conditions: an application for replacement maintenance of tidal energy infrastructure. Issue 5 (4th May 2019)

• Record Type: Journal Article
• Authors: de Nie, Ruben; Leontaris, Georgios; Hoogendoorn, Don; Wolfert, A. R. M. (Rogier)
• Is Part Of: Structure and infrastructure engineering. Volume 15:Issue 5(2019)
• Page Start: 600
• Page End: 617
• Publication Date: 2019-05-04
• Subjects: Offshore energy infrastructure -- uncertain operational windows -- vine copulas -- environmental time series -- probabilistic planning -- tidal energy converter -- replacement maintenance; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2018.1558268
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: Installation and maintenance operations of offshore assets are impacted by local environmental conditions such as wave height and period, wind speed and current velocity. These parameters are substantially of influence for the asset planning (time and costs) given the uncertainty of operational windows. In this article, a method is proposed to construct realistic time series of the aforementioned dependent conditions using a vine copulas approach. This method makes it possible to obtain a large number of realizations of these conditions at a certain location. It is shown that the operational windows remain persistent with the original limited dataset. Moreover, this method enables the incorporation of environmental uncertainties in the operational planning processes. To illustrate the value of this method, an application regarding replacement maintenance of a tidal energy infrastructure is examined. For this purpose, the maintenance activities are represented as a semi-Markov decision process. For every synthetic environmental time series, the algorithm finds the optimal set of decisions and the corresponding maintenance plans, including replacement costs and revenue losses. It is shown that the proposed method is effective in replacement maintenance decision making, while taking into account the environmental uncertainties.