Digitalization of North Sea energy systems. North Sea Energy 2020-2022
report
Digitalization in relation to offshore energy systems is a topic of interest in the context of North Sea
Energy (NSE) program. This report presents an overview on how digitalization can result in cost and
emission reductions in the offshore energy sector. To this aim, nine digitalization technologies are
reviewed and the potential barriers towards its implementation are highlighted. To showcase the
potential benefits of a future NSE digital twin, simple examples based on possible components of such a
digital twin are presented. A simplified case study using a dynamic simulator framework (TNO's
PyDOLPHIN) illustrates the gains in optimizing component sizing and operational strategies, whereas a
second example using AI methods highlights the importance of forecasting system boundary conditions
(e.g., electricity demand) under uncertainties. Finally, the benefits and necessary steps to deploy digital
innovations for the NSE are discussed using three reference scenarios. In particular, it is highlighted how
digitalization will play a key role to achieve efficient operations, for predictive maintenance, and for how
autonomous vehicles will assist in inspections, repairs, installation and decommissioning of assets such
as wind turbines. This study was done as a part of WP5 logistics of the NSE 4 program but since the
scope of the work was broader than only digitalization of the logistics and services, it was decided to
deliver a separate report on this topic.
Energy (NSE) program. This report presents an overview on how digitalization can result in cost and
emission reductions in the offshore energy sector. To this aim, nine digitalization technologies are
reviewed and the potential barriers towards its implementation are highlighted. To showcase the
potential benefits of a future NSE digital twin, simple examples based on possible components of such a
digital twin are presented. A simplified case study using a dynamic simulator framework (TNO's
PyDOLPHIN) illustrates the gains in optimizing component sizing and operational strategies, whereas a
second example using AI methods highlights the importance of forecasting system boundary conditions
(e.g., electricity demand) under uncertainties. Finally, the benefits and necessary steps to deploy digital
innovations for the NSE are discussed using three reference scenarios. In particular, it is highlighted how
digitalization will play a key role to achieve efficient operations, for predictive maintenance, and for how
autonomous vehicles will assist in inspections, repairs, installation and decommissioning of assets such
as wind turbines. This study was done as a part of WP5 logistics of the NSE 4 program but since the
scope of the work was broader than only digitalization of the logistics and services, it was decided to
deliver a separate report on this topic.
Topics
TNO Identifier
979625
Publisher
TNO
Collation
42 p.