Details
| Original language | English |
|---|---|
| Title of host publication | 30th International Ocean and Polar Engineering Conference |
| Publisher | International Society of Offshore and Polar Engineers |
| Pages | 472-479 |
| Number of pages | 8 |
| ISBN (electronic) | 9781880653845 |
| Publication status | Published - 2020 |
| Event | 30th International Ocean and Polar Engineering Conference, ISOPE 2020 - Virtual, Online Duration: 11 Oct 2020 → 16 Oct 2020 |
Publication series
| Name | Proceedings of the International Offshore and Polar Engineering Conference |
|---|---|
| Volume | 2020-October |
| ISSN (Print) | 1098-6189 |
| ISSN (electronic) | 1555-1792 |
Abstract
This work presents a multi-leveled model based on Colored Generalized Stochastic Petri nets (CGSPN) approach for offshore wind energy installation. The offshore logistics, which describes the organization of offshore operations, is embedded at the root level. The offshore operations, e.g., loading and sailing, are implemented at the secondary level using sub-models. The large scale of the wind turbine components and the ever-changing offshore weather conditions make the scheduling difficult. The aim is to support the project operators and managers in making decisions with the knowledge of the system behavior obtained through stochastic simulation, in which historical weather data measured on the German North Sea from 1958 to 2007 is used. The numerical results show the influence of decision variables, e.g. initial inventory, on a designed offshore wind farm with a size of 80 wind turbines.
Keywords
- Colored generalized stochastic Petri nets, Data-driven simulation, Multi-leveled modeling, Offshore installation
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
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30th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers, 2020. p. 472-479 (Proceedings of the International Offshore and Polar Engineering Conference; Vol. 2020-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Modeling and simulation of offshore wind farm installation with multi-leveled cgspn approach
AU - Peng, Shengrui
AU - Szczerbicka, Helena
AU - Becker, Matthias
N1 - Funding information: The authors gratefully acknowledge the financial support by the DFG (German Research Foundation) for the Project “OffshorePlan”, grant number LU 2049/1-1; SZ 51/33-1.
PY - 2020
Y1 - 2020
N2 - This work presents a multi-leveled model based on Colored Generalized Stochastic Petri nets (CGSPN) approach for offshore wind energy installation. The offshore logistics, which describes the organization of offshore operations, is embedded at the root level. The offshore operations, e.g., loading and sailing, are implemented at the secondary level using sub-models. The large scale of the wind turbine components and the ever-changing offshore weather conditions make the scheduling difficult. The aim is to support the project operators and managers in making decisions with the knowledge of the system behavior obtained through stochastic simulation, in which historical weather data measured on the German North Sea from 1958 to 2007 is used. The numerical results show the influence of decision variables, e.g. initial inventory, on a designed offshore wind farm with a size of 80 wind turbines.
AB - This work presents a multi-leveled model based on Colored Generalized Stochastic Petri nets (CGSPN) approach for offshore wind energy installation. The offshore logistics, which describes the organization of offshore operations, is embedded at the root level. The offshore operations, e.g., loading and sailing, are implemented at the secondary level using sub-models. The large scale of the wind turbine components and the ever-changing offshore weather conditions make the scheduling difficult. The aim is to support the project operators and managers in making decisions with the knowledge of the system behavior obtained through stochastic simulation, in which historical weather data measured on the German North Sea from 1958 to 2007 is used. The numerical results show the influence of decision variables, e.g. initial inventory, on a designed offshore wind farm with a size of 80 wind turbines.
KW - Colored generalized stochastic Petri nets
KW - Data-driven simulation
KW - Multi-leveled modeling
KW - Offshore installation
UR - http://www.scopus.com/inward/record.url?scp=85090876927&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85090876927
T3 - Proceedings of the International Offshore and Polar Engineering Conference
SP - 472
EP - 479
BT - 30th International Ocean and Polar Engineering Conference
PB - International Society of Offshore and Polar Engineers
T2 - 30th International Ocean and Polar Engineering Conference, ISOPE 2020
Y2 - 11 October 2020 through 16 October 2020
ER -