[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.

[pdf]

Note:

Abstract: The installation of offshore wind farms represents a major driver of offshore wind energy costs due to the high level of associated risks. Substantial cost reduction potential can be realized if these risks are effectively identified and controlled, preventing project delays and financial damages. Therefore, we propose a new method to identify, analyse and evaluate installation risks for use in project planning. This paper provides an in-depth walkthrough of the steps involved in the development of a simulation-based quantitative risk analysis model. At the example of a turbine installation case study, project plan-related risks are identified and quantified by conducting a literature review and expert interviews. The corresponding risk likelihoods and consequences are modelled with probability distributions to simulate their impact on total project duration. Using Monte Carlo simulation, the distribution of results is statistically evaluated to derive the expected project delay caused by the analysed risks as well as reference values for optimistic and pessimistic scenarios. Regarding the underlying model assumptions, the most significant risks (e.g. supply chain failure) are identified and examined in a sensitivity analysis. The developed model can serve as a basis for developing more reliable schedule estimations and contribute to minimizing installation delays and costs.