A Probabilistic Approach to Introduce Risk Measurement Indicators to an Offshore Wind Project Evaluation – Improvement to an Existing Tool Ecume

Douard, Fanny; Domecq, Célia; Lair, William

doi:10.1016/j.egypro.2012.06.107

Cited by 28 publications

(29 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A probabilistic failure event model is used to simulate failure occurrences using an inverse transformation sampling algorithm. It formulates dates according to distributions based on the lifecycle of the component related to the bathtub curve [20], as shown in Fig. 2 [21].…”

Section: Offshore Wind Modelmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of offshore wind farm operation and maintenance cost and availability

et al. 2016

View full text Add to dashboard Cite

Operation and Maintenance (O&M) costs are estimated to account for 14%-30% of total Offshore Wind Farm (OWF) project lifecycle expenditure according to a range of studies. In this respect, identifying factors affecting operational costs and availability are vital for wind farm operators to achieve the most profitable decisions. Many OWFs are built in stages and the important factors may not be consistent for the different phases. To address this issue, three OWF case studies are defined to represent two phases and a complete project. An initial qualitative screening sensitivity analysis was conducted to identify the most important factors of O&M affecting operating cost and availability. The study concluded that the important factors for total O&M cost were access and repair costs along with failure rates for both minor and major repairs. For time-based availability, the important factors identified were those related to the length of time conducting the maintenance tasks, i.e. the operation duration and the working day length. It was found that the two stages had similar results, but these were different compared to the complete project. In this case, the results provide valuable information to OWF operators during the project development and decision making process

show abstract

Section: Offshore Wind Modelmentioning

confidence: 99%

“…The offshore wind O&M tool used for the analysis is described by Douard [20]. The tool evaluates the annual and total O&M cost and the cost of wind farm unavailability.…”

Section: Offshore Wind Modelmentioning

confidence: 99%

Sensitivity analysis of offshore wind farm operation and maintenance cost and availability

et al. 2016

View full text Add to dashboard Cite

show abstract

“…A Monte Carlo analysis determines the probability distribution of O&M costs. More information on the model can be found in [13].…”

Section: University Of Stavanger Offshore Wind Simulation Modelmentioning

confidence: 99%

“…A Monte Carlo analysis determines the probability distribution of O&M costs. More information on the model can be found in [13].Strathclyde University, Centre for Doctoral Training offshore wind OPEX model This model has been developed at the Centre for Doctoral Training in Wind Energy Systems to provide a complementary analysis tool to a previously developed probabilistic model [14]. The initial model development focussed on the use of specialist heavy-lift vessels for offshore wind and to inform decision support for operational strategies [15].…”

mentioning

confidence: 99%

Reference Cases for Verification of Operation and Maintenance Simulation Models for Offshore Wind Farms

et al. 2015

View full text Add to dashboard Cite

Due to lack of operating experience in the field of offshore wind energy and large costs associated with maintaining offshore wind farms, there is a need to develop accurate operation and maintenance models for strategic planning purposes. This paper provides an approach for verifying such simulation models and demonstrates it by describing the verification process for four models. A reference offshore wind farm is defined and simulated using these models to provide test cases and benchmark results for verification for wind farm availability and O&M costs. This paper also identifies key modelling assumptions that impact the results. The calculated availabilities for the four models show good agreement apart from cases where maintenance resources are heavily constrained. There are also larger discrepancies between the cost results. All the differences in the results can be explained by different modelling assumptions. Therefore, the models can be regarded as verified based on the presented approach. INTRODUCTION MotivationOffshore wind energy is a new area for operation and maintenance (O&M) research, and the operational legacy of the industry is only just over a decade. Operation and maintenance cost modelling software tools are being developed to support activities in this field. Because of the novelty of offshore wind energy generation and lack of real data, there are limited options for validation and verification of these models. Verification and validation of a simulation model is essential if the model is to say something useful about the system it is meant to represent. We define verification as ensuring that the simulation model is implemented according to the specifications of the conceptual model of the system; validation is defined as ensuring that this conceptual model is in fact a faithful representation of the real system for the purposes of the model [1]. It may prove difficult for researchers to acquire suitable data to perform model validation. For full operational validation [1], necessary historical data would include repair and logistical costs, statistical information on component reliability and performance indicators such as total operations costs or availability. This type of information is possessed by the farm owner/operator, turbine manufacturer or non-existent for new generation wind turbines. BackgroundSeveral O&M simulation models for offshore wind farms have been developed, of which Hofmann [2] provides a thorough overview. Often, the intended applications of the models differ slightly. For example, one model will focus on assessing heavy-lift vessels, whereas another will be used for maintenance strategy optimisation. [3]. One position is that models are never entirely validated because it is not practicable to assess correspondence between the system and the model for its entire domain of applicability [1]. Even if the system is observable and a comparison of model output and system output is possible, one is often interested in predicting system behaviour under circumst...

show abstract

“…Several models with a focus on O&M strategies are reviewed in [18]. Studies [19][20][21] investigate the impact of meteorological parameters on availability and maintenance planning where, for instance, significant changes in production loss and availability are observed, and the addition of alternative access methods, e.g., a helicopter, is suggested for future studies. Research works [22,23] perform cost-based optimizations by considering different transport vessels, seasonal environment, component changes, etc., in order to find the optimum preventive maintenance schedules and the number of permanent maintenance teams.…”

Section: Introductionmentioning

confidence: 99%

Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

2017

View full text Add to dashboard Cite

This paper proposes a model for strategic maintenance scheduling of offshore wind farms (SMSOWF) in a deregulated power system. The objective of the model is to plan the maintenance schedules in a way to maximize the profit of the offshore wind farm. In addition, some network constraints, such as transmission lines capacity, and wind farm constraints, such as labor working shift, wave height limit and wake effect, as well as unexpected outages, are included in deterministic and stochastic studies. Moreover, the proposed model provides the ability to incorporate information from condition monitoring systems. SMSOWF is formulated through a bi-level formulation and then transformed into a single-level through Karush-Kuhn-Tucker conditions. The model is validated through a test system, and the results demonstrate applicability, advantages and challenges of harnessing the full potential of the model.

show abstract

A Probabilistic Approach to Introduce Risk Measurement Indicators to an Offshore Wind Project Evaluation – Improvement to an Existing Tool Ecume

Cited by 28 publications

References 3 publications

Sensitivity analysis of offshore wind farm operation and maintenance cost and availability

Sensitivity analysis of offshore wind farm operation and maintenance cost and availability

Reference Cases for Verification of Operation and Maintenance Simulation Models for Offshore Wind Farms

Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

Contact Info

Product

Resources

About