Marine logistics decision support for operation and maintenance of offshore wind parks with a multi method simulation model

Endrerud, Ole-Erik Vestøl; Liyanage, Jayantha P.; Keseric, Nenad

doi:10.1109/wsc.2014.7020021

Cited by 24 publications

(35 citation statements)

References 8 publications

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“…The Norwegian Offshore Wind power lifecycle cost and benefit tool (NOWIcob), developed by the SINTEF Energy Research Centre, used an eventbased MCS to simulate the wind farm O&M costs considering weather, maintenance and logistics. 30 Endrerud et al 28,29 presented a logistics decision support model using an agent-based simulation for O&M of offshore wind farms. This model was later integrated into a commercial tool, Shoreline.…”

Section: Literature Reviewmentioning

confidence: 99%

“…18 Although input data uncertainty is a critical factor that wind energy system operators need to deal with, 19 existing simulation models vastly ignore the uncertainties of data in reliability and O&M cost evaluation. 9,[20][21][22][23][24][25][26][27][28][29][30][31][32] Therefore, this paper focuses on modelling two types of uncertainties related to reliability data and failure cost of OWT components. An OWT simulation framework employing a timesequential Monte Carlo simulation (TSMCS), probabilistic techniques and fuzzy logic is proposed to evaluate the impacts of reliability and failure cost uncertainties on the OWT availability, energy production and cost of wind energy.…”

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confidence: 99%

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Offshore wind turbine reliability and operational simulation under uncertainties

2020

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The fast‐growing offshore wind energy sector brings opportunities to provide a sustainable energy resource but also challenges in offshore wind turbine (OWT) operation and maintenance management. Existing operational simulation models assume deterministic input reliability and failure cost data, whereas OWT reliability and failure costs vary depending on several factors, and it is often not possible to specify them with certainty. This paper focuses on modelling reliability and failure cost uncertainties and their impacts on OWT operational and economic performance. First, we present a probabilistic method for modelling reliability data uncertainty with a quantitative parameter estimation from available reliability data resources. Then, failure cost uncertainty is modelled using fuzzy logic that relates a component's failure cost to its capital cost and downtime. A time‐sequential Monte Carlo simulation is presented to simulate operational sequences of OWT components. This operation profile is later fed into a fuzzy cost assessment and coupled with a wind power curve model to evaluate OWT availability, energy production, operational expenditures and levelised cost of energy. A case study with different sets of reliability data is presented, and the results show that impacts of uncertainty on OWT performance are magnified in databases with low components' reliability. In addition, both reliability and cost uncertainties can contribute to more than 10% of the cost of energy variation. This research can provide practitioners with methods to handle data uncertainties in reliability and operational simulation of OWTs and help them to quantify the variability and dependence of wind power performance on data uncertainties.

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Section: Literature Reviewmentioning

confidence: 99%

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Offshore wind turbine reliability and operational simulation under uncertainties

2020

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“…A multi-method simulation modelling method was chosen for this model consisting of both the agent-based and the discrete-event paradigms. More information on the model can be found in [12]. ECUME Model Version 8.2 of the ECUME model has been developed by EDF R&D to be used internally to support the group's activities in the offshore wind industry.…”

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confidence: 99%

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

et al. 2015

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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...

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“…This approach is not as complex in terms of resource optimization as some models (such as Refs. [29][30][31][32][33][34][35]), but is detailed enough to identify different cost centres. The objective is not logistic optimization over the project deployment lifetime, but the identification of cost reduction pathways for operations during the technology design phase.…”

Section: Development Of the Opera Oandm Modelmentioning

confidence: 99%

“…All operations are considered independent of one The first step of the model is to define different systems where the operations will be divided (e.g., mooring, structure, PTO). Once general characteristics and operations are identified, specific resources for each operation must be defined such as vessels, equipment, labour and spares, duration of the operation, uncertainty level, and mean time between failures, which are taken from the DTOcean library, local suppliers and the literature [34][35][36]. The real sea experience gives inputs into the model, such as failure rates and operations design; operation time; environmental conditions; equipment; and ships.…”

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O&M Models for Ocean Energy Converters: Calibrating through Real Sea Data

et al. 2019

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Of the cost centres that combine to result in Levelised Cost of Energy (LCOE), O&M costs play a significant part. Several developers have calculated component costs, demonstrating how they can become commercially competitive with other forms of renewable energy. However, there are uncertainties relating to the O&M figures that can only be reduced through lessons learned at sea. This work presents an O&M model calibrated with data from real sea experience of a wave energy device deployed at the Biscay Marine energy Platform (BiMEP): the OPERA O&M Model. Two additional case studies, utilising two other O&M calculation methodologies, are presented for comparison with the OPERA O&M Model. The second case study assumes the inexistence of an O&M model, utilising a Simplified Approach. The third case study applies DTOcean’s (a design tool for ocean energy arrays) O&M module. The results illustrate the potential advantages of utilising real sea data for the calibration and development of an O&M model. The Simplified Approach was observed to overestimate LCOE when compared to the OPERA O&M Model. This work also shows that O&M models can be used for the definition of optimal maintenance plans to assist with OPEX reduction.

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Marine logistics decision support for operation and maintenance of offshore wind parks with a multi method simulation model

Cited by 24 publications

References 8 publications

Offshore wind turbine reliability and operational simulation under uncertainties

Offshore wind turbine reliability and operational simulation under uncertainties

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

O&M Models for Ocean Energy Converters: Calibrating through Real Sea Data

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