The Role of Logistics in Practical Levelized Cost of Energy Reduction Implementation and Government Sponsored Cost Reduction Studies: Day and Night in Offshore Wind Operations and Maintenance Logistics

Poulsen, Thomas Tuxen; Hasager, Charlotte Bay; Jensen, Christian Munk

doi:10.3390/en10040464

Cited by 20 publications

(14 citation statements)

References 44 publications

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“…The cost of Operation and Maintenance (O&M) of wind power plants is having a significant effect on the levelized cost of energy (LCOE). () A key challenge in O&M management is achieving a reliable prediction of mechanical failures. This could enable a better understanding of deterioration mechanisms and provide benefits by allowing a sufficient lead time for making decisions.…”

Section: Introductionmentioning

confidence: 99%

Predictive repair scheduling of wind turbine drive‐train components based on machine learning

2019

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We devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

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Section: Introductionmentioning

confidence: 99%

Predictive repair scheduling of wind turbine drive‐train components based on machine learning

2019

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“…The trends clearly indicate that offshore wind farms (OWFs) occupying a larger area, with wind turbines of higher rated power, located in deeper waters, and at larger distances to shore, will be favoured (Rodrigues et al, 2015). Due to their significant contribution to the operational expenditures (OpEx) and to the overall cost of energy, operation and maintenance (O&M) of the devices will play a pivotal role in determining the profitability of a project (Poulsen et al, 2017). For this reason, flexible and comprehensive tools are needed to grasp the complex dynamics of the OWF and provide support in the choice and dispatch of the O&M assets.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms

et al. 2019

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This article explores the use of genetic algorithms to optimize the operation and maintenance assets of an offshore wind farm. Three different methods are implemented in order to demonstrate the approach. The optimization problem simultaneously considers both the reliability characteristics of the offshore wind turbines and the composition of the maintenance fleet, seeking to identify the optimal configurations for the strategic assets. These are evaluated in order to minimize the operating costs of the offshore farm while maximizing both its reliability and availability. The considerations used for the application of genetic algorithms as an effective way to support the assets management are described, and a case study to show the applicability of the approach is presented. The variation of the economic performance indicators as a consequence of the optimization procedure is discussed, and the implementation of this method in a wider computational framework for the operation and maintenance assets improvement is introduced.

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“…In Germany, the Wind Energy Institute estimated service and spare parts costs as 26% of the variable costs for German turbines operation and maintenance costs [15]. More recently, [16] indicated that the relative OPEX share of total levelized costs of energy may vary from 13% to 57% over the life-cycle of an offshore wind farm. This adds another important aspect to the analysis of operation and maintenance costs, i.e., its wide variation between different cases and situations.…”

Section: Introductionmentioning

confidence: 99%

Spare Parts Cost Management for Long-Term Economic Sustainability: Using Fuzzy Activity Based LCC

2019

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The concept of life cycle sustainability assessment provides an interdisciplinary space to discuss the main challenges in addressing sustainability from a long-term perspective. However, uncertainty related to cost parameters constitutes one of the main challenges in LCC. This uncertainty prevents managers from precisely anticipating the exact values of important variables. These variables include the costs of spare parts inventories, which in some cases can constitute high percentages of the total cost of a product or service. We propose an activity-based costing model that handles uncertainty using triangular numbers, along with a matrix representation, and Weibull functions to incorporate the projected equipment reliability into the model. A case study based on a spare parts distribution center, which serves the mining industry in Northern Chile, is presented. The results obtained are compared with a traditional costing method. The advantage of using the proposed approach over the traditional one is evident. Future research is directed towards the implementation of a larger number of inventory policies, along with experimentation with other types of fuzzy numbers.

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The Role of Logistics in Practical Levelized Cost of Energy Reduction Implementation and Government Sponsored Cost Reduction Studies: Day and Night in Offshore Wind Operations and Maintenance Logistics

Cited by 20 publications

References 44 publications

Predictive repair scheduling of wind turbine drive‐train components based on machine learning

Predictive repair scheduling of wind turbine drive‐train components based on machine learning

Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms

Spare Parts Cost Management for Long-Term Economic Sustainability: Using Fuzzy Activity Based LCC

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