On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

Izquierdo, Juan Santiago Murgui; Márquez, Adolfo Crespo; Uribetxebarria, Jone; Erguido, Asier

doi:10.1016/j.renene.2020.02.048

Cited by 17 publications

(10 citation statements)

References 64 publications

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“…The optimization model based on real options [54] provides promising ways to address the economic aspects of prognostics and health management. Another model is based on artificial neural networks that dynamically calculate the impact of operational conditions of the failures of wind turbines [110].…”

Section: Maintenance and Life Cycle Cost (M_cost)mentioning

confidence: 99%

New Tendencies in Wind Energy Operation and Maintenance

et al. 2021

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Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.

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Section: Maintenance and Life Cycle Cost (M_cost)mentioning

confidence: 99%

New Tendencies in Wind Energy Operation and Maintenance

et al. 2021

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“…The logical relationship between the number of maintenance tasks performed at time interval t and t−1 is formulated in Equation (10). The initial and terminal values of decision variables related to the number of maintenance tasks are given in Equation (11).…”

Section: Constraints Related To Maintenance Tasksmentioning

confidence: 99%

“…Previous studies [6][7][8][9][10][11][12][13][14][15] discussed task scheduling, resource allocation, vessel routing, and fleet composition in the maintenance optimization for a single OWF and a single O&M base. Yet, resource coordination among multiple OWFs has not been involved.…”

Section: Introductionmentioning

confidence: 99%

“…To consider a more sophisticated planning of maintenance activities, vessel routing [7,8] and fleet composition [9] were jointly optimized with the maintenance scheduling for OWFs. To balance the maintenance cost and power production loss due to maintenance, the OWF reliability was integrated with the maintenance cost in optimization objective functions [10,11]. As the reliability cannot directly reflect impacts of maintenance tasks on the power production of OWFs, performancebased contracting (PBC) was utilized to enhance the system operational availability [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A robust model for scheduling power productions of multiple offshore wind farms using one‐to‐many maintenance services

Zhang

2021

IET Renewable Power Gen

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This paper studies the power production optimization problem of multiple offshore wind farms (OWFs) considering different maintenance demands and requirements under the wind power uncertainty. A decentralized robust operation optimization model is developed, in which OWFs and a maintenance service provider (MSP) are treated as individual stakeholders with different interests but coupled by maintenance resources. Based on the analytical target cascading (ATC) algorithm, the model is decoupled into independent models for MSP and each OWF. The operation and objectives of individual entities can be autonomously optimized only considering their own conditions. For each OWF, the power production planning coupled with maintenance scheduling is formulated as a two-stage robust optimization model and solved by a column-and-constraint generation (C&CG) algorithm to tackle the wind power uncertainty. Numerical experiments demonstrate the effectiveness of the proposed model and the applicability of the integrated solution method to the studied problem. Results illustrate that the developed model shows a better guarantee for individual interests of OWFs, lower communication burden, and better information privacy than the centralized optimization model, especially in large-scale systems. The integrated solution framework can guarantee a quick convergence and optimality. Moreover, it reduces impacts of uncertain factors on power productions and maintenance schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…For instance, the authors in [12], proposed a multi-criteria decision-making model for determining the maintenance periods of the most critical equipment in a hydroelectric power plant. On the other hand, the reliability of wind power farms was determined using artificial neural networks that dynamically estimated the impact of operational conditions on the failures of wind turbines, as has been presented in [13]. Alternative approaches for increasing reliability and durability in enterprises in the energy sector based on digitization, digital twins, blockchain and Industry 4.0 can be found in [14].…”

Section: Literature Overviewmentioning

confidence: 99%

Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets

2021

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Rapid changes in electricity power markets have increased the production costs of coal-fired power plants and pushed their production to the limits of profitability. For power plants currently in operation, a possible approach to cope with this issue is to introduce novel methods that increase the plant’s reliability and availability. Coal mills are a subsystem that should ensure a plant’s availability without unexpected breakdowns. Remediation-based maintenance is defined as a set of actions performed after fault detection that do not require instant shutdown due to safety reasons. The aim of this paper was to provide a scientific confirmation that by implementing a novel remediation-based maintenance strategy, electricity production breakdowns can be significantly reduced. First, the performance of the proposed maintenance method was proved in simulation where coal mills were modeled by generalized stochastic Petri nets. The maintenance strategy was then experimentally verified in a 220 MW coal-fired power plant located in Croatia, where the plant’s availability, reliability and efficiency were increased.

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On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

Cited by 17 publications

References 64 publications

New Tendencies in Wind Energy Operation and Maintenance

New Tendencies in Wind Energy Operation and Maintenance

A robust model for scheduling power productions of multiple offshore wind farms using one‐to‐many maintenance services

Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets

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