Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction

Ge, Xiaolin; Chen, Quan; Fu, Yang; Chung, C.Y.; Mi, Yang

doi:10.1016/j.epsr.2020.106298

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…It also restricts that the number of vessels returned to the MSP cannot exceed the number of vessels leaving the MSP in each time interval. Equation (15) describes that the total number of vessels of each type staying offshore is bounded by its total available number in the MSP base at each time interval. Equation ( 16) ensures the total number of technicians which could be accommodated by vessels staying at OWF n is larger than the number of technicians required to conduct tasks at OWF n at time t.…”

Section: Constraints Related To Fleet Compositionmentioning

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%

“…[13], the PBC-based model also integrated dynamic wind conditions to improve the revenue and system availability of OWFs. Moreover, spare parts inventory management [14] and the wake effect [15] were considered to formulate more realistic O&M optimization models to reduce the maintenance cost and the downtime power loss.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Constraints Related To Fleet Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Chinmoy et al (2019) took the comprehensive cost into consideration and built a clean energy collaborative optimization model from an overall perspective [10]. Ge et al (2020) proposed an optimization model to make offshore wind farm maintenance plans by considering the minimum maintenance cost as the constraint [11]. Kucuksari et al (2019) calculated the energy economic cost indicators of the offshore wind power and developed a cost model [12].…”

Section: Introductionmentioning

confidence: 99%

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Niu

et al. 2021

Sustainability

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Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.

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“…Stock-Williams et al 166 demonstrated automated daily maintenance planning for OWFs. Few other researchers studied about offshore WTs O&M in different locations 9,144,[167][168][169][170][171][172][173][174][175][176].…”

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confidence: 99%

Maintenance cost minimization models for offshore wind farms: A systematic and critical review

Tusar

Sarker

2021

Intl J of Energy Research

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Feasible and sustainable source of power is a burning question nowadays. The offshore wind farms can be a solution to power generation problem, but it is relatively more expensive. Its installation cost is more than twice of its similar size onshore counterpart. The cost that matters most is the operations and maintenance (O&M) cost. Expensive and sophisticated transfer vehicles as well as highly skilled technicians are needed to conduct O&M activities, which results in a remarkably higher O&M cost for an offshore wind farm project.Few researchers proposed some strategies that could resolve the problem nicely, although there is no universal maintenance strategy, which will fit all the conditions. Optimal maintenance strategy depends on a lot of factors such as cost of energy, level of reliability needed, weather conditions, availability of skilled technicians, availability of crew transfer vehicles, and to mention a few.Total 190 research articles related to offshore maintenance have been reviewed, and some prominent models have been discussed in detail. Some risk and reliability-based models reduced annual maintenance cost by 23%, whereas some other opportunistic maintenance strategy was able to minimize 32% of production loss and transportation cost. Compatibility and usability of different models and results are highlighted. This critical review is aimed at identifying the relevant research outcomes and comparing their critical aspects to provide a good guideline about optimal maintenance strategy to the maintenance managers.

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Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction

Cited by 21 publications

References 42 publications

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

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

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Maintenance cost minimization models for offshore wind farms: A systematic and critical review

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