Routing in offshore wind farms: A multi-period location and maintenance problem with joint use of a service operation vessel and a safe transfer boat

Irawan, Chandra Ade; Starita, Stefano; Chan, Hing Kai; Eskandarpour, Majid; Reihaneh, Mohammad

doi:10.1016/j.ejor.2022.07.051

Cited by 13 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it is important to consider alternative methods to address this issue. Other algorithms such as ant columns or another version of a mixed-integer linear programming (MILP) model proposed by Irawan et al [43] allow a new vision of the problem and special attention should be paid to it. Concerning the model presented here, a number of improvements could be made in the context of future work.…”

Section: Other Ways To Solve the Problemmentioning

confidence: 99%

Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty

Hadjoudj

Pandit

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

The growth of offshore wind farms depends significantly on how well offshore wind turbines (OWTs) are operated and maintained in the long term. The operation and maintenance (O&M) activities for offshore wind are relatively more challenging due to uncertain environmental conditions than onshore and due to this, vessel routing for offshore onsite repair is remain complex and unreliable. Here, an improved data-driven decision tool is proposed to robust the vessel routing for O&M tasks under numerous environmental conditions. A novel data-driven technique based on operational datasets is presented to incorporate weather uncertainties, such as wind speed, wave period and wave height (significantly influence offshore crew repair works), into the O&M decision-making process. Results show: (1) The inclusion of weather conditions improves the O&M model uncertainty and accuracy, (2) the implementation of a model allowing weather conditions to evolve has been added to vary the probabilities of successful transfers throughout the day, and (3) the reduction of risk of transfer failure by 15%. These conclusions are further supported by the performance error metrics and uncertainty calculations. Last but not least, by generating a variety of policies for consideration, this tool gave wind turbine operators a systematic and transparent way to evaluate trade-offs and enable choices pertaining to offshore O&M. The full paper highlights the strengths and weaknesses of the proposed technique for offshore vessel routing as well as how the environmental conditions affect them. INTRODUCTIONRecently, the UK Government established an ambitious target in 2020 to reduce emissions in the UK by at least 68% by 2030 compared to 1990 levels [1]. Furthermore, UK's long-term goal (by 2050) is to reduce all emissions by at least 80% from 1990 levels, which means domestic emissions must be reduced by at least 3% per year [2]. With over 24% of the world's renewable energy coming from wind power, it is the second most widely used renewable energy source [3], and demand for wind energy is increasing rapidly and offshore wind output is predicted to triple by 2024 [4]. Therefore, wind energy is considered to be a formidable instrument for achieving net zero by decarbonisation to achieve the net-zero target and meet the nation's future energy demands. For instance, the installed offshore wind capacity in the EU, which was 14.6 GW in 2021, is projected toThis 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.

show abstract

Section: Other Ways To Solve the Problemmentioning

confidence: 99%

Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty

Hadjoudj

Pandit

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Saleh et al [13] developed the self-adaptive maintenance policy for offshore wind turbines by intelligent Petri nets, which avoids dispensable maintenance behaviors and reduces the operation and maintenance costs related to downtime. Ade Irawan et al [14] used service operation vessels and safe transfer boats to make maintenance routing in offshore wind farms. O'Neil et al [15] studied the joint maintenance and orienteering strategy for complex wind turbines.…”

Section: Literature Reviewmentioning

confidence: 99%

Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost

Dui

Zhang

2023

Mathematics

View full text Add to dashboard Cite

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

show abstract

“…Lavinia Amorosi et al [ 22 ] uses mixed integer linear programming (MILP) models to provide a solution that minimizes installation costs and weather uncertainties to address optimization challenges during offshore wind farm installation planning. Chandra Ade Irawan et al [ 23 ] addresses the logistical challenge of optimizing maintenance activities for selected turbines in offshore wind farms by coordinating a service operation vessel (SOV) and a safe transfer boat (STB), utilizing a mixed-integer linear programming model to minimize total maintenance costs. The aforementioned studies have largely achieved significant results in optimizing vessel inspection routes and scheduling, utilizing many efficient methods for similar Nondeterministic Polynomial (NP) class problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of offshore wind farm inspection paths based on K-means-GA

Peng,

Sun,

Tong

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

show abstract

Routing in offshore wind farms: A multi-period location and maintenance problem with joint use of a service operation vessel and a safe transfer boat

Cited by 13 publications

References 32 publications

Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty

Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty

Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost

Optimization of offshore wind farm inspection paths based on K-means-GA

Contact Info

Product

Resources

About