Layout optimisation for an installation port of an offshore wind farm

Irawan, Chandra Ade; Song, Xiang; Jones, Dylan; Akbari, Negar

doi:10.1016/j.ejor.2016.09.032

Cited by 23 publications

(8 citation statements)

References 48 publications

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“…Two recent studies deal with port developments in the offshore wind sector. Irawan et al (2017) have developed a mathematical optimisation model for finding the optimal layout configuration of a port for the installation phase of the offshore wind farm. Their model considers different compartments within the port and suggests an optimal layout that minimizes the total transportation cost of moving the components between the different compartments.…”

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confidence: 99%

A combined supply chain optimisation model for the installation phase of offshore wind projects

Irawan

Akbari

Jones

et al. 2017

International Journal of Production Research

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This paper proposes a combined model for port selection and supply chain optimisation for the installation phase of an offshore wind farm. Two strategic models are proposed where the first model, based on Analytical Hierarchy Process (AHP), aims to select the most suitable installation port. The second model is developed using Integer Linear Programming (ILP) in order to determine the optimal transportation schedule of the components from suppliers to the chosen installation port. The proposed models are evaluated for the West Gabbard (UK) offshore wind farm located in southern part of the North Sea. According to the computational results, the AHP model chooses port of Oostende, Belgium as the most suitable installation port for this offshore wind farm whereas the proposed supply chain model shows that the total transportation cost makes up 9% of total supply chain cost.

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confidence: 99%

A combined supply chain optimisation model for the installation phase of offshore wind projects

Irawan

Akbari

Jones

et al. 2017

International Journal of Production Research

Self Cite

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“…The layout of the installation port is an important issue in the offshore wind industry [11][12][13]. In this sense, several authors explain some aspects about it [14]. Akbari et al [15] explain that the storage space availability, as part of a port's layout, as an importance of 0.289 under 1 regarding the criteria to select an installation port.…”

Section: Introductionmentioning

confidence: 99%

How important are ports for the offshore wind industry?: the case of Spain

Filgueira-Vizoso¹,

Puime-Guillén²,

Cordal-Iglesias³

et al. 2023

RE&PQJ

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Offshore wind is becoming a new technology to develop a better sustainable world. Its progress is linked to the use of port facilities, where the offshore wind farms can be stored or pre-installed. The aim of this paper is to analyse the storage space availability for ports in terms of being used for the new offshore wind sector. The case of study will be focused on analysing the port facilities in Spain, country with a great offshore wind resource in some specific areas. Results indicate the ports that can be used for the development of offshore wind in Spain. This work is important in order to establish a roadmap of the offshore wind business in Spain, which can repair the economic and social damaged due to SARS-CoV-2 pandemic.

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“…The UK has the most substantial share of this offshore wind capacity at 44%, followed by Germany (34%) and Denmark (7%) of the EU capacity [1]. Due to complex logistics and transportation, offshore wind farm construction is challenging as well as costly, and operation and maintenance (O&M) costs are substantial, [2, 3]. Due to the steady evolution of more cost‐effective technology, wind sector has experienced rapid development during recent decades.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Pandit

Kolios

Infield

2020

IET Renewable Power Generation

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Wind energy is an attractive alternative to conventional sources of electricity generation due to its effectively zero carbon emissions. Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various timescales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavourable weather prevents repair and maintenance activities. Delayed repair results in increased downtime and reduced wind farm availability and energy yield. This paper proposes two data-driven models for long-term weather conditions forecasting to improve the wind farm availability and support operation and maintenance (O&M) decision-making process. These two data-driven approaches are Long Short-Term Memory Network, abbreviated as LSTM, and Markov chain. A LSTM is an artificial recurrent neural network (RNN), capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather datasets are obtained from FINO3 database to train and validate the performance of these data-driven models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The full paper outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities.

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Layout optimisation for an installation port of an offshore wind farm

Cited by 23 publications

References 48 publications

A combined supply chain optimisation model for the installation phase of offshore wind projects

A combined supply chain optimisation model for the installation phase of offshore wind projects

How important are ports for the offshore wind industry?: the case of Spain

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

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