Minimizing transportation and installation costs for turbines in offshore wind farms

Sarker, Bhaba R.; Faiz, Tasnim Ibn

doi:10.1016/j.renene.2016.09.014

Cited by 71 publications

(21 citation statements)

References 24 publications

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“…The installation cost of the wind turbines is a function of the vessel day rates (VDR), the number of vessels (workboats, heavy lift vessels, Special Operations Vessels (SOVs), and jack up vessels; N v ), the duration of the installation (T Instal ), and the cost for the personnel (C pers ) required for carrying out the installation. Specifically for the installation of the wind turbines, the onshore pre-assembly method (M Assemb;T ) is also expected to greatly affect the cost of installation (Sarker and Faiz 2017). Although installation usually takes place during spring and summer time in order to avoid adverse weather conditions, they still play an important role to activities taking place offshore (Kaiser 2009); hence, for estimating the final installation cost of the wind farm, a weather adjustment factor (AdjW) was also considered, an approach used also by other authors in the literature (Sarker and Faiz 2017;Kaiser and Snyder 2012).…”

Section: Cost Model Of Ow Farm With Fixed Monopilementioning

confidence: 99%

“…Specifically for the installation of the wind turbines, the onshore pre-assembly method (M Assemb;T ) is also expected to greatly affect the cost of installation (Sarker and Faiz 2017). Although installation usually takes place during spring and summer time in order to avoid adverse weather conditions, they still play an important role to activities taking place offshore (Kaiser 2009); hence, for estimating the final installation cost of the wind farm, a weather adjustment factor (AdjW) was also considered, an approach used also by other authors in the literature (Sarker and Faiz 2017;Kaiser and Snyder 2012). Therefore, the cost is expressed as C 3:…”

Section: Cost Model Of Ow Farm With Fixed Monopilementioning

confidence: 99%

See 1 more Smart Citation

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

Ιωάννου

Angus

Brennan

2018

Energy Sources, Part B: Economics, Planning, and Policy

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Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (P WT), water depth (WD), distance from port (D), and wind farm capacity (P WF). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.

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Section: Cost Model Of Ow Farm With Fixed Monopilementioning

confidence: 99%

Section: Cost Model Of Ow Farm With Fixed Monopilementioning

confidence: 99%

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

Ιωάννου

Angus

Brennan

2018

Energy Sources, Part B: Economics, Planning, and Policy

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“…Fixed-bottom offshore wind turbines are already commercialized, but there is still opportunity for reducing their cost. In particular, the installation phase is an area of ongoing innovation (Sarker and Faiz 2017), with a focus on new piling and alternative installation technologies that avoid transition-piece-related reliability issues, while at the same time reducing environmental impact.…”

Section: Innovations To Reduce Lcoementioning

confidence: 99%

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

Dykes

Veers

Lantz

et al. 2019

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Research advanced erosion-resistant composite materials for use on the leading edge of wind turbine blades Research, development, and scale-up of thermoplastic resin systems to reduce wind turbine blade manufacturing cycle times, reduce blade costs, enable thermal welding of bond lines, increase recyclability, and enable advanced in-field blade repair techniques Research advanced thermoset materials that can be recovered and reused Research metals and other noncomposite materials for development of lightweight, high-strength, and high-stiffness turbine components Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a virtual factory (or digital twin of a factory) to understand advantages and disadvantages of targeted automation for both wind turbine blade molding and finishing operations Identify and quantify all cost inputs for wind turbine component manufacturing (e.g., blades, towers) to identify production steps that would benefit from automation Develop automated production methods for the continuous manufacturing of one-piece wind turbine towers for on-site manufacturing Develop advanced design tools to optimize blade and tower design with respect to manufacturing with automation Research and develop automation technology able to locate complex wind turbine blade geometry in space to allow for effective automated finishing operations, such as sanding, drilling, and cutting Develop methods to automate the delivery, placement, and inspection of core material into blade skin molds during the composite laminate skin lay-up Integrate real-time inspection and quality control into automated production steps for wind turbine blades, towers, and other components Develop large-scale, low-cost, high-throughput, high-performance automated additive manufacturing technologies (e.g., 3D printing, automated fiber placement and tape layup, filament winding) for the production of towers, blade skins, blade spars, and other turbine components Develop embedded metrology and out-of-mold indexing technologies Grand Challenge 3: On-Site Manufacturing Develop robust composite materials for use in broad on-site environmental conditions Research the chemistry and processing of in-situ thermoplastic resin systems to enable thermally welded joints in an on-site manufacturing environment Develop targeted wind turbine blade finishing automation techniques designed to reduce the overall footprint of on-site finishing operations and minimize production facility floor space Research additive manufacturing to print wind turbine blade molds and tooling at on-site manufacturing locations IEA Wind TCP Task 11

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“…This model determines the optimal schedule for maintaining the turbines and the optimal routes for the crew transfer vessels to visit the turbines along with the number of technicians required for each vessel. Sarker and Faiz (2017) developed a model for wind turbine installation and transportation in an oshore wind farm to minimise the total cost where the eects of wind farm and vessel parameters on cost are investigated. Recently, Barlow et al (2018) proposed a mixed-method framework to address current oshore wind farm installation projects.…”

Section: Applied a Simulationmentioning

confidence: 99%

An optimisation model for scheduling the decommissioning of an offshore wind farm

Irawan

Wall²,

Jones³

2019

OR Spectrum

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An optimisation model is proposed for scheduling the decommissioning of an oshore wind farm in order to minimise the total cost which is comprised of jack-up vessel, barge (transfer) vessel, inventory, processing and on-land transportation costs. This paper also presents a comprehensive review of the strategic issues relating to the decommissioning process and of scheduling models that have been applied to oshore wind farms. A mathematical model using integer linear programming (ILP) is developed to determine the optimal schedule considering several constraints such as the availability of vessels and planning delays. As the decommissioning problem is challenging to solve, a matheuristic approach based on the hybridization of a heuristic approach and an exact method is also proposed to nd near optimal solutions for a test-set of problems. A set of computational experiments has been carried out to assess the proposed approach.

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Minimizing transportation and installation costs for turbines in offshore wind farms

Cited by 71 publications

References 24 publications

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

An optimisation model for scheduling the decommissioning of an offshore wind farm

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