A life cycle cost model for floating offshore wind farms

Maienza, Carmela; Avossa, Alberto Maria; Ricciardelli, Francesco; Coiro, Domenico P.; Troise, G.; Georgakis, Christos Τ.

doi:10.1016/j.apenergy.2020.114716

Cited by 87 publications

(45 citation statements)

References 18 publications

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“…The cost consequence can be predicted by the cost function, and NPV models proposed by Maienza et al, 4 Nkoi et al, 24 Nkoi et al, 25 and Ozturk et al, 11 such that:

normalThe normalcost normalof normalthe normalassociated normalfailure normalmodes = \sum_{i}^{n} P_{i} C_{i}

where

normaln

represents the subsystem,

P_{i}

is the probability of failure of

normalith

element, and

C_{i}

is the cost consequence per

normalith

failure.

\begin{matrix} true \\ right The energy loss due to downtime ([], kWh) & = & left Capacity factor \times wind turbine nominal power ((), kW) \\ left \times Downtime per failures false[normalhours false] \times Annual failure rate \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…The operation and maintenance costs can be modeled using Equations (). The operational cost accounted for the associated costs, such as the insurance cost, grid access cost, and the seabed rental costs, while the maintenance cost captures the preventive and corrective maintenance, respectively 4

{normalO}_{normalC} = \sum_{i = 1}^{normaln} {normalO}_{C, i}, (i = 1, 2 \dots \dots \dots . n),

{normalM}_{normalDC} = \sum_{i = 1}^{normalm} {normalP}_{normali} \cdot λ_{, i} \cdot {normalM}_{DC, i}, (i = 1, 2 \dots \dots \dots m),

{normalM}_{normalIC} = \sum_{i = 1}^{normalm} (1 - {normalP}_{normali}) \cdot λ_{, i} \cdot {normalM}_{IC, i}, (i = 1, 2 \dots \dots \dots m)

where

O_{C}

are the likely operational expenditures,

M_{IC}

is the direct maintenance cost, which includes but not limited to port fees, vessel hiring fixed cost, maintenance planning cost, and weather forecasting cost,

M_{DC}

is the corrective/replacement maintenance cost of

normalith

elements of the gearbox system with

r_{normalf, normali}

annual failure rate, and

P_{i}

is the anticipated probabilit...…”

Section: Methodsmentioning

confidence: 99%

“…Several improvements in offshore renewable energy harvesting ranging from wave energy, 1 wind energy, 2 and ocean thermal energy 3 have increased over the past decades. In recent times, global energy contributions from offshore energy sources have improved and contributed to over 40% of world energy sources 4 . The recent development in the Australian offshore energy sector and China investment in large offshore wind energy generating farms across the country have confirmed the global shift to maximizing cleaner energy.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic failure analysis of renewable energy systems in the remote offshore environments

Nitonye

Adumene

Sigalo

et al. 2020

Quality & Reliability Eng

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For effective integrity management of marine renewable energy systems in the dynamic and uncertain ocean environments, understanding the failure dynamics is crucial. The cost of investment in marine/offshore renewable energy infrastructures and the associated cost due to failure and loss of energy production necessitate a predictive monitoring methodology that is dynamic and adaptive. This paper presents an integrated multi‐state pure‐birth‐pure‐death Markovian‐net profit value model for the offshore turbine subsystem failure analysis and its cost‐based consequences. The integrated model captures the offshore turbine subsystem's dynamic failure states and its economic implications due to the cost of energy loss and downtime for the period under consideration. The model applies a phase‐type exponential distribution to describe the monotonic state of failure. The methodology is demonstrated with an offshore wind turbine gearbox, and it captures the dynamic state of the system and its failure mechanisms. The cumulative effect of the subelements deterioration decreases the gearbox performance by over 35% within the first 2 years of operation.

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“…The cost consequence can be predicted by the cost function, and NPV models proposed by Maienza et al, 4 Nkoi et al, 24 Nkoi et al, 25 and Ozturk et al, 11 such that:

normalThe normalcost normalof normalthe normalassociated normalfailure normalmodes = \sum_{i}^{n} P_{i} C_{i}

where

normaln

represents the subsystem,

P_{i}

is the probability of failure of

normalith

element, and

C_{i}

is the cost consequence per

normalith

failure.

\begin{matrix} true \\ right The energy loss due to downtime ([], kWh) & = & left Capacity factor \times wind turbine nominal power ((), kW) \\ left \times Downtime per failures false[normalhours false] \times Annual failure rate \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

{normalO}_{normalC} = \sum_{i = 1}^{normaln} {normalO}_{C, i}, (i = 1, 2 \dots \dots \dots . n),

{normalM}_{normalDC} = \sum_{i = 1}^{normalm} {normalP}_{normali} \cdot λ_{, i} \cdot {normalM}_{DC, i}, (i = 1, 2 \dots \dots \dots m),

{normalM}_{normalIC} = \sum_{i = 1}^{normalm} (1 - {normalP}_{normali}) \cdot λ_{, i} \cdot {normalM}_{IC, i}, (i = 1, 2 \dots \dots \dots m)

where

O_{C}

are the likely operational expenditures,

M_{IC}

is the direct maintenance cost, which includes but not limited to port fees, vessel hiring fixed cost, maintenance planning cost, and weather forecasting cost,

M_{DC}

is the corrective/replacement maintenance cost of

normalith

elements of the gearbox system with

r_{normalf, normali}

annual failure rate, and

P_{i}

is the anticipated probabilit...…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic failure analysis of renewable energy systems in the remote offshore environments

Nitonye

Adumene

Sigalo

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Estimating these costs for a floating offshore farm is challenging due to the absence of commercial operating farms and the complexity of the systems [2]. The models proposed in [34,33,10] provide the best guides available. According to these models, the life-cycle costs of offshore wind farms can be associated with the following stages: i) planning, development, and design; ii) production and installation; iii) operation; and iv) decommissioning.…”

Section: Life-cycle Cost Modelmentioning

confidence: 99%

“…The main appeal of this concept is that it unlocks locations with water depths greater than 50 m, where bottom-fixed concepts are either technically infeasible or economically unfeasible [1]. However, the capital costs of floating projects can be as large as twice the cost for shallow waters [10]. These additional costs are partially explained by the extra length financial closure and regulatory compliance of the project.…”

Section: Introductionmentioning

confidence: 99%

A flexibility-based approach for the design and management of floating offshore wind farms

2021

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Floating offshore wind farms have become a gateway to reach locations that are technically and economically infeasible to exploit using fixed platforms. However, the high capital investments and the uncertainty associated with the reliability, capacity factor, technology evolution, electricity demand, and regulatory frameworks negatively affect the cost of energy of this approach. Alternative strategies, such as designing for flexibility, have been shown to increase the value of engineering systems subject to highly uncertain environments. In this article, an analysis based on life-cycle costs and Monte-Carlo simulation is used to determine if floating wind farms with flexible installed capacity result in lower costs of energy than traditionally designed wind farms. Flexibility is introduced using an adaptable platform strategy and an over-dimensioned platform strategy. The results show that the adaptable platform strategy has the potential to reduce the cost of energy up to 18% by increasing the energy generation and the lifetime of some components of the wind farm. Nonetheless, the benefits of flexibility depend on new legislation that allows for lifetime extensions and proper flexibility management policies that utilize the potential built into the systems.

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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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A life cycle cost model for floating offshore wind farms

Cited by 87 publications

References 18 publications

Dynamic failure analysis of renewable energy systems in the remote offshore environments

Dynamic failure analysis of renewable energy systems in the remote offshore environments

A flexibility-based approach for the design and management of floating offshore wind farms

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

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