Selection index for Wave Energy Deployments (SIWED): A near-deterministic index for wave energy converters

Lavidas, George

doi:10.1016/j.energy.2020.117131

Cited by 49 publications

(25 citation statements)

References 46 publications

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“…Lavidas [24] (13) with CoV H s the Coefficient of Variation of the significant wave height H s = 4 √ m 0 , CF the capacity factor, H EVA the value of return waves based on extreme value analysis, and H max the maxima value of wave height from the dataset. The index reduces uncertainties and bridges the power capabilities with resource dependence, providing an unbiased selection of WEC.…”

Section: Hybrid Methods Based Indicesmentioning

confidence: 99%

“…CF values thus depend on the adaptability of the WEC technology to a given wave climate. Indeed, high rated WEC will require high energy levels, and will show reduced performance in locations with moderate resources; hence CF is dependent on WEC selection e.g., [6,24,42]. Availability of production is another metric that is considered to determine WEC operations between a start limit P in and an upper limit P out .…”

Section: Post-production Metricsmentioning

confidence: 99%

“…Particular attention must also be dedicated to the time scale considered that must be long enough to encompass resource annual and seasonal variabilities [23]. Wave energy resource assessment is therefore a complex multivariable problem that requires a detailed understanding of all influencing parameters by relying on specific statistical analyses, energy metrics and selection indexes [24]. Although a part of these aspects appeared in technical specifications, we proposed here a review of the most recent resource assessments encompassing applications based on (i) available observations (in situ and satellite) and (ii) hindcast databases, and (iii) refined simulations specifically implemented for resource characterization.…”

Section: Introductionmentioning

confidence: 99%

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Wave Energy Resource Assessment for Exploitation—A Review

Guillou

Lavidas²,

Chapalain

2020

JMSE

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Over recent decades, the exploitation of wave energy resources has sparked a wide range of technologies dedicated to capturing the available power with maximum efficiency, reduced costs, and minimum environmental impacts. These different objectives are fundamental to guarantee the development of the marine wave energy sector, but require also refined assessments of available resource and expected generated power to optimize devices designs and locations. We reviewed here the most recent resource characterizations starting from (i) investigations based on available observations (in situ and satellite) and hindcast databases to (ii) refined numerical simulations specifically dedicated to wave power assessments. After an overall description of formulations and energy metrics adopted in resource characterization, we exhibited the benefits, limitations and potential of the different methods discussing results obtained in the most energetic locations around the world. Particular attention was dedicated to uncertainties in the assessment of the available and expected powers associated with wave–climate temporal variability, physical processes (such as wave–current interactions), model implementation and energy extraction. This up-to-date review provided original methods complementing the standard technical specifications liable to feed advanced wave energy resource assessment.

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Section: Hybrid Methods Based Indicesmentioning

confidence: 99%

Section: Post-production Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wave Energy Resource Assessment for Exploitation—A Review

Guillou

Lavidas²,

Chapalain

2020

JMSE

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“…CapEx and maintenance (OM) are costs expressed in Euro (e) discounted at present values (PV). Annual energy production (AEP) is the estimation of energy production by the joint probability distribution of H m0 and T peak [31,32,[46][47][48]. To provide a feasibility estimate, the simple Pay Back Period (PBP) [44] is considered (see Equation 2), using the guaranteed selling price of electricity (300 e/MWh) (R n ) given by the Italian government [49].…”

Section: Methodsmentioning

confidence: 99%

Blue Growth Development in the Mediterranean Sea: Quantifying the Benefits of an Integrated Wave Energy Converter at Genoa Harbour

2020

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Coastal resilience is often achieved by traditional civil engineering projects, such as dikes and breakwaters. However, given the pressing nature of Climate Change, integrating energy converters in “classical” structures can enhance innovation, and help in pursuing decarbonisation targets. In this work, we present an alternative for integrating a wave energy converter at a vertical wall breakwater, following past successful projects. Our approach is based on a high spatio-temporal wave dataset to properly quantify expected energy production, but also focus on the hours for which other time-dependent renewables cannot produce, i.e., solar. Our analysis evaluates the power performance and assesses the economic parameters and viability of the proposed installation. Our integrated solution shares the main capital with the breakwater and can produce from 390 MWh–2300 MWh/year, displacing more than 1760 Tn of CO2 annually. In addition to power generated, we estimated the payback period for most cases being approximately 10–15 years, but when accounting avoided oil CO2 emissions, the installation is highly attractive with payback in less than 9 years, with favourable financing indicating 3.4 years.

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“…The particular advantages of wave energy (e.g., high energy density, small energy loss, predictability) render its exploitation promising not only in areas with high wave energy flux like the Atlantic Ocean but also in areas with relatively calm wave climate and thus with intermediate levels of power availability, like the Mediterranean Sea; see, for example, [1,3,4]. Usually, the later areas are characterized by low variability achieving better survivability, without increase of capital expenditure [5]. In order to make wave energy production feasible and economically viable, it is necessary, among others, to deploy wave energy converters (WECs) based on the accurate knowledge of the wave climate and its temporal variability in the area of interest [6].…”

Section: Introductionmentioning

confidence: 99%

Joint Modelling of Wave Energy Flux and Wave Direction

Soukissian

Karathanasi

2021

Processes

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In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

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Selection index for Wave Energy Deployments (SIWED): A near-deterministic index for wave energy converters

Cited by 49 publications

References 46 publications

Wave Energy Resource Assessment for Exploitation—A Review

Wave Energy Resource Assessment for Exploitation—A Review

Blue Growth Development in the Mediterranean Sea: Quantifying the Benefits of an Integrated Wave Energy Converter at Genoa Harbour

Joint Modelling of Wave Energy Flux and Wave Direction

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