Uncertainty analysis of wave energy farms financial indicators

Guanche, Raúl; Andrés, Adrián de; Díaz-Simal, Pedro; Vidal, C. R.; Losada, Íñigo J.

doi:10.1016/j.renene.2014.02.046

Cited by 65 publications

(45 citation statements)

References 11 publications

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“…Secondly, the OPEX was calculated as a percentage of the CAPEX following [6,7]. However, this approach is inaccurate as it relies on the CAPEX calculation and does not take into account the number of devices per MW, which is a key parameter for the sizing study.…”

Section: Sensitivity Analysis: Capex and Opexmentioning

confidence: 99%

“…Moreover, some authors were found to account for risk in an LCOE analysis by considering the probability of a return on the investment for different sized wave farms through calculation of the Value at Risk (VaR) and Conditional Value at Risk (CVaR) [5]. Also, O'Connor et al [6] studied the OPEX costs for wave energy projects, and Guanche et al [7] This paper will present a case study of the techno-economic performance of an inherently resonant point absorber wave energy converter with several ratings. Performance will be tested at various locations in order to check the location dependency of the rating decision.…”

Section: Introductionmentioning

confidence: 99%

“…O'Connor et al [6] showed different figures of OPEX calculated as a percentage of CAPEX. In this case, following Guanche et al [7], it was assumed that the initial OPEX was 8% of CAPEX.…”

mentioning

confidence: 99%

“…In this case, as the first units produced, a factor of 0.82 is selected as suggested as an optimistic scenario in Guanche et al [7]. For OPEX, a learning rate of 0.92 is applied (normally OPEX shows slower learning than CAPEX).…”

mentioning

confidence: 99%

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Techno-Economic Related Metrics for a Wave Energy Converters Feasibility Assessment

et al. 2016

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Abstract:When designing "multi-MW arrays" of Wave Energy Converters (WECs), having a low number of converters with high individual power ratings can be beneficial as the Operation and Maintenance (O&M) costs may be reduced. However, having converters of small dimensions or small power ratings could also be beneficial, as suggested by previous works, due to a reduction in material costs as compared to power production, and the use of small, inexpensive vessels. In this work, a case study investigating the optimum size of WEC for a 20 MW array is performed. Analysis is carried out based on the CorPower Ocean technology. In this case study, firstly a Levelized Cost of Energy (LCOE) model is created. This model incorporates the latest Capital Expenditure (CAPEX) estimates for CorPower Ocean's 250 kW prototype. Using this techno-economic model, several sizes/ratings of WEC are tested for use in a 20 MW array. Operational Expenditure (OPEX) is calculated using two different calculation approaches in order to check its influence on final indicators. OPEX is firstly calculated as a percentage of CAPEX, as shown in previous works, and secondly using a failure-repair model, taking into account individual failures of WECs in the array. Size/rating analysis is carried out for several European locations in order to establish any dependence between site location and optimal WEC size/rating. Several metrics for techno-economic assessment of marine energy converters, other than LCOE, are compared in this work. A comparison of several devices with each these metrics is performed within this study.

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Section: Sensitivity Analysis: Capex and Opexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…O'Connor et al [6] showed different figures of OPEX calculated as a percentage of CAPEX. In this case, following Guanche et al [7], it was assumed that the initial OPEX was 8% of CAPEX.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Techno-Economic Related Metrics for a Wave Energy Converters Feasibility Assessment

et al. 2016

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“…Uno de los campos de aplicación del análisis de indicadores financieros es la evaluación del desempeño financiero de un determinado sector económico, como el petróleo en Tailandia (Wattanatorn y Kanchanapoom, 2012), empresas internacionales de capital de riesgo (Gerschewski y Xiao, 2014), el sector bancario en turquía (Mercan et al, 2003) y plantas de energía undimotriz en las Islas Canarias -España (Guanche et al, 2014). Otra línea de investigación se ha enfocado en el uso de indicadores financieros para evaluar el efecto de una variable externa en el desempeño de una determinada actividad económica, como el impacto de la implementación de un tratado de libre comercio en una industria lechera intensiva en México (Villegas y Dávalos, 2005), identificar en retrospectiva si las empresas italianas distribuidoras de gas que optaron por especializarse tuvieron mejores resultados que las que eligieron la diversificación (Capece et al, 2010), si la privatización incidió en los rendimientos financieros de cinco distribuidoras de energía eléctrica del nordeste de Brasil (Silvestre et al, 2010), el efecto amortiguador de los indicadores financieros sobre la responsabilidad de las empresas suecas según su antigüedad (Wiklund et al, 2010), la incidencia de nuevas políticas regulatorias en el desempeño financiero de empresas italianas del sector gas (Capece et al, 2012), y el desempeño de compañías energéticas italianas luego de la liberalización del mercado (Capece et al, 2013).…”

Section: Introductionunclassified

Análisis de indicadores financieros del sector manufacturero del cuero y marroquinería: un estudio sobre las empresas colombianas

et al. 2015

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Se presenta un análisis financiero de empresas colombianas del sector marroquinería y cuero, que busca tener información sobre el desempeño financiero de esta actividad económica y generar criterios que apoyen la toma de decisiones estratégicas. Se realizó un estudio descriptivo, de carácter documental y exploratorio, en el que se usaron estados financieros de cincuenta y siete empresas clasificadas en cuatro actividades económicas, y se calcularon catorce indicadores de liquidez, rentabilidad, apalancamiento, administración de activos, y generación de valor, por actividad económica y por año. Se encontró que el número de empresas con grandes activos ha disminuido, que el sector afronta dificultades de liquidez, rentabilidad y baja rotación de activos, y que la generación de valor es baja, aunque la razón corriente y el nivel de apalancamiento son adecuados. Se detectaron diferencias entre el desempeño financiero de las cuatro actividades económicas que conforman el sector, lo cual se recomienda sea tomado en consideración cuando se evalúe la industria del cuero y marroquinería de forma integral. Se plantea cómo el análisis de la información financiera, al contrastarlo con las conclusiones de estudios sectoriales y de prospectiva, puede generar criterios para la toma de decisiones y la planeación estratégica.

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Met‐ocean conditions influence on floating offshore wind farms power production

et al. 2015

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Met-ocean conditions may affect the performance of a floating wind turbine, since a harsh climate could lead the system to exceed its operating thresholds and thus to force the machine shutdown.In this paper, it is a proposed methodology to evaluate the effect of met-ocean conditions on the long-term dynamic behaviour, and energy production, of a floating wind farm. For a sample of 500 MW farm located off the coast of Aberdeen (Scotland), 20 years of met-ocean data are generated by means of meteorological reanalysis techniques. A subset of 1000 hourly conditions is selected, by means of a maximum dissimilarity algorithm, and input to a dynamic floating wind turbine model. Numerical results are then interpolated for the whole set of met-ocean data, using radial basis functions. This approach allows to dramatically reduce the global computation time. Tower inclination and hub acceleration are chosen as relevant operating parameters: the former mainly depends on mean wind speed and direction, being largest at rated wind speed. The latter is also affected by significant wave height, and reaches its highest values when wind and waves are aligned. For each simulation, any machine exceeding the selected safety threshold is considered to be shut down. Assuming continuous operation, the average lifespan capacity factor of the farm is 50.2%; more restrictive tolerances result in a nonlinear reduction of the energy production. This approach may help both at the design and the operational stage, in determining the best trade-off between energy production and safe operation.

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Uncertainty analysis of wave energy farms financial indicators

Cited by 65 publications

References 11 publications

Techno-Economic Related Metrics for a Wave Energy Converters Feasibility Assessment

Techno-Economic Related Metrics for a Wave Energy Converters Feasibility Assessment

Análisis de indicadores financieros del sector manufacturero del cuero y marroquinería: un estudio sobre las empresas colombianas

Met‐ocean conditions influence on floating offshore wind farms power production

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