A Bayesian model for wind farm capacity factors

Wilkie, David; Galasso, Carmine

doi:10.1016/j.enconman.2021.114950

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Wilke and Galasso used the technique to assess WT capacity factors in a recent study. 30 This is arguably the most closely aligned application of the methodology to the case study presented here. However, to the authors' best knowledge, there are no studies which apply hierarchical models to questions of strategy or maintainability of WTs.…”

Section: Bayesian Hierarchical Modelling In Offshore Windmentioning

confidence: 80%

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A Bayesian hierarchical assessment of night shift working for offshore wind farms

et al. 2023

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This article presents a Bayesian data‐modelling approach to assessing operational efficiency at offshore wind farms. Input data are provided by an operational database provided by a large offshore wind farm which employs an advanced data management system. We explore the combination of datasets making up the database, using them to train a Bayesian hierarchical model which predicts weekly lost production from corrective maintenance and time‐based availability. The approach is used to investigate the effect of technician work shift patterns, specifically addressing a strategy involving night shifts for corrective maintenance which was employed at the site throughout the winter. It was found that, for this particular site, there is an approximate annual increase in time‐based technical availability of 0.64%. We explore the effect of modelling assumptions on cost savings; specifically, we explore variations in failure rate, price of electricity, number of technicians working night shift, extra staff wages, months of the year employing 24/7 working and extra vessel provision. Results vary quite significantly among the scenarios investigated, exemplifying the need to consider the question on a farm‐by‐farm basis.

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Section: Bayesian Hierarchical Modelling In Offshore Windmentioning

confidence: 80%

“…BHMs have seen a limited application to modelling operational data from OWFs. Wilke and Galasso used the technique to assess WT capacity factors in a recent study 30 . This is arguably the most closely aligned application of the methodology to the case study presented here.…”

Section: Literature Reviewmentioning

confidence: 98%

A Bayesian hierarchical assessment of night shift working for offshore wind farms

et al. 2023

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“…It is usually defined as the ratio of the predicted power generation to the rated capacity. The capacity factor depends on the parameters and performance of the wind turbine and the characteristics of wind resources [41]. Many studies have shown that the larger the capacity factor is, the higher the utilization rate of wind energy is.…”

Section: Predicted Power Generation and Capacity Factormentioning

confidence: 99%

Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang

et al. 2022

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As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.

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“…Su principal ventaja es su predictibilidad. Sin embargo -debido a la naturaleza cíclica de la marea y la eficiencia de la turbina-su factor de capacidad es de aproximadamente un 30 %, lo cual es considerablemente menor que el logrado por la eólica offshore [24] [25].…”

Section: Energía De Las Mareasunclassified

Desarrollo de metodologías de estimación para sistemas de almacenamiento de energía no convencionales en topologías híbridas

Fornaro¹

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Los sistemas de almacenamiento de energía son esenciales para alcanzar la sustentabilidad energética. Hoy día son un eslabón fundamental en el desarrollo de vehículos eléctricos, así como en la implementación a gran escala de los esquemas de generación eléctrica basados en energías renovables. Sin embargo, aún existen ciertas limitaciones tecnológicas, algunas de ellas intrínsecas al proceso de fabricación y otras dependientes del uso que se le da a estos sistemas. Teniendo este último aspecto en consideración, en esta tesis se presentan y desarrollan los principales avances en el área de la caracterización de módulos de almacenamiento de energía no convencionales (MANC). En particular, los esfuerzos se centran en el área de la estimación y observación para supercapacitores, baterías de Ión-Litio y baterías de flujo redox, aunque los desarrollos no se limitan a estos sistemas. Partiendo de un estudio exhaustivo de los modelos disponibles en la bibliografía, se realizan aportes en el área del modelado y se propone un esquema que permite observar los estados fundamentales de estos así como medidas validadas de los parámetros internos de los modelos adoptados. Los desarrollos de esta tesis se basan en la combinación de una multiplicidad de algoritmos, y el éxito de esta combinación requiere de un conocimiento detallado de cada uno de los métodos involucrados. Más precisamente, se emplean algoritmos de observación, estimación y control por modos deslizantes, algoritmos continuos y discretos de estimación. Por estos motivos, en primer lugar se realiza una presentación que condensa los aportes e ideas originales realizadas en las áreas de observación, estimación y modelado para MANC. Luego, en último lugar se presentan y analizan los principales resultados obtenidos.

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A Bayesian model for wind farm capacity factors

Cited by 8 publications

References 18 publications

A Bayesian hierarchical assessment of night shift working for offshore wind farms

A Bayesian hierarchical assessment of night shift working for offshore wind farms

Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang

Desarrollo de metodologías de estimación para sistemas de almacenamiento de energía no convencionales en topologías híbridas

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