Mutriku has recently become the first commercial wave farm to release its operating data. The plant has 14 OWC operating turbines, and this study has conducted an analysis of hourly data corresponding to the 2014–2016 period. The plant's capacity factor has been calculated for this period, and its seasonal evolution characterized. Additionally, a plant efficiency index has been defined as the ratio between the wave energy flux at a reference buoy and the average power generation across the active turbines. The Mutriku wave farm's annual output in the period analysed has been 246,468.7 kW-h, with an average of around ten working turbines. The results indicate that Mutriku's average capacity factor is around 0.11, with higher values in winter than in summer. These values are below the capacity factors reported for other renewable energy sources. The plant efficiency index is 0.26, and further advances in regulation and control may also raise this parameter's values, as may lower rated power alternators. This will also help to improve the Mutriku wave plant's capacity factor, and OWC technology in general.MINECO CGL2016-76561-R (MINECO/EU ERDF), University of the Basque Country UPV/EHU (GIU14/03 and PES17/23

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Historical wave trends are studied via ECMWF's reanalyses over the 20th century. • ERA20 is calibrated via quantile-matching and validated against buoy measurements. • The wave energy resource increases over 40% off the west coast of Ireland. • A 30% surplus of AMPP is observed for different WECs due to resource variations. • Extreme events occurrence doubled, doubling the time WECs spend in survival mode.

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In this article, offshore wind energy potential is measured around the West Mediterranean using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson's correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation's spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

In addition to human error, manufacturing tolerances for blades and hubs cause pitch angle misalignment in wind turbines. As a consequence, a significant number of turbines used by existing wind farms experience power production loss and a reduced turbine lifetime. Existing techniques, such as photometric technology and laser-based methods, have been used in the wind industry for on-field pitch measurements. However, in some cases, regular techniques have difficulty achieving good and accurate measurements of pitch angle settings, resulting in pitch angle errors that require cost-effective correction on wind farms. Here, the authors present a novel patented method based on laser scanner measurements. The authors applied this new method and achieved successful improvements in the Annual Energy Production of various wind farms. This technique is a benchmarking-based approach for pitch angle calibration. Two case studies are introduced to demonstrate the effectiveness of the pitch angle calibration method to yield Annual Energy Production increase.

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Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( C F ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that C F values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.