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An enhanced database (RetroDB) of the Spanish wind energy resources, derived from a high spatial resolution integration with the WRF model, is proposed and evaluated. RetroDB provides hourly capacity factor (CF) values for the Spanish regions, along the period of 2007–2020, with an unprecedented spatial resolution. RetroDB estimates were benchmarked based on the ERA5 global reanalysis. A comprehensive evaluation study of both RetroDB and ERA5 estimates was conducted using surface and tall mast measurements, along with actual CF values. The extent to which RetroDB and ERA5 reproduced the CF spatial variability, distribution, and ramp distribution were specifically addressed. The results showed no differences between the global and regional reanalysis performance regarding nationally aggregated wind energy estimates. Nevertheless, RetroDB clearly shows a superior performance reproducing the wind speeds’ and CFs’ spatial and temporal distributions. This was found to be related to the higher reliability of RetroDB reproducing the aloft winds in complex topographic areas. Overall, the results clearly indicate that, in areas such as the study region, where the wind resources are mostly associated with topographic enhancements, high spatial resolution regional reanalyses are preferable over relative coarse reanalyses (e.g., ERA5), particularly for wind energy integration studies. RetroDB database is made publicly available.

Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation.

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

Wind turbines are often placed in complex terrains, where benefits from orography-related speed up can be capitalized. However, accurately modeling the wind resource over the extended areas covered by a typical wind farm is still challenging over a flat terrain, and over a complex terrain, the challenge can be even be greater. Here, a novel approach for wind resource modeling is proposed, where a linearized flow model is combined with a machine learning approach based on the k-nearest neighbor (k-NN) method. Model predictors include combinations of distance, vertical shear exponent, a measure of the terrain complexity and speedup. The method was tested by performing cross-validations on a complex site using the measurements of five tall meteorological towers. All versions of the k-NN approach yield significant improvements over the predictions obtained using the linearized model alone; they also outperform the predictions of non-linear flow models. The new method improves the capabilities of current wind resource modeling approaches, and it is easily implemented.

Small Wind Turbines (SWTs) are promissory for distributed generation using renewable energy sources; however, their deployment in a broad sense requires to address topics related to their cost-efficiency. This paper aims to survey recent developments about SWTs holistically, focusing on multidisciplinary aspects such as wind resource assessment, rotor aerodynamics, rotor manufacturing, control systems, and hybrid micro-grid integration. Wind resource produces inputs for the rotor’s aerodynamic design that, in turn, defines a blade shape that needs to be achieved by a manufacturing technique while ensuring structural integrity. A control system may account for the rotor’s aerodynamic performance interacting with an ever-varying wind resource. At the end, the concept of integration with other renewable source is justified, according to the inherent variability of wind generation. Several commercially available SWTs are compared to study how some of the previously mentioned aspects impact performance and Cost of Electricity (CoE). Understanding these topics in the whole view may permit to identify both tendencies and unexplored topics to continue expanding SWTs market.

Urban wind energy research is crucial for the success or failure of wind turbines installed in the built environment. Research in this field is fragmented into various research groups working on different topics in isolation with seemingly few efforts of integrating the various fields. This review aims at highlighting the synergies between the various advances, particularly in aerodynamics, but also in other areas. Past and current work has been focused on establishing reliable wind statistics at the site of interest. Advances in building aerodynamics have provided new insight on the local flow occurring at the rotor location. An outlook toward future research and the need to treat the different flow scales in a holistic manner is emphasized given also the recent advances in rotor aerodynamics related to the effect of flow skewness and turbulence. This will shed light on the critical issues that need to be addressed by scientists in order to make urban wind energy viable for decentralized generation. Various other present challenges are discussed briefly including structural aspects, noise emissions, economics and visual impact. Research in this field should be the guidepost for more targeted certification standards, in an effort to regularize the small wind energy market.

Land based site selection for a wind farm has some challenging criteria, namely, cost for electricity generation and distribution, acquiring ownership of the site, potential barriers from various laws and permits, security concerns, access issues, feasibility of accommodation, etc. However, wind resource assessment deems the first criterion to rule out a site before other criteria can play roles in the selection process. In this paper, a Computational Fluid Dynamics (CFD) study has been performed on a complex mountainous terrain near a shore in the west coast of the US to assess the wind resource in order to spot potential suitable sites for wind turbines. Average wind speed at a height of 10 m at the centre (44°22 ′ 12.0 ′ ′ N, 123°59 ′ 24.0 ′ ′ W) of the chosen region under study has been compared with the simulated data for validation. Results from the study, which yields a continuous map of flow field variables, have revealed much more detailed features than the available state-wise wind maps. For example, it has revealed as high as 147% variation in wind speeds and 438% in wind power, making it possible to choose suitable sites without the need for, or perhaps in advance of, expensive direct measurements. This type of analysis may help in preliminary assessments and expedite the site selection process.