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Advances in Numerical Weather Prediction Modelling to Improve Wind Resource and Wind Energy Production Assessment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 7528

Special Issue Editor


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Guest Editor
National Research Council, Institute of Biometeorology (CNR-IBIMET), Via Caproni 8, 50145 Firenze, Italy
Interests: air quality; air pollutant dispersion models; road transportation; wind energy; wind resource assessment; boundary layer meteorology
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Special Issue Information

Dear Colleagues,

Numerical weather prediction (NWP) models are generally a mandatory starting point to address a (pre)feasibility assessment of wind resource potential of a site to develop a wind power plant. Although representing a well-established approach addressed in the literature, their use is hampered by two crucial limitations: (i) large input data requirement and (ii) high computational cost. In the last few decades, various improvements have been accomplished, in order to cope with these limitations, including implementation of geostatistical techniques as spatial interpolation or topography correction, vertical wind speed profiling methods, optimal tuning of the setting parameters of models, and computation of turbulence-related parameters. Optimal mesoscale-to-microscale modeling coupling and more effective grid nesting also proved key to addressing this issue. First and foremost, this Special Issue will bridge the knowledge gap between NWP modelers (atmosphere physicists, meteorologists, environment experts) and wind farm developers (engineers, investors, and power utilities), who often have very different backgrounds. In detail, this Special Issue welcomes research articles bringing novel contribution to NWP models implementation that span between the two opposing stages of wind project development: (i) ex-ante analysis, i.e., (pre)feasibility wind resource assessment; (ii) ex-post analysis, i.e. model testing based on measured energy production data. A challenging new research frontier is represented by the use of NWP models for wind farm micrositing, thus accurately providing, e.g., estimation of wake effects that develop between wind turbines (WTs), turbulence-related parameters, short-term wind power forecasting, etc. High resolution implemented at the wind farm-scale and also capable of ingesting very detailed input data, they may serve as a more feasible solution than very computationally-expensive tools such as computational fluid dynamics (CFD) models. As they are capable of finely calculating all power losses affecting a real wind farm, their net production estimations may be directly compared with measured wind energy production data. NWP applications over complex environments such as offshore, ridged, hilly, sloped locations are strongly encouraged. Is also hoped that this Special Issue will attract valuable review articles that describe the current state of the art and possibly draw future research areas that need to be addressed.

Potential topics include but are not limited to the following:

● Models application for (pre)feasibility studies

● Smart assimilation of wind atlas data

● Developing wind potential mapping

● Mesoscale-to-microscale modeling coupling

● Geostatistical methods (e.g., spatial interpolation or topography correction)

● Vertical wind speed profiling methods

● Optimal tuning of model setting parameters

● Models application over challenging (e.g., offshore, hilly, sloped) locations

● Models application for wind farm micrositing

● Short-term wind power forecasting

● Estimation of wind turbulence parameters

● Estimation of wakes due to mutual wind turbine interactions

● Testing models accuracy based on measured energy production data

● New metrics to specifically rate models' scores for wind energy applications

 

Dr. Giovanni Gualtieri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • NWP models
  • mesoscale-to-microscale modeling coupling
  • wind potential mapping
  • wind farm micrositing
  • challenging locations
  • geostatistical methods
  • vertical wind speed profiling methods

Published Papers (2 papers)

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Research

17 pages, 8048 KiB  
Article
A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind
by Christopher M. Rozoff and Stefano Alessandrini
Energies 2022, 15(5), 1718; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051718 - 25 Feb 2022
Cited by 5 | Viewed by 1643
Abstract
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost [...] Read more.
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain. Full article
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21 pages, 2143 KiB  
Article
Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers
by Giovanni Gualtieri
Energies 2021, 14(14), 4169; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144169 - 10 Jul 2021
Cited by 71 | Viewed by 5003
Abstract
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 [...] Read more.
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. Full article
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