Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2022
Research article |
| 02 Jun 2022
| 02 Jun 2022
Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands
National Renewable Energy Laboratory, Golden, CO, USA
Lindsay M. Sheridan
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, WA, USA
Patrick Conry
Los Alamos National Laboratory, Los Alamos, NM, USA
Dimitrios K. Fytanidis
Argonne National Laboratory, Argonne, IL, USA
Dmitry Duplyakin
National Renewable Energy Laboratory, Golden, CO, USA
Sagi Zisman
National Renewable Energy Laboratory, Golden, CO, USA
Nicolas Duboc
Los Alamos National Laboratory, Los Alamos, NM, USA
Matt Nelson
Los Alamos National Laboratory, Los Alamos, NM, USA
Rao Kotamarthi
Argonne National Laboratory, Argonne, IL, USA
Rod Linn
Los Alamos National Laboratory, Los Alamos, NM, USA
Marc Broersma
EAZ Wind, Rijswijk, the Netherlands
Timo Spijkerboer
EAZ Wind, Rijswijk, the Netherlands
Heidi Tinnesand
National Renewable Energy Laboratory, Golden, CO, USA
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A total of 12 months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms; however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
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Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
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Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
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Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
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Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
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Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
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Short summary
Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Adoption of distributed wind turbines for energy generation is hindered by challenges associated...
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