Abstract
Wind energy has the potential to play a vital role in fulfilling the world's rising energy needs. Due to the cubic relationship between wind speed and power, slight wind speed variations substantially impact power generation. Various wind speed assessment methods are available in the literature, and the Numerical Weather Prediction (NWP) methods are among them. However, its sensitivity analysis is required under the NWP model to estimate wind speed effectively. The planetary boundary layer (PBL) scheme is crucial in the sensitivity analysis. This work used a strategic approach with sensitivity analysis to estimate wind fields in the southern part of Andhra Pradesh, India, using the NWP model. The wind field is simulated using the WRF model, and the best configuration is determined by simulating nine distinct PBL parameterization combinations for four locations. Despite overestimation from 3 am to 8 pm and underestimation from 8 pm to 3 am, the mean absolute percentage errors are determined to be 6%, 9%, 14%, and 16%, respectively. The Weibull distribution mean, shape parameter, and scale parameter all have relative errors of 9.2%, 8.9%, and 15.36%, respectively. The developed methodology would be valuable in assessing wind resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon request.
Abbreviations
- AGL:
-
Above ground level
- CU:
-
Cumulus parameterization
- ECMWF:
-
European Centre for Medium-Range Global Weather Forecasts
- Eta:
-
Eta-coordinate model
- GFS:
-
Global Forecast System
- LSM:
-
Land-surface model
- MP:
-
Microphysics
- NCEP:
-
National Center for Environmental Prediction
- NWP:
-
Numerical Weather Prediction
- PBL:
-
Planetary boundary layer
- RA:
-
Radiation
- WRF:
-
Weather Research and Forecasting Model
- WWEA:
-
World Wind Energy Association
- IRENA:
-
International Agency for Renewable Energy
- NWP:
-
Numerical Weather Prediction
- OhitS:
-
Objective Hit Score
References
Al-yahyai S, Charabi Y, Gastli A (2010) Review of the use of numerical weather prediction (NWP) models for wind energy assessment. Renew Sustain Energy Rev 14(9):3192–3198. https://doi.org/10.1016/j.rser.2010.07.001
Banks RF et al (2016) Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign. Atmos Res 176–177:185–201. https://doi.org/10.1016/j.atmosres.2016.02.024
Carvalho D, Rocha A, Gómez-Gesteira M, Santos C (2012) A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environ Model Softw 33:23–34. https://doi.org/10.1016/j.envsoft.2012.01.019
Carvalho D, Rocha A, Santos CS, Pereira R (2013) Wind resource modelling in complex terrain using different mesoscale-microscale coupling techniques. Appl Energy 108:493–504. https://doi.org/10.1016/j.apenergy.2013.03.074
Carvalho D, Rocha A, Gómez-Gesteira M, Silva Santos C (2014) WRF wind simulation and wind energy production estimates forced by different reanalyses: comparison with observed data for portugal. Appl Energy 134:57–64. https://doi.org/10.1016/j.apenergy.2013.12.001
Carvalho D, Rocha A, Gómez-Gesteira M, Silva Santos C (2014b) Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula. Appl Energy 135:234–246. https://doi.org/10.1016/j.apenergy.2014.08.082
Global Wind Energy Council (2014) Global wind report annual market update 201, Gwec, p. 80
Dey A, Truax DD, Magbanua BS (2011) Optimisation of operating parameters of intermittent aeration-type activated sludge process for nitrogen removal: a simulation-based approach. Water Environ Res 83(7):636–642. https://doi.org/10.2175/106143010X12851009156600
Division MM, NCAR MD (2009) Version 3 modeling systems user’s guide January 2009
Energy Overview (2019) Accessed 20th Apr 2019. https://www.worldbank.org/en/topic/energy/overview
Floors R, Vincent CL, Gryning S, Peña A, Batchvarova E (2013) The wind profile in the coastal boundary layer: wind lidar measurements and numerical modelling. Bound-Layer Meteorol 147(3):469–491. https://doi.org/10.1007/s10546-012-9791-9
Ellabban O, Abu-Rub H, Blaabjerg F (2014) Renewable energy resources: current status, future prospects and their enabling technology. Renew Sustain Energy Rev 39:748–764. https://doi.org/10.1016/j.rser.2014.07.113
El-samra R, Zeid EB, El-fadel M (2013) Evaluation of WRF high resolution dynamical downscaling
Giannakopoulou EM, Nhili R (2014) WRF model methodology for offshore wind energy applications. Adv Meteorol. https://doi.org/10.1155/2014/319819
Han Z, Ueda H, An J (2008) Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models. Atmos Environ 42(2):233–249. https://doi.org/10.1016/j.atmosenv.2007.09.053
Hansen JP, Narbel PA, Aksnes DL (2017) Limits to growth in the renewable energy sector. Renew Sustain Energy Rev 70:769–774. https://doi.org/10.1016/j.rser.2016.11.257
Jee J-B, Kim S (2017) Sensitivity study on high-resolution WRF precipitation forecast for a heavy rainfall event. Atmosphere (basel) 8(12):96. https://doi.org/10.3390/atmos8060096
Krogsaeter O, Reuder J (2015) Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications—part I: average wind speed and wind shear. Wind Energy 18(5):769–782. https://doi.org/10.1002/we.1727
Lopes D, Ferreira J, Hoi KI, Miranda AI, Yuen KV, Mok KM (2019) Weather research and forecasting model simulations over the pearl river delta region. Air Qual Atmos Heal 12(1):115–125. https://doi.org/10.1007/s11869-018-0636-7
Mohammadpour Penchah M, Malakooti H, Satkin M (2017) Evaluation of planetary boundary layer simulations for wind resource study in east of Iran. Renew Energy 111:1–10. https://doi.org/10.1016/j.renene.2017.03.040
New Page 1 (2020) Accessed from 23 Jan 2020. http://www.imdhyderabad.gov.in/apsite/january.html
“NREDCAP | New & Renewable Energy Development Corporation of AP Ltd. Accessed from 22 Jan 2020. https://nredcap.in/GuidelinesWindPowerProjects.aspx
Olaofe ZO (2017) Modeling and sensitivity of the seasonal ocean winds to local effects at west and south coasts of South Africa. Sustain Energy Technol Assess 19:24–41. https://doi.org/10.1016/j.seta.2016.11.002
Skamarock WC et al (2008) A description of the advanced research WRF version 3. Tech Rep. https://doi.org/10.5065/D6DZ069T
Smith RK, Montgomery MT, Thomsen GL (2014) Sensitivity of tropical-cyclone models to the surface drag coefficient in different boundary-layer schemes. Q J R Meteorol Soc 140(680):792–804. https://doi.org/10.1002/qj.2057
Soares PMM, Cardoso RM, Miranda PMA, de Medeiros J, Belo-Pereira M, Espirito-Santo F (2012) WRF high resolution dynamical downscaling of ERA-Interim for Portugal. Clim Dyn 39(9–10):2497–2522. https://doi.org/10.1007/s00382-012-1315-2
Teixeira J et al (2011) Tropical and subtropical cloud transitions in weather and climate prediction models: The GCSS/WGNE pacific cross-section intercomparison (GPCI). J Clim 24(20):5223–5256. https://doi.org/10.1175/2011JCLI3672.1
Untitled Document (2020) Accessed from 23 Jun 2020. http://www.imdhyderabad.gov.in/apsite/may.html
Xie B, Fung JCH, Chan A, Lau A (2012) Evaluation of non-local and local planetary boundary layer schemes in the WRF model. J Geophys Res Atmos. https://doi.org/10.1029/2011JD017080
Acknowledgements
The first author is grateful to the Ministry of Human Resource Development (MHRD), Government of India, for providing financial support through a fellowship for the research work.
Funding
Authors have not received any grant for this research.
Author information
Authors and Affiliations
Contributions
SBP: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Validation; Visualization; Writing—original draft, MK: Conceptualization; Resources; Software; Supervision; Writing—review & editing, RPS: Conceptualization; Resources; Software; Supervision; Project administration; Writing—review & editing.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Penugonda, S.B., Singhal, M.K. & Saini, R.P. A sensitivity study of surface wind simulations to PBL schemes for the southern part of Andhra Pradesh, India. Stoch Environ Res Risk Assess 37, 3763–3777 (2023). https://doi.org/10.1007/s00477-023-02478-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-023-02478-1