IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223028050.html
   My bibliography  Save this article

Wind field simulation using WRF model in complex terrain: A sensitivity study with orthogonal design

Author

Listed:
  • Mi, Lihua
  • Shen, Lian
  • Han, Yan
  • Cai, C.S.
  • Zhou, Pinhan
  • Li, Kai

Abstract

Accurately simulating wind speed is of utmost importance for wind power assessments. The objective of this study is to investigate the performance of wind speed simulation in complex terrain using different parameterization schemes from the Weather Research and Forecasting (WRF) model, employing an orthogonal design methodology. Specifically, nine WRF simulations are conducted based on the orthogonal test, considering various configurations of the planetary boundary layer (PBL), microphysics (MP), and land surface (LS) options. The numerical results are then compared to actual wind data obtained from a measuring station at two different heights (50 m and 80 m) during summer and winter periods. Furthermore, range and variance analyses are employed to rank the three schemes and identify the optimal combination. Moreover, we examine the impact of each parameterization scheme on the accuracy of wind speed predictions based on the results obtained from the orthogonal simulations. Additionally, we discuss the influence of using different evaluation indices within the orthogonal test on the outcomes and analyze the WRF simulated results under the optimal scheme combination. Lastly, we conduct an uncertainty analysis of results from the optimal scheme combination. The findings reveal that both the PBL and MP schemes exhibit highly significant effects on the accuracy of wind speed predictions (significance: **), followed by the LS scheme (significance: *). The order of importance for these three options is ranked as follows: PBL > MP > LS, which is independent of the seasons. The optimal configurations vary from summer and winter periods. Specifically, the optimal scheme combination is determined to be PBL-ACM2, MP-Kessler, and LS-Noah MP in summer, while PBL-BouLac, MP-Lin, and LS-Noah MP in winter. The simulating accuracy of the wind speeds is satisfactory under this optimal combination when considering the uncertainty of on-site measurements during these two periods. These results provide valuable insights for selecting appropriate PBL, MP, and LS options (from the WRF model) for wind speed estimates and wind power development in the studied region.

Suggested Citation

  • Mi, Lihua & Shen, Lian & Han, Yan & Cai, C.S. & Zhou, Pinhan & Li, Kai, 2023. "Wind field simulation using WRF model in complex terrain: A sensitivity study with orthogonal design," Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028050
    DOI: 10.1016/j.energy.2023.129411
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223028050
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129411?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2022. "Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration," Energy, Elsevier, vol. 254(PB).
    2. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
    3. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    4. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "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," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    5. Gil Ruiz, Samuel Andrés & Cañón Barriga, Julio Eduardo & Martínez, J. Alejandro, 2022. "Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia," Energy, Elsevier, vol. 244(PB).
    6. Tanvir Islam & Prashant Srivastava & Miguel Rico-Ramirez & Qiang Dai & Manika Gupta & Sudhir Singh, 2015. "Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1473-1495, April.
    7. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    8. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
    9. Ban, Marko & Perković, Luka & Duić, Neven & Penedo, Ricardo, 2013. "Estimating the spatial distribution of high altitude wind energy potential in Southeast Europe," Energy, Elsevier, vol. 57(C), pages 24-29.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Xiong & Zou, Ruilin & Sheng, Tao & Zeng, Weilin & Ye, Xiaoling, 2023. "An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed," Energy, Elsevier, vol. 283(C).
    2. Gil Ruiz, Samuel Andrés & Cañón Barriga, Julio Eduardo & Martínez, J. Alejandro, 2022. "Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia," Energy, Elsevier, vol. 244(PB).
    3. Perini de Souza, Noele Bissoli & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Wind mapping using the mesoscale WRF model in a tropical region of Brazil," Energy, Elsevier, vol. 240(C).
    4. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
    5. Costoya, X. & Rocha, A. & Carvalho, D., 2020. "Using bias-correction to improve future projections of offshore wind energy resource: A case study on the Iberian Peninsula," Applied Energy, Elsevier, vol. 262(C).
    6. Jared A. Lee & Paula Doubrawa & Lulin Xue & Andrew J. Newman & Caroline Draxl & George Scott, 2019. "Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set," Energies, MDPI, vol. 12(14), pages 1-22, July.
    7. Laura Castro-Santos & Maite deCastro & Xurxo Costoya & Almudena Filgueira-Vizoso & Isabel Lamas-Galdo & Americo Ribeiro & João M. Dias & Moncho Gómez-Gesteira, 2021. "Economic Feasibility of Floating Offshore Wind Farms Considering Near Future Wind Resources: Case Study of Iberian Coast and Bay of Biscay," IJERPH, MDPI, vol. 18(5), pages 1-16, March.
    8. Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
    9. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model," Energy, Elsevier, vol. 239(PB).
    10. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    11. Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Im," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    12. Lunney, E. & Ban, M. & Duic, N. & Foley, A., 2017. "A state-of-the-art review and feasibility analysis of high altitude wind power in Northern Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 899-911.
    13. Hugo Díaz & Carlos Guedes Soares, 2021. "A Multi-Criteria Approach to Evaluate Floating Offshore Wind Farms Siting in the Canary Islands (Spain)," Energies, MDPI, vol. 14(4), pages 1-18, February.
    14. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    15. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    16. Adhikari, Jeevan & Sapkota, Rajesh & Panda, S.K., 2018. "Impact of altitude and power rating on power-to-weight and power-to-cost ratios of the high altitude wind power generating system," Renewable Energy, Elsevier, vol. 115(C), pages 16-27.
    17. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    18. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    19. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    20. Pavković, D. & Hoić, M. & Deur, J. & Petrić, J., 2014. "Energy storage systems sizing study for a high-altitude wind energy application," Energy, Elsevier, vol. 76(C), pages 91-103.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028050. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.