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A refined wind farm parameterization for the weather research and forecasting model

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  • Wu, Chunlei
  • Luo, Kun
  • Wang, Qiang
  • Fan, Jianren

Abstract

In the original wind farm parameterization of the Weather Research and Forecasting model, the standard constant air density is a source of errors in power output estimation, and the hub-height wind speed is inappropriate to represent the wind speed through the whole rotor area. To overcome these issues, a refined model incorporating variable air density is developed, and the hub-height wind speed is replaced with the rotor equivalent wind speed (REWS) in this study. To keep consistency, the power coefficients corresponding to the REWS under variable air density conditions are modified. The influences of atmospheric stability are investigated to evaluate the performance of wind power estimation between the original and the current refined models. The refined wind farm parameterization has few impacts on the flow field, however, the average power generation becomes more scattered and is notably decreased by nearly 7% due to the reduced variable air density. Meanwhile, the REWS slightly increases the power estimation by around 1.1%. Both REWS and variable air density can decrease the estimation uncertainty of the average power induced by complex atmospheric stability. Moreover, atmospheric stability plays a more significant role in power outputs, and the refined model can capture a stronger correlation between the atmospheric condition and wind power generation.

Suggested Citation

  • Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "A refined wind farm parameterization for the weather research and forecasting model," Applied Energy, Elsevier, vol. 306(PB).
  • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013684
    DOI: 10.1016/j.apenergy.2021.118082
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    References listed on IDEAS

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    1. Sun, Mucun & Feng, Cong & Zhang, Jie, 2019. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation," Applied Energy, Elsevier, vol. 256(C).
    2. Amirinia, Gholamreza & Kamranzad, Bahareh & Mafi, Somayeh, 2017. "Wind and wave energy potential in southern Caspian Sea using uncertainty analysis," Energy, Elsevier, vol. 120(C), pages 332-345.
    3. Bilal, Muhammad & Birkelund, Yngve & Homola, Matthew & Virk, Muhammad Shakeel, 2016. "Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell," Renewable Energy, Elsevier, vol. 99(C), pages 647-653.
    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. Zhenhai Guo & Xia Xiao, 2014. "Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-15, July.
    6. Mattar, Cristian & Borvarán, Dager, 2016. "Offshore wind power simulation by using WRF in the central coast of Chile," Renewable Energy, Elsevier, vol. 94(C), pages 22-31.
    7. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    8. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    9. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    10. Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
    11. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    12. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
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