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Wind farm power short-term prediction using WRF model and Kalman filtering

par Mamani, Rober ;Hendrick, Patrick
Référence Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems(32: 23-28 June 2019: Wrocklaw), Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Institute of Thermal Technology, Gliwice, Poland, Vol. 1, Ed. 1, page (4769)
Publication Publié, 2019-06-23

Publication dans des actes

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

The growing wind power industry requires more reliable power forecasting due tohigher penetration of this technology in electricity generation. Due to the stochasticbehaviour of wind, additional tools for improving the electricity forecast are required.This research parameterizes WRF model for wind power forecasting in Qollpana windfarm -Bolivia. Qollpana is a wind farm over a very complex terrain and 2800 metersabove sea level (masl) located on the Andes. Wind power forecasting is performed fordifferent periods of the year, such as, January, April, June, and October. Finally, theresults are improved using a Kalman filter and compared with the power generated inQollpana wind farm. The simulated WRF results of energy generated in the wind farmshowed between 50 and 30 % of error. However using a Kalman filter the errorsdiminished up to 10 % of the energy generated by the wind farm. The time of thehighest errors (at sunlight time) suggests that topographic conditions play an importantrole in the wind farm electricity production.