Improvement of short-term numerical wind predictions

• Main Author: Bédard, Joël
• Format: Article in Journal/Newspaper
• Language: English
• Published: École de technologie supérieure 2010
• Subjects: Vents Prévision; Vents Simulation par ordinateur; Énergie éolienne; prévision de puissance; prévisions numériques; couche limite; atmosphérique; vent de surface; sites complexes; statistiques de sortie; analyse d’erreur; Canada; North Cape; Prince Edward Island
• Online Access: https://espace.etsmtl.ca/id/eprint/296/; https://espace.etsmtl.ca/id/eprint/296/1/B%C3%A9dard_Jo%C3%ABl.pdf; https://espace.etsmtl.ca/id/eprint/296/2/B%C3%A9dard_Jo%C3%ABl-web.pdf

With the sustained growth of wind energy installed capacity for electricity generation, electricity system operators have increasing challenges balancing the electricity grid, notably in regards to minimizing the cost of other energy sources dispatch. Due to the variability of wind, wind power generation forecasting is an important issue for the economic viability of wind energy, whether in regulated or open markets. Therefore, there is a pressing need for robust short-term (up to 48 hours) surface wind forecast models, and eventually wind power forecast models, in order to sustain the integration of wind energy in electricity portfolios of jurisdictions. Computed for the needs of the wind energy industry, three years of experimental meteorological forecasts in Eastern Canada are available from Environment Canada Numerical Weather Prediction (NWP) model configured on a limited-area (GEM-LAM 2.5 km) for wind power predictions. These data include forecasts for the region of North Cape (Prince Edward Island) where the Wind Energy Institute of Canada runs a test site for wind turbines. Although the model spatial resolution is already relatively high (2.5 km), preliminary statistical analysis and site inspection revealed that the model does not have sufficient grid spacing refinement to properly represent the meteorological phenomena on this complex coastal site. For this reason, a Geophysic Model Output Statistic (GMOS) module has been developed and applied to optimize the use of the short-term NWP. GMOS differs from other MOS that are widely used by meteorological centers in the following aspects: 1) it takes into accounts the surrounding geophysical parameters such as surface roughness, terrain height, etc. along with the wind direction; 2) GMOS can be directly applied for model output correction without any training although a training of the GMOS will further improve the results. This statistical module was trained and tested over the North Cape site and it basically improves the predictions RMSE by 25 – 30 % ...