Environment Canada (EC) and Hydro-Québec (HQ) have been collaborating in a Research & Development and Demonstration project on a high resolution wind energy dedicated forecasting system (SPÉO: Système de Prévision ÉOlien under its French acronym). This project emphasizes the operational tests and the forecast of high impact events, e.g. wind ramps. It was found that SPÉO improves the Canadian Regional Deterministic Prediction System (RDPS), by about 18% in terms of the RMSE (Root Mean Square Error) of the predicted wind speed when compared with mast observations from three wind power plants. The improvement is most significant in the cold season. When the average wind speed measured at all wind turbines (nacelle anemometer) is used as a reference, SPÉO improves the RMSE of the average wind speed at a wind power plant in complex terrain (24%) compared with that of RDPS. However, there is almost no improvement for two other wind power plants located in less complex terrain. The average wind speed is corrected with the average wind speed measured at all turbines, and is then fed into a wind-to-power conversion module for power production forecasts. The power production forecast is improved by 6% on average in complex terrain when SPÉO winds are used as input compared to the RDPS. The most important finding of this project is SPÉO's ability to predict ramps due to mountain waves/downslope winds. The proposed forecast index for ramps based on the Froude number is useful for predicting the onset of this kind of ramp when a high resolution NWP model is unavailable.

Recent studies undertaken by Hydro-Québec evaluate three aspects of the integration of wind generation on their system reliability/security. In an operations setting, the impacts on intra-hourly operating reserves and on extra-hourly balancing reserves are examined. On an operations planning horizon, the wind power capacity credit is evaluated for winter peak loading conditions, when very cold temperatures risk disabling part of the wind generation. Depending on the study, various mathematical tools were used to generate the statistical characteristics of the load and anticipated wind generation: time-series analysis, wind simulation at new/future wind plant sites, power system simulation and a posteriori determination of forecast errors. However, in each case the measure used to quantify the impact of wind generation has been related to the change in the variance of the total system uncertainty as a result of the addition of wind power generation.

Hydro-Quebec intends to integrate 3500 MW of wind energy into its hydroelectric generating fleet. Wind integration studies were initiated to analyse the impact of large amounts of wind generation on operational and balancing reserves, and on the reliability of Hydro-Quebec power system. To support these studies, a diagnostic method using historical met data was applied to simulate hourly time series over 36 years at wind plants. Wind power capacity credit is very sensitive to a limited number of extreme cold weather events, corresponding to the annual electricity demand peaks. To improve the accuracy of the wind conditions during those critical events, a high resolution mesoscale numeric weather prediction model from the Canadian Meteorological Centre is used. The comparisons over a validation period show better results with the dynamic numerical simulations method, particularly for complex sites. Moreover, wind power generation estimations during the critical events confirm favourable wind conditions during peak load events.

The province of Quebec in Canada has begun a decisive move towards wind energy with a series of wind farm projects totaling 4,000 MW in capacity. This paper describes the SAGIPE system that gathers in real time, through multiple data links, a very large volume of wind generation and weather data. It then uses this data to produce, using external forecasting engines, the short term wind generation forecast for most of the wind farms in the province. It also includes a massive data bank for statistical analyses that permits running fruitful studies on the characteristics of wind generation models.