An optimal dispatching method and system for a wind power generation and energy storage combined system are provided. Uncertainty of a wind turbine output is characterized based on spatio-temporal coupling of the wind turbine output and an interval uncertainty set. Compared with a traditional symmetric interval uncertainty set, the uncertainty set that considers spatio-temporal effects effectively excludes some extreme scenarios with a very small probability of occurrence and reduces conservativeness of a model. A two-stage robust optimal dispatching model for the wind power generation and energy storage combined system is constructed, and a linearization technology and a nested column-and-constraint generation (C&CG) strategy are used to efficiently solve the model.

An optimal dispatching method and system for a wind power generation and energy storage combined system are provided. Uncertainty of a wind turbine output is characterized based on spatio-temporal coupling of the wind turbine output and an interval uncertainty set. Compared with a traditional symmetric interval uncertainty set, the uncertainty set that considers spatio-temporal effects effectively excludes some extreme scenarios with a very small probability of occurrence and reduces conservativeness of a model. A two-stage robust optimal dispatching model for the wind power generation and energy storage combined system is constructed, and a linearization technology and a nested column-and-constraint generation (C&CG) strategy are used to efficiently solve the model.

The invention relates to a method of determining the vertical profile of the wind speed upstream from a wind turbine (1), wherein wind speed measurements are performed by a LiDAR sensor (2), then the exponent α of the power law is determined by an unscented Kalman filter and measurements, and the exponent α is applied to the power law in order to determine the vertical wind speed profile.

The present invention is designed and calculated to use different wind regimes, namely, laminar, turbulent and hurricane-wind regimes, to exploit at the industrial level the enormous wind potential in the different regions of the planet. The result is a wind-based electrical power generation system that couples a mechanical system to an electrical system.

The invention relates to a method of determining the vertical profile of the wind speed upstream from a wind turbine (1), wherein wind speed measurements are performed by means of a LiDAR sensor (2), then the exponent of the power law is determined by means of an unscented Kalman filter and measurements, and this exponent is applied to the power law in order to determine the vertical wind speed profile.

A system and method for predicting power output of a wind farm are disclosed. The method includes determining first and second parameter values of a power curve for a plurality of wind turbines. A second relationship is determined between the densities associated with the wind turbines and the values of the first parameter. A third relationship is determined between the densities associated with the wind turbines and the values of the second parameter. A value of the first parameter for a specified wind farm density is determined based on the second relationship. A value of the second parameter for the specified wind farm density is determined based on the third relationship. An indication of a power output for the specified wind farm density is generated by applying the determined values of the first and second parameters to the power curve.