Abstract: The present application discloses a chance constrained extreme learning machine method for nonparametric interval forecasting of wind power, which belongs to the field of renewable energy generation forecasting. The method combines an extreme learning machine with a chance constrained optimization model, ensures that the interval coverage probability is no less than the confidence level by chance constraint, and takes minimizing the interval width as the training objective. The method avoids relying on the probability distribution hypothesis or limiting the interval boundary quantile level, so as to directly construct prediction intervals with well reliability and sharpness. The present application also proposes a bisection search algorithm based on difference of convex functions optimization to achieve efficient training for the chance constrained extreme learning machine.

Abstract: The present invention discloses an operating reserve quantification method for power systems using probabilistic wind power forecasting and belongs to the field of power system operation optimization. This method constructs an operating reserve optimization model of power systems using probabilistic wind power forecasting, which utilizes extreme learning machine to output non-parametric prediction intervals of wind power and determines the positive and negative operating reserve requirements of the system by upper and lower boundaries of the prediction intervals. The cost-benefit trade-offs of reserve decision are realized by taking reserve provision cost and deficit penalty as a loss function of machine learning. The resultant reserve decision can effectively reduce system operation cost on the premise of ensuring good reliability.

Abstract: The present application discloses a chance constrained extreme learning machine method for nonparametric interval forecasting of wind power, which belongs to the field of renewable energy generation forecasting. The method combines an extreme learning machine with a chance constrained optimization model, ensures that the interval coverage probability is no less than the confidence level by chance constraint, and takes minimizing the interval width as the training objective. The method avoids relying on the probability distribution hypothesis or limiting the interval boundary quantile level, so as to directly construct prediction intervals with well reliability and sharpness. The present application also proposes a bisection search algorithm based on difference of convex functions optimization to achieve efficient training for the chance constrained extreme learning machine.