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A probabilistic prediction interval (PI) model based on variational mode decomposition (VMD) and a kernel extreme learning machine using the firefly algorithm (FA-KELM) is presented to tackle the problem of photovoltaic (PV) power for intra-day-ahead prediction. Firstly, considering the non-stationary and nonlinear characteristics of a PV power output sequence, the decomposition of the original PV power output series is carried out using VMD. Secondly, to further improve the prediction accuracy, KELM is established for each decomposed component and the firefly algorithm is introduced to optimize the penalty factor and kernel parameter. Finally, the point predicted value is obtained through the summation of predicted results of each component and then using the nonlinear kernel density estimation to fit it. The cubic spline interpolation algorithm is applied to obtain the shortest confidence interval. Results from practical cases show that this probabilistic prediction interval could achieve higher accuracy as compared with other prediction models.

With concerned environmental problem, a large number of electric vehicles (EVs) has been adopted to replace the oil-fueled vehicles. If electric vehicles are charged simultaneously on a large-scale, it may cause peak load increase. Therefore, it is of great practical significance to study the influence of controlled charging behavior of electric vehicles on power grid. Firstly, Gaussian Mixture Model is used to modeling electric vehicles. Secondly, Monte Carlo method is studied to determine the charging load of electric vehicles, and the influence of uncontrolled charging of electric vehicles on the power grid is analyzed. Then the peak and valley hours are divided according to the membership function and the time-of-use pricing to minimize the difference between peak and valley load. Furthermore, the influence of controlled charging of EVs on power grid is analyzed. Finally, the model is applied to simulate and analyze the distribution network of Yangjiang, a coastal city in South China. The case study shows that the uncontrolled charging of EVs will increase the peak load of the power grid. The proposed controlled charging strategy can effectively transfer the charging load of EVs and lessen peak load demand.

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