With the rapid growth of photovoltaic (PV) power in recent years, the stability of system operation, the performance of system contingency analysis as well as the power quality of the power grid are threatened by the inherent uncertainty and fluctuation of PV output. It is necessary to have the knowledge of PV output characteristics for reliable power system dispatching. Day-ahead PV power forecasting is an effective support for achieving optimal dispatching. Probabilistic forecasting can describe the uncertainty that is difficult to depict by deterministic forecasting, and the forecasting results are more comprehensive. An ensemble nonparametric probabilistic forecasting model of PV output is proposed based on the traditional deterministic forecasting method. Quantile regression averaging (QRA) is used to ensemble a group of independent long short-term memory (LSTM) deterministic forecasting models for obtaining the probabilistic forecasting of PV output. Real measured data are used to verify the effectiveness of this nonparametric probabilistic forecasting model. Additionally, in comparison with the benchmark methods, LSTM-QRA has higher prediction performance due to the better forecasting accuracy of independent deterministic forecasts.
With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.
Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.
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