Zilu Li’s research while affiliated with Guangdong University of Technology and other places

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program.

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.