Data-driven methods for situation awareness and operational adjustment of sustainable energy integration into power systems

In the context of increasing complexity in power system operations due to the integration of renewable energy sources, two main challenges arise: accurate short-term wind power forecasting and power flow convergence control. Accurate wind power forecasting plays a crucial role in power system scheduling, while controlling power flow convergence is essential for system stability. This study proposes a concise short-term wind power generation prediction model that combines a feature selection-based convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) model. By effectively screening multidimensional feature datasets, the model optimizes the selection of highly correlated feature parameters and assigns weights to input data based on feature correlation. The CNN-BiLSTM combination model is then employed to establish a predictive model for wind power generation based on multiple features. Additionally, this study introduces an automatic adjustment model for power flow convergence using the D3QN (Double Dueling Q Network) reinforcement learning algorithm. This addresses the challenge of power imbalance leading to flow non-convergence, enabling effective control of power flow convergence and adaptive adjustment of operating modes. Experiments conducted using the KDD Cup 2022 wind power prediction dataset validate the wind power prediction method. The results demonstrate that the CNN-BiLSTM model effectively utilizes time-series data, surpassing other neural networks in prediction accuracy. Simulation results based on the PYPOWER case39 standard case reveal that the reinforcement learning model’s reward value increases with training rounds and stabilizes at 40. Remarkably, more than 72% of abnormal flow samples achieve rapid convergence within 10 steps, affirming the proposed method's efficacy and computational efficiency. The findings of this study contribute to enhancing the accurate awareness of new energy integration into power systems and provide a novel adaptive control method for power flow

PbXND1 Results in a Xylem-Deficient Dwarf Phenotype through Interaction with PbTCP4 in Pear (Pyrus bretschneideri Rehd.)

Dwarfing is an important agronomic characteristic in fruit breeding. However, due to the lack of dwarf cultivars and dwarf stocks, the dwarfing mechanism is poorly understood in pears. In this research, we discovered that the dwarf hybrid seedlings of pear (Pyrus bretschneideri Rehd.), ‘Red Zaosu,’ exhibited a xylem-deficient dwarf phenotype. The expression level of PbXND1, a suppressor of xylem development, was markedly enhanced in dwarf hybrid seedlings and its overexpression in pear results in a xylem-deficient dwarf phenotype. To further dissect the mechanism of PbXND1, PbTCP4 was isolated as a PbXND1 interaction protein through the pear yeast library. Root transformation experiments showed that PbTCP4 promotes root xylem development. Dual-luciferase assays showed that PbXND1 interactions with PbTCP4 suppressed the function of PbTCP4. PbXND1 expression resulted in a small amount of PbTCP4 sequestration in the cytoplasm and thereby prevented it from activating the gene expression, as assessed by bimolecular fluorescence complementation and co-location analyses. Additionally, PbXND1 affected the DNA-binding ability of PbTCP4, as determined by utilizing an electrophoretic mobility shift assay. These results suggest that PbXND1 regulates the function of PbTCP4 principally by affecting the DNA-binding ability of PbTCP4, whereas the cytoplasmic sequestration of PbTCP4 is only a minor factor. Taken together, this study provides new theoretical support for the extreme dwarfism associated with the absence of xylem caused by PbXND1, and it has significant reference value for the breeding of dwarf varieties and dwarf rootstocks of the pear

The Fine Characterization and Potential Photocatalytic Effect of Semiconducting Metal Minerals in Danxia Landforms

The Danxia landform is representative of the Cretaceous continental red sediment. The careful identification and potential environmental effects of minerals in Danxia red beds have yet to be clearly reported. In this work, reddish sandstone samples were collected from Lang Mountain Danxia landform in Xinning, Hunan province, China, and their mineral phases, element distribution, microstructure, and the spatial relationship of different minerals were investigated using polarizing optical microscope, environmental scanning electron microscopy, energy-dispersive X-ray analysis, electron probe microanalysis, micro-Raman spectra, micro- X-ray diffraction, X-ray fluorescence spectroscopy, and high-resolution transmission electron microscopy. The results revealed that iron oxide (mainly hematite) and titanium oxide (mainly anatase) were the dominant minerals in Danxia red layers. Microcrystalline hematite was suggested as being the coloring mineral. Anatase, reported here for the first time in Danxia red beds, constituted the content of titanium in the red layer (0.17⁻0.57%) and was present in a significantly higher amount than the adjacent limestone formation (0.13%). Over 95% of Fe/Ti oxides served as a cementation agent along the framework of coarse-grain minerals (quartz and feldspar). The hematite and anatase were visible-light-responsive semiconductors, with a band gap of 2.01 eV and 3.05 eV, respectively. Photoelectrochemical experiments were performed on synthetic hematite, anatase, and their coupled material. The inactive hematite displayed an enhanced 23-fold photocurrent at 0.8 V (vs. Ag/AgCl) when coupled with anatase. Furthermore, in a photodegradation experiment using methyl orange dye under simulated sunlight, the coupled material showed decolorizing efficiency 2.4 times that of hematite. The anatase, therefore, prominently improved the photocatalytic activities of hematite. It is proposed that these semiconducting minerals in red beds produce oxygen reactive species and have significant environmental effects, which is of great importance