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Saline-alkali land is an important component of terrestrial ecosystems and may serve as a carbon sink but its net contribution to the overall terrestrial carbon sink is unknown. Using methods recommended by the IPCC, this study evaluates the impacts of interconverting saline-alkali and non-saline-alkali land on terrestrial carbon stocks by measuring two major carbon pools (soil organic carbon and vegetation carbon) in the saline-alkali land of China's Songnen Plain. Distinct phases in the evolution of the region's terrestrial carbon stock were delineated, factors contributing to transitions between phases were identified, and the effects of changes in the saline-alkali land carbon stock on the overall terrestrial carbon sink were estimated. Between 2005 and 2020, the region's saline-alkali land carbon stock initially increased, then declined, and finally increased again. However, the overall terrestrial carbon stock decreased by 0.5 Tg (1 Tg = 1012 g), indicating that the increase in the saline-alkali land carbon stock was due primarily to expansion of the saline-alkali land area. The conversion of non-saline-alkali land to saline-alkali land was a carbon-emitting process; consequently, in areas undergoing saline-alkali land change, the lower carbon density bound was equal to the carbon density of unconverted saline-alkali land and the upper bound was equal to the carbon density of unconverted non-saline-alkali land. In general, changes in the carbon stock of saline-alkali land correlated negatively with changes in the overall terrestrial carbon stock. The conversion of saline-alkali land into grassland and cropland through biochar improvement and the planting of saline-tolerant crops (Leymus chinensis, salt-tolerant rice) has a positive effect on promoting the enhancement of terrestrial carbon stocks. [ABSTRACT FROM AUTHOR]

Land-use change is an important factor affecting terrestrial carbon balance, and it is crucial to explore the response of terrestrial carbon stocks to land-use change, especially in the Songnen Plain, which faces a fierce conflict between the rapid growth of production activities and ecosystem degradation. In this study, we measured soil organic carbon and vegetation biocarbon stocks in the Songnen Plain based on IPCC-recommended methodologies, and explored the characteristics of carbon stock changes in land-use trajectories, land-use drivers, and specific land-use change scenarios (cropland cultivation, returning cropland to forests, the expansion of land for construction, deforestation, greening, and land degradation). The results showed that soil organic carbon stock in the Songnen Plain decreased by 1.63 × 105 t, and vegetation biocarbon stock increased by 2.10 × 107 t from 2005 to 2020. Human factors and natural factors jointly contributed to the land-use change, but the extent of the role of human factors was greater than that of natural factors. The increase in land-use trajectory led to the decrease in soil organic carbon stock and the increase in vegetation biocarbon stock. There was no difference in the effects of human-induced and natural-induced land-use changes on vegetation biocarbon stocks, but the effects on soil organic carbon stocks were diametrically opposite, increasing by 43.27 t/km2 and decreasing by 182.02 t/km2, respectively. The reclamation of arable land, returning cropland to forests, and greening led to a net increase in terrestrial carbon stocks (+813,291.84 t), whereas land degradation, deforestation, and land-use expansion led to a decrease in terrestrial carbon stocks (−460,710.2 t). The results of this study can provide a reference for the adjustment of land-use structure and the increase in terrestrial carbon stock in the Songnen Plain. [ABSTRACT FROM AUTHOR]

Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation. [ABSTRACT FROM AUTHOR]

With the high degree of wind power penetration integrated into multi-area AC/DC interconnected power grids, the frequency regulation capacity of automatic generation control (AGC) units is not sufficient in the wind power-penetrated area, making it difficult to effectively suppress the frequency stability caused by the fluctuation of wind power. Therefore, a coordinated control strategy for AGC units across areas based on bi-level model predictive control is proposed in this paper to achieve resource sharing. The control scheme uses economic model predictive control to realize steady power optimal allocation of the AGC units across areas in the upper layer and distributed model predictive control to realize dynamic frequency optimization control of the multi-area AGC units in the lower layer. Taking a three-area AC/DC interconnected power grid with a wind farm as an example, the simulation results show that, compared with model predictive control using tie-line frequency bias control (TBC) mode, the proposed control strategy can not only effectively maintain tie-line safety and frequency stability, but can also reduce the regulation cost of multi-area AGC units. [ABSTRACT FROM AUTHOR]