As a clean energy source, solar power plays an important role in reducing the high carbon emissions of China’s electricity system. However, the intermittent nature of the system limits the effective use of photovoltaic power generation. This paper addresses the problem of low accuracy of ultra-short-term prediction of distributed PV power, compares various deep learning models, and innovatively selects the Informer model with multi-head probability sparse self-attention mechanism for prediction. The results show that the CEEMDAN-Informer model proposed in this paper has better prediction accuracy, and the error index is improved by 30.88% on average compared with the single Informer model; the Informer model is superior to other deep learning models LSTM and RNN models in medium series prediction, and its prediction accuracy is significantly better than the two. The power prediction model proposed in this study improves the accuracy of PV ultra-short-term power prediction and proves the feasibility and superiority of the deep learning model in PV power prediction. Meanwhile, the results of this study can provide some reference for the power prediction of other renewable energy sources, such as wind power.

Photovoltaic power generation as a green energy source is often used in power systems, but the volatility of PV output and randomness of the problem affect the stability of the power-grid power supply; so, for the problem of low prediction accuracy of photovoltaic power generation under different weather conditions, this paper proposes a Variational Mode Decomposition (VMD), combined with a Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) secondary decomposition method for the original signal decomposition, to reduce the signal volatility and reduce the complexity of feature mapping the PV data, followed by the use of a BiLSTM model to model the timing information of the decomposed IMF. Simultaneously, the Informer model predicts the components obtained from the secondary decomposition, and finally, the subsequence is reconstructed and superimposed to obtain the PV power prediction value. The results show that the RMSE and MAE of the proposed model are improved by up to 10.91% and 17.33% on the annual PV dataset, with high prediction accuracy and stability, which can effectively predict the ultra-short-term power of PV power plants.

The third-monthly (about 10 days in a time-step) hydropower scheduling, typically a challenging nonlinear optimization, is one of the essential tasks in a power system with operational storage hydropower reservoirs. This work formulates the problem into quadratic programming (QP), which is solved successively, with the linearization updated on the nonlinear constraint of the firm hydropower yield from all the cascaded hydropower reservoirs. Notably, the generating discharge is linearly concaved with two planes, and the hydropower output is defined as a quadratic function of reservoir storage, release, and generating discharge. The application of the model and methods to four cascaded hydropower reservoirs on the Jinsha River reveals several things: the successive quadratic programming (SQP) presented in this work can derive results consistent with those by the dynamic programming (DP), typically with the difference in water level within 0.01m; it has fast convergence and computational time increasing linearly as the number of reservoirs increases, with the most significant improvement in the objective at the second iteration by about 20%; and it is capable of coordinating the cascaded reservoir very well to sequentially maximize the firm hydropower yield and the total hydropower production.

In renewable energy generation, uncertainties mainly refer to power output fluctuations caused by the intermittency, variability, and forecasting errors of wind and photovoltaic power. These uncertainties have adverse effects on the secure operation of the power systems. Probabilistic load supply capability (LSC) serves as an effective perspective for evaluating power system security under uncertainties. Therefore, this paper studies the influence of renewable energy generation on probabilistic LSC to quantify the impact of these uncertainties on the secure operation of the power systems. Global sensitivity analysis (GSA) is introduced for the first time into probabilistic LSC evaluation. It can quantify the impact of renewable energy generation on the system’s LSC and rank the importance of renewable energy power stations based on GSA indices. GSA necessitates multiple rounds of probabilistic LSC evaluation, which is computationally intensive. To address it, this paper introduces a novel probabilistic repeated power flow (PRPF) algorithm, which employs a basis-adaptive sparse polynomial chaos expansion (BASPCE) model as a surrogate model for the original repeated power flow model, thereby accelerating the probabilistic LSC evaluation. Finally, the effectiveness of the proposed methods is verified through case studies on the IEEE 39-bus system. This study provides a practical approach for analyzing the impact of renewable generation uncertainties on power system security, contributing to more informed planning and operational decisions.

Energy activities are the primary source of carbon dioxide emissions. Real-time and accurate monitoring of their carbon emission dynamics is a crucial foundation for governments and enterprises in formulating carbon emission control measures. However, traditional accounting methods struggle to achieve high-frequency monitoring, and direct measurement approaches are cost-prohibitive. This results in a current lack of low-cost, high-frequency carbon emission calculation methods. Therefore, this study proposes an electricity-based carbon measurement (ECM) method. This method is predicated on the assumption of a stable relationship between electricity consumption and carbon emissions over a defined period and estimates emissions by leveraging power sector big data in conjunction with historical carbon emission inventory data. First, after explaining the basic principles of this method, the study systematically analyzes the related data requirements and demonstrates its feasibility. Subsequently, a calculation model is constructed and empirically tested using data from several provinces. The results show that, within the scope of our empirical validation and under conditions of a relatively stable electricity-carbon relationship, this method demonstrates good computational accuracy and application potential. Finally, a predictive analysis is performed based on 2021–2024 electricity-carbon data from an industrial park in Yunnan, and case studies of energy-intensive industries such as cement, steel, and electrolytic aluminum are used to demonstrate the potential utility of the method at both the enterprise and industry levels. This work provides a scientific basis to promote energy conservation, emission reduction, and green transformation of enterprises.

Network-feasible regulation headroom does not ensure that aggregated resources can deliver a dispatch instruction within its activation window. This study combines joint response calibration with robust linear network constraints to construct an aggregate regulation region. A maximum standardized shortfall score provides simultaneous lower response bounds from independent calibration episodes. These bounds enter a robust command-space model, and directional linear programs generate an inner approximation with guaranteed active increment and capacitive reactive support. All results are obtained on the public 33-bus feeder with an explicitly specified synthetic response generator (400 training, 800 calibration and 2000 test episodes per scenario) and are not operational performance figures. In this benchmark, boundary-command delivery failure at 95% nominal coverage ranges from 1.95% to 4.25%; the compound scenario reduces region area by 19.15%, and fixed 0.8 derating yields 11.85% delivery failure there. Sensitivity sweeps over response dispersion, correlation and activation time keep joint-calibrated failure below 5%, whereas fixed derating reaches 39%. Full-boundary AC checks on 2000 episodes show no violation. Under an abrupt response shift, frozen calibration fails in 73% of episodes; an alarm-and-reset recalibration restores about 3%. Findings are limited to a balanced static feeder and synthetic responses.