Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

As the prevalence of electric vehicles (EVs) continues to grow, their charging and discharging behaviors pose a challenge to the stable operation of power systems. Therefore, this paper analyzes the charging demand of EV users through GPS trajectory data and proposes an EV-discharging-optimization model based on vehicle-to-grid interaction (V2G). Firstly, the spatial–temporal distribution of EV-charging demand is obtained by cleaning and mining the big data of traveling vehicles, considering dynamic energy consumption theory and users’ willingness; secondly, a probabilistic model of EV users’ participation in V2G-demand response is constructed based on expected utility theory, which both considers the heterogeneity of users and reflects the interactive influence of users’ decisions; finally, a scheduling model of EV discharging in the regional grid is established. The results show that the proposed model can explore the potential of user participation in V2G in the study area, and the V2G response resources can reduce the grid fluctuation and enable users to obtain certain benefits, which achieves a win–win situation between the grid side and the user side.

Under the "dual carbon" target in China, virtual power plants (VPPs) play an important role in improving grid security and promoting clean and low-carbon energy transformation. VPPs can integrate and control distributed resources to participate in the energy market and make full use of distributed resources. However, the intermittency and volatility of renewable energy and the "heat-driven" working mode of CHP units create contradictions that seriously affect the peak-shaving ability of VPPs and lead to high carbon emissions. To solve these problems, this paper aggregates CHP units, wind power, photovoltaics, carbon capture, hydrogen energy storage, and electric boilers into a new type of virtual power plant. The "hydrogen energy storage-electric boiler" joint decoupling CHP working mode is used to strengthen the coupling relationship between electric-thermal-hydrogen load. At the same time, a tiered carbon trading mechanism is considered, with the net profit of the VPP as the optimization objective, balancing economic and environmental considerations. A low-carbon economic dispatch model for VPPs is established, and a genetic algorithm is used for optimization. Three different scheduling strategies are set, and simulations are conducted in three different seasonal scenarios. The results show that the net profit in the cooling season increased by 50.4%, and carbon emissions decreased by 42.3%. In the transitional season, the net profit increased by 39.2%, and carbon emissions decreased by 44.9%. In the heating season, the net profit increased by 19.4%, and carbon emissions decreased by 43.4%. Overall, the proposed dispatch strategy can improve the new energy consumption capacity and total revenue of VPPs while achieving the goal of reducing carbon emissions.