For the alternating current(AC)/direct current(DC) distribution network, on the one hand, the power transfer between feeders is realized through dynamic reconstruction, and on the other hand, the mobile energy storage provides power support in the local area. The two complement each other and cooperate to achieve the power balance across the entire region. To this end, this paper proposes a collaborative two-stage robust optimization method of AC/DC distribution network dynamic reconfiguration and mobile energy storage. Firstly, aiming at the operation range of the charging and discharging power of the actual energy storage battery, a scientific sampling method is adopted to obtain discrete data points. Based on these sampling points, the originally complex measurement-based energy storage battery model is transformed into a linear model that is convenient for analysis and calculation, accurately simulating the nonlinear attenuation characteristics of the power of the energy storage battery. Then, a hyperplane polyhedral set driven by data is used to describe the uncertainty of the power output of distributed photovoltaics. In order to achieve the efficient and economic operation of the AC/DC distribution network, based on the column and constraint generation (C&CG) algorithm, a strong robust model for the dynamic topology optimization of the AC/DC distribution network that incorporates the coordinated operation of mobile energy storage is established. Subsequently, a simulation experiment is carried out by using the improved IEEE 33-bus system. The results fully demonstrate that this optimization strategy can significantly improve the economic performance of the system, proving the effectiveness of the method proposed in this paper.
In the novel power system, the large-scale integration of distributed generation imposes multiple challenges for distribution network fault diagnosis, including complex multi-source information interaction, weakened fault electrical characteristics, and uncertain bidirectional power flows. To address these issues, this paper focuses on fault diagnosis algorithms based on multi-modal feature fusion. Firstly, it employs Variational Mode Decomposition (VMD) to preprocess raw data, reducing noise and non-stationarity, thereby improving the accuracy of the fault diagnosis algorithm. Subsequently, a novel spatio-temporal dual-stream parallel feature extraction network is innovatively designed to mine multi-modal information from both spatial and temporal dimensions. Specifically, a Graph Attention Network (GAT) models the distribution network topology to extract spatial correlation features among nodes, while a Bidirectional Gated Recurrent Unit (BiGRU) captures the temporal evolution patterns of electrical quantities at each node. Furthermore, an adaptive multi-modal feature fusion mechanism is constructed. A self-attention layer dynamically weights and deeply fuses the aforementioned heterogeneous features, thereby generating a more discriminative comprehensive representation for accurate fault classification. The core innovations of this study lie in proposing a novel spatio-temporal dual-stream network architecture and designing an efficient adaptive multi-modal feature fusion strategy, effectively addressing the problems of insufficient fault feature extraction and underutilization of heterogeneous information under high penetration of distributed generation. Experimental results show that the proposed method achieves a significant improvement in fault diagnosis accuracy compared to existing mainstream single-model and hybrid-model diagnostic approaches, demonstrating its superiority, robustness, and strong generalization capability in complex new power system scenarios.
With the increasing penetration of new energy such as wind power and photovoltaic in distribution network, the traditional distribution network is gradually evolving into a complex new power system with multi-source energy supply and two-way power flow. However, frequent power flow switching may lead to reverse power overload of the transformer and accelerate its life decay. Therefore, this paper proposes a multi-time scale life optimization strategy for distribution network transformers facing reverse overload. The regulation mechanism of on-load tap changer (OLTC) and the influence of the load rate on the transformer life loss are analyzed, and then, the coordinated scheduling model of distribution network is constructed. The model aims to reduce the transformer loss cost and the comprehensive operation cost of the distribution network, and realizes the life optimization in the transformer reverse overload scenario through the day-ahead and intra-day rolling optimization scheduling strategy. An example is verified based on the improved IEEE-33 node system. The results show that the proposed method can effectively reduce the reverse load ratio of the transformer under the condition of frequent power flow switching, reduce the life loss of the transformer and reduce the comprehensive operation cost, so as to significantly increase the operation life of the transformer under the reverse overload condition, and improve the safety and economy of the system.
Dual-mode communication breaks down communication medium barriers and achieves complementary advantages of network resources. It has shown significant advantages in the collection of total curve data in the substation area and has become a key component of the smart grid. However, existing research mainly focuses on micro level aspects such as node random links and node networking. This paper proposes a synchronization capability analysis method for dual-mode communication network based on complex network theory, and derives the mathematical correlation between the hyper Laplacian matrix and its eigenvalues of dual-mode communication and the coupling strength within and between layers. Theoretical analysis shows that the synchronization capability of dual-mode communication networks is determined by the minimum non-zero eigenvalue of their Laplacian matrix. The simulation results indicate that the improvement of interlayer coupling is limited by the strength of intra layer connections. It is recommended to prioritize increasing the intra layer coupling strength to a critical value, and then increasing the inter layer coupling strength until the synchronization capability approaches saturation. If resources are sufficient, the optimization method will shift from parameter optimization to topology optimization by adding proxy coordinator（PCO）nodes. The experimental results confirm that the synchronization ability decreases with the expansion of network scale, but the trend is consistent. The failure of key nodes leads to a sharp drop in synchronization ability. Therefore, key node protection is crucial for maintaining synchronization stability.
A distribution network system dominated by photovoltaic (PV) power is gradually becoming an important direction for power grid development. However, the continuous increase in its penetration rate poses challenges to the stable operation of the distribution network, including voltage violations, increased distribution network losses, reverse power flow, and reduced power quality. To this end, this paper proposes a carrying capacity assessment method considering high PV penetration. A carrying capacity model for distribution network with high PV penetration and an evaluation index system for carrying capacity are established, and the carrying capacity model is solved based on second-order cone relaxation. To assess the impact of high PV penetration on the hosting capacity of the distribution network, an improved priority-CRITIC (criteria importance through intercriteria correlation) combined weighting method is used for comprehensive evaluation. To explore the effectiveness of different carrying capacity enhancement measures, the proposed carrying capacity assessment method is applied to compare and analyze various enhancement schemes. The IEEE 33-node distribution network system is used as a case study. By comparing the proposed method with existing carrying capacity evaluation methods, the comprehensiveness and effectiveness of the proposed approach are validated, and the enhancement measures are shown to effectively improve the carrying capacity of the distribution network.
