The identification accuracy of low‐voltage distribution consumer–transformer relationship and phase are crucial to three‐phase unbalanced regulation and error correction in consumer–transformer relationships. However, owing to the rapid increase in the number of consumers and the upgrade of the feed lines for low‐voltage distribution systems, the timely update of the consumer‐transformer relationship and phase information of consumers is challenging. This influences the accuracy of the basic information of the power grid. Thus, this study proposes a low‐voltage distribution network consumer–transformer relationship and phase identification method based on anomaly detection and the clustering algorithm. First, the improved fast dynamic time warping distance based on the filter search between voltage sequences is used to measure the similarity between voltage curves. Subsequently, an abnormal consumer detection method based on the local outlier factor is used to identify consumers with mismatched consumer‐transformer relationships by determining the local outlier factor scores of voltage curves. Furthermore, the phase information of normal consumers is identified through clustering by fast search and find of density peaks. Finally, the proposed method is validated using case studies of practical low‐voltage distribution systems in China. The proposed method can effectively improve phase identification accuracy and maintain high adaptability in various data environments.

The need to accelerate the innovation and application of the supply chain has been suggested by the State Council of China. To solve the problem of data isolation caused by privacy protection in the power material supply chain, a data traceability and sharing mechanism based on blockchain is designed in this paper. Firstly, the existing problems of the power material supply chain are introduced, and the applicability of blockchain in the power material supply chain in view of these problems is analyzed. Secondly, blockchain-based power material supply deployment and application structures are proposed. Then, considering the problem of data isolation in the material inspection and distribution links between suppliers and the material company, a data traceability mechanism based on blockchain is designed to provide evidence for the data authenticity and a proxy re-encryption method is used to ensure security and privacy in data sharing. Finally, the effectiveness of the proposed data traceability and sharing mechanism is verified using the Hyperledger Fabric platform for power material case studies. The simulation results show that the combination of proxy re-encryption and blockchain technology in the power material supply chain can confirm the validity of the historical data and keep the private data of the material company confidential, so as to realize the traceability and sharing of the power material supply data.

The long-term three-phase imbalance in distribution systems can lead to increased energy losses during transmission, reduced efficiency in energy utilization, and have serious implications for power supply security, power quality, and economic operations of the power system. The automatic load commutation device is an automatic device designed to address the issue of three-phase load imbalances in low-voltage distribution networks. By adjusting the phase of users without interrupting the power supply, this automatic device evenly distributes the load across all phases and effectively resolves the three-phase imbalance problem. Therefore, aiming at the above issue, a robust optimization method is adopted to address the control problem of automatic load commutation devices for a low-voltage distribution network. First, using historical data from photovoltaic and wind power generation systems as well as user load, a forecast analysis is conducted and uncertainty models for renewable energy and load demand are established. Then, a robust control strategy for the automatic load commutation device is proposed, which considers uncertainties of both source and load using the robust optimization method. The proposed model is then linearized using second-order cone technology and strong dual theory, and the column-and-constrained generation (C&CG) algorithm is employed to solve the problem iteratively. Finally, a modified IEEE 33-bus system is taken to verify the effectiveness of the proposed strategy. The simulation results show that the proposed load commutation device robust control model can enhance the ability of the distribution network to respond to load demand and renewable energy fluctuations while ensuring the economic operation of the distribution network. In addition, by adjusting the deviation amount and uncertainty parameters of load demand and renewable energy, a good balance between the robustness and economy of the proposed model can be achieved.