With the rapid development of renewable energy generation and multi‐energy system technologies, reviewing and discussing the emerging power system restoration methods and key technologies suitable for renewable‐dominated electric power systems and the Energy Internet are important. Based on this, the backgrounds of renewable‐dominated electric power systems and the Energy Internet are first introduced here. Subsequently, the power system restoration process is divided into three phases: the black‐start, network reconfiguration, and the load restoration phases; relative restoration strategy research on these three phases is reviewed. Moreover, the boundaries between these three phases are occasionally not sufficiently apparent or even cross in most cases owing to the severity of blackouts and other various factors. Therefore, the key technologies for power system restoration considering multiple phases are analysed in detail. Moreover, the major gaps between existing research and real‐world applications and the outlooks on the restoration technologies under renewable‐dominated electric power systems are discussed.
Islanded multi-microgrids formed by interconnections of microgrids will be conducive to the improvement of system economic efficiency and supply reliability. Due to the lack of support from a main grid, the requirement of real-time power balance of the islanded multi-microgrid is relatively high. In order to solve real-time dispatch problems in an island multi-microgrid system, a real-time cooperative power dispatch framework is proposed by using the multi-agent consensus algorithm. On this basis, a regulation cost model for the microgrid is developed. Then a consensus algorithm of power dispatch is designed by selecting the regulation cost of each microgrid as the consensus variable to make all microgrids share the power unbalance, thus reducing the total regulation cost. Simulation results show that the proposed consensus algorithm can effectively solve the real-time power dispatch problem for islanded multi-microgrids.
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.
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