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From flexible interconnection among feeders to hybrid alternating current (AC) and direct current (DC) distribution structures of future smart distribution systems, medium-voltage DC distribution centers with flexibly interlinked multiple microgrids (MGs) will have wide applications on the demand side. A generic coordinated control framework based on a distributed cooperation scheme is proposed for such DC centers, as opposed to centralized control structures. A novel unified control only using local measurements is proposed for these interlinking converters. During normal power disturbances, automatic coordinated power control and mutual support among subsystems can be realized, thereby improving DC voltage and AC frequency stability to enable multiple MGs to be treated as a real unified cluster. Moreover, with this method, interlinking converters can realize seamless transition in power dispatching mode, common DC bus voltage control mode, and MG support mode without communication and control system switching. A simplified dynamic model has been developed to verify the proposed control strategy. This work is expected to provide a new solution for flexible interconnection and operational control of large-scale MG clusters.

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Question: This study aimed to explore the nutrient-cycling patterns, supply mechanisms and limiting characteristics of elements in saplings of tropical and subtropical forest ecosystems.Location: Tropical seasonal rainforests, tropical montane rainforests and mid-montane moist evergreen broad-leaved forests in Yunnan, China. Methods:We sampled sapling roots from 21 vascular plant species that are dominant in the upper and lower canopies of three forest types. We assessed stoichiometric contents of carbon (C), nitrogen (N) and phosphorus (P), and investigated their relationships with soil nutrients.

With the proposal of smart grid, the demand of both source and load for fine monitoring and control of power load is becoming increasingly prominent. Non-intrusive load monitoring is a technical means to better meet this demand. However, the research at home and abroad focuses on the existing data sets and labeled data to improve the accuracy of load identification, while the research on the training method of the model under the massive unlabeled monitoring data in the actual scene is still in a relatively blank stage. Aiming at the problem of how to make full use of unlabeled monitoring data for model training, a non-intrusive-load monitoring method based on self-supervised learning is proposed in this paper. This method designs a self-supervised learning task, so that the model can make full use of the massive unlabeled monitoring data for training, eliminating the step of manually labelling the data; Based on the encoder-decoder structure, a deep learning model is established, and the load is identified through the load characteristic vector output by the encoder, so that the method has generalization performance. In this paper, AMPds2 data set is used to verify the method, and test examples verify the effectiveness of the method.