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Smart distribution networks (SDNs) can integrate the flexible resources from source-network-load-storage (SNLS) to cope with the fluctuation due to a high proportion of distributed generation (DG). However, such SNLS resources are characterized by complex coupling relationships; their control authority may belong to different stakeholders. Challenged by the above, the laminar flow structure from the communication field is introduced for distributed optimal dispatching in SDNs. A day-ahead laminar dispatching method considering the effective interaction of SNLS resources is proposed. First, the applicability of the laminar flow structure is analyzed. An upper-layer dispatching model for the SDN and a lower-layer dispatching model for users with DG are established. Then, by introducing intermediate variables, the lower dispatching model is transformed into a quadratic programming problem and the upper dispatching model is transformed into a second-order cone relaxation programming problem. Selecting the tie-line power flow as the exchanged information in the boundary, the upper- and lower-layer models are alternately solved until the convergence criterion is met. Finally, an improved IEEE 33-bus system is experimentally analyzed. We find that the SNLS flexible resource dispatching scheme can be obtained with only a few iterations, and the DG consumption can be significantly improved.

With the popularity of electric vehicles (EVs), vehicle-to-grid (V2G) technology is attracting increasing attention due to its crucial merit of enabling bidirectional power flows between EVs and grid, so as to enhance the grid security and stability by regulated dispatching. However, the existing V2G approaches are confronted with several unrealizable challenges because of high computational complexity for large-scale EVs and impracticality for future power data acquisition. In this article, an edge computing framework is proposed in a distributed manner to ensure the dispatching efficiently and provide the raw dataset flexibly. Meanwhile, the long short-term memory network is applied to prediction merely by the past and present power data. Moreover, attention mechanism and data clustering are utilized to improve the prediction accuracy and operation robustness. Experiments involving real dataset demonstrated that the proposed V2G scheme is able to achieve very satisfactory dispatching performance with the prediction accuracy up to 98.89%. [ABSTRACT FROM AUTHOR]

Nowadays, distributed power generation is highly valued and fully developed since the energy crisis is worsening. At the same time, the distribution system operator is becoming a new stakeholder to take part in the dispatch of the active distribution network (ADN) with the power market being further reformed. Some new challenges to the dispatching of the ADN are brought by these distribution system operators (DSO), which break the traditional requirement of the lowest operating cost. In this paper, the relationship between the maximum revenue and the minimum operating cost of the ADN is fully considered, and the model of the bi-level distributed ADN considering the benefits and privacy protection of multi-stakeholder is established precisely. Further, the model is solved by using the alternating direction method of multipliers (ADMM) in which the safety and economy of the ADN are fully considered. Finally, the validity of the model and the feasibility of the algorithm are verified by using the adjusted IEEE 33 bus. [ABSTRACT FROM AUTHOR]