Due to the diversity and complexity of distribution networks, the classical modular software development method may face the difficulty in modular division, data sharing and collaboration of different specialties. Inspired by the idea of separating the control plane from the data forwarding plane in software-defined networking (SDN), a method of power data virtual plane is proposed in this paper to improve software development efficiency. Layered processing of power data virtual plane is designed to meet the diversity characteristics of intelligent distribution network and the multi-source and heterogeneous characteristics of information. This paper introduces the design idea and implementation process of virtual plane in detail. The main component of the power data virtual plane, power data warehouse and application scheduling are presented. Finally, the performance of the proposed virtual plane method is verified by practical distribution network examples with different communication networks and information.

Small hydropower-dominated microgrids enable power exchange with the main grid during grid-connected operation but face frequency stability challenges during sudden islanding (e.g., line faults), requiring prompt generation curtailment or load shedding. In communication-constrained mountainous regions, conventional methods such as high-frequency tripping or low-frequency load shedding often struggle to achieve precise frequency regulation A hierarchical strategy integrating master station centralized decision making and substation local control is proposed. This study theoretically analyzes the post-islanding frequency dynamics of small hydropower microgrids. The master station formulates optimal shedding decisions using regional power flow data, while substations execute decisions via local measurements to mitigate communication issues. A constrained mathematical model is established, solved using a heuristic algorithm, validated through electromagnetic transient simulations, and compared with traditional schemes. The proposed scheme achieves precise surplus capacity shedding, enhancing frequency stability during abrupt islanding with reduced over-/under-tripping compared to that of conventional methods. This hierarchical strategy enhances frequency regulation capability under communication constraints, ensuring reliable operation of small hydropower microgrids during sudden islanding and providing a practical solution for remote regions with limited communication infrastructure.

Using electric vehicles (EVs) for grid support requires reliable flexibility estimates and effective voltage control from limited online information. We propose a behavior-informed interacting multiple-model estimator (BIMM) and an availability-aware voltage-control strategy. The estimator combines predicted behavior-mode probabilities with station-level measurements to estimate aggregate upward and downward energy flexibility and its uncertainty. The controller combines behavior-derived availability priors with online measurements to update station-level reactive-power limits and allocate voltage support. In paired simulations, explicit behavior-prior pooling reduces the normalized continuous ranked probability score (nCRPS) for energy by 54.0% and 61.8% in IID and OOD, respectively, relative to the same-bank no-pooling reference. Relative to the standard interacting multiple-model estimator (Standard-IMM), the normalized root-mean-square error (NRMSE) for energy decreases by 23.36% and 39.48% in the batch-charging scenarios S1 and S2, respectively; energy losses in ordinary IID/OOD operation are higher than those of Standard-IMM. In ordinary control scenarios, mean action-time request-exceedance rates are lower than for fast-measurement-only voltage control (FastOnly). These results demonstrate the use of aggregate behavior information for probabilistic flexibility estimation and availability-aware reactive-power dispatch.