A virtual boundary node driven decoupling method for boundary interaction

By introducing virtual boundary nodes and a deep reconstruction model into the distribution network, and combining deep reinforcement learning with particle swarm optimization, the problem of real-time interaction dependency of cross-cluster boundary states in the distribution network is solved, achieving efficient local control optimization and communication concealment, and improving the robustness and real-time performance of the system.

CN122136880AActive Publication Date: 2026-06-02HEFEI UNIV OF TECH +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEFEI UNIV OF TECH
Filing Date
2026-05-06
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing distributed control methods in power distribution networks suffer from strong real-time interaction dependence across cluster boundary states, long communication links, queuing congestion, and large closed-loop delays, making it difficult to balance control accuracy, robustness, and real-time performance.

Method used

A boundary interaction decoupling method driven by virtual boundary nodes is adopted. By constructing virtual boundary nodes and a local dynamic discrete state space model, boundary state estimation is performed using a state observer and a deep reconstruction model. Local optimization control is then performed by combining deep reinforcement learning and particle swarm optimization algorithm to achieve boundary state reconstruction and communication culling of local information.

Benefits of technology

It effectively reduces the rigid dependence on cross-domain communication, improves control response speed and accuracy, reduces communication burden, and achieves fast and high-quality local control optimization.

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Abstract

This invention discloses a virtual boundary node-driven boundary interaction decoupling method, comprising the following steps: dividing the entire distribution network into M control clusters and identifying physical boundary nodes between adjacent clusters; constructing a corresponding virtual boundary node for each physical boundary node; establishing a local dynamic discrete state space model for each control cluster; calculating the boundary reconstruction error, model uncertainty index, and boundary consistency deviation of the virtual boundary state; constructing a communication blanking criterion based on preset boundary reconstruction error thresholds, consistency deviation thresholds, and minimum confidence thresholds; constructing a local optimization objective function for distribution network voltage control in the rolling time domain; and distributing the optimal control solution to controllable equipment in the distribution network for execution. This invention achieves rapid response and high-quality solution for local control optimization through a joint solution method of "deep reinforcement learning for rapid initial values ​​of actions + particle swarm optimization for fine-grained search."
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