The rapid growth of smart grids has created an urgent demand for power flow analysis methods that are both scalable and responsive. In this work, we introduce a distributed framework built on Robot Operating System (ROS 2) to carry out Newton–Raphson Load Flow (NRLF) calculations for a simple two-bus network. Two distinct cases are examined: one with fixed or static loads (Scenario A) and another with loads that change every five seconds (Scenario B). Bus data and related computations are handled by independent ROS 2 nodes, which exchange information through topics such as power flow output. Initial results show computation times in the range of 0.0001–0.0005 s, which, in certain contexts, outperform more conventional centralized tools. Beyond speed, the system’s modular design and ability to adapt in real time make it particularly relevant for cyber-physical energy systems, including applications in grid monitoring and microgrid management. Looking ahead, we plan to extend the approach to larger multi-bus networks and to incorporate advanced visualization capabilities for deeper analysis.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Dynamic Power Flow Analysis with ROS 2 for Next-Generation Smart Grids

  • Vivek Kumar

摘要

The rapid growth of smart grids has created an urgent demand for power flow analysis methods that are both scalable and responsive. In this work, we introduce a distributed framework built on Robot Operating System (ROS 2) to carry out Newton–Raphson Load Flow (NRLF) calculations for a simple two-bus network. Two distinct cases are examined: one with fixed or static loads (Scenario A) and another with loads that change every five seconds (Scenario B). Bus data and related computations are handled by independent ROS 2 nodes, which exchange information through topics such as power flow output. Initial results show computation times in the range of 0.0001–0.0005 s, which, in certain contexts, outperform more conventional centralized tools. Beyond speed, the system’s modular design and ability to adapt in real time make it particularly relevant for cyber-physical energy systems, including applications in grid monitoring and microgrid management. Looking ahead, we plan to extend the approach to larger multi-bus networks and to incorporate advanced visualization capabilities for deeper analysis.