Probabilistic Visualisation Approach Using Polar Histograms to Examine the Influence of Networked Distributed Generation

The variability of renewable energy sources, coupled with the decentralised configuration of distributed generation (DG), significantly complicates grid management, necessitating sophisticated visual analytics to enhance power system performance and energy distribution. This paper presents a probabilistic visualisation technique based on polar histograms to identify the dynamic influence zones of DG units by analysing line current flows. The proposed framework explicitly accounts for the probabilistic representation of reverse power flows, which provides an overall view of DG impacts on distribution networks. Quasi-dynamic simulations are conducted on a 33-bus distribution system using DIgSILENT PowerFactory 2020, MATLAB R2020, and Python 3.8. The results demonstrate that the polar histogram approach provides intuitive insights into DG influence, revealing zones of grid-dominated, DG-dominated, and shared interactions. These findings act as a potential practical tool for voltage management, demand balancing, and secure integration of renewable DG units into modern power grids.