PowerPlots.jl: An Open Source Power Grid Visualization and Data Analysis Framework for Academic Research

TL;DR

PowerPlots.jl addresses the need for easy, flexible, and customizable power-grid visualization in academic research. The paper presents the design, data-models, and plotting workflow that convert PowerModels.jl structures into graph and dataframe representations, enabling both network visualization and data analysis via VegaLite.jl. Through case studies and performance benchmarks, the work demonstrates how researchers can explore grid data, perform graph and tabular analyses, and produce publication-ready figures. The framework thus offers a versatile, open-source tool that supports transmission and distribution networks and can extend to other critical-infrastructure domains.

Abstract

Data visualization is essential for developing an understanding of a complex system. The power grid is one of the most complex systems in the world and effective power grid research visualization software must 1) be easy to use, 2) support unique data that may arise in research, and 3) be capable of creating custom figures for publication and presentation. However, no current software addresses all three of these needs. PowerPlots is an open-source data visualization tool for power grids that does address these needs. In addition, several tools created to support this software facilitate the analysis of power grid data by transforming the data into graph topology or data-frame data formats that are more compatible for some analyses. In this work, we use PowerPlots to investigate several case studies that involve exploring power grid data. These case studies demonstrate the valuable insights that are possible when using network visualization and how it can be applied to research applications.

Figures (10)

• Figure 1: 39 Node EPRI Network
• Figure 2: It is quickly apparent that the 89 node network in Fig. \ref{['fig:89']} has a tight clustering of nodes in the center of the graph that are densely connected. The 118 node IEEE network is shown in Fig. \ref{['fig:118']} as a comparison of a more typical network that lacks this density.
• Figure 3: Layouts of a 39 node network, sorted by time to compute the layout. The Kamada Kawai layout method generally produces the best visual layout to view the components of the network, but has a much longer computation time. Buses are shown in blue, generators in orange, and loads in red. Lines are shown in green, and dashed gray lines indicate where generators and loads connect to buses.
• Figure 4: Partial fixed layout of power network. Bus data is known, but generator locations are not. The generator locations are computed using SFDP. The figure shows a plan for a Public Safety Power Shutoff plan to reduce wildfire risk by turning off power lines. Components in gray indicate that they have been de-energized rhodes2020balancing.
• Figure 5: Fig. \ref{['fig:lmp']} shows a plot of the Locational Marginal Price at each node, with binding transmission limits shown in red. In Fig. \ref{['fig:lmp-interactive']}, a user hovers over a binding transmission line to see detailed information.