Open Datasets for Grid Modeling and Visualization: An Alberta Power Network Case
Ben Cheng, Yize Chen
TL;DR
The paper tackles the lack of fine-grained, locational grid data by integrating open datasets to recover Alberta's transmission topology and line-flow directions using an optimization- and heuristic-driven pipeline. It constructs a graph of $G=(oldsymbol{B},oldsymbol{E})$, assigns nodal demand through a Population-based Demand Index, and applies a tree- and BFS-driven method to orient lines, followed by a linear-programming dispatch proxy to allocate line loads. Key contributions include a transparent, open-source workflow that maps public data to topology, a BFS-based topology recovery approach, and LP-based dispatch visualization that yields consistent load allocations without exposing sensitive parameters. The framework enables synthetic data generation, improved visualization, and exploratory analytics for grid resilience, renewables integration, and carbon-emission assessments, with demonstrated applicability to the Alberta grid and generalizability to other regions.
Abstract
In the power and energy industry, multiple entities in grid operational logs are frequently recorded and updated. Thanks to recent advances in IT facilities and smart metering services, a variety of datasets such as system load, generation mix, and grid connection are often publicly available. While these resources are valuable in evaluating power grid's operational conditions and system resilience, the lack of fine-grained, accurate locational information constrain the usage of current data, which further hinders the development of smart grid and renewables integration. For instance, electricity end users are not aware of nodal generation mix or carbon emissions, while the general public have limited understanding about the effect of demand response or renewables integration if only the whole system's demands and generations are available. In this work, we focus on recovering power grid topology and line flow directions from open public dataset. Taking the Alberta grid as a working example, we start from mapping multi-modal power system datasets to the grid topology integrated with geographical information. By designing a novel optimization-based scheme to recover line flow directions, we are able to analyze and visualize the interactions between generations and demand vectors in an efficient manner. Proposed research is fully open-sourced and highly generalizable, which can help model and visualize grid information, create synthetic dataset, and facilitate analytics and decision-making framework for clean energy transition.