NextGW: Power Grid Hosting Capacity Visualization

March 6, 2026

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NextGW: Power Grid Hosting Capacity Visualization develops interactive geospatial visualizations of power grid hosting capacity for new electrical loads, including AI data centers and EV charging stations, across U.S. transmission networks, using open-source grid models, ISO generation data, and interconnection queue datasets to assess present and future capacity.

Mentor:

Detailed Description

The rapid growth of AI data centers and electric vehicle (EV) charging infrastructure is placing unprecedented new demands on the US electric transmission grid. This team will develop an interactive map-based visualization platform that displays the hosting capacity, which is the amount of new load a given region or substation can absorb without violating reliability constraints across US transmission networks. Students will work with open-source synthetic grid models (such as the ACTIVSg, The Breakthrough Energy Model and similiar datasets), publicly available generation capacity data from regional ISOs (e.g., MISO, PJM, CAISO, ERCOT, NYISO), and FERC interconnection queue datasets to map both current and projected future hosting capacity. The team will also analyze interconnection queue trends to forecast how approved and pending generation projects will alter hosting capacity in future years. Ultimately, the project delivers a publicly accessible tool to help planners, policymakers, and communities understand where new large loads can be sited on the grid today and in the years ahead. 

Relevant Technologies

  • Geospatial data visualization
  • Power systems analysis
  • Data science and open-source energy datasets

Prerequisite Knowledge

  • Recommended (not required): Basic programming experience in Python; familiarity with data analysis or visualization tools (e.g., Pandas, Matplotlib); introductory coursework in electrical engineering or energy systems is a plus but not required. Students from engineering, computer science, data science, and related fields are encouraged to apply. No prior power systems knowledge is necessary - mentors will provide onboarding resources.