This research develops a Generation Expansion Planning (GEP) framework that supports long-term, macro-scale energy planning and serves as a decision-support tool for planners and policymakers. The project objectives are to quantify the human health externalities associated with power grid expansion plans and optimize distribution systems for resilient grid expansion.

This project focuses on building a more useful and realistic energy planning tool for policymakers by upgrading traditional generation expansion models with detailed demand-side dynamics, transmission/distribution constraints, and the ability to estimate health impacts using AI. This involves integrating high-resolution demand-side features (e.g., demand response and grid-edge technologies) and incorporating operational constraints and flexibility through day-ahead dynamics. The research also evaluates the health externalities of electricity generation using AI-based modeling approaches and tests emerging technologies (e.g., fuel cells) under different demand and reliability scenarios. An open-source modeling suite with documentation, continuous updates, and public access via GitHub is being developed.

The entire modeling framework is open-source to foster widespread use and support cleaner, more equitable energy decisions.

Publication: Beacher, M., Arasu, T., Rodgers, M., Coit, D., & Senick, J. (2024). Assessing the public health impacts: A comprehensive analysis of health externalities related to power grid expansion plans. IISE Annual Conference. Proceedings, 1–6.  https://iise.confex.com/iise/2024/meetingapp.cgi/Paper/7911