ISTC Webinar, Dr. Maxwell Brown and Austin Puckett

Abstract: Open-source techno-economic models are increasingly important for analyzing electric power systems. This webinar is a discussion on the methods and use cases of these tools. The session will begin with an overview of the motivations behind open-source modeling, who can benefit from it, and which scenarios it can be applied to. The topic will then shift to the technical details of capacity expansion and dispatch optimization using the Python-based modeling framework PyPSA, with emphasis on model construction, data preparation, implementation, and interpretation of results. Using a case study examining the introduction of AI data centers into a regional power system, the presentation will demonstrate how publicly available datasets and open-source software can be combined to evaluate alternative planning scenarios and investment decisions. The session will conclude with a discussion of other modeling tools such as ReEDS as well as recent analyses in the field.  

Biography: Dr. Maxwell Brown (“Max”) is an Assistant Professor in the Department of Economics and Business at Colorado School of Mines, a University Affiliate with the National Laboratory of the Rockies, and a Research Fellow with the Payne Institute for Public Policy. He uses engineering-informed modeling to study material supply chains, energy systems, and industrial policy, with recent publications in Science, Nature Reviews, Joule, and other energy and policy journals. At Mines, he teaches computational economics and mathematical economics, with emphasis on optimization, equilibrium modeling, resource systems, and applied policy analysis. 

Austin Puckett recently completed an M.S. in Mineral and Energy Economics at the Colorado School of Mines, where he served as a Research Assistant under Dr. Maxwell Brown. His research focused on techno-economic modeling of electric power systems, with an emphasis on evaluating the impacts of large new electricity loads, including AI data centers, using open-source capacity expansion and dispatch optimization models. He has developed models in Python/PyPSA to analyze generation, storage, transmission, and system costs under alternative planning scenarios. His broader interests include electricity markets, optimization, energy systems analysis, and the use of open-source modeling to support transparent, evidence-based energy policy and infrastructure planning.