Our effort builds on a decade’s worth of effort to develop Tools for Energy Model Optimization and Analysis (Temoa), an open-source, Python-based energy system optimization model (ESOM). The motivation to develop Temoa was twofold: (1) publicly archive model source code and data in order to enable third parties to replicate published analysis, and (2) design the model to perform rigorous uncertainty analysis that can take advantage of high performance computing resources.
Temoa represents an energy system as a network in which technologies (green boxes) are linked together by the flow of energy commodities (blue circles), as shown in the figure. Each process is defined by a set of techno-economic parameters such as investment costs, operations and maintenance costs, conversion efficiencies, emission rates, and availability factors. Temoa minimizes the total system-wide cost of energy supply by optimizing the installation and utilization of energy technologies across the network to meet a set of end-use demands. The user-defined model time horizon typically spans multiple decades and consists of a set of time periods, which are further decomposed into time slices that capture short-term variations in supply and demand. Model constraints enforce rules governing energy system performance, and user-defined constraints can be added to represent limits on technology expansion, fuel availability, and system- wide emissions. In addition to a mature code base, several different input databases have been constructed for model-based analysis with Temoa. We have already constructed a nine-region US database, which will serve as a starting point for this project. If you are interested to learn more about Temoa, please visit the model website.
We are in the process of building this GitHub repository that will house all of the Temoa-related model code, input data, and documentation associated with the Open Energy Outlook for the United States.