@article{hunter_sreepathi_decarolis_2013, title={Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)}, volume={40}, ISSN={["1873-6181"]}, DOI={10.1016/j.eneco.2013.07.014}, abstractNote={This paper introduces Tools for Energy Model Optimization and Analysis (Temoa), an open source framework for conducting energy system analysis. The core component of Temoa is an energy economy optimization (EEO) model, which minimizes the system-wide cost of energy supply by optimizing the deployment and utilization of energy technologies over a user-specified time horizon. The design of Temoa is intended to fill a unique niche within the energy modeling landscape by addressing two critical shortcomings associated with existing models: an inability to perform third party verification of published model results and the difficulty of conducting uncertainty analysis with large, complex models. Temoa leverages a modern revision control system to publicly archive model source code and data, which ensures repeatability of all published modeling work. From its initial conceptualization, Temoa was also designed for operation within a high performance computing environment to enable rigorous uncertainty analysis. We present the algebraic formulation of Temoa and conduct a verification exercise by implementing a simple test system in both Temoa and MARKAL, a widely used commercial model of the same type. In addition, a stochastic optimization of the test system is presented as a proof-of-concept application of uncertainty analysis using the Temoa framework.}, journal={ENERGY ECONOMICS}, author={Hunter, Kevin and Sreepathi, Sarat and DeCarolis, Joseph F.}, year={2013}, month={Nov}, pages={339–349} }
 @article{decarolis_hunter_sreepathi_2012, title={The case for repeatable analysis with energy economy optimization models}, volume={34}, ISSN={["1873-6181"]}, DOI={10.1016/j.eneco.2012.07.004}, abstractNote={Energy economy optimization (EEO) models employ formal search techniques to explore the future decision space over several decades in order to deliver policy-relevant insights. EEO models are a critical tool for decision-makers who must make near-term decisions with long-term effects in the face of large future uncertainties. While the number of model-based analyses proliferates, insufficient attention is paid to transparency in model development and application. Given the complex, data-intensive nature of EEO models and the general lack of access to source code and data, many of the assumptions underlying model-based analysis are hidden from external observers. This paper discusses the simplifications and subjective judgments involved in the model building process, which cannot be fully articulated in journal papers, reports, or model documentation. In addition, we argue that for all practical purposes, EEO model-based insights cannot be validated through comparison to real world outcomes. As a result, modelers are left without credible metrics to assess a model's ability to deliver reliable insight. We assert that EEO models should be discoverable through interrogation of publicly available source code and data. In addition, third parties should be able to run a specific model instance in order to independently verify published results. Yet a review of twelve EEO models suggests that in most cases, replication of model results is currently impossible. We provide several recommendations to help develop and sustain a software framework for repeatable model analysis.}, number={6}, journal={ENERGY ECONOMICS}, author={DeCarolis, Joseph F. and Hunter, Kevin and Sreepathi, Sarat}, year={2012}, month={Nov}, pages={1845–1853} }