1Institute for Energy Studies, University of North Dakota, Grand Forks, USA
American Journal of Energy Research.
2023,
Vol. 11 No. 2, 63-81
DOI: 10.12691/ajer-11-2-2
Copyright © 2023 Science and Education PublishingCite this paper: Michael Ryder, Solomon Evro, Caleb Brown, Olusegun S. Tomomewo. Multi-Model Approach of Global Energy Model Validation: Times and EN-ROADS Models.
American Journal of Energy Research. 2023; 11(2):63-81. doi: 10.12691/ajer-11-2-2.
Correspondence to: Solomon Evro, Institute for Energy Studies, University of North Dakota, Grand Forks, USA. Email:
solomon.evro@und.eduAbstract
Energy System Modeling tools are becoming ever-prevalent in global society to help decide factors in energy policymaking, power production methods, and means of environmental impact assessment. Energy system engineers need to be aware of the use of energy system models due to the complexity of the systems and the demand for model use in evaluating an energy system. This literature review will cover the importance of energy system models and the most recent advances in modeling technology, the accepted methods of model evaluation and validation before the use of an energy system model, and lastly, demonstrate a comparative analysis validation technique with three case studies using a multi-model approach, by applying two widely accepted global energy models. The two global energy models evaluated are the Integrated MARKAL-EFOM System (TIMES) and Energy-Rapid Overview and Decision-Support (EN-ROADS) models. The comparative analysis will be demonstrated by reviewing three base cases, whether 2.5°C average warming is achievable within the desired timeline, the projected global energy supply, and practical climate change mitigation scenarios. The comparative analysis results show that two globally accepted energy system models still predict different outcomes with the same inputs. The comparative analysis results exemplify the necessity for energy system engineers or other model users to properly benchmark and validate any model they decide to use for decision-making before accepting model results.
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