Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts

DeCarolis, Joseph F.; Jaramillo, Paulina; Johnson, Jeremiah X.; McCollum, David; Trutnevyte, Evelina; Daniels, D.; Akın-Olçum, Gökçe; Bergerson, Joule; Cho, Soolyeon; Choi, Joon-Ho; Craig, Michael; Queiroz, Anderson Rodrigo de; Eshraghi, Hadi; Galik, Christopher S.; Gutowski, Timothy G.; Haapala, Karl R.; Hodge, Bri‐Mathias; Hoque, Shamia; Jenkins, Jesse D.; Jenn, Alan; Johansson, Daniel; Kaufman, Noah; Kiviluoma, Juha; Lin, Zhenhong; MacLean, Heather L.; Masanet, Eric; Masnadi, Mohammad S.; McMillan, Colin; Nock, Destenie; Patankar, Neha; Patiño‐Echeverri, Dalia; Schivley, Greg; Siddiqui, Sauleh; Smith, Amanda D.; Venkatesh, Aranya; Wagner, Gernot; Yeh, Sonia; Zhou, Yuyu

doi:10.1016/j.joule.2020.11.002

Cited by 26 publications

(8 citation statements)

References 8 publications

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“…With these enhanced uncertainty quantification strategies, the energy system community could begin to use ensembles of climate data [110], so the energy system community should prioritise addressing remaining conceptual and computational challenges. Distributed modelling teams that incorporate experts in uncertainty quantification and climate science could partially alleviate the challenges with implementation of these uncertainty quantification strategies [111].…”

Section: Add Climate-related Uncertainty Analysis To Default Energy S...mentioning

confidence: 99%

Overcoming the disconnect between energy system and climate modeling

Craig

Wohland

Stoop

et al. 2022

Joule

Self Cite

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Section: Add Climate-related Uncertainty Analysis To Default Energy S...mentioning

confidence: 99%

Overcoming the disconnect between energy system and climate modeling

Craig

Wohland

Stoop

et al. 2022

Joule

Self Cite

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“…40 Comparable efforts across global, regional and non-governmental domains are being developed to support the transition to U4RIA focused EMoPS. Notable examples include the Open Energy Outlook project covering the United States 41,42 and similar initiatives being undertaken broadly within the openmod 25 community.…”

Section: Stakeholder Involvementmentioning

confidence: 99%

Energy system analytics and good governance -U4RIA goals of Energy Modelling for Policy Support

Howells

Quirós-Tortós

Morrison³

et al. 2021

Preprint

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Energy modelling is the process of using mathematical models to develop abstractions and then seek insights into future energy systems. It can be an abstract academic activity. Or, it can insert threads that influence our development. We argue therefore, that energy modelling that provides policy support (EMoPS) should not only be grounded in rigorous analytics, but also in good governance principles. As, together with other policy actions, it should be accountable. Almost all aspects of society and much of its impact on the environment are influenced by our use of energy. In this context, EMoPS can inspire, motivate, calibrate, and ‘post assess’ energy policy. But, such modeling is often undertaken by too few analysts under time and resource pressure. Building on the advances of ‘class leaders’, we propose that EMoPS should reach for practical goals — including engagement and accountability with the communities it involves, and those it will later affect. (We use the term Ubuntu, meaning ‘I am because you are’ to capture this interdependency). We argue that Ubuntu, together with retrievability, repeatability, reconstructability, interoperability and auditability (U4RIA) of EMoPS should be used to signal the beginnings of a new default practice. We demonstrate how the U4RIA principles can contribute in practice using recent modelling of aspirational energy futures by Costa Rica as a case study. This modelling effort includes community involvement and interfaces and integrates stakeholder involvement. It leaves a trail that allows for its auditing and accountability, while building capacity and sustainable institutional memory.

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“…Energy system optimization models (ESOMs) enable the study of energy transitions. 7 These models typically rely on least-cost optimization to determine the optimal deployment of resources, considering existing and new technologies, within a speci ed time horizon and subject to various constraints.…”

Section: Introductionmentioning

confidence: 99%

Diverse Decarbonization Pathways Under Near Cost-Optimal Futures

Sinha,

Venkatesh,

Jordan

et al. 2023

Preprint

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Energy system optimization models offer insights into energy and emissions futures through least-cost optimization. However, real-world energy systems often deviate from deterministic scenarios, highlighting the need for rigorous exploration of uncertainty in macro-energy system modeling. This study uses modeling techniques to generate a diverse array of near cost-optimal net-zero CO2 pathways for the United States’ energy system. Our findings reveal several consistent trends across these pathways, including the rapid expansion of solar and wind power generation, substantial reductions in petroleum use, near elimination of coal combustion, and increased end-use electrification. We also observe varying levels of deployment for natural gas, hydrogen, direct air capture of CO2, and synthetic fuels. Notably, coal with carbon capture and sequestration and synthetic liquid fuels exhibit high adoption rates but only in a limited subset of decarbonization pathways. We uncover interconnected technologies by analyzing technology adoption correlations and energy system interactions. These results demonstrate that diverse pathways for deep decarbonization exist at comparable system-level costs, providing insights into the portfolio of technologies that enable a near cost-optimal net-zero CO2 future.

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Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts

Cited by 26 publications

References 8 publications

Overcoming the disconnect between energy system and climate modeling

Overcoming the disconnect between energy system and climate modeling

Energy system analytics and good governance -U4RIA goals of Energy Modelling for Policy Support

Diverse Decarbonization Pathways Under Near Cost-Optimal Futures

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