catalyst_cites.bib

@article{schwartz2023fusion,
  title={The value of fusion energy to a decarbonized United States electric grid},
  author={Schwartz, Jacob A and Ricks, Wilson and Kolemen, Egemen and Jenkins, Jesse D},
  journal={Joule},
  volume={7},
  number={4},
  pages={675--699},
  year={2023},
  publisher={Elsevier}
}

@phdthesis{jordanPhd,
  author  = {Katherine Halloran Jordan},
  title   = {An Energy Systems Model Approach to US Decarbonization: Technological Solutions, Policy Pathways, and Equity Outcomes},
  school  = {Carnegie Mellon University},
  year    = {2024}
}

@article{meedev2,
title = {Construction and Application of the Micro-level Engineering, Environmental, and Economic Detail of Electricity (MEEDE) Dataset, Version 2},
journal = {Environmental Economics Working Paper Series},
volume = {2023},
year = {2023},
number = {2023-03},
url = {https://www.epa.gov/environmental-economics/construction-and-application-micro-level-engineering-environmental-and},
author = {Candise Henry and Jared Woollacott and Alison Bean de Hernández and Andrew Schreiber and David A. Evans},
keywords = {electricity, pollution control, social accounting matrices, matrix balancing, air pollution, costs of pollution control, pollution control and economic incentives, energy, electric power, modeling, C69, Q43, Q52, Q53},
abstract = {The Micro-level Engineering, Economic, and Environmental Detail of Electricity (MEEDE, Version 2) dataset provides a unit-level representation of the United States electricity sector based on public sources. The data draw on a disparate set of engineering,
environmental, and economic data, primarily from the Environmental Protection Agency and the Department of Energy's Energy Information Administration, to characterize all utility-scale electric generating units in the U.S. in terms of their physical inputs of energy, outputs of electricity and pollution, generating and pollution control equipment configurations, and economic costs (capital, labor, energy, and materials) associated with their operation. The combination of complete unit-level physical details of the US grid with economic characteristics is a key distinction between the MEEDE data and other publicly-available sources. The MEEDE data provide a highly-valuable tool for generating descriptive statistics and supporting advanced partial or general equilibrium modeling efforts that require technology-rich representations of the U.S. electricity grid. We demonstrate how these data can be integrated into social accounting matrices for use in economy-wide modeling applications.}
}

@article{KASSEL2025124732,
title = {A method to analyze the costs and emissions tradeoffs of connecting ERCOT to WECC},
journal = {Applied Energy},
volume = {378},
pages = {124732},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.124732},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924021159},
author = {Drew A. Kassel and Joshua D. Rhodes and Michael E. Webber},
keywords = {Grid reliability, Electric power systems, Energy systems engineering, Interregional transmission, Capacity expansion modeling, Operational dispatch modeling, ERCOT, WECC},
abstract = {Grid reliability in Texas is an increasingly highlighted concern due to recent winter storms and heat waves threatening the reliability of the power sector. In this analysis, we compare two improvements on power grid reliability: building more firm generation capacity and connecting the Texas electricity grid to other regions, like WECC. To do so, we created a novel analytical framework that is comprised of the following four elements: (1) integration of open-source modeling tools, such as PowerGenome, pyGRETA, and GenX; (2) synthesis of multiple datasets containing information on historic weather, existing power fleet, energy technology performance factors, future projections of economics, etc.; (3) development of unique zonal profiles for load projections and weather dependent renewable resource performance; and (4) a newly consolidated network of 20 model regions representing the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, and the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous US. Our flexible modeling approach can be applied globally to other grid modeling regions, though the method is demonstrated in this work with unique zonal profiles and a 20-region network, specific to our use case of connecting ERCOT to WECC. These 20 regions were used to simulate different developmental pathways of capacity expansion and operational dispatch across the combined regions while simultaneously optimizing cost and avoiding outage events by planning for winter storm. The primary focus of our work is to analyze the trade-offs of connecting two independently functioning grids and how their future development might be impacted. This analysis considered eight primary forward-looking grid modeling scenarios in ERCOT and WECC. We also completed two sensitivity analyses on some of the critical parameters needed to define the eight primary scenarios. All analyses found that building power plants and transmission connecting ERCOT and WECC lowers total system cost and avoids future CO2 emissions across both regions when compared to solely expanding ERCOT’s power plant capacity. Further, the analyses found that plant hardening and weatherization is important, and can be done in parallel with transmission development for maximum benefit.}
}

@article{DERJAGT2024100132,
title = {Understanding the role and design space of demand sinks in low-carbon power systems},
journal = {Energy and Climate Change},
volume = {5},
pages = {100132},
year = {2024},
issn = {2666-2787},
doi = {https://doi.org/10.1016/j.egycc.2024.100132},
url = {https://www.sciencedirect.com/science/article/pii/S2666278724000084},
author = {Sam van {der Jagt} and Neha Patankar and Jesse D. Jenkins},
keywords = {Demand sinks, Decarbonization, Macro-energy systems, Power systems, Hydrogen, Direct air capture, Flexible loads},
abstract = {As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or ‘demand sinks’, could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.}
}

@article{doi:10.1021/acs.est.4c03719,
author = {Jordan, Katherine H. and Dennin, Luke R. and Adams, Peter J. and Jaramillo, Paulina and Muller, Nicholas Z.},
title = {Climate Policy Reduces Racial Disparities in Air Pollution from Transportation and Power Generation},
journal = {Environmental Science \& Technology},
volume = {58},
number = {49},
pages = {21510-21522},
year = {2024},
doi = {10.1021/acs.est.4c03719},
    note ={PMID: 39593208},
URL = {https://doi.org/10.1021/acs.est.4c03719},
eprint = {https://doi.org/10.1021/acs.est.4c03719}
}

@misc{Tehranchi_2024,
  title={Pypsa-Usa: A Flexible Open-Source Energy System Model and Optimization Tool for the United States},
  author={Kamran Tehranchi and Trevor Barnes and Martha Frysztacki and Ines Azevedo},
  year={2024},
  month={nov},
  doi={10.2139/ssrn.5029120},
  publisher={SSRN},
  url={https://dx.doi.org/10.2139/ssrn.5029120},
}

@techreport{McKinley_2024,
  title={Short Circuited: Costly Transitions under The Clean Air Act},
  author={Andrew McKinley},
  year={2024},
  month={dec},
  doi={10.2139/ssrn.4972091},
  publisher={SSRN},
  institution={Booth School of Business, University of Chicago},
  url={https://dx.doi.org/10.2139/ssrn.4972091},
}

@techreport{Singh2024-wl,
  title={Trends and drivers of utility costs in California},
  author={Madalsa Singh and Allison Ong and Rayan Sud},
  year={2024},
  month={oct},
  doi={10.2139/ssrn.4987198},
  publisher={SSRN},
  institution={Stanford University},
  url={https://dx.doi.org/10.2139/ssrn.4987198},
}

@article{Jacobson_2024,
  doi = {10.1088/2753-3751/ad6d6f},
  url = {https://dx.doi.org/10.1088/2753-3751/ad6d6f},
  year = {2024},
  month = {sep},
  publisher = {IOP Publishing},
  volume = {1},
  number = {3},
  pages = {035009},
  author = {Jacobson, Anna F and Mauzerall, Denise L and Jenkins, Jesse D},
  title = {Quantifying the impact of energy system model resolution on siting, cost, reliability, and emissions for electricity generation},
  journal = {Environmental Research: Energy},
  abstract = {Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.}
}

@INPROCEEDINGS{10780659,
  author={Hanna, Bavly and Xu, Guandong and Wang, Xianzhi and Hossain, Jahangir},
  booktitle={2024 11th International Conference on Behavioural and Social Computing (BESC)},
  title={Leveraging Artificial Intelligence for Affordable and Clean Energy: Advancing UN Sustainable Development Goal 7},
  year={2024},
  volume={},
  number={},
  pages={1-9},
  keywords={Green energy;Technological innovation;Sociotechnical systems;Social computing;Reviews;Collaboration;Stability analysis;Stakeholders;Artificial intelligence;Sustainable development;Artificial Intelligence;Sustainable Development Goals;Energy Efficiency;Clean Energy;Renewable Energy},
  doi={10.1109/BESC64747.2024.10780659}}


@misc{zheng2024optimal,
      title={Optimal transmission expansion minimally reduces decarbonization costs of {U}.{S}. electricity},
      author={Rangrang Zheng and Greg Schivley and Patricia Hidalgo-Gonzalez and Matthias Fripp and Michael J. Roberts},
      year={2024},
      eprint={2402.14189},
      archivePrefix={arXiv},
      primaryClass={econ.GN},
      url={https://doi.org/10.48550/arXiv.2402.14189},
      doi={10.48550/arXiv.2402.14189},
      abstract={Solar and wind power are cost-competitive with fossil fuels, yet their
      intermittent nature presents challenges. Significant temporal and geographic
      differences in land, wind, and solar resources suggest that long-distance
      transmission could be particularly beneficial. Using a detailed, open-source
      model, we analyze optimal transmission expansion jointly with storage, generation,
      and hourly operations across the three primary interconnects in the United States.
      Transmission expansion offers far more benefits in a high-renewable system than in
      a system with mostly conventional generation. Yet while an optimal nationwide plan
      would have more than triple current interregional transmission, transmission
      decreases the cost of a 100% clean system by only 4% compared to a plan that
      relies solely on current transmission. Expanding capacity only within existing
      interconnects can achieve most of these savings. Adjustments to energy storage and
      generation mix can leverage the current interregional transmission infrastructure
      to build a clean power system at a reasonable cost.}
}

@misc{DVN/7QRME4_2024,
  author = {Dotson, Samuel and Shaver, Lee and Gignac, James},
  publisher = {Harvard Dataverse},
  title = {{Storing the Future: A modeling analysis of Illinois storage needs}},
  year = {2024},
  version = {V1},
  doi = {10.7910/DVN/7QRME4},
  url = {https://doi.org/10.7910/DVN/7QRME4}
}

@misc{PowerGenome,
  author       = {Greg Schivley and
                  Ethan Welty and
                  Neha Patankar and
                  Anna Jacobson and
                  Qingyu Xu and
                  Aneesha Manocha and
                  Braden Pecora and
                  Riti Bhandarkar and
                  Jesse D. Jenkins and
                  Matthias Fripp},
  title        = {PowerGenome/PowerGenome: v0.6.3},
  month        = {may},
  year         = {2024},
  publisher    = {Zenodo},
  howpublished = {Git{H}ub repository archived on {Z}enodo},
  version      = {v0.6.3},
  doi          = {10.5281/zenodo.4426096},
  url          = {https://doi.org/10.5281/zenodo.4426096}
}

@article{10.1257/jep.37.4.155,
      Author = {Davis, Lucas W. and Hausman, Catherine and Rose, Nancy L.},
      Title = {Transmission Impossible? Prospects for Decarbonizing the {U}.{S}. Grid},
      Journal = {Journal of Economic Perspectives},
      Volume = {37},
      Number = {4},
      Year = {2023},
      Month = {December},
      Pages = {155-80},
      DOI = {10.1257/jep.37.4.155},
      URL = {https://www.aeaweb.org/articles?id=10.1257/jep.37.4.155},
      abstract = {Encouraged by the declining cost of grid-scale renewables, recent
      analyses conclude that the United States could reach net zero carbon dioxide
      emissions by 2050 at relatively low cost using currently available technologies.
      While the cost of renewable generation has declined dramatically, integrating
      these renewables would require a large expansion in transmission to deliver that
      power. Already there is growing evidence that the United States has insufficient
      transmission capacity, and current levels of annual investment are well below what
      would be required for a renewables-dominated system. We describe a variety of
      challenges that make it difficult to build new transmission and potential policy
      responses to mitigate them, as well as possible substitutes for some new
      transmission capacity.}
}

@techreport{FlatPowerDemandOver2023,
    title={The era of flat power demand is over},
    author={Wilson, John D. and Zimmerman, Zach},
    institution ={Grid Strategies LLC},
    year={2023},
    month={December},
    url={https://gridstrategiesllc.com/wp-content/uploads/2023/12/National-Load-Growth-Report-2023.pdf},
}

@article{Miller_2023,
doi = {10.1088/1748-9326/acc119},
url = {https://dx.doi.org/10.1088/1748-9326/acc119},
year = {2023},
month = {April},
publisher = {IOP Publishing},
volume = {18},
number = {4},
pages = {044020},
author = {Gregory J Miller and Gailin Pease and Wenbo Shi and Alan Jenn},
title = {Evaluating the hourly emissions intensity of the {U}.{S}. electricity system},
journal = {Environmental Research Letters},
abstract = {High-quality data for the greenhouse gas and air pollution emissions
            associated with electricity generation and consumption are increasingly
            important to enable effective and targeted action to decarbonize the
            electric grid and to inform research in a range of academic disciplines
            including environmental economics, lifecycle assessment, and environmental
            health. To inform the broadest range of use cases, such data should ideally
            have a high temporal and spatial resolution, be available in as close to
            real-time as possible, represent the complete power sector, use the
            highest-quality measured data, have complete historical coverage, and
            represent both generated and consumed emissions. To date, no published
            datasets have achieved all of these characteristics. This work is the first
            to publish a comprehensive, plant-level dataset of hourly-resolution
            generation, fuel consumption, and direct CO2, NOx, and SO2 emissions for the
            entire U.S. power sector. This data is published as part of the public and
            open-source Open Grid Emissions Initiative, which also includes hourly,
            consumption-based emissions intensities for every grid balancing area in the
            country.  Using insights generated by this new dataset, this paper also
            interrogates how several of the assumptions implicit in the use of existing
            power sector emissions datasets may under-count or misrepresent the climate
            and health impacts of air emissions from the U.S. power sector. We envision
            the Initiative becoming a central repository of, and hub of activity for
            addressing open research questions related to power sector emissions data,
            and the go-to source for high-quality, high-resolution data for future
            research on grid emissions.}
}

@techreport{NBERw30297,
 title = {Policy Uncertainty in the Market for Coal Electricity: The Case of Air Toxics Standards},
 author = {Gowrisankaran, Gautam and Langer, Ashley and Zhang, Wendan},
 institution = {National Bureau of Economic Research},
 type = {Working Paper},
 series = {Working Paper Series},
 number = {30297},
 year = {2022},
 month = {July},
 doi = {10.3386/w30297},
 URL = {http://www.nber.org/papers/w30297},
 abstract = {Government policy uncertainty affects irreversible decisions including technology adoption and exit. This paper quantifies uncertainty surrounding the Mercury and Air Toxics Standard (MATS). We estimate a dynamic oligopoly model for coal-fired electricity generators that recovers generators' beliefs regarding future MATS enforcement. We develop the Approximate Belief Oligopoly Equilibrium concept where players understand that their decisions impact aggregate market states. MATS enforcement created substantial uncertainty: the perceived enforcement probability dropped to 43%. Resolving uncertainty early would increase profits by $1.39 billion but also pollution costs by $0.652–1.776 billion. Had exit been unlikely, resolving uncertainty early would have decreased pollution.},
}

@article{ZHANG2022112215,
  title = {A review of publicly available data sources for models to study renewables integration in China's power system},
  journal = {Renewable and Sustainable Energy Reviews},
  volume = {159},
  pages = {112215},
  year = {2022},
  issn = {1364-0321},
  doi = {https://doi.org/10.1016/j.rser.2022.112215},
  url = {https://www.sciencedirect.com/science/article/pii/S1364032122001381},
  author = {Xiaodong Zhang and Dalia Patino-Echeverri and Mingquan Li and Libo Wu},
  keywords = {Power system operation, Renewable energy, Model, Simulation, Data quality, China},
  abstract = {The ongoing transformation of the world's energy system requires detailed power-system models that help plan a cost-effective and reliable integration of variable renewables and demand-side resources. The quality and depth of the results of these models depend on the existence of trustworthy, complete, and high-resolution data on extant electric power assets and the demand they serve, wind and solar resources, and projections on costs and performance of technologies that could be developed during the next three decades. This paper assesses the quality of China's power system's publicly available data compared to the U.S. It concludes that despite growing use of power system models to inform and analyze Chinese energy policy, the availability of necessary data is still a significant barrier that severely limits the transparency, replicability, relevance, and usefulness of their results.}
}

@techreport{CarbonStranding2021,
  author      = {Tyler Fitch},
  editor      = {Tyler H. Norris},
  title       = {Carbon Stranding: Climate Risk and Stranded Assets in Duke's Integrated Resource Plan},
  institution = {Energy Transition Institute},
  year        = {2021},
  month       = {January},
  url         = {https://votesolar.org/wp-content/uploads/2021/02/ETI_CarbonStrandingReport_2021.pdf},
  urldate     = {2021-10-14}
}


@techreport{PutGasOnStandby,
  author      = {Sims, Jonathan and
                 Hillenbrand von der Neyen, Catharina and
                 D‘souza, Durand and
                 Chau, Lily and
                 González-Jiménez, Nicolás and
                 Sani, Lorenzo},
  abstract    = {Unabated gas plants’ future role in the power system should be predominantly limited
                 to backup reserve to allow for flexible low carbon forms of supply to fully emerge.
                 Events in 2021 have brought the extreme levels of price and supply volatility
                 present in the global gas market to the fore of discussions over future energy
                 system dynamics.  Wholesale gas prices have risen to record highs across key supply
                 hubs, highlighting the levels of  market  risk  that  gas-fired  power stations  are
                 exposed  to,  and demonstrating  the  urgent  requirement for increased investment
                 in alternative forms of flexible supply which are steadily emerging.  Even before
                 this year’s crisis however, gas-fired power stations across Europe and the US were
                 already  confronting  declining operating  profitability and rapidly  growing
                 competition  from  low carbon sources. A steady shift towards the primary use of
                 such technologies is vital if net zero emissions goals are to be achieved.  While
                 small amounts of unabated gas-fired capacity may well be required to remain
                 available in these regions and to sit predominantly idle as back up peaking capacity
                 to ensure long- term system supply security, we believe that this should be the
                 limit to such units’ future role.  This report aims to demonstrate that stakeholders
                 committing to the long-term funding of such assets are already risking the loss of
                 billions of dollars, while the risks of continued investment will only grow
                 further.},
  institution = {Carbon Tracker Initiative},
  title       = {Put Gas On Standby},
  year        = {2021},
  url         = {https://carbontracker.org/reports/put-gas-on-standby/},
  urldate     = {2021-10-30}
}

@article{TransmissionSyndicate,
  author  = {Ari Peskoe},
  journal = {Energy Law Journal},
  number  = {1},
  title   = {Is the Utility Transmission Syndicate Forever?},
  volume  = {42},
  year    = {2021},
  url     = {https://www.eba-net.org/assets/1/6/5_-_%5BPeskoe%5D%5B1-66%5D.pdf},
  urldate = {2021-11-01},
  doi     = {10.2139/ssrn.3770740}
}

@article{doi:10.1146/annurev-environ-020220-061831,
author = {Donti, Priya L. and Kolter, J. Zico},
title = {Machine Learning for Sustainable Energy Systems},
journal = {Annual Review of Environment and Resources},
volume = {46},
number = {1},
pages = {719-747},
year = {2021},
doi = {10.1146/annurev-environ-020220-061831},
URL = {https://doi.org/10.1146/annurev-environ-020220-061831},
eprint = {https://doi.org/10.1146/annurev-environ-020220-061831},
abstract = {In recent years, machine learning has proven to be a powerful tool for
            deriving insights from data. In this review, we describe ways in which machine
            learning has been leveraged to facilitate the development and operation of
            sustainable energy systems. We first provide a taxonomy of machine learning
            paradigms and techniques, along with a discussion of their strengths and
            limitations. We then provide an overview of existing research using machine
            learning for sustainable energy production, delivery, and storage. Finally,
            we identify gaps in this literature, propose future research directions, and
            discuss important considerations for deployment.}
}

@book{o2021quantifying,
  title={Quantifying Operational Resilience Benefits of the Smart Grid},
  author={O'Fallon, Cheyney and Gopstein, Avi},
  year={2021},
  doi={10.6028/NIST.TN.2137},
  url={https://doi.org/10.6028/NIST.TN.2137},
  publisher={Department of Commerce. National Institute of Standards and Technology},
  abstract={Automated systems for network protection, outage management, and restoration
            enable electric utilities to maintain service continuity through network
            reconfiguration. We evalu- ate the impact of interoperability investments on
            distribution system resilience during Hur- ricane Irma through a reduced
            form analysis of sustained customer outages. The expected number of
            interruption hours during that hurricane was relatively lower for regions of
            the Florida distribution grid that invested more in interoperability
            enhancements, all else being equal. We use advanced metering infrastructure
            penetration as a proxy and leading indica- tor of investment in
            interoperability enhancements. Employing only publicly available data
            resources, we conservatively estimate that Florida counties that made these
            investments re- alized nearly $1.7 billion of operational resilience
            benefits in the form of avoided customer interruption costs during Hurricane
            Irma.}
}

@article{huppmann2021pyam,
  title={pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios},
  author={Huppmann, Daniel and Gidden, Matthew J and Nicholls, Zebedee and H{\"o}rsch, Jonas and Lamboll, Robin and Kishimoto, Paul N and Burandt, Thorsten and Fricko, Oliver and Byers, Edward and Kikstra, Jarmo and others},
  journal={Open Research Europe},
  volume={1},
  year={2021},
  publisher={European Commission, Directorate General for Research and Innovation}
}


@techreport{CoalCostCrossover2021,
  author      = {Eric Gimon and Amanda Meyers and Mike O'{B}oyle},
  title       = {Coal Cost Crossover 2.0},
  institution = {Energy Innovation},
  year        = {2021},
  url         = {https://energyinnovation.org/publication/the-coal-cost-crossover-2021/},
  urldate     = {2021-10-14}
}

@misc{OSClimate,
  author       = {Open Source Climate},
  title        = {Open Source Climate Initiative},
  howpublished = {Website},
  url          = {https://www.os-climate.org/},
  urldate      = {2021-10-15}
}

@article{OpenEnergyOutlook,
  author  = {Joseph F. DeCarolis and
             Paulina Jaramillo and
             Jeremiah X. Johnson and
             David L. McCollum and
             Evelina Trutnevyte and
             David C. Daniels and
             Gökçe Akın-Olçum and
             Joule Bergerson and
             Soolyeon Cho and
             Joon-Ho Choi and
             Michael T. Craig and
             Anderson R. de Queiroz and
             Hadi Eshraghi and
             Christopher S. Galik and
             Timothy G. Gutowski and
             Karl R. Haapala and
             Bri-Mathias Hodge and
             Simi Hoque and
             Jesse D. Jenkins and
             Alan Jenn and
             Daniel J.A. Johansson and
             Noah Kaufman and
             Juha Kiviluoma and
             Zhenhong Lin and
             Heather L. MacLean and
             Eric Masanet and
             Mohammad S. Masnadi and
             Colin A. McMillan and
             Destenie S. Nock and
             Neha Patankar and
             Dalia Patino-Echeverri and
             Greg Schivley and
             Sauleh Siddiqui and
             Amanda D. Smith and
             Aranya Venkatesh and
             Gernot Wagner and
             Sonia Yeh and
             Yuyu Zhou},
  title   = {Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts},
  journal = {Joule},
  volume  = {4},
  issue   = {12},
  pages   = {2523-2526},
  year    = {2020},
  month   = {12},
  url     = {https://doi.org/10.1016/j.joule.2020.11.002},
  doi     = {10.1016/j.joule.2020.11.002}
}

@techreport{EconFlexSolar,
  author      = {Steven Dahlke and
                 Mahesh Morjaria and
                 Vahan Gevorgian and
                 Barry Mather},
  institution = {First Solar},
  title       = {The Economics of Flexible Solar for Electricity Markets in Transition},
  year        = {2020},
  url         = {https://www.firstsolar.com/es-CSA/-/media/First-Solar/Documents/Grid-Evolution/The_Economics_of_Flexible_Solar_for_Electricity_Markets_in_Transition.ashx},
  urldate     = {2021-11-01}
}

@article{BISTLINE2020114941,
  title    = {Parameterizing open-source energy models: Statistical learning to estimate unknown power plant attributes},
  journal  = {Applied Energy},
  volume   = {269},
  pages    = {114941},
  year     = {2020},
  issn     = {0306-2619},
  doi      = {https://doi.org/10.1016/j.apenergy.2020.114941},
  url      = {https://www.sciencedirect.com/science/article/pii/S0306261920304530},
  urldate  = {2021-11-01},
  author   = {Bistline, John E.T. and Merrick, James H.},
  abstract = {Energy systems models are used to perform energy and environmental policy
              analysis, inform company strategy, and understand implications of technological
              change. Although open-source models can promote transparency and reproducibility, data
              availability and cost can be prohibitive barriers for researchers and other
              stakeholders. This paper presents a novel application of a statistical approach to
              predict unknown power plant parameters in Canada using available data from the United
              States, which can be applied in other settings where critical model inputs are
              missing. We apply two statistical learning methods, linear regression and
              k-nearest-neighbors, and compare their performance on unseen portions of the United
              States data before applying the learned functions to unknown Canadian data. Results
              indicate that reasonable predictions of heatrates and, to a lesser extent, operation
              and maintenance costs are possible even with limited data about age, capacity, and
              power plant types. The nearest-neighbor approach generally outperforms linear
              regressions for the datasets and applications to power plant parameters investigated
              here.}
}

@techreport{CoalCostCrossover2019,
  author      = {Eric Gimon and Mike O'{B}oyle and Christopher T M Clack and Sarah A McKee},
  title       = {The Coal Cost Crossover: Economic Viability of Existing Coal Compared to New Local Wind and Solar Resources},
  institution = {Energy Innovation and Vibrant Clean Energy},
  year        = {2019},
  url         = {https://energyinnovation.org/publication/the-coal-cost-crossover/},
  urldate     = {2021-10-14}
}

@techreport{EIFossilToClean,
  author      = {Ron Lehr},
  title       = {Utility Transition Financial Impacts: From Fossil to Clean},
  institution = {Energy Innovation},
  year        = {2018},
  url         = {https://energyinnovation.org/wp-content/uploads/2018/12/From-Fossil-to-Clean-Brief_12.3.18.pdf},
  urldate     = {2021-10-15}
}