diff --git a/docs/source/model_documentation.md b/docs/source/model_documentation.md index 0e38631f6..d424911b9 100644 --- a/docs/source/model_documentation.md +++ b/docs/source/model_documentation.md @@ -1302,9 +1302,9 @@ ReEDS assumes electrolyzer units have a 20-year lifespan and a 10-year electroly | Technology | Year | Capital Cost (\$/kW) | Variable O&M (\$/kWh) | Fixed O&M (\$/kW-yr) | Electricity Use (kWh/kg) | Natural Gas Use (MMBtu/kg) | |:--:|:--:|:--:|:--:|:--:|:--:|:--:| -| Electrolyzer | 2020 | 1750 | 0 | 101.9 | 56.1 | -- | -| Electrolyzer | 2035 | 550 | 0 | 32.0 | 53.8 | -- | -| Electrolyzer | 2050 | 550 | 0 | 25.0 | 51.5 | -- | +| Electrolyzer | 2020 | 1750 | 0 | 87.5 | 56.1 | -- | +| Electrolyzer | 2035 | 550 | 0 | 27.5 | 53.8 | -- | +| Electrolyzer | 2050 | 550 | 0 | 27.5 | 51.5 | -- | | Steam methane reforming | 2020 | 649 | 0.087 | 20.9 | 0.88 | 0.192 | | Steam methane reforming | 2035 | 634 | 0.087 | 20.4 | 0.88 | 0.192 | | Steam methane reforming | 2050 | 622 | 0.087 | 20.0 | 0.88 | 0.192 | @@ -1379,8 +1379,8 @@ Fixed and variable O&M assumptions are taken from Exhibit 5-19 (Case B31A) of th ### Direct Air Capture The model can also procure negative emissions by removing and storing CO2 from the atmosphere using direct air capture. -DAC is represented as a sorbent design that uses only electricity as an input, with an energy consumption of 3.72 MWh per tonne of CO2 removed. -Overnight capital costs are assumed to be \$1,932 per tonne-year capture capacity, with annual FOM costs of 4.6% of the capital costs and nonfuel VOM costs of \$21 per tonne. +DAC is represented as a sorbent design that uses only electricity as an input, with an energy consumption of 4.40 MWh per tonne of CO2 removed. +In 2019 dollars, overnight capital costs are assumed to be \$931 per tonne-year of capture capacity, with annual FOM costs of 3.74% of the capital costs and nonfuel VOM costs of \$46.69 per tonne. ### CO2 Transport and Storage @@ -2901,9 +2901,8 @@ For imported electricity, the model calculates the regional emissions rate (metr In scenarios that also have a national carbon cap that reaches zero emissions, the emission intensity of California imports is also set to zero for years when the national carbon cap is zero. Because California's greenhouse gas reduction targets are legislated for all economic sectors whereas ReEDS models only the electricity sector, we rely on published economywide modeling results to estimate electric-sector-specific caps that are used in ReEDS. -In particular, we apply power sector caps based on the annual California electric sector emissions (from in-state and imported electricity) from the California Public Utilities Commission {cite}`cpucDecisionSettingRequirements2018`, which provides guidance for a 42 million tCO2 cap by 2030. -We enforce that cap from 2030 to 2050. -The pre-2030 cap ramps linearly from 60 million tCO2 in 2020 to the 42 million tCO2 in 2030. +In particular, we apply power sector caps based on annual California electric sector emissions (from in-state and imported electricity) and guidance from the California Public Utilities Commission {cite}`cpucDecisionSettingRequirements2018,cpuc_2022`. +The modeled trajectory declines from approximately 59 million tCO2 in 2020 to 38 million tCO2 in 2030 and 15 million tCO2 in 2045, then remains at 15 million tCO2 through 2050. California's RPS policy is also included in ReEDS. #### Delaware carbon cap @@ -3159,7 +3158,8 @@ For these states, the nuclear power plants are not allowed to retire until after The policy end dates are taken from EIA {cite:year}`eiaElectricPowerMonthly2019a`. In addition, there are [several states that do not allow new nuclear power](https://www.ncsl.org/environment-and-natural-resources/states-restrictions-on-new-nuclear-power-facility-construction). -These states include California, Illinois, Maine, Massachusetts, Minnesota, New York (Long Island only), Oregon, Rhode Island, and Vermont. Nuclear ban is not imposed for Connecticut since Act25-127 allows construction of advanced nuclear facilities in municipalities with referendums. +These states include California, Maine, Massachusetts, Minnesota, New York (Long Island only), Oregon, Rhode Island, and Vermont. +A nuclear ban is not imposed for Connecticut since Act25-127 allows construction of advanced nuclear facilities in municipalities with referendums. ### Other Policy Capabilities diff --git a/docs/source/references.bib b/docs/source/references.bib index 8316c2a93..5aad7b9a9 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -1,3 +1,13 @@ +@techreport{_, + title = {Queued {{Up}}: 2026 {{Edition}}, {{Characteristics}} of {{Power Plants Seeking Transmission Interconnection As}} of the {{End}} of 2025}, + author = {Rand, Joseph and Cheyette, Anna and Talley, Chris and Zhang, Steven and Gorman, Will and Wiser, Ryan H and Seel, Joachim and Jeong, Seongeun and Kahrl, Fredrich}, + year = 2026, + month = may, + address = {Berkeley, CA}, + institution = {Lawrence Berkeley National Laboratory}, + url = {https://emp.lbl.gov/publications/queued-2026-edition-characteristics} +} + @misc{_federalenergyregulatorycommission, title = {Reliability {{Explainer}}}, author = {Federal Energy Regulatory Commission}, @@ -153,6 +163,15 @@ @article{2025_catania_etal abstract = {The field of energy system modelling is experiencing significant development, driven by the urgent need to redesign the national energy systems to achieve carbon neutrality. A growing interest regards long-term energy system models, which enable tracking the pathway and not only the final need for installations. The increase in complexity may easily lead them to face computational limits. Therefore, modelling approaches are required that cluster data to reduce the size of the problem while limiting errors and inaccuracies. This article studies the impact of temporal clustering on the performances of a sector-integrated energy system model, considering the double-layer clustering scheme operating on two distinct temporal scales: intra-year and inter-year. The former is addressed through typical-day clustering (k-means and k-medoids), while the latter introduces multi-year gaps between representative years. This methodology is implemented in the open-source framework oemof, which is customized to the dual clustering approach. The study addresses a sector-integrated energy system, built on the Italian structure, with a multi-vector and multi-sector perspective along the 2020--2050 horizon. The impact is investigated by comparing multiple options with varying number of typical days and multi-year gap, comparing each configuration with a benchmark without clustering. The approach yields coherent representations of the energy system evolution, simultaneously reducing the memory usage down to 4~\%. The outcomes show the benefits of balancing the number of representative years with the number of representative days, suggesting that such a trade-off leads to significant computational advantages. Although literature shows that time-series reduction is case-dependent, the double-layer clustering scheme appears promising for application on even more complex models, where a full-hour resolution would be computationally intractable.} } +@techreport{2025_rand_etal, + title = {Queued {{Up}}: 2025 {{Edition}}, {{Characteristics}} of {{Power Plants Seeking Transmission Interconnection As}} of the {{End}} of 2024}, + author = {Rand, Joseph and Manderlink, Nick and Zhang, Steven and Talley, Chris and Gorman, Will and Wiser, Ryan H and Seel, Joachim and Kemp, Julie Mulvaney and Jeong, Seongeun and Kahrl, Fredrich}, + year = 2025, + month = dec, + address = {Berkeley, CA}, + institution = {Lawrence Berkeley National Lab} +} + @techreport{2025_wilson_etal, title = {Power {{Demand Forecasts Revised Up}} for {{Third Year Running}}, {{Led}} by {{Data Centers}}}, author = {Wilson, John D and Meyer, Sophie and Zimmerman, Zach and Gramlich, Rob}, @@ -348,6 +367,24 @@ @article{abdelazizAssessingLongtermFuture2024 langid = {english} } +@inproceedings{abdelkaderTwoStageOptimizationFramework2026, + title = {Two-{{Stage Optimization Framework}} for {{Dynamic Line Rating Implementation}}}, + booktitle = {2026 {{IEEE}}/{{PES Transmission}} and {{Distribution Conference}} and {{Exposition}} ({{T}}\&{{D}})}, + author = {Abdelkader, Alaaeldein and Chaudhary, Chandan and Pei, Yansong and Benidris, Mohammed and Mitra, Joydeep}, + year = 2026, + month = may, + pages = {1--5}, + publisher = {IEEE}, + address = {Chicago, IL, USA}, + doi = {10.1109/TD48022.2026.11562678}, + url = {https://ieeexplore.ieee.org/document/11562678/}, + urldate = {2026-06-23}, + abstract = {Transmission line capacities are the limiting factor in integrating new renewable energy resources. Expanding transmission capacity is often unavoidable; however, constructing new lines is economically burdensome and time-consuming. As an alternative, dynamic line rating (DLR) approaches offer a practical and cost-effective means to enhance network capacity. Most existing DLR implementations are driven by line congestion indicators, and typically target the most congested lines. While this approach is optimal when a single line is congested, it may not yield the best results in multi-line congestion scenarios. It can potentially remove congestion from other lines or, conversely, intensify it. Such interactions are often overlooked in current practice, leading to suboptimal deployment decisions. To address this challenge, we propose a two-stage optimization framework for the strategic implementation of DLR, accounting for networkwide impacts, associated costs, and technology-specific characteristics. Furthermore, the framework incorporates the cost of load shedding into the decision-making process. In the first stage, system operating costs including generation costs and load shedding penalties, are minimized. In the second stage, the framework optimizes DLR deployment by evaluating implementation costs alongside the interdependence and congestion impacts across all transmission lines. This comprehensive approach enables more efficient and economically justified DLR investments, enhancing both reliability and cost-efficiency in transmission network operation. Validation using the IEEE Reliability Test System under realistic operating conditions including heatwave events demonstrates significant cost savings through strategic DLR deployment.}, + copyright = {https://doi.org/10.15223/policy-029}, + isbn = {979-8-3315-5569-6}, + langid = {english} +} + @article{abdullaLimitsDeploymentNuclear2019, title = {Limits to Deployment of Nuclear Power for Decarbonization: {{Insights}} from Public Opinion}, shorttitle = {Limits to Deployment of Nuclear Power for Decarbonization}, @@ -380,6 +417,19 @@ @article{abi-samraWhenBoughBreaks2014 abstract = {Preparing for, responding to, coping with, and managing the aftermath of severe impacts on electrical power system grids due to extreme weather events resulting from climate change are all receiving growing attention. Recent severe events in North America and across the globe have stressed the ability of electrical utilities to maintain the continuity and reliability of supply, with many customers experiencing extended power outages. Hurricane Sandy, recent severe winter snow and ice storms in the northeastern United States and eastern Canada, Supercyclone Phailin in India, and Typhoon Haiyan in the Philippines are prime examples. This article is sponsored by members of the Climate Change Technology Subcommittee of the IEEE Power \& Energy Society Energy Development and Power Generation Committee, whose chair is Tom Baumann.} } +@techreport{abou-jaoudeLiteratureReviewAdvanced2023, + title = {Literature {{Review}} of {{Advanced Reactor Cost Estimates}}}, + author = {{Abou-Jaoude}, Abdalla and Lin, Linyu and Bolisetti, Chandrakanth and Worsham, Elizabeth and Larsen, Levi and Epiney, Aaron}, + year = 2023, + month = jun, + number = {INL/RPT--23-72972-Rev000, 1986466}, + pages = {INL/RPT--23-72972-Rev000, 1986466}, + doi = {10.2172/1986466}, + url = {https://www.osti.gov/servlets/purl/1986466/}, + urldate = {2026-07-07}, + langid = {english} +} + @techreport{aboujaoudeLiteratureReviewAdvanced2023, title = {Literature {{Review}} of {{Advanced Reactor Cost Estimates}}}, author = {Abou Jaoude, Abdalla and Bolisetti, Chandrakanth and Lin, Linyu and Larsen, Levi and Epiney, Aaron and Worsham, Elizabeth}, @@ -1300,6 +1350,21 @@ @techreport{astrapeconsultingEstimationMarketEquilibrium2021 month = jan } +@article{asuegaTechnoeconomicAnalysisAdvanced2023, + title = {Techno-Economic Analysis of Advanced Small Modular Nuclear Reactors}, + author = {Asuega, Anthony and Limb, Braden J. and Quinn, Jason C.}, + year = 2023, + month = mar, + journal = {Applied Energy}, + volume = {334}, + pages = {120669}, + issn = {0306-2619}, + doi = {10.1016/j.apenergy.2023.120669}, + url = {https://www.sciencedirect.com/science/article/pii/S0306261923000338}, + urldate = {2026-07-07}, + abstract = {Small modular nuclear reactors (SMRs) represent a robust opportunity to develop low-carbon and reliable power with the potential to meet cost parity with conventional power systems. This study presents a detailed, bottom-up economic evaluation of a 12~\texttimes ~77 MWe (924 MWe total) light-water SMR (LW-SMR) plant, a 4~\texttimes ~262 MWe (1,048 MWe) gas-cooled SMR (GC-SMR) plant, and a 5~\texttimes ~200 MWe (1,000 MWe total) molten salt SMR (MS-SMR) plant. Cost estimates are derived from equipment costs, labor hours, material inputs, and process-engineering models. The advanced SMRs are compared to natural gas combined cycle plants with and without post-combustion carbon capture and a conventional large nuclear reactor. Overnight capital cost (OCC) and levelized cost of energy (LCOE) estimates are developed. The OCC of the LW-SMR, GC-SMR, and MS-SMR are found to be \$4,844/kW, \$4,355/kW, and \$3,985/kW respectively. The LCOE of the LW-SMR, GC-SMR, and MS-SMR are found to be \$89.6/MWh, \$81.5/MWh, and \$80.6/MWh respectively. A Monte Carlo analysis is performed, for which the OCC and construction time of the LW-SMR is found to have a lower mean and standard deviation than a conventional large reactor. The LW-SMR OCC is found to have a mean of \$5,233/kW with a standard deviation of \$658/kW and a 90~\% probability of remaining between \$4,254/kW and \$6,399/kW, while the construction duration is found to have a mean of 4.5~years with a standard deviation of 0.8~years and a 90~\% probability of remaining between 3.4 and 6.0~years. The economic impact of economies of scale, simplification, modularization, and construction time for SMRs are discussed. Additionally, policy implications for direct SMR capital subsidies and the impact of a carbon tax on natural gas emissions are explored.} +} + @misc{ATB2023, title = {Annual {{Technology Baseline}} 2023}, shorttitle = {{{ATB2023}}}, @@ -1749,17 +1814,6 @@ @techreport{barboseStateRenewablesPortfolio2024 langid = {english} } -@techreport{barboseStateRenewablesPortfolio2026, - title = {{{State Electricity Resource Standards}}: 2026 {{Data Update}}}, - author = {Barbose, Galen}, - year = 2026, - month = jun, - address = {Berkeley, CA}, - institution = {Lawrence Berkeley National Laboratory}, - url = {https://emp.lbl.gov/sites/default/files/2026-06/State%20Electricity%20Resource%20Standards-2026%20Data%20Update.pdf}, - langid = {english} -} - @techreport{barboseTrackingSun102017, title = {Tracking the {{Sun}} 10: {{The Installed Price}} of {{Residential}} and {{Non-Residential Photovoltaic Systems}} in the {{United States}}}, author = {Barbose, Galen and Darghouth, Naim}, @@ -1875,6 +1929,17 @@ @article{basoreComparingSupplyDemand langid = {english} } +@misc{basoreQ12025SolarPhotovoltaic, + title = {Q1-2025 {{Solar Photovoltaic System Cost Benchmarks}}}, + author = {Basore, Paul and Dummit, Krysta and Thomas, Andy and Taylor, Margaret and Benoliel, Peter and Feldman, David and Narayanaswami, Meenakshi and Ramasamy, Vignesh and Woodhouse, Michael and Zuboy, Jarett and Braid, Jennifer and Jost, Norman and Sproul, Evan}, + pages = {7 files.}, + publisher = {National Laboratory of the Rockies}, + doi = {10.7799/3009689}, + url = {https://www.osti.gov/servlets/purl/3009689}, + urldate = {2026-07-07}, + abstract = {The U.S. Department of Energy's solar office and its national laboratory partners analyze cost data for U.S. solar photovoltaic systems to develop cost benchmarks to measure progress toward goals and guide research and development programs. The cost data is available in an Excel spreadsheet model that can be downloaded here.} +} + @article{batasbjelicSimulationbasedOptimizationSustainable2015, title = {Simulation-Based Optimization of Sustainable National Energy Systems}, author = {Batas Bjeli{\'c}, Ilija and Rajakovi{\'c}, Nikola}, @@ -4271,19 +4336,19 @@ @techreport{campbellWeatherRelatedPowerOutages2012 url = {https://www.ourenergypolicy.org/wp-content/uploads/2016/02/R42696.pdf} } -@techreport{canadaenergyregulatorCanadasEnergyFuture2026, +@techreport{canadaenergyregulatorCanadasEnergyFuture2023, title = {Canada's {{Energy Future Data Appendices}}}, author = {{Canada Energy Regulator}}, - year = 2026, + year = 2023, institution = {Canada Energy Regulator}, url = {https://doi.org/10.35002/zjr8-8x75} } -@misc{canadaenergyregulatorElectricityTradeSummary2026, +@misc{canadaenergyregulatorElectricityTradeSummary2024, title = {Electricity {{Trade Summary}}}, author = {{Canada Energy Regulator}}, - year = 2026, - month = jun, + year = 2024, + month = dec, url = {https://www.cer-rec.gc.ca/en/data-analysis/energy-commodities/electricity/statistics/electricity-trade-summary/index.html} } @@ -4640,8 +4705,7 @@ @misc{cerElectricityTradeSummary2024 url = {https://www.cer-rec.gc.ca/en/data-analysis/energy-commodities/electricity/statistics/electricity-trade-summary/index.html}, urldate = {2024-09-12}, abstract = {Electricity Trade Summary}, - langid = {english}, - annotation = {Last Modified: 2024-06-14} + langid = {english} } @article{chadalavadaISONewEngland, @@ -5079,25 +5143,7 @@ @techreport{cigreGuideSelectionWeather2006 year = 2006, institution = {CIGRE}, url = {https://www.e-cigre.org/publications/detail/299-guide-for-the-selection-of-weather-parameters-for-bare-overhead-conductor-ratings.html}, - langid = {english}, - annotation = {Tapani Seppa\\ -Afshin Salehian\\ -Kresimir Bakic\\ -William Chisholm\\ -Nicholas DeSantis\\ -Svein Fikke\\ -Dale Douglass\\ -Michelle Gaudry\\ -Anand Goel\\ -Sven Hoffmann\\ -Javier Iglesias\\ -Andrew Maxwell\\ -Dennis Mize\\ -Ralf Puffer\\ -Jerry Reding\\ -Jimmy Robinson\\ -Rob Stephen\\ -Woodrow Whitlatch} + langid = {english} } @techreport{cigreGuideThermalRating2014, @@ -5318,8 +5364,7 @@ @techreport{cohenAdvancedHydropowerPSH2022 @unpublished{cohenClimateWaterElectricityEvaluatingClimateinduced[inreview], title = {Climate-{{Water-Electricity}}: {{Evaluating}} Climate-Induced Changes to the Future Development of the {{U}}.{{S}}. Electricity Sector}, - author = {Cohen, Stuart M. and Meldrum, James R. and Macknick, Jordan and Averyt, Kristen and Rangwala, Imtiaz}, - year = {[in review]} + author = {Cohen, Stuart M. and Meldrum, James R. and Macknick, Jordan and Averyt, Kristen and Rangwala, Imtiaz} } @techreport{cohenComponentLevelBottomUpCost2023, @@ -6102,6 +6147,14 @@ @techreport{cowiestollContributionsSolarStorage2020 langid = {english} } +@techreport{cpuc_2022, + title = {D2202004 {{Decision Adopting}} 2021 {{Preferred System Plan}}}, + author = {CPUC}, + year = 2022, + institution = {California Public Utilities Commission}, + url = {https://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M451/K412/451412947.PDF} +} + @techreport{cpucceccaisoRootCauseAnalysis2021, title = {Root {{Cause Analysis}}}, author = {CPUC, CEC, CAISO}, @@ -7659,8 +7712,7 @@ @techreport{doePathwaysCommercialLiftoff2024 month = apr, institution = {DOE}, url = {https://liftoff.energy.gov/innovative-grid-deployment/}, - langid = {english}, - annotation = {https://liftoff.energy.gov/wp-content/uploads/2024/05/Liftoff\_Innovative-Grid-Deployment\_Final\_5.2-1.pdf} + langid = {english} } @misc{doePendingApplicationsDepartment2016, @@ -9296,6 +9348,13 @@ @techreport{electricreliabilitycounciloftexasSeasonalAssessmentResource2022 url = {https://www.ercot.com/files/docs/2022/05/16/SARA_Summer2022.pdf} } +@article{EliminatingDerateCarbon, + title = {Eliminating the {{Derate}} of {{Carbon Capture Retrofits}}}, + author = {{K. Buchheit} and {N. Kuehn}}, + journal = {National Energy Technology Laboratory}, + langid = {english} +} + @techreport{ellerEnergyStorageFot2016, title = {Energy {{Storage}} Fot the {{Grid}} and {{Ancillary Services}}}, author = {Eller, Alex and Dehamna, Anissa}, @@ -10287,8 +10346,7 @@ @article{falcioneOptimalCostEnergy2022 url = {https://www.politesi.polimi.it/handle/10589/198255}, urldate = {2025-10-03}, abstract = {LAUREA MAGISTRALE}, - langid = {english}, - annotation = {Accepted: 2023-04-19T14:55:38Z} + langid = {english} } @article{falthMENAComparedEurope2020, @@ -10517,6 +10575,15 @@ @techreport{ferc2023AssessmentDemand2023 langid = {english} } +@techreport{FERC2024Energy, + title = {Energy Primer: {{A}} Handbook of Energy Market Basics}, + author = {{FERC}}, + year = 2023, + month = dec, + institution = {Federal Energy Regulatory Commission}, + url = {https://www.ferc.gov/media/energy-primer-handbook-energy-market-basics} +} + @misc{fercAdvancedNoticeProposed2024, title = {Advanced {{Notice}} of {{Proposed Rulemaking}}: {{Implementation}} of {{Dynamic Line Ratings}}}, author = {{FERC}}, @@ -10738,6 +10805,21 @@ @misc{FormEIA860Detailed2018 urldate = {2020-12-30} } +@article{fossas-tenasEmpiricalEvidenceSlack2026, + title = {Empirical Evidence for Slack in {{Modeling}} to {{Generate Alternatives}}}, + author = {{Fossas-Tenas}, Ariadna and Wen, Xin and Trutnevyte, Evelina}, + year = 2026, + month = sep, + journal = {Energy}, + volume = {360}, + pages = {141552}, + issn = {0360-5442}, + doi = {10.1016/j.energy.2026.141552}, + url = {https://www.sciencedirect.com/science/article/pii/S0360544226016580}, + urldate = {2026-06-29}, + abstract = {Modeling to Generate Alternatives (MGA) has been increasingly used with energy system optimization models to tackle uncertainty, stability of results, non-modeled objectives, and other complexities of the real-world energy transition. However, MGA results are highly contingent on the subjectively assumed value of slack, which defines how much higher the total system costs can be with respect to the costs of the cost-optimal scenario. Here, electricity system hindcasting in 31 European countries is used to quantify deviations between costs of the cost-optimal scenario and the real-world transition represented in an optimization model to provide first-of-the-kind empirical evidence for slack. For a multi-year model, the results indicate that the median value of cost deviations across the countries and across various parametric cost assumptions is 44\% if modeling 30 years ahead and if accounting for costs during the modeled period only. A single-year model for 25 years ahead had a median cost deviation of 59\%. As most current literature uses slack values that are lower or equal to 10\%, these results underscore the importance of using larger and country-specific slack, and at the same time open more fundamental questions about the use of cost-optimization models.} +} + @article{fossDynamicLineRating1990, title = {Dynamic Line Rating in the Operating Environment}, author = {Foss, S.D. and Maraio, R.A.}, @@ -12806,6 +12888,19 @@ @article{hamiltonHowDoesWind2020 langid = {english} } +@techreport{hammondWindfarmOperationsMaintenance2022, + title = {Windfarm {{Operations}} and {{Maintenance}} Cost-{{Benefit Analysis Tool}} ({{WOMBAT}})}, + author = {Hammond, Rob and Cooperman, Aubryn}, + year = 2022, + month = oct, + number = {NREL/TP-5000-83712, 1894867, MainId:84485}, + pages = {NREL/TP-5000-83712, 1894867, MainId:84485}, + doi = {10.2172/1894867}, + url = {https://www.osti.gov/servlets/purl/1894867/}, + urldate = {2026-07-07}, + langid = {english} +} + @techreport{handRenewableElectricityFutures2012, title = {Renewable {{Electricity Futures Study}}. {{Volume}} 1. {{Exploration}} of {{High-Penetration Renewable Electricity Futures}}}, author = {Hand, M. M. and Baldwin, S. and DeMeo, E. and Reilly, J. M. and Mai, T. and Arent, D. and Porro, G. and Meshek, M. and Sandor, D.}, @@ -12866,6 +12961,18 @@ @article{hansenStatusPerspectives1002019 abstract = {This article shows that research in the design of 100\% renewable energy systems in scientific articles is fairly new but has gained increasing attention in recent years. In total, 180 articles published since 2004 have been identified and analysed. Many of these articles have a predominant focus on the electricity sector. However, an increasing number of studies apply a cross-sectoral holistic approach on the entire energy system. Most studies analyse energy systems for the final 100\% renewable state, while a small, though increasing, number also investigate energy transition pathways; how to reach the target. Europe, and thereafter the US and Australia, are well researched, while other parts of the world lack behind, and there is a focus on individual country studies. Henceforward, there is a need for applying a cross-sectoral holistic approach as well as coordinating individual country studies with the global context.} } +@misc{hansonGeothermalElectricityTechnology2013, + title = {Geothermal {{Electricity Technology Evaluation Model}} ({{GETEM}}) {{Individual Case Files}} and {{Summary Spreadsheet}} ({{GETEM}} Version {{Spring}} 2013)}, + author = {Hanson, Steven}, + year = 2013, + publisher = {DOE Geothermal Data Repository; Idaho National Laboratory (INL), Idaho Falls, ID (United States)}, + doi = {10.15121/1148822}, + url = {http://www.osti.gov/servlets/purl/1148822/}, + urldate = {2026-07-07}, + abstract = {This group of files-- 10 GETEM individual case files and 1 summary spreadsheet-- contain final data from the revisions between summer 2011 and spring 2013.}, + langid = {english} +} + @article{haoExtendedTransmissionLine2008, title = {Extended Transmission Line Loadability Curve by Including Voltage Stability Constrains}, author = {Hao, Jin and Xu, Wilsun}, @@ -13091,6 +13198,21 @@ @article{haszeldineCarbonCaptureStorage2009 langid = {english} } +@article{haugenRepresentationUncertaintyMarket2023, + title = {Representation of Uncertainty in Market Models for Operational Planning and Forecasting in Renewable Power Systems: A Review}, + shorttitle = {Representation of Uncertainty in Market Models for Operational Planning and Forecasting in Renewable Power Systems}, + author = {Haugen, Mari and Farahmand, Hossein and Jaehnert, Stefan and Fleten, Stein-Erik}, + year = 2023, + month = jul, + journal = {Energy Systems}, + issn = {1868-3967, 1868-3975}, + doi = {10.1007/s12667-023-00600-4}, + url = {https://link.springer.com/10.1007/s12667-023-00600-4}, + urldate = {2026-05-25}, + abstract = {As the power system is becoming more weather-dependent and integrated to meet decarbonization targets, the level and severity of uncertainty increase and inevitably introduce higher risk of demand rationing or economic loss. This paper reviews the representation of uncertainty in power market models for operational planning and forecasting. A synthesis of previous reviews is used to find the prevalence of stochastic tools in power and energy system applications, and it concludes that most approaches are deterministic. A selection of power market tools handling uncertainty is reviewed in terms of the uncertain parameters they capture, and the methods used to describe them. These all use probabilistic methods and typically cover weather-related uncertainty, including demand. Random outages are also covered by several short-term power market models, while uncertainty in fuel and CO2 emission prices were generally not found to be included, nor other types of uncertainty. A gap in power market models representing multiple dimensions of uncertainty, solvable on a realistic, large-scale system in a reasonable time, is identified. The paper concludes with a discussion on topics to address when representing uncertainty, where the main challenges are that uncertainty can be difficult to describe and quantify, and including uncertainty adds additional complexity and computational burden to the problem.}, + langid = {english} +} + @article{hauglustaineClimateBenefitFuture2022, title = {Climate Benefit of a Future Hydrogen Economy}, author = {Hauglustaine, Didier and Paulot, Fabien and Collins, William and Derwent, Richard and Sand, Maria and Boucher, Olivier}, @@ -13666,6 +13788,24 @@ @article{hledikEconomicPotentialEnergy2018 langid = {english} } +@article{hobbsAdaptiveTransmissionPlanning2016, + title = {Adaptive {{Transmission Planning}}: {{Implementing}} a {{New Paradigm}} for {{Managing Economic Risks}} in {{Grid Expansion}}}, + shorttitle = {Adaptive {{Transmission Planning}}}, + author = {Hobbs, Benjamin F. and Xu, Qingyu and Ho, Jonathan and Donohoo, Pearl and Kasina, Saamrat and Ouyang, Jasmine and Park, Sang Woo and Eto, Joseph and Satyal, Vijay}, + year = 2016, + month = jul, + journal = {IEEE Power and Energy Magazine}, + volume = {14}, + number = {4}, + pages = {30--40}, + issn = {1540-7977}, + doi = {10.1109/MPE.2016.2547280}, + url = {http://ieeexplore.ieee.org/document/7491446/}, + urldate = {2026-05-26}, + copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}, + langid = {english} +} + @article{hochartWindTurbinePerformance2008, title = {Wind Turbine Performance under Icing Conditions}, author = {Hochart, Clement and Fortin, Guy and Perron, Jean and Ilinca, Adrian}, @@ -14511,6 +14651,15 @@ @techreport{inlAdvancedConductorScan2023 url = {https://inl.gov/content/uploads/2024/10/23-50856_R12a_-AdvConductorsScanProjectReportCompressed.pdf} } +@techreport{inlRealTimeForecastedDLR2023, + title = {Real-{{Time}} and {{Forecasted DLR Use Cases}}}, + author = {{INL}}, + year = 2023, + number = {INL/RPT-22-69578}, + institution = {Idaho National Laboratory}, + url = {https://inl.gov/content/uploads/2023/03/Real-Time-and-Forecasted-DLR-Use-Cases.pdf} +} + @misc{inskeep50StatesSolar2015, title = {The 50 {{States}} of {{Solar}}}, author = {Inskeep, Benjamin and Case, Ethan and Daniel, Kate and Lips, Brian and Proudlove, Autumn and Shreshta, Achyut and Wright, Kathryn and Cook, Ryan and Laurent, Chad and Gessesse, Eskedar and Hanley, Will}, @@ -14623,6 +14772,22 @@ @techreport{irenaRenewablePowerGeneration2019 url = {https://www.irena.org/publications/2019/May/Renewable-power-generation-costs-in-2018} } +@article{irizarry-martinezOptimalGenerationExpansion2027, + title = {Optimal Generation Expansion Planning with Weather-Dependent Generation and Transmission}, + author = {{Irizarry-Martinez}, Giancarlos and Roald, Line}, + year = 2027, + month = feb, + journal = {Electric Power Systems Research}, + volume = {263}, + pages = {113783}, + issn = {03787796}, + doi = {10.1016/j.epsr.2026.113783}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0378779626010746}, + urldate = {2026-08-18}, + abstract = {Generation Expansion Planning is used to obtain future planning decisions that ensure the system reliability while minimizing cost. However, the past decade has seen an increase in weather-dependent faults in the electric grid, higher shares of weather-dependent renewable generation, and increased reliance on weather-dependent dynamic line ratings. To account for those dependencies, we propose a two-stage stochastic generation expansion planning model integrates leverages a spatial weather model to assess the generation and transmission line availability. This includes both variations of renewable generation capacity factors and dynamic line rating, and weather-dependent outage rates. The proposed framework is tested using a synthetic network, demonstrating that dynamic line rating outperforms static line ratings by reducing total operational costs and load shedding.}, + langid = {english} +} + @article{irizarry-riveraCaseStudyResidential2018, title = {A {{Case Study}} of {{Residential Electric Service Resiliency Through Renewable Energy Following Hurricane Maria}}}, author = {{Irizarry-Rivera}, A. and {Montano-Martinez}, K. and {Alzate-Drada}, S. I. and Andrade, F.}, @@ -15041,6 +15206,12 @@ @article{jalilianAluminumConductorSteelSupported2024 langid = {english} } +@article{james_et_al_2023, + title = {Cost and {{Performance Baseline}} for {{Fossil Energy Plants}}, {{Volume}} 3 -- {{Low Rank Coal}} and {{Natural Gas}} to {{Electricity}}}, + author = {Turner, Marc and James, Robert and Woods, Mark and Schmitt, Tommy and Oakes, Matt and Konrade, Jacob and Bleckinger, Mark and Sturdivan, Mark}, + langid = {english} +} + @misc{jameslittlefieldLifeCycleAnalysis2019, title = {Life {{Cycle Analysis}} of {{Natural Gas Extraction}} and {{Power Generation}}}, author = {{James Littlefield} and {Selina Roman-White} and {Dan Augustine} and {Ambica Pegallapati} and {George G. Zaimes} and {Srijana Rai} and {Gregory Cooney} and {Timothy J. Skone}}, @@ -15080,8 +15251,7 @@ @misc{javanbakht2024IEPRData2025 author = {Javanbakht, Heidi}, year = 2025, month = jul, - url = {https://www.energy.ca.gov/sites/default/files/2025-07/2024_IEPR_Data_Center_Forecast_Recap_ada.pdf}, - annotation = {California Energy Comission} + url = {https://www.energy.ca.gov/sites/default/files/2025-07/2024_IEPR_Data_Center_Forecast_Recap_ada.pdf} } @article{jayadevElectricityInfrastructureFuture2020, @@ -15132,8 +15302,7 @@ @techreport{jenkinsEnhancedDecisionSupport2017 url = {https://dspace.mit.edu/handle/1721.1/130589}, urldate = {2026-04-02}, abstract = {The electric power sector is currently undergoing several important transitions, which individually and collectively have the potential to transform the design, operation, and characteristics of electricity systems, including: decarbonization of electricity supplies; increased adoption of variable renewable energy and distributed energy resources; digitization of power systems; and electrification of greater shares of heating, transportation, and industry. In the face of these transformations, many conventional electricity resource capacity expansion models are no longer adequate for rigorous decision support and policy analysis. This working paper describes the formulation of ``GenX," a highly-configurable electricity resource capacity expansion model that incorporates several state-of-the-art improvements in electricity system modeling to offer improved decision support for a changing electricity landscape. GenX is a constrained optimization model that determines the mix of electricity generation, storage, and demand-side resource investments and operational decisions to meet electricity demand in a future planning year at lowest cost subject to a variety of power system operational constraints and specified policy constraints, such as CO2 emissions limits. The appropriate level of model resolution with regards to chronological variability of electricity demand and renewable energy availability, power system operational detail and unit commitment constraints, and transmission and distribution network representation each vary for a given planning problem or policy question. As such, the GenX model is designed to be highly configurable, with several different degrees of resolution possible on each of these three key dimensions. The model is capable of representing a full range of conventional and novel electricity resources, including thermal generators, variable renewable resources (wind and solar), run-of-river, reservoir and pumped-storage hydroelectric generators, energy storage devices, demand-side flexibility, and several advanced technologies such as high temperature nuclear reactors and carbon capture and storage. Two optional modules also allow modeling of heat storage, industrial heat demand, and co-generation of heat and power; and distributed energy resources deployed at distribution voltages. The model has been implemented in Julia Language.}, - langid = {american}, - annotation = {Accepted: 2021-05-13T17:24:21Z} + langid = {american} } @article{jenkinsGettingZeroCarbon2018, @@ -15258,6 +15427,12 @@ @article{jiLargescaleDataAnalysis2016 langid = {english} } +@misc{JobsEconomicDevelopment, + title = {Jobs and {{Economic Development Impact Models}} \textbar{} {{NLR}}}, + url = {https://www.nlr.gov/analysis/jedi/}, + urldate = {2026-07-07} +} + @techreport{johntsoukalis_et_al_2020, title = {Western {{Energy Imbalance Service}} and {{SPP Western RTO Participation Benefits}}}, author = {Tsoukalis, John and Pfeifenberger, Johannes and Celebi, Metin and Leamon, Sophie and Peacock, Carson and Ganjam, Sharan}, @@ -17157,6 +17332,24 @@ @misc{leeCANOPIContingencyAwareNodal2025 archiveprefix = {arXiv} } +@inproceedings{leeImpactsDynamicLine2022, + title = {Impacts of {{Dynamic Line Ratings}} on the {{ERCOT Transmission System}}}, + booktitle = {2022 {{North American Power Symposium}} ({{NAPS}})}, + author = {Lee, Thomas and Nair, Vineet Jagadeesan and Sun, Andy}, + year = 2022, + month = oct, + pages = {1--6}, + publisher = {IEEE}, + address = {Salt Lake City, UT, USA}, + doi = {10.1109/NAPS56150.2022.10012241}, + url = {https://ieeexplore.ieee.org/document/10012241/}, + urldate = {2026-07-21}, + abstract = {Grid regulators and participants are paying increasing attention to Dynamic Line Ratings (DLR) as a new approach to address transmission system bottlenecks. In this paper, a thorough comparison of DLR, Ambient Adjusted Ratings (AAR), and the traditional Static Line Ratings (SLR) are conducted on a synthetic ERCOT grid. Estimates of DLR and AAR are calculated using an equation based on heat balance physics, along with high-resolution weather data of temperature and wind velocities. A constraint generation method for contingency screening is developed for solving security-constrained optimal power flow. Numerical results suggest that employing DLR could double the benefits compared to those of AAR relative to SLR, in terms of system costs, renewable curtailment, and emissions. Index Terms---Dynamic line ratings, transmission capacity, security-constrained optimal power flow, reliability.}, + copyright = {https://doi.org/10.15223/policy-029}, + isbn = {978-1-6654-9921-7}, + langid = {english} +} + @article{leeInteractionsComplementaritiesTensions2012, title = {Interactions, {{Complementarities}} and {{Tensions}} at the {{Nexus}} of {{Natural Gas}} and {{Renewable Energy}}}, author = {Lee, April and Zinaman, Owen and Logan, Jeffrey and Bazilian, Morgan and Arent, Douglas and Newmark, Robin L.}, @@ -20412,8 +20605,7 @@ @article{morilhatNuclearPowerPlant2019 pages = {32--41}, address = {Germany}, issn = {1435-3199}, - abstract = {Based upon existing experience feedback of French nuclear power plants operated by EDF (Electricit\'e de France), this paper shows that flexible operation of nuclear reactors is possible and has been applied in France by EDF's 58 reactors for more than 30 years without any noticeable or unmanageable impacts: no effects on safety or on the environment, and no noticeable additional maintenance costs, with an additional unplanned capability load factor estimated at only 05 \% EDF's nuclear reactors have the capability to vary their output between 20 \% and 100 \% within 30 minutes, twice a day, when operating in load-following mode Flexible operation requires sound plant design (safety margins, auxiliary equipment) and appropriate operator skills, and early modifications were made to the initial Westinghouse design to enable flexible operation (eg, use of "grey" control rods to vary reactor core thermal power more rapidly than with conventional "black" control rods) The nominal capacities of the present power stations are sufficient, safe and adequate to balance generation against demand and allow renewables to be inserted intermittently, without any additional CO2 emissions It is a clear demonstration of full complementarity between nuclear and renewable energies}, - annotation = {INIS Reference Number: 52059914} + abstract = {Based upon existing experience feedback of French nuclear power plants operated by EDF (Electricit\'e de France), this paper shows that flexible operation of nuclear reactors is possible and has been applied in France by EDF's 58 reactors for more than 30 years without any noticeable or unmanageable impacts: no effects on safety or on the environment, and no noticeable additional maintenance costs, with an additional unplanned capability load factor estimated at only 05 \% EDF's nuclear reactors have the capability to vary their output between 20 \% and 100 \% within 30 minutes, twice a day, when operating in load-following mode Flexible operation requires sound plant design (safety margins, auxiliary equipment) and appropriate operator skills, and early modifications were made to the initial Westinghouse design to enable flexible operation (eg, use of "grey" control rods to vary reactor core thermal power more rapidly than with conventional "black" control rods) The nominal capacities of the present power stations are sufficient, safe and adequate to balance generation against demand and allow renewables to be inserted intermittently, without any additional CO2 emissions It is a clear demonstration of full complementarity between nuclear and renewable energies} } @article{moryadeeInvestigatingPotentialEffects2014, @@ -21760,9 +21952,8 @@ @misc{nrelReEDSModelDocumentation2025 title = {{{ReEDS}} Model Documentation}, shorttitle = {{{ReEDS}} Docs}, author = {{NREL}}, - year = 2025, - month = nov, - url = {https://natlabrockies.github.io/ReEDS-2.0/model_documentation.html} + year = 2026, + url = {https://reeds-model.github.io/ReEDS/model_documentation.html} } @misc{nrelRegionalEnergyDeployment2020, @@ -21815,6 +22006,20 @@ @misc{NSRDB_web url = {https://nsrdb.nlr.gov/} } +@techreport{nunemakerORBITOffshoreRenewables2020, + title = {{{ORBIT}}: {{Offshore Renewables Balance-of-System}} and {{Installation Tool}}}, + shorttitle = {{{ORBIT}}}, + author = {Nunemaker, Jacob and Shields, Matthew and Hammond, Robert and Duffy, Patrick}, + year = 2020, + month = aug, + number = {NREL/TP-5000-77081, 1660132, MainId:26027}, + pages = {NREL/TP-5000-77081, 1660132, MainId:26027}, + doi = {10.2172/1660132}, + url = {https://www.osti.gov/servlets/purl/1660132/}, + urldate = {2026-07-07}, + langid = {english} +} + @article{nunesEnablingSolarElectricity2015, title = {Enabling Solar Electricity with Electric Vehicles Smart Charging}, author = {Nunes, Pedro and Farias, Tiago and Brito, Miguel C.}, @@ -21929,6 +22134,20 @@ @techreport{nyserdaNewYorkState2018 url = {http://documents.dps.ny.gov/public/Common/ViewDoc.aspx?DocRefId={2A1BFBC9-85B4-4DAE-BCAE-164B21B0DC3D}} } +@techreport{oakesCostPerformanceBaseline2023, + title = {Cost and {{Performance Baseline}} for {{Fossil Energy Plants}}, {{Volume}} 5: {{Natural Gas Electricity Generating Units}} for {{Flexible Operation}}}, + shorttitle = {Cost and {{Performance Baseline}} for {{Fossil Energy Plants}}, {{Volume}} 5}, + author = {Oakes, Matt and Konrade, Jacob and Bleckinger, Mark and Turner, Marc and Hughes, Sydney and Hoffman, Hannah and Shultz, Travis and Lewis, Eric}, + year = 2023, + month = may, + number = {DOE/NETL--2023/3855, 1973266}, + pages = {DOE/NETL--2023/3855, 1973266}, + doi = {10.2172/1973266}, + url = {https://www.osti.gov/servlets/purl/1973266/}, + urldate = {2026-07-07}, + langid = {english} +} + @article{ockoClimateConsequencesHydrogen2022, title = {Climate Consequences of Hydrogen Emissions}, author = {Ocko, Ilissa B. and Hamburg, Steven P.}, @@ -23416,6 +23635,14 @@ @techreport{ramasamySolarPhotovoltaicSystem2022 langid = {english} } +@article{ramasamyUSSolarPhotovoltaic2022, + title = {U.{{S}}. {{Solar Photovoltaic System}} and {{Energy Storage Cost Benchmarks}}, {{With Minimum Sustainable Price Analysis}}: {{Q1}} 2022}, + author = {Ramasamy, Vignesh and Zuboy, Jarett and O'Shaughnessy, Eric and Feldman, David and Desai, Jal and Woodhouse, Michael and Basore, Paul and Margolis, Robert}, + year = 2022, + journal = {Renewable Energy}, + langid = {english} +} + @article{ramJobCreationGlobal2020, title = {Job Creation during the Global Energy Transition towards 100\% Renewable Power System by 2050}, author = {Ram, Manish and Aghahosseini, Arman and Breyer, Christian}, @@ -24083,6 +24310,23 @@ @inproceedings{rivasmauricioa.GeopressuredGeothermalSystems2024 volume = {48} } +@article{roaldPowerSystemsOptimization2023, + title = {Power Systems Optimization under Uncertainty: {{A}} Review of Methods and Applications}, + shorttitle = {Power Systems Optimization under Uncertainty}, + author = {Roald, Line A. and Pozo, David and Papavasiliou, Anthony and Molzahn, Daniel K. and Kazempour, Jalal and Conejo, Antonio}, + year = 2023, + month = jan, + journal = {Electric Power Systems Research}, + volume = {214}, + pages = {108725}, + issn = {03787796}, + doi = {10.1016/j.epsr.2022.108725}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0378779622007842}, + urldate = {2026-05-25}, + abstract = {Electric power systems and the companies and customers that interact with them are experiencing increasing levels of uncertainty due to factors such as renewable energy generation, market liberalization, and climate change. This raises the important question of how to make optimal decisions under uncertainty. This paper aims to provide an overview of existing methods for modeling and optimization of problems affected by uncertainty, targeted at researchers with a familiarity with power systems and optimization. We also review some important applications of optimization under uncertainty in power systems and provide an outlook to future directions of research.}, + langid = {english} +} + @techreport{rocha-garridoProposedChangesPJM2019, title = {Proposed {{Changes}} to {{PJM Manual}} 21: {{Calculation}} of {{Capacity Values}} for {{Wind}} and {{Solar Capacity Resources}}}, author = {{Rocha-Garrido}, Patricio}, @@ -24591,6 +24835,22 @@ @article{sasseCosteffectiveOptionsRegional2023 abstract = {With its European Green Deal, the European Union (EU) aims to substantially increase renewable electricity generation and decrease greenhouse gas emissions in the electricity sector by 2035. European countries independently set these targets, but might not account for cross-border infrastructure interdependencies that may facilitate or impede their implementation. This study quantifies regional interdependencies among 36 European countries aggregated to eight regions by applying electricity sector modeling, Modeling to Generate Alternatives, tree-based ensemble method, and scenario discovery. Cost-effectively reaching EU targets in 2035 allows for flexibility in uptake of electricity system infrastructure across European regions, with few exceptions. Reaching EU targets with low costs requires high renewable capacities in Central and Nordic Europe and France, steep reductions of fossil fuel capacities in Central and Eastern Europe, keeping existing nuclear capacities in France, and high storage capacities in Alpine and Central Europe. Regional targets are most influential for increasing renewable capacities, while EU targets are most influential for decreasing greenhouse gas emissions. If EU targets are met, reaching regional targets of Central Europe and keeping nuclear capacities in France substantially reduce infrastructure requirements in Eastern Europe, highlighting the importance of regional cooperation for electricity system planning and policy.} } +@article{sasseDistributionalTradeoffsRegionally2019, + title = {Distributional Trade-Offs between Regionally Equitable and Cost-Efficient Allocation of Renewable Electricity Generation}, + author = {Sasse, Jan-Philipp and Trutnevyte, Evelina}, + year = 2019, + month = nov, + journal = {Applied Energy}, + volume = {254}, + pages = {113724}, + issn = {03062619}, + doi = {10.1016/j.apenergy.2019.113724}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261919314114}, + urldate = {2026-07-06}, + abstract = {Decentralized renewable electricity generation (DREG) has been growing at an unprecedented pace, yet the appropriate spatial allocation and associated regional equity implications remain underinvestigated. In this study, we quantify the trade-offs between cost-efficient (least-cost) and regionally equitable DREG allocation in terms of electricity generation costs, investment needs, and DREG capacity requirements. Using the case of the ambitious and publicly-approved Swiss Energy Strategy 2050, we set up a bottom-up, technology-rich electricity system model EXPANSE with Modeling to Generate Alternatives at a spatial resolution of 2'258 Swiss municipalities. In order to measure regional equity implication, we adapt the concepts of the Lorenz curve and the Gini coefficient. We find a significant trade-off by 2035 in Switzerland: 50\% increase in regional equity when allocating DREG to various Swiss regions on the basis of population or electricity demand leads to 18\% higher electricity generation costs. Least-cost allocation implies concentrating DREG and associated investments to few most productive locations only. Solar PV is the key technology for increasing regional equity. We conclude that in countries with spatially-uneven DREG resources like Switzerland, any policies that focus on cost efficiency should anticipate regional equity implications in advance and, if desired, minimize them by promoting solar PV.}, + langid = {english} +} + @article{sasseLowcarbonElectricitySector2023, title = {A Low-Carbon Electricity Sector in {{Europe}} Risks Sustaining Regional Inequalities in Benefits and Vulnerabilities}, author = {Sasse, Jan-Philipp and Trutnevyte, Evelina}, @@ -24893,6 +25153,28 @@ @article{schmidtProjectingFutureLevelized2019 abstract = {Summary The future role of stationary electricity storage is perceived as highly uncertain. One reason is that most studies into the future cost of storage technologies focus on investment cost. An appropriate cost assessment must be based on the application-specific lifetime cost of storing electricity. We determine the levelized cost of storage (LCOS) for 9 technologies in 12 power system applications from 2015 to 2050 based on projected investment cost reductions and current performance parameters. We find that LCOS will reduce by one-third to one-half by 2030 and 2050, respectively, across the modeled applications, with lithium ion likely to become most cost efficient for nearly all stationary applications from 2030. Investments in alternative technologies may prove futile unless significant performance improvements can retain competitiveness with lithium ion. These insights increase transparency around the future competitiveness of electricity storage technologies and can help guide research, policy, and investment activities to ensure cost-efficient deployment.} } +@techreport{schmitt_et_al_2022, + title = {Cost and {{Performance Baseline}} for {{Fossil Energy Plants}}. {{Volume}} 1: {{Bituminous Coal}} and {{Natural Gas}} to {{Electricity}}}, + author = {{Schmitt, Tommy} and {Leptinsky, Sarah} and {Turner, Marc} and {Zoelle, Alexander} and {White, Charles W.} and {Hughes, Sydney} and {Homsy, Sally} and {Woods, Mark} and {Hoffman, Hannah} and {Shultz, Travis} and {James, Robert}}, + year = 2022, + institution = {National Energy Technology Laboratory}, + url = {https://www.osti.gov/servlets/purl/1893822/}, + urldate = {2026-07-07} +} + +@techreport{schmittCostPerformanceRetrofitting2023, + title = {Cost and {{Performance}} of {{Retrofitting NGCC Units}} for {{Carbon Capture}} -- {{Revision}} 3}, + author = {Schmitt, Tommy and Homsy, Sally and Mantripragada, Hari and Woods, Mark and Hoffman, Hannah and Shultz, Travis and Fout, Timothy and Hackett, Gregory}, + year = 2023, + month = may, + number = {DOE/NETL-2023/3848, 1961845}, + pages = {DOE/NETL-2023/3848, 1961845}, + doi = {10.2172/1961845}, + url = {https://www.osti.gov/servlets/purl/1961845/}, + urldate = {2026-07-07}, + langid = {english} +} + @techreport{schneiderWorldNuclearIndustry2022, title = {The {{World Nuclear Industry Status Report}} 2022}, author = {Schneider, Mycle and Froggatt, Antony and Hazemann, Julie and Ramana, M.V. and Sailer, Michael and Suzuki, Tatsujiro and Von Hirschhausen, Christian and Wimmers, Alexander James and Schneider, Nina and Stienne, Agnes and Meinass, Friedhelm}, @@ -25680,6 +25962,22 @@ @inproceedings{simpkinsdouglasEnergyStorageDeep2023 volume = {47} } +@misc{singhSensitivityAnalysisDynamic2026, + title = {Sensitivity {{Analysis}} of {{Dynamic Line Rating}} for {{ACSR Conductors}} Using {{IEEE-738}}}, + author = {Singh, Shashank and Mishra, Ashish Kumar and P, Vinod M. and Romeis, Christian}, + year = 2026, + month = jul, + number = {arXiv:2607.23536}, + eprint = {2607.23536}, + primaryclass = {eess.SY}, + publisher = {arXiv}, + doi = {10.48550/arXiv.2607.23536}, + url = {http://arxiv.org/abs/2607.23536}, + urldate = {2026-08-18}, + abstract = {Dynamic Line Rating (DLR) is a novel technique that enhances the utilization of transmission line capacity. It is nevertheless unclear how much measurement uncertainty in important environmental parameters affects the DLR calculation. Using the IEEE-738 standard, this paper presents a systematic parametric sensitivity analysis of DLR for a 795 kcmil ACSR Drake conductor. The DLR computation encompasses 832 operating points which includes daytime/nighttime solar scenarios, clear/non-clear atmospheric clearness conditions, wind speeds ranging from 0 to 15.25 m/s, and ambient temperatures between 15 and 50 \textopenbullet C. According to sensitivity analysis, wind sensitivity tends to decrease at higher ambient temperatures, whereas temperature sensitivity tends to increase with wind speed. Pearson correlation analysis indicates a strong negative linear association between DLR and ambient temperature, and a strong positive linear association with wind speed. A regression model incorporates both individual and interactive effects of wind and temperature, and explains over 93\% of the observed DLR variability across all cases. Finally, the observations serve as a guide for operational planning and uncertainty assessment in DLR-based transmission systems.}, + archiveprefix = {arXiv} +} + @article{singhSolarPowerGeneration2013, title = {Solar Power Generation by {{PV}} (Photovoltaic) Technology: {{A}} Review}, shorttitle = {Solar Power Generation by {{PV}} (Photovoltaic) Technology}, @@ -26705,6 +27003,25 @@ @article{sunDataDrivenRepresentativeDay2019 langid = {english} } +@inproceedings{sunDynamicLineRating2026, + title = {Dynamic {{Line Rating}} for {{Offshore Wind Submarine Cables}}: {{Case Studies}} in {{Selected U}}.{{S}}. {{Locations}}}, + shorttitle = {Dynamic {{Line Rating}} for {{Offshore Wind Submarine Cables}}}, + booktitle = {2026 {{IEEE}}/{{PES Transmission}} and {{Distribution Conference}} and {{Exposition}} ({{T}}\&{{D}})}, + author = {Sun, Mucun and Abboud, Alexander W. and Alam, S M Shafiul and Gentle, Jake P.}, + year = 2026, + month = may, + pages = {1--5}, + publisher = {IEEE}, + address = {Chicago, IL, USA}, + doi = {10.1109/TD48022.2026.11562629}, + url = {https://ieeexplore.ieee.org/document/11562629/}, + urldate = {2026-06-23}, + abstract = {Dynamic line rating (DLR) adjusts the thermal capacity of transmission infrastructure based on real-time atmospheric conditions, enabling improved utilization of existing assets. While DLR has been widely studied for overhead lines, its application to submarine export cables for offshore wind (OSW) projects remains underexplored. This paper presents a quantitative framework for evaluating DLR feasibility for high-voltage submarine cables by integrating the IEC 60287 thermal model with oceanographic data from the National Oceanic and Atmospheric Administration's (NOAA) Real-Time Ocean Forecast System (RTOFS). Case studies covering representative sites across major U.S. coastal regions (e.g., Pacific, New England, Mid-Atlantic, South Atlantic, and Gulf Coast) have been conducted. Case study results indicate that regions with greater seasonal seabed temperature variability, such as the Mid-Atlantic, South Atlantic, and Gulf Coast, can achieve up to 10--15\% ampacity gains over static ratings, translating into enhanced operational flexibility and deferred infrastructure upgrades. Conversely, stable cold-water regions like the Pacific exhibit limited benefits. The results demonstrate that applying DLR to OSW submarine cables is technically feasible and may improve grid reliability when combined with appropriate sensing and monitoring systems.}, + copyright = {https://doi.org/10.15223/policy-029}, + isbn = {979-8-3315-5569-6}, + langid = {english} +} + @techreport{sunElectrificationFuturesStudy2020a, title = {Electrification {{Futures Study}}: {{Methodological Approaches}} for {{Assessing Long-Term Power System Impacts}} of {{End-Use Electrification}}}, shorttitle = {Sun2020}, @@ -29654,8 +29971,7 @@ @techreport{xendee2024MarketSurvey2024 author = {{Xendee}}, year = 2024, institution = {Xendee}, - url = {https://xendee.com/industry-survey-report}, - annotation = {https://6572318.fs1.hubspotusercontent-na1.net/hubfs/6572318/MK-5157-Xendee-SoM\_Report-2024\_FINAL.pdf?\_\_hstc=43606970.a684e4dbc748817cf46cbbde6ac9ef4c.1735666957538.1735666957538.1735666957538.1\&\_\_hssc=43606970.1.1735666957538\&\_\_hsfp=897878061} + url = {https://xendee.com/industry-survey-report} } @article{xieDistributionalLabourChallenges2023, @@ -30467,6 +30783,15 @@ @article{zotero-8568 doi = {10.1016/j.isci.2022.103915} } +@techreport{zotero-item-10814, + title = {2025 {{Long-Term Reliability Assessment}}}, + author = {{NERC}}, + year = 2026, + month = jan, + institution = {North American Electric Reliability Corporation}, + url = {https://www.nerc.com/globalassets/our-work/assessments/nerc_ltra_2025.pdf} +} + @misc{ZoteroConnectors, title = {Zotero \textbar{} {{Connectors}}}, url = {https://www.zotero.org/download/connectors}, @@ -30505,163 +30830,3 @@ @misc{zuluagaNodalCapacityExpansion2025 archiveprefix = {arXiv}, langid = {english} } - -@online{JobsEconomicDevelopment, - title = {Jobs and Economic Development Impact Models {\textbar} {NLR}}, - url = {https://www.nlr.gov/analysis/jedi/}, - urldate = {2026-07-07}, - file = {Jobs and Economic Development Impact Models | NLR:files/8170/jedi.html:text/html}, -} - -@article{asuegaTechnoeconomicAnalysisAdvanced2023, - title = {Techno-economic analysis of advanced small modular nuclear reactors}, - volume = {334}, - issn = {0306-2619}, - url = {https://www.sciencedirect.com/science/article/pii/S0306261923000338}, - doi = {10.1016/j.apenergy.2023.120669}, - pages = {120669}, - journaltitle = {Applied Energy}, - shortjournal = {Applied Energy}, - author = {Asuega, Anthony and Limb, Braden J. and Quinn, Jason C.}, - urldate = {2026-07-07}, - date = {2023-03-15}, - keywords = {Levelized cost of energy, Overnight capital cost, Small modular reactors, {SMRs}, Techno-economic analysis}, - file = {ScienceDirect Snapshot:files/8183/S0306261923000338.html:text/html;Submitted Version:files/8182/Asuega et al. - 2023 - Techno-economic analysis of advanced small modular nuclear reactors.pdf:application/pdf}, -} - -@report{abou-jaoudeLiteratureReviewAdvanced2023, - title = {Literature Review of Advanced Reactor Cost Estimates}, - url = {https://www.osti.gov/servlets/purl/1986466/}, - doi = {10.2172/1986466}, - pages = {INL/RPT--23--72972--Rev000, 1986466}, - number = {{INL}/{RPT}--23-72972-Rev000, 1986466}, - author = {Abou-Jaoude, Abdalla and Lin, Linyu and Bolisetti, Chandrakanth and Worsham, Elizabeth and Larsen, Levi and Epiney, Aaron}, - urldate = {2026-07-07}, - date = {2023-06-01}, - langid = {english}, - file = {PDF:files/8184/Abou-Jaoude et al. - 2023 - Literature Review of Advanced Reactor Cost Estimates.pdf:application/pdf}, -} - -@misc{basoreQ12025SolarPhotovoltaic, - title = {Q1-2025 Solar Photovoltaic System Cost Benchmarks}, - url = {https://www.osti.gov/servlets/purl/3009689}, - doi = {10.7799/3009689}, - abstract = {The U.S. Department of Energy’s solar office and its national laboratory partners analyze cost data for U.S. solar photovoltaic systems to develop cost benchmarks to measure progress toward goals and guide research and development programs. The cost data is available in an Excel spreadsheet model that can be downloaded here.}, - publisher = {National Laboratory of the Rockies}, - author = {Basore, Paul and Dummit, Krysta and Thomas, Andy and Taylor, Margaret and Benoliel, Peter and Feldman, David and Narayanaswami, Meenakshi and Ramasamy, Vignesh and Woodhouse, Michael and Zuboy, Jarett and Braid, Jennifer and Jost, Norman and Sproul, Evan}, - urldate = {2026-07-07}, - note = {Artwork Size: 7 files. -Pages: 7 files.}, - keywords = {14 {SOLAR} {ENERGY}, battery cost 2025, battery system cost, pv cost 2025, pv module cost, pv system cost}, -} - -@report{hammondWindfarmOperationsMaintenance2022, - title = {Windfarm Operations and Maintenance cost-Benefit Analysis Tool ({WOMBAT})}, - url = {https://www.osti.gov/servlets/purl/1894867/}, - doi = {10.2172/1894867}, - pages = {NREL/TP--5000--83712, 1894867, MainId:84485}, - number = {{NREL}/{TP}-5000-83712, 1894867, {MainId}:84485}, - author = {Hammond, Rob and Cooperman, Aubryn}, - urldate = {2026-07-07}, - date = {2022-10-01}, - langid = {english}, - file = {PDF:files/8187/Hammond and Cooperman - 2022 - Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT).pdf:application/pdf}, -} - -@misc{hansonGeothermalElectricityTechnology2013, - title = {Geothermal Electricity Technology Evaluation Model ({GETEM}) Individual Case Files and Summary Spreadsheet ({GETEM} version Spring 2013)}, - url = {http://www.osti.gov/servlets/purl/1148822/}, - doi = {10.15121/1148822}, - abstract = {This group of files-- 10 {GETEM} individual case files and 1 summary spreadsheet-- contain final data from the revisions between summer 2011 and spring 2013.}, - publisher = {{DOE} Geothermal Data Repository; Idaho National Laboratory ({INL}), Idaho Falls, {ID} (United States)}, - author = {Hanson, Steven}, - urldate = {2026-07-07}, - date = {2013}, - langid = {english}, - keywords = {15 Geothermal Energy, geothermal, Geothermal Electricity Technology Evaluation Model, {GETEM}, {LCOE}, Levelized Cost of Electricity}, -} - -@report{schmitt_et_al_2022, - title = {Cost and Performance Baseline for Fossil Energy Plants. Volume 1: Bituminous Coal and Natural Gas to Electricity}, - url = {https://www.osti.gov/servlets/purl/1893822/}, - institution = {National Energy Technology Laboratory}, - author = {{Schmitt, Tommy} and {Leptinsky, Sarah} and {Turner, Marc} and {Zoelle, Alexander} and {White, Charles W.} and {Hughes, Sydney} and {Homsy, Sally} and {Woods, Mark} and {Hoffman, Hannah} and {Shultz, Travis} and {James, Robert}}, - urldate = {2026-07-07}, - date = {2022}, -} - -@article{james_et_al_2023, - title = {Cost and Performance Baseline for Fossil Energy Plants, Volume 3 – Low Rank Coal and Natural Gas to Electricity}, - author = {James, Robert and Woods, Mark and Turner, Marc and Schmitt, Tommy and Oakes, Matt and Konrade, Jacob and Bleckinger, Mark and Sturdivan, Mark}, - langid = {english}, - file = {PDF:files/8176/James et al. - Cost and Performance Baseline for Fossil Energy Plants, Volume 3 – Low Rank Coal and Natural Gas to.pdf:application/pdf}, -} - -@report{oakesCostPerformanceBaseline2023, - title = {Cost and Performance Baseline for Fossil Energy Plants, Volume 5: Natural Gas Electricity Generating Units for Flexible Operation}, - url = {https://www.osti.gov/servlets/purl/1973266/}, - doi = {10.2172/1973266}, - shorttitle = {Cost and Performance Baseline for Fossil Energy Plants, Volume 5}, - pages = {DOE/NETL--2023/3855, 1973266}, - number = {{DOE}/{NETL}--2023/3855, 1973266}, - author = {Oakes, Matt and Konrade, Jacob and Bleckinger, Mark and Turner, Marc and Hughes, Sydney and Hoffman, Hannah and Shultz, Travis and Lewis, Eric}, - urldate = {2026-07-07}, - date = {2023-05-05}, - langid = {english}, - file = {PDF:files/8189/Oakes et al. - 2023 - Cost and Performance Baseline for Fossil Energy Plants, Volume 5 Natural Gas Electricity Generating.pdf:application/pdf}, -} - -@article{EliminatingDerateCarbon, - title = {Eliminating the Derate of Carbon Capture Retrofits}, - journaltitle = {National Energy Technology Laboratory}, - author = {{K. Buchheit} and {N. Kuehn}}, - langid = {english}, - file = {PDF:files/8178/K. Buchheit and N. Kuehn - Eliminating the Derate of Carbon Capture Retrofits.pdf:application/pdf}, -} - -@report{schmittCostPerformanceRetrofitting2023, - title = {Cost and Performance of Retrofitting {NGCC} Units for Carbon Capture – Revision 3}, - url = {https://www.osti.gov/servlets/purl/1961845/}, - doi = {10.2172/1961845}, - pages = {DOE/NETL--2023/3848, 1961845}, - number = {{DOE}/{NETL}-2023/3848, 1961845}, - author = {Schmitt, Tommy and Homsy, Sally and Mantripragada, Hari and Woods, Mark and Hoffman, Hannah and Shultz, Travis and Fout, Timothy and Hackett, Gregory}, - urldate = {2026-07-07}, - date = {2023-05-31}, - langid = {english}, - file = {PDF:files/8191/Schmitt et al. - 2023 - Cost and Performance of Retrofitting NGCC Units for Carbon Capture – Revision 3.pdf:application/pdf}, -} - -@report{nunemakerORBITOffshoreRenewables2020, - title = {{ORBIT}: Offshore Renewables Balance-of-System and Installation Tool}, - url = {https://www.osti.gov/servlets/purl/1660132/}, - doi = {10.2172/1660132}, - shorttitle = {{ORBIT}}, - pages = {NREL/TP--5000--77081, 1660132, MainId:26027}, - number = {{NREL}/{TP}-5000-77081, 1660132, {MainId}:26027}, - author = {Nunemaker, Jacob and Shields, Matthew and Hammond, Robert and Duffy, Patrick}, - urldate = {2026-07-07}, - date = {2020-08-10}, - langid = {english}, - file = {PDF:files/8193/Nunemaker et al. - 2020 - ORBIT Offshore Renewables Balance-of-System and Installation Tool.pdf:application/pdf}, -} - -@article{ramasamyUSSolarPhotovoltaic2022, - title = {U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks, With Minimum Sustainable Price Analysis: Q1 2022}, - journaltitle = {Renewable Energy}, - author = {Ramasamy, Vignesh and Zuboy, Jarett and O'Shaughnessy, Eric and Feldman, David and Desai, Jal and Woodhouse, Michael and Basore, Paul and Margolis, Robert}, - date = {2022}, - langid = {english}, - file = {PDF:files/8171/Ramasamy et al. - 2022 - U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks, With Minimum Sustainable Price An.pdf:application/pdf}, -} - - -@techreport{reznicekCostAnalysisHeavyDuty2026, - title = {Cost {{Analysis}} of {{Heavy-Duty Vehicle Proton Exchange Membrane Fuel Cell Stationary Power Plants}}}, - author = {Reznicek, Evan and Berres, Andreas and Li, Yijin and others}, - year = 2026, - institution = {National Laboratory of the Rockies}, - doi = {10.2172/3018256}, - url = {https://doi.org/10.2172/3018256}, - langid = {english} -} diff --git a/inputs/canada_imports/README.md b/inputs/canada_imports/README.md index d4d012d2e..946c086a4 100644 --- a/inputs/canada_imports/README.md +++ b/inputs/canada_imports/README.md @@ -1,5 +1,8 @@ ## Canadian Input Files +All files in this folder are generated by [`exogenous_canadian_trade/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/exogenous_canadian_trade) in the ReEDS_Input_Processing repo, +from historical trade data and future province-level projections published by the Canada Energy Regulator. + - `can_exports.csv`: Annual exports [MWh] to Canada by county - `can_exports_szn_frac.csv`: Fraction of annual exports [fraction] to Canada by season. diff --git a/inputs/capacity_exogenous/README.md b/inputs/capacity_exogenous/README.md index ce7af1a7c..4b6a02a7d 100644 --- a/inputs/capacity_exogenous/README.md +++ b/inputs/capacity_exogenous/README.md @@ -1,42 +1,16 @@ ## Capacity Exogenous Input Files -- `cappayments.csv`: - -- `cappayments_ba.csv`: +- `cappayments_ba.csv`: Capacity payment to existing (pre-2010) generators [billion $] by region and year + - Not used by the model itself (`runfiles.csv` marks it "not used"); it is read only by the BokehPivot system cost report, which inflates it and adds it to `inv_investment_capacity_costs` + - Generated by [`existing_capacity_payments/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/existing_capacity_payments) in the ReEDS_Input_Processing repo - `demonstration_plants.csv`: Nuclear-smr demonstration plants [MW] - Active when `GSw_NuclearDemo = 1` - For more information, refer to the **notes** section of [https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0](https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0) -- `exog_cap_geohydro_allkm_reference.csv`: - -- `exog_cap_geohydro_reference.csv`: - -- `exog_cap_upv_*.csv`: - - `exog_cap_upv_limited.csv`: - - `exog_cap_upv_open.csv`: - - `exog_cap_upv_reference.csv`: - -- `exog_cap_wind-ons_*.csv`: - - `exog_cap_wind-ons_limited.csv`: - - `exog_cap_wind-ons_open.csv`: - - `exog_cap_wind-ons_reference.csv`: - -- `interconnection_queues.csv`: - -- `prescribed_builds_wind-ofs_meshed_*.csv`: - - `prescribed_builds_wind-ofs_meshed_limited.csv`: - - `prescribed_builds_wind-ofs_meshed_open.csv`: - - `prescribed_builds_wind-ofs_meshed_reference.csv`: - -- `prescribed_builds_wind-ofs_radial_*.csv`: - - `prescribed_builds_wind-ofs_radial_limited.csv`: - - `prescribed_builds_wind-ofs_radial_open.csv`: - - `prescribed_builds_wind-ofs_radial_reference.csv`: - -- `prescribed_builds_wind-ons_*.csv`: - - `prescribed_builds_wind-ons_limited.csv`: - - `prescribed_builds_wind-ons_open.csv`: - - `prescribed_builds_wind-ons_reference.csv`: +- `interconnection_queues.csv`: Near-term capacity [MW] in the interconnection queues by county, technology group, and year + - Aggregated to model zones during input processing to form `cap_limit`, which caps total capacity deployed since the start of the interconnection queue data + - Generated by [`interconnection_queues/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/interconnection_queues) in the ReEDS_Input_Processing repo -- `ReEDS_generator_database_final_EIA-NEMS.csv`: ReEDS generator database derived from the EIA NEMS plant file and EIA 860M. See https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/nems_database_processing. +- `ReEDS_generator_database_final_EIA-NEMS.csv`: ReEDS generator database derived from the EIA NEMS plant file and EIA 860M + - Generated by [`nems_database_processing/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/nems_database_processing) in the ReEDS_Input_Processing repo diff --git a/inputs/capacity_exogenous/cappayments.csv b/inputs/capacity_exogenous/cappayments.csv deleted file mode 100644 index f141748fb..000000000 --- a/inputs/capacity_exogenous/cappayments.csv +++ /dev/null @@ -1,42 +0,0 @@ -year,existingcap -2010,0.740922159602699 -2011,0.722533159125209 -2012,0.710703058731268 -2013,0.688949824091009 -2014,0.674110881803549 -2015,0.656819176737328 -2016,0.644425838635469 -2017,0.625587800255759 -2018,0.607914937307489 -2019,0.582883418941709 -2020,0.557430112429799 -2021,0.529093441852649 -2022,0.455071666883908 -2023,0.434603318451538 -2024,0.352467760698059 -2025,0.299374387284299 -2026,0.279149123885009 -2027,0.228154522909259 -2028,0.150594149514599 -2029,0.067510568363769 -2030,0 -2031,0 -2032,0 -2033,0 -2034,0 -2035,0 -2036,0 -2037,0 -2038,0 -2039,0 -2040,0 -2041,0 -2042,0 -2043,0 -2044,0 -2045,0 -2046,0 -2047,0 -2048,0 -2049,0 -2050,0 diff --git a/inputs/climate/README.md b/inputs/climate/README.md index c2f706569..707a60bd8 100644 --- a/inputs/climate/README.md +++ b/inputs/climate/README.md @@ -1,5 +1,9 @@ ## Climate Input Files -- `climate_heuristics_finalyear.csv`: +- `climate_heuristics_finalyear.csv`: Climate heuristic adjustment [fraction] applied in the final year, by climate parameter + - Covers hydropower capacity credit, thermal summer capacity, and transmission summer capacity deltas + - Scenario is selected by the `GSw_ClimateHeuristics` switch -- `climate_heuristics_yearfrac.csv`: +- `climate_heuristics_yearfrac.csv`: Annual scaling factor [fraction] applied to the final-year climate heuristic adjustment + - Phases the adjustments in `climate_heuristics_finalyear.csv` in over time, reaching their full value in the final year + - Scenario is selected by the `GSw_ClimateHeuristics` switch diff --git a/inputs/consume/README.md b/inputs/consume/README.md index ee903e9e9..d4a4b3722 100644 --- a/inputs/consume/README.md +++ b/inputs/consume/README.md @@ -20,10 +20,10 @@ - `dollaryear.csv`: Dollar year for DAC costs -- `h2_demand_county_share.csv`: The fraction of national hydrogen demand in that year that corresponds to each county +- `h2_demand_county_share.csv`: The fraction [fraction] of national hydrogen demand in that year that corresponds to each county - Demand estimates come from [https://data.openei.org/submissions/5655](https://data.openei.org/submissions/5655) - 2021 demand shares correspond to the "Reference" scenario with light-duty vehicles / biofuels / methanol demand removed and 2050 shares correspond to the "Low Cost Electrolysis" scenario -- `h2_exogenous_demand.csv`: Exogenous hydrogen demand by industries other than the power sector per year +- `h2_exogenous_demand.csv`: Exogenous hydrogen demand [metric tons/yr] by industries other than the power sector - `h2_transport_and_storage_costs.csv`: Transport and storage costs of hydrogen per year (in $2004) diff --git a/inputs/ctus/README.md b/inputs/ctus/README.md index 1fd97a48d..f0f39a883 100644 --- a/inputs/ctus/README.md +++ b/inputs/ctus/README.md @@ -1,7 +1,10 @@ ## Ctus Input Files -- `co2_site_char.csv`: +- `co2_site_char.csv`: Characteristics of each CO2 storage site: maximum injection rate [metric tons/hour], maximum storage capacity [Mton], and break-even storage cost [$/metric ton] at a range of capacity factors + - Break-even cost columns are named `bec_{CF}`; the column used is selected by the `GSw_CO2_BEC` switch + - Costs are in the dollar year given by `dollaryear.csv` and are deflated during input processing -- `cs.csv`: +- `cs.csv`: List of CO2 storage sites (geologic formations) recognized by the model + - A superset of the sites in `co2_site_char.csv`; only sites with characteristics are available for storage -- `dollaryear.csv`: +- `dollaryear.csv`: U.S. dollar year for each cost-related input file in this folder diff --git a/inputs/degradation/README.md b/inputs/degradation/README.md index b797bc3e5..78fe5fc6f 100644 --- a/inputs/degradation/README.md +++ b/inputs/degradation/README.md @@ -1,3 +1,5 @@ ## Degradation Input Files -- `degradation_annual_default.csv`: +- `degradation_annual_default.csv`: Annual capacity degradation rate [fraction/year] by technology + - Applied in the model as `(1 - rate)^years` to derate capacity as plants age + - The file is selected by the `degrade_suffix` switch diff --git a/inputs/demand_response/README.md b/inputs/demand_response/README.md index 577d08a86..a3de752af 100644 --- a/inputs/demand_response/README.md +++ b/inputs/demand_response/README.md @@ -1,5 +1,8 @@ ## Demand Response Input Files -- `dr_shed_avail_scalar.csv`: +- `dr_shed_avail_scalar.csv`: Peak availability [fraction of capacity] of shed demand response by year + - The hourly shed-DR availability profile is normalized to its own maximum and then scaled by this value -- `dr_shed_capacity_scalar_demo_data_January_2025.csv`: +- `dr_shed_capacity_scalar_demo_data_January_2025.csv`: Shed demand response capacity multiplier [unitless] over time by technology and region + - The file is selected by the `dr_shedscen` switch and is only used when `GSw_DRShed` is on + - Generated by [`demand_response/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/demand_response) in the ReEDS_Input_Processing repo diff --git a/inputs/dgen_model_inputs/README.md b/inputs/dgen_model_inputs/README.md index f634fc567..1a0f0692a 100644 --- a/inputs/dgen_model_inputs/README.md +++ b/inputs/dgen_model_inputs/README.md @@ -1,20 +1,29 @@ ## dGen Input Files -- `stscen2023_electrification/distpvcap_stscen2023_electrification.csv`: +- `stscen2023_electrification/distpvcap_stscen2023_electrification.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 electrification scenario + - Selected using `distpvscen` -- `stscen2023_highng/distpvcap_stscen2023_highng.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with high NG (including distpv) costs +- `stscen2023_highng/distpvcap_stscen2023_highng.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high natural gas price scenario + - Selected using `distpvscen` -- `stscen2023_highre/distpvcap_stscen2023_highre.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with high RE (including distpv) costs +- `stscen2023_highre/distpvcap_stscen2023_highre.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high renewable energy cost scenario (including high distributed PV capital costs based on the 2023 Annual Technology Baseline) + - Selected using `distpvscen` -- `stscen2023_lowng/distpvcap_stscen2023_lowng.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with low NG (including distpv) costs +- `stscen2023_lowng/distpvcap_stscen2023_lowng.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 low natural gas price scenario + - Selected using `distpvscen` -- `stscen2023_lowre/distpvcap_stscen2023_lowre.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with low RE (including distpv) costs +- `stscen2023_lowre/distpvcap_stscen2023_lowre.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high renewable energy cost scenario (including low distributed PV capital costs based on the 2023 Annual Technology Baseline) + - Selected using `distpvscen` -- `stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv`: +- `stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 mid-case scenario + - Selected using `distpvscen` -- `stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv`: +- `stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 95% CO2 emission reduction by 2035 scenario + - Selected using `distpvscen` -- `stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv`: +- `stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 95% CO2 emission reduction by 2050 scenario + - Selected using `distpvscen` -- `stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv`: +- `stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 mid-case scenario where tax credits (including for distributed PV) are extended through 2050 + - Selected using `distpvscen` diff --git a/inputs/disaggregation/README.md b/inputs/disaggregation/README.md index 0f7671480..3d84fa0a6 100644 --- a/inputs/disaggregation/README.md +++ b/inputs/disaggregation/README.md @@ -3,6 +3,8 @@ - `county_population.csv`: The population of each county, relative values are used as multipliers for downselecting data - Data come from the U.S. Census Bureau 2021 county population estimates ([https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-total.html](https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-total.html)) -- `county_state_lpf.csv`: +- `county_state_lpf.csv`: Load participation factor [fraction] used to disaggregate state-level quantities to counties + - Values sum to 1 within each state + - Generated by `write_lpf_naris.py` in [`transmission/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/transmission) of the ReEDS_Input_Processing repo, which aggregates NARIS nodal load to counties and normalizes within each state - `disagg_hydroexist.csv`: The hydropower capacity fraction of each county within a given ReEDS BA, used as multipliers for downselecting data diff --git a/inputs/emission_constraints/README.md b/inputs/emission_constraints/README.md index 72f9a561b..b37ff2099 100644 --- a/inputs/emission_constraints/README.md +++ b/inputs/emission_constraints/README.md @@ -17,8 +17,116 @@ A range of values for global warming potentials (GWP) of CH4 and N2 caps for RGGI states in metric tons +- `rggicon.csv`: CO2 cap [metric tons] for RGGI states - Citation: [https://www.rggi.org/allowance-tracking/allowance-distribution](https://www.rggi.org/allowance-tracking/allowance-distribution) + - Generated by [`rggi/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/rggi) in the ReEDS_Input_Processing repo ### Other files -- `co2_tax.csv`: Annual CO2 tax +- `ccs_link.csv`: Link from each CCS technology to its uncontrolled (non-CCS) counterpart + +- `ccs_link_water.csv`: Same as `ccs_link.csv` for the expanded cooling/water technologies + +- `co2_tax.csv`: CO2 tax [2004$/metric ton] by year, with one column per tax scenario + - Applied to CO2 emissions in all regions when `GSw_CarbTax` is on; the scenario column is selected by `GSw_CarbTaxOption` + - Scenario columns are named `t{level}_{year}`, ramping to $`{level}`/metric ton by `{year}` and holding flat thereafter; the `default` column escalates over the full horizon instead + - Enters the objective function as a cost on process (and, with `Sw_Upstream`, upstream) emissions + +- `county_co2_share_egrid_2022.csv`: Share [fraction] of national 2022 CO2 emissions attributable to each county, calculated from 2022 eGRID data + - Summed over the counties in `GSw_Region` to scale national emission caps down to the modeled region + - Generated by [`emissions/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/emissions) in the ReEDS_Input_Processing repo + +- `csapr_group1_ex.csv`: CSAPR states that cannot trade allowances with those in group 2 + +- `csapr_group2_ex.csv`: CSAPR states that cannot trade allowances with those in group 1 + +- `csapr_ozone_season.csv`: CSAPR ozone-season NOx budget and assurance levels [metric tons] by state and year + - Caps NOx emissions during the May-September ozone season + - Active when `GSw_CSAPR` is on + +- `gwp.csv`: Global warming potentials by pollutant for each IPCC report and time horizon (`AR4-100`, `AR6-20`, etc.) + - The column used is selected by the `GSw_GWP` switch; see the Global Warming Potentials section above + +- `h2_leakage_rate.csv`: Hydrogen leakage rate [fraction] from upstream and process emissions by hydrogen production technology + - Columns are named `{study}/{Low,Med,High}`; the column used is selected by the `GSw_H2LeakageScen` switch + +- `methane_leakage_rate.csv`: Fractional leakage of methane from upstream natural gas production by year, for a range of named scenarios + - The column used is selected by the `GSw_MethaneLeakageScen` switch, which also accepts a fixed float rate + +- `state_cap.csv`: State-level CO2 cap [metric tons CO2] by state and year + - Active when `GSw_StateCap` is on + +### Clean Air Act, Section 111 + +#### The regulation itself + +The Clean Air Act, Section 111 is a federal regulation from the Environmental Protection Agency (EPA), which features carbon pollution standards to reduce greenhouse gas emissions from power plants. +These regulations are called Section 111 for the section of the tax code which it is implemented in. +The regulations stipulate a couple of compliance mechanisms for plants to comply with these regulations. + +The first compliance mechanism, and the strictest, is a best system of emissions reduction (BSER) for every technology and vintage (existing vs new plants). +This is enforced at the plant level i.e. each plant must meet its BSER. +The BSERs are listed below: + +- Existing coal plants: + - If the plant will permanently cease operations before Jan. 1, 2032: they are not subject to these standards + - If the plant is operating on or after Jan. 1, 2032, and demonstrate that they plan to permanently cease operation before Jan. 1, 2039: they must cofire with 40% natural gas from 2030 through 2039 + - If the plant plant to operate on or after Jan. 1, 2039: they must upgrade with CCS with 90% capture by Jan. 1, 2032 +- New coal plants: + - No regulations since no new coal plants are being built these days +- Existing gas-CCs and gas-CTs: + - No regulations +- New gas-CCs and gas-CTs: + - If the plant is operating at <= 40% capacity factor: they are unregulated + - If a plant is operating above 40% CF: they must upgrade with CCS by 2032. + +The second compliance mechanism, which is slightly more lenient, is a emissions rate-based mechanism. +This is enforced at the state level. +If a state opts into this compliance mechanism, the emissions rate (tons CO2/MWh) of their coal fleet must be less than or equal to the emissions rate of a 90% coal-CCS plant. +This in theory enables some unabated coal plants to remain online after 2032, even though they won't be able to generate much. +This is only possible if that state also has coal-CCS plants with high capture rates that stay online and generate, to average out the emissions rate to below the threshold. + +##### Other resources +- [Simplified presentation on the final regulations](https://www.epa.gov/system/files/documents/2024-04/cps-presentation-final-rule-4-24-2024.pdf) +- [Final regulations](https://www.federalregister.gov/documents/2024/05/09/2024-09233/new-source-performance-standards-for-greenhouse-gas-emissions-from-new-modified-and-reconstructed) +- [History of the Clean Air Act](https://www.epa.gov/clean-air-act-overview/evolution-clean-air-act) + +#### Implementation in ReEDS +In ReEDS, new gas plants must adhere to their BSER and existing coal plants adhere to an emissions rate-based standard. + +For new gas plants, this is the code implementation: + +1. `inputs/scalars.csv` + - `caa_gas_max_cf` = 0.40. + This is the maximum capacity factor that new gas plants (CCs or CTs) can operate at without being regulated under Clean Air Act, Section 111, expressed as a fraction. +2. `c_model.gms` - `eq_caa_max_cf` enforces the maximum capacity factor for new gas plants. + + +For existing coal plants, this is the code implementation: + +1. `inputs/scalars.csv` + - `caa_coal_retire_year` = 2032. + This is the year in which coal capacity is forced to either retire or upgrade with CCS to meet the emissions requirements under Clean Air Act, Section 111. + - `caa_first_year` = 2024. + This is the year in which the emissions requirements under Clean Air Act, Section 111 are first active. + - `caa_rate_emis_standard` = 0.1039. + This is the emissions rate (metric tons CO2 per MWh) equivalent to average emissions from a new coal-CCS plant, assuming 90% capture rate. + The emissions rate from a new coal-CCS plant in ReEDS is 0.051956 metric tons CO2 per MWh (see `emit_rate` parameter) which assumes 95% capture. + For 90% capture, the emissions rate is double that or 0.1039 metric tons CO2 per MWh, which we use as the standard. +2. `reeds/input_processing/WriteHintage.py` + - Coal plants are binned at the unit level if `GSw_Clean_Air_Act=1` so that each coal unit can independently choose to retire or upgrade. + - Coal plants maintain their exogenous retirement assumption, except after 2032, when the Clean Air Act regulations begin and coal can retire endogenously. + For example, if the NEMS data states that a plant will retire in 2029, we maintain that assumption and that plant will retire in 2029. + However, if the NEMS data previously stated that a plant will retire in 2040, they are now subject to the Clean Air Act regulations and may retire sooner than their previously stated retirement date. +3. `b_inputs.gms` + - `numhintage` is set to 300, so that we can accommodate a large number of coal bins since they are binned at the unit level. + - Revise `m_capacity_exog` so that it matches with which coal capacity is allowed. + - if `caa_coal_retire_year` is not in the set of years being modeled for this run, then set it to the first year that is modeled after `caa_coal_retire_year`. + For example, if running 5 year solves, then instead of enforcing coal retirement in 2032, it will be enforced in 2035. + +4. `c_model.gms` + - `eq_caa_rate_standard(st,t)` - this constraint enforces the rate-based emissions standard by setting the maximum coal emissions rate per state under Clean Air Act Section 111. + +#### Assumptions +- We do not include the compliance mechanism for coal plants to cofire with natural gas in the model. +We have modeled this in ReEDS previously in our analysis of these regulations and found that coal plants almost never choose this compliance mechanism. +They would prefer to upgrade with CCS or retire. diff --git a/inputs/employment/README.md b/inputs/employment/README.md index bd9640f23..c2a261521 100644 --- a/inputs/employment/README.md +++ b/inputs/employment/README.md @@ -1,6 +1,7 @@ ## Power Sector Employment Data ### Data input options -- `employment_factor_plant_jedi.csv`: Employment factor data for power plants of different technologies, taken from the JEDI/WIRED model. +- `employment_factor_plant_jedi.csv`: Employment factors for power plants by technology and job type, from the JEDI/WIRED model + - `construction` and `fom` are in job-years per MW; `vom` is in job-years per MWh Sources for employment data of individual technologies in JEDI/WIRED are shown in the table below. | Technology | Source | diff --git a/inputs/financials/README.md b/inputs/financials/README.md index 2b72fd3e0..79f566378 100644 --- a/inputs/financials/README.md +++ b/inputs/financials/README.md @@ -1,57 +1,71 @@ ## Financials Input Files -- `cap_penalty.csv`: +- `cap_penalty.csv`: Cost penalty [$/MW] for deploying capacity above the interconnection cap limit, by technology group -- `construction_schedules_default.csv`: +- `construction_schedules_default.csv`: Fraction of total construction expenditure incurred in each year of construction, by schedule name + - Columns are the schedule identifiers referenced by the `construction_sch` column of the `financials_tech_*` and `financials_transmission_*` files; selected by the `construction_schedules_suffix` switch -- `construction_times_default.csv`: +- `construction_times_default.csv`: Construction time [years] by technology and online year + - Selected by the `construction_times_suffix` switch -- `currency_incentives.csv`: +- `currency_incentives.csv`: Dollar year of the `ptc_value` column in each incentives file - `deflator.csv`: Dollar year deflator to convert values to 2004$ -- `depreciation_schedules_default.csv`: +- `depreciation_schedules_default.csv`: Fraction of capital depreciated in each year, by depreciation schedule + - Column names are the schedule identifiers (e.g. `5`, `7`, `15`, `20`) referenced by the `depreciation_sch` column of the financials files; selected by the `depreciation_schedules_suffix` switch -- `energy_communities.csv`: +- `energy_communities.csv`: Counties that qualify as energy communities for the purposes of the tax credit bonus + - See `nuclear_energy_communities.csv` for the separate nuclear-specific list -- `financials_hydrogen.csv`: +- `financials_hydrogen.csv`: Financial assumptions for hydrogen production capacity: construction start year, depreciation schedule, construction time and schedule, real financing differential, ITC fraction, tax equity penalty, and annual degradation -- `financials_sys_ATB2023.csv`: +- `financials_sys_ATB2023.csv`: System-wide financial assumptions by year from the 2023 ATB: nominal interest rate, nominal return on equity, debt fraction, and tax rate + - Selected with `financials_sys_suffix = ATB2023` -- `financials_sys_ATB2024.csv`: +- `financials_sys_ATB2024.csv`: System-wide financial assumptions by year from the 2024 ATB: nominal interest rate, nominal return on equity, debt fraction, and tax rate + - Selected with `financials_sys_suffix = ATB2024` -- `financials_tech_ATB2023_CRP20.csv`: +- `financials_tech_ATB2023_CRP20.csv`: Same as `financials_tech_ATB2023.csv` but with a 20-year capital recovery period + - Selected with `financials_tech_suffix = ATB2023_CRP20` -- `financials_tech_ATB2023.csv`: +- `financials_tech_ATB2023.csv`: Technology-specific financial assumptions by technology and year from the 2023 ATB: depreciation schedule, evaluation period [years], construction schedule, and real financing differential + - Selected with `financials_tech_suffix = ATB2023` -- `financials_tech_ATB2024.csv`: +- `financials_tech_ATB2024.csv`: Technology-specific financial assumptions by technology and year from the 2024 ATB: depreciation schedule, evaluation period [years], construction schedule, and real financing differential + - Selected with `financials_tech_suffix = ATB2024` -- `financials_transmission_30ITC_0pen_2022_2031.csv`: +- `financials_transmission_30ITC_0pen_2022_2031.csv`: Transmission financial assumptions with a 30% ITC and no tax equity penalty applied over 2022-2031 -- `financials_transmission_default.csv`: +- `financials_transmission_default.csv`: Default financial assumptions for transmission capacity by construction start year: depreciation schedule, construction time and schedule, real financing differential, ITC fraction, and tax equity penalty + - Selected by the `financials_trans_suffix` switch -- `incentives_*.csv`: - - `incentives_annual.csv`: - - `incentives_biennial.csv`: - - `incentives_ira_45q_45v_extension.csv`: - - `incentives_ira.csv`: - - `incentives_noira.csv`: - - `incentives_none.csv`: - - `incentives_obbba_conservative.csv`: - - `incentives_obbba.csv`: +- `incentives_*.csv`: Federal tax incentive definitions by technology and construction start year, selected by the `incentives_suffix` switch + - Columns cover the production tax credit (`ptc_value`, `ptc_perc_bonus`, `ptc_dur`, `ptc_tax_equity_penalty`), the investment tax credit (`itc_frac`, `itc_energy_comm_bonus`, `itc_percpt_domestic_bonus`, `itc_tax_equity_penalty`), the 45Q carbon capture credit (`co2_capture_value`, `co2_capture_dur`), and the 45V hydrogen credit (`h2_ptc_value`, `h2_ptc_dur`), together with the eligibility window (`safe_harbor`, `t_max_online`) + - `ptc_value` is in the dollar year given by `currency_incentives.csv` + - `incentives_annual.csv`: Pre-IRA incentives with credit values given for each individual year + - `incentives_biennial.csv`: Pre-IRA incentives with credit values set to two-year averages of the `incentives_annual.csv` values during credit step-down periods, for runs that solve every two years + - `incentives_ira_45q_45v_extension.csv`: Inflation Reduction Act incentives with the 45Q (carbon capture) and 45V (clean hydrogen) credits extended + - `incentives_ira.csv`: Incentives as defined by the Inflation Reduction Act of 2022 + - `incentives_noira.csv`: Incentives as they stood before the Inflation Reduction Act + - `incentives_none.csv`: No incentives + - `incentives_obbba_conservative.csv`: A more conservative interpretation of the One Big Beautiful Bill Act (OBBBA) incentives + - `incentives_obbba.csv`: Incentives as defined by the One Big Beautiful Bill Act (OBBBA) of 2025 - `inflation_default.csv`: Annual inflation factors from 1914 through 2200 - historical values use the avg-avg values from [https://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008/](https://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008/) - `nuclear_energy_communities.csv`: Counties belonging to metropolitan statistical areas (MSAs) for which at least 0.17% of direct employment has been related to nuclear power at any point since 2010 - These are determined partly by following the process described in Section 2.6 of [https://home.treasury.gov/system/files/8861/EnergyCommunities_Data_Documentation.pdf](https://home.treasury.gov/system/files/8861/EnergyCommunities_Data_Documentation.pdf) and substituting in the NAICS code for nuclear electric power generation (221113) and partly by determining counties that belong to MSAs where the number of people employed by national labs engaged in nuclear research and development (PNNL, INL, ORNL, SNL, LLNL, Argonne, and LANL) has been at least 0.17 percent of the MSA's total employment at any point since 2010 + - Generated by [`financials/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/financials) in the ReEDS_Input_Processing repo (`calculate_nuclear_energy_communities.py`) -- `reg_cap_cost_diff_default.csv`: Region-specific differences for capital cost of all resources +- `reg_cap_cost_diff_default.csv`: Region-specific capital cost difference [fraction] for each technology group + - Applied as `1 + reg_cap_cost_diff`, so 0 means no regional adjustment - Add 1 to produce a multiplier + - Generated by [`capital_cost_multipliers/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/capital_cost_multipliers) in the ReEDS_Input_Processing repo -- `retire_penalty.csv`: +- `retire_penalty.csv`: Penalty for retiring a power plant, expressed as a fraction of FOM, by year -- `supply_chain_adjust.csv`: - -- `tc_phaseout_schedule_ira2022.csv`: +- `tc_phaseout_schedule_ira2022.csv`: Tax credit phaseout multiplier by the number of years after the phaseout trigger is met, following the Inflation Reduction Act of 2022 + - Selected by the `GSw_TCPhaseout_schedule` switch; the phaseout starts the year after the emissions threshold is met diff --git a/inputs/financials/supply_chain_adjust.csv b/inputs/financials/supply_chain_adjust.csv deleted file mode 100644 index 88a526aa6..000000000 --- a/inputs/financials/supply_chain_adjust.csv +++ /dev/null @@ -1,41 +0,0 @@ -2010,0 -2011,0 -2012,0 -2013,0 -2014,0 -2015,0 -2016,0 -2017,0 -2018,0 -2019,0 -2020,0 -2021,0 -2022,0.1 -2023,0.08 -2024,0.06 -2025,0.04 -2026,0.02 -2027,0 -2028,0 -2029,0 -2030,0 -2031,0 -2032,0 -2033,0 -2034,0 -2035,0 -2036,0 -2037,0 -2038,0 -2039,0 -2040,0 -2041,0 -2042,0 -2043,0 -2044,0 -2045,0 -2046,0 -2047,0 -2048,0 -2049,0 -2050,0 diff --git a/inputs/fuelprices/README.md b/inputs/fuelprices/README.md index 3a190de4f..80cebcc84 100644 --- a/inputs/fuelprices/README.md +++ b/inputs/fuelprices/README.md @@ -1,13 +1,16 @@ ## Fuel Prices Input Files +The `*_AEO_{YYYY}_*` files in this folder are generated by [`aeo_updates/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/aeo_updates) in the +ReEDS_Input_Processing repo, which scrapes the EIA API for AEO fuel prices and natural gas demand. + - `alpha_AEO_{YYYY}_*.csv`: census division alpha values, used in the calculation of natural gas demand curves ($2004 dollar year) - `alpha_AEO_{YYYY}_HOG.csv`: High Oil and Gas Resource and Technology scenario - `alpha_AEO_{YYYY}_LOG.csv`: Low Oil and Gas Resource and Technology scenario - `alpha_AEO_{YYYY}_reference.csv`: Reference scenario -- `cd_beta0.csv`: Reference census division beta levels electric sector ($2004 dollar year) +- `cd_beta0.csv`: Reference census division beta levels [$/MMBtu per Quad] for the electric sector ($2004 dollar year) -- `cd_beta0_allsector.csv`: Reference census division beta levels all sectors ($2004 dollar year) +- `cd_beta0_allsector.csv`: Reference census division beta levels [$/MMBtu per Quad] across all sectors ($2004 dollar year) - `coal_AEO_2025_reference.csv`: [AEO2025](https://www.eia.gov/outlooks/aeo/pdf/2025/AEO2025-narrative.pdf) Reference case census division fuel price [$/MMBtu] of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic @@ -22,24 +25,29 @@ - `h2-combustion_30.csv`: $30/MMBtu - `h2-combustion_reference.csv`: $20/MMBtu -- `ng_AEO_{YYYY}_*.csv`: census division fuel price [$/MMBtu] of natural gas ($2004 dollar year) +- `gasreg_price_adj_regression_params.csv`: Regression parameters used to adjust natural gas prices by gas region, with cooling and heating degree-day coefficients (`beta_CDD`, `beta_HDD`) and an overall plus per-month intercept (`alpha`, `alpha_JAN` through `alpha_DEC`) + - Used only when `GSw_GasPriceAdjMethod = 2` + +- `ng_AEO_{YYYY}_*.csv`: census division fuel price [$/MMBtu] of natural gas + - Deflated to 2004$ during input processing using the dollar year given in `dollaryear.csv` - `ng_AEO_{YYYY}_HOG.csv`: High Oil and Gas Resource and Technology scenario - `ng_AEO_{YYYY}_LOG.csv`: Low Oil and Gas Resource and Technology scenario - `ng_AEO_{YYYY}_reference.csv`: Reference scenario - - `ng_AEO_{YYYY}_baseline.csv`: + - `ng_AEO_{YYYY}_baseline.csv`: Baseline scenario (the central AEO case, named `baseline` beginning with AEO2026 in place of the `reference` naming used for earlier vintages) -- `ng_demand_AEO_{YYYY}_*.csv`: census division natural gas demand [Quads] for the **electric sector**, used in the calculation of natural gas demand curves ($2004 dollar year) +- `ng_demand_AEO_{YYYY}_*.csv`: census division natural gas demand [Quads] for the **electric sector**, used in the calculation of natural gas demand curves - `ng_demand_AEO_{YYYY}_HOG.csv`: High Oil and Gas Resource and Technology census division natural gas demand - `ng_demand_AEO_{YYYY}_LOG.csv`: Low Oil and Gas Resource and Technology census division natural gas demand - `ng_demand_AEO_{YYYY}_reference.csv`: Reference census division natural gas demand - - `ng_demand_AEO_{YYYY}_baseline.csv`: + - `ng_demand_AEO_{YYYY}_baseline.csv`: Baseline census division natural gas demand -- `ng_tot_demand_AEO_{YYYY}_*.csv`: census division natural gas demand [Quads] across **all sectors**, used in the calculation of natural gas demand curves ($2004 dollar year) +- `ng_tot_demand_AEO_{YYYY}_*.csv`: census division natural gas demand [Quads] across **all sectors**, used in the calculation of natural gas demand curves - `ng_tot_demand_AEO_{YYYY}_HOG.csv`: High Oil and Gas Resource and Technology census division natural gas demand - `ng_tot_demand_AEO_{YYYY}_LOG.csv`: Low Oil and Gas Resource and Technology census division natural gas demand - `ng_tot_demand_AEO_{YYYY}_reference.csv`: Reference census division natural gas demand - - `ng_tot_demand_AEO_{YYYY}_baseline.csv`: + - `ng_tot_demand_AEO_{YYYY}_baseline.csv`: Baseline census division natural gas demand -- `uranium_AEO_2025_*.csv`: - - `uranium_AEO_2025_reference.csv`: - - `uranium_AEO_2026_baseline.csv`: +- `uranium_AEO_{YYYY}_*.csv`: National uranium fuel price [$/MMBtu] by year, applied uniformly to all regions + - Deflated to 2004$ during input processing using the dollar year given in `dollaryear.csv` + - `uranium_AEO_2025_reference.csv`: Reference scenario + - `uranium_AEO_2026_baseline.csv`: Baseline scenario (the central AEO case, named `baseline` beginning with AEO2026 in place of the `reference` naming used for earlier vintages) diff --git a/inputs/geothermal/README.md b/inputs/geothermal/README.md index d7261e9a1..4c4584ca4 100644 --- a/inputs/geothermal/README.md +++ b/inputs/geothermal/README.md @@ -1,11 +1,16 @@ ## Geothermal Input Files -- `geo_discovery_BAU.csv`: +- `geo_discovery_BAU.csv`: Business-as-usual trajectory for the fraction [fraction] of undiscovered geothermal resource that has been discovered, by year + - selected with `geodiscov = BAU` -- `geo_discovery_factor_ATB_2023.csv`: +- `geo_discovery_factor_ATB_2023.csv`: Factor [fraction] representing the share of geothermal resource already discovered, by technology and region, from the ATB 2023 geohydro supply curve + - Selected by the `geohydrosupplycurve` switch -- `geo_discovery_factor_reV.csv`: +- `geo_discovery_factor_reV.csv`: Factor [fraction] representing the share of geothermal resource already discovered, by technology and region, derived from reV + - Selected by the `geohydrosupplycurve` switch -- `geo_discovery_TI.csv`: +- `geo_discovery_TI.csv`: Accelerated trajectory for the fraction [fraction] of undiscovered geothermal resource that has been discovered, by year + - selected with `geodiscov = TI` -- `geo_rsc_ATB_2023.csv`: +- `geo_rsc_ATB_2023.csv`: Geothermal supply curve by technology and region for the EGS and geohydro technologies + - `sc_cat = cap` gives available capacity [MW]; `sc_cat = cost` gives the supply-curve capital cost adder [$/MW], which is deflated during input processing diff --git a/inputs/growth_constraints/README.md b/inputs/growth_constraints/README.md index 9bdf08c7d..b6429d93c 100644 --- a/inputs/growth_constraints/README.md +++ b/inputs/growth_constraints/README.md @@ -1,9 +1,10 @@ ## Growth Constraints Input Files -- `gbin_min.csv`: +- `gbin_min.csv`: Minimum size of the first (zero cost) growth bin [MW] by technology group + - Based on the representative plant size for a single plant in that technology group -- `growth_bin_size_mult.csv`: +- `growth_bin_size_mult.csv`: Multiplier [unitless] setting the size of each growth bin relative to the prior solve year's annual deployment -- `growth_limit_absolute.csv`: Maximum expected annual builds [MW/year] for wind, batteries, and UPV from 2024-2026 using observed record builds +- `growth_limit_absolute.csv`: Maximum allowed annual builds [MW/year] for specified technology groups from 2024-2026 using anticipated record builds -- `growth_penalty.csv`: +- `growth_penalty.csv`: Multiplier penalty [unitless] applied to the capital cost for growth in each bin diff --git a/inputs/hydro/README.md b/inputs/hydro/README.md index 147d52719..8348c501b 100644 --- a/inputs/hydro/README.md +++ b/inputs/hydro/README.md @@ -2,12 +2,13 @@ - `cap_existing_hydro.csv`: Annual capacities [MW] for hydro plants spanning 2007-2022, which come from ORNL's Existing Hydropower Assets dataset -- `hyd_fom.csv`: Regional FOM costs for hydro +- `hyd_fom.csv`: Regional fixed O&M cost [$/MW-year] for hydro technologies -- `hydcf_fixed.csv`: Fixed monthly zonal hydro capacity factor data partially created by ORNL and partially derived from ORNL's Existing Hydropower Assets dataset +- `hydcf_fixed.csv`: Fixed monthly zonal hydro capacity factor [fraction] partially created by ORNL and partially derived from ORNL's Existing Hydropower Assets dataset -- `hydro_mingen.csv`: +- `hydro_mingen.csv`: Minimum hydropower loading factor [fraction of capacity] by technology, region, and quarter - `net_gen_existing_hydro.csv`: Monthly net generation values [MWh] for hydro plants spanning 2007-2022, which come from ORNL's Existing Hydropower Assets dataset -- `SeaCapAdj_hy.csv`: +- `SeaCapAdj_hy.csv`: Maximum capacity adjustment [fraction] for dispatchable hydro by technology, month, and region + - Weighted into seasonal values during input processing to produce `cap_hyd_szn_adj` diff --git a/inputs/load/README.md b/inputs/load/README.md index b4e3a6d14..6cb0ff2d2 100644 --- a/inputs/load/README.md +++ b/inputs/load/README.md @@ -2,6 +2,16 @@ ### Load Growth Projections +The `demand_AEO_*` files are generated by [`aeo_updates/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/aeo_updates) in the ReEDS_Input_Processing repo, +which combines EIA retail sales and behind-the-meter PV for historical years with AEO electricity +consumption growth for projected years. + +Each file gives an annual load growth multiplier [unitless] by state (`r`) and year, normalized to +1.0 in the file's first year. `hourly_load.py` scales the historical hourly state load profiles to +match annual totals in that baseline year, then applies these multipliers to project load forward. +The scenario is chosen with the `demandscen` switch, and the file is copied into `inputs_case/` as +`load_multiplier.csv`. + - `demand_AEO_2025_high.csv`: Load growth projection from the AEO2025 High Economic Growth scenario - `demand_AEO_2025_low.csv`: Load growth projection from the AEO2025 Low Economic Growth scenario @@ -15,11 +25,16 @@ - `demand_AEO_2026_low.csv`: Load growth projection from the AEO2026 Low Economic Growth scenario ### Load Growth Multipliers -- `cangrowth.csv`: Canada load growth multiplier +- `cangrowth.csv`: Canadian load growth multiplier [unitless] by province and year, relative to the first year + - Used only when `GSw_Canada` is on -- `mex_growth_rate.csv`: Mexico load growth multiplier +- `mex_growth_rate.csv`: National Mexican load growth multiplier [unitless] by year, relative to 2016 + - Scales the exogenous Mexican load in each region and hour ### Other -- `EIA_loadbystate.csv`: +- `EIA_loadbystate.csv`: Historical annual electricity load [MWh] by state and year, from EIA + - Provides the annual totals that the historical hourly state load profiles are scaled to match -- `loadsite_country_test.csv`: \ No newline at end of file +- `loadsite_country_test.csv`: Load trajectory [MW] to be optimally sited, by load-site region and year + - The file is selected by the `GSw_LoadSiteTrajectory` switch and is only used when `GSw_LoadSiteCF` > 0 + - The first column may use any region hierarchy level (`st`, `transreg`, `transgrp`, `interconnect`, etc.), which must match the level named in the file title: `loadsite_{level}_{name}.csv` \ No newline at end of file diff --git a/inputs/national_generation/README.md b/inputs/national_generation/README.md index 91daaaf98..b929be16e 100644 --- a/inputs/national_generation/README.md +++ b/inputs/national_generation/README.md @@ -1,78 +1,12 @@ ## National Generation Input Files -### Clean Air Act, Section 111 +### Input files -#### The regulation itself +- `gen_mandate_trajectory.csv`: Required clean generation share [fraction] by year for each national generation standard trajectory + - Rows are the trajectory names; the row used is selected by the `GSw_GenMandateScen` switch -The Clean Air Act, Section 111 is a federal regulation from the Environmental Protection Agency (EPA), which features carbon pollution standards to reduce greenhouse gas emissions from power plants. -These regulations are called Section 111 for the section of the tax code which it is implemented in. -The regulations stipulate a couple of compliance mechanisms for plants to comply with these regulations. +- `nat_gen_tech_frac.csv`: Fraction [fraction] of each technology's generation that counts toward the national generation standard, given separately for the `RE`, `Nuclear`, `NuclearCCS`, and `RE_NoCombust` technology lists + - The column used is selected by the `GSw_GenMandateList` switch -The first compliance mechanism, and the strictest, is a best system of emissions reduction (BSER) for every technology and vintage (existing vs new plants). -This is enforced at the plant level i.e. each plant must meet its BSER. -The BSERs are listed below: - -- Existing coal plants: - - If the plant will permanently cease operations before Jan. 1, 2032: they are not subject to these standards - - If the plant is operating on or after Jan. 1, 2032, and demonstrate that they plan to permanently cease operation before Jan. 1, 2039: they must cofire with 40% natural gas from 2030 through 2039 - - If the plant plant to operate on or after Jan. 1, 2039: they must upgrade with CCS with 90% capture by Jan. 1, 2032 -- New coal plants: - - No regulations since no new coal plants are being built these days -- Existing gas-CCs and gas-CTs: - - No regulations -- New gas-CCs and gas-CTs: - - If the plant is operating at <= 40% capacity factor: they are unregulated - - If a plant is operating above 40% CF: they must upgrade with CCS by 2032. - -The second compliance mechanism, which is slightly more lenient, is a emissions rate-based mechanism. -This is enforced at the state level. -If a state opts into this compliance mechanism, the emissions rate (tons CO2/MWh) of their coal fleet must be less than or equal to the emissions rate of a 90% coal-CCS plant. -This in theory enables some unabated coal plants to remain online after 2032, even though they won't be able to generate much. -This is only possible if that state also has coal-CCS plants with high capture rates that stay online and generate, to average out the emissions rate to below the threshold. - -##### Other resources -- [Simplified presentation on the final regulations](https://www.epa.gov/system/files/documents/2024-04/cps-presentation-final-rule-4-24-2024.pdf) -- [Final regulations](https://www.federalregister.gov/documents/2024/05/09/2024-09233/new-source-performance-standards-for-greenhouse-gas-emissions-from-new-modified-and-reconstructed) -- [History of the Clean Air Act](https://www.epa.gov/clean-air-act-overview/evolution-clean-air-act) - -#### Implementation in ReEDS -In ReEDS, new gas plants must adhere to their BSER and existing coal plants adhere to an emissions rate-based standard. - -For new gas plants, this is the code implementation: - -1. `inputs/scalars.csv` - - `caa_gas_max_cf` = 0.40. - This is the maximum capacity factor that new gas plants (CCs or CTs) can operate at without being regulated under Clean Air Act, Section 111, expressed as a fraction. -2. `c_model.gms` - `eq_caa_max_cf` enforces the maximum capacity factor for new gas plants. - - -For existing coal plants, this is the code implementation: - -1. `inputs/scalars.csv` - - `caa_coal_retire_year` = 2032. - This is the year in which coal capacity is forced to either retire or upgrade with CCS to meet the emissions requirements under Clean Air Act, Section 111. - - `caa_first_year` = 2024. - This is the year in which the emissions requirements under Clean Air Act, Section 111 are first active. - - `caa_capture_rate_standard` = 0.90. - This is the CO2 capture rate that a state's coal fleet must match on average. - It is applied to each unit's own uncontrolled emissions rate, taken as the sum of the `emit_rate` and `capture_rate` parameters, so a unit that captures 90% of its CO2 complies regardless of its heat rate. - A unit that captures more than 90%, such as a new coal-CCS plant at 95%, emits less than its own allowance and so creates headroom for other coal in that state. -2. `reeds/input_processing/WriteHintage.py` - - Coal plants are binned at the unit level if `GSw_Clean_Air_Act=1` so that each coal unit can independently choose to retire or upgrade. - - Coal plants maintain their exogenous retirement assumption, except after 2032, when the Clean Air Act regulations begin and coal can retire endogenously. - For example, if the NEMS data states that a plant will retire in 2029, we maintain that assumption and that plant will retire in 2029. - However, if the NEMS data previously stated that a plant will retire in 2040, they are now subject to the Clean Air Act regulations and may retire sooner than their previously stated retirement date. -3. `b_inputs.gms` - - `numhintage` is set to 300, so that we can accommodate a large number of coal bins since they are binned at the unit level. - - Revise `m_capacity_exog` so that it matches with which coal capacity is allowed. - - if `caa_coal_retire_year` is not in the set of years being modeled for this run, then set it to the first year that is modeled after `caa_coal_retire_year`. - For example, if running 5 year solves, then instead of enforcing coal retirement in 2032, it will be enforced in 2035. - -4. `c_model.gms` - - `eq_caa_rate_standard(st,t)` - this constraint enforces the rate-based emissions standard by setting the maximum coal emissions per state under Clean Air Act Section 111. - Generation on both sides is weighted by `hours` over representative periods, so the standard is applied on an MWh-weighted basis. - -#### Assumptions -- We do not include the compliance mechanism for coal plants to cofire with natural gas in the model. -We have modeled this in ReEDS previously in our analysis of these regulations and found that coal plants almost never choose this compliance mechanism. -They would prefer to upgrade with CCS or retire. +- `national_rps_frac_allScen.csv`: Required national RPS share [fraction] by year for a range of named scenarios + - Used when `GSw_StateRPS` is on diff --git a/inputs/plant_characteristics/README.md b/inputs/plant_characteristics/README.md index 4c7a3c3ee..ae397f223 100644 --- a/inputs/plant_characteristics/README.md +++ b/inputs/plant_characteristics/README.md @@ -1,156 +1,201 @@ ## Plant Characteristics Input Files -- `battery_ATB_2024_*.csv`: - - `battery_ATB_2024_advanced.csv`: - - `battery_ATB_2024_conservative.csv`: - - `battery_ATB_2024_moderate.csv`: - -- `beccs_BVRE_2021_*.csv`: - - `beccs_BVRE_2021_high.csv`: - - `beccs_BVRE_2021_low.csv`: - - `beccs_BVRE_2021_mid.csv`: - -- `beccs_lowcost.csv`: - -- `beccs_reference.csv`: - -- `biopower_ATB_2024_moderate.csv`: - -- `ccsflex_ATB_2020_*.csv`: - - `ccsflex_ATB_2020_cost.csv`: - - `ccsflex_ATB_2020_perf.csv`: - -- `coal-ccs_ATB_2024_*.csv`: - - `coal-ccs_ATB_2024_advanced.csv`: - - `coal-ccs_ATB_2024_conservative.csv`: - - `coal-ccs_ATB_2024_moderate.csv`: - -- `coal_ATB_2024_moderate.csv`: - -- `cost_opres_*.csv`: - - `cost_opres_default.csv`: - - `cost_opres_market.csv`: - -- `csp_ATB_2023_*.csv`: - - `csp_ATB_2023_advanced.csv`: - - `csp_ATB_2023_conservative.csv`: - - `csp_ATB_2023_moderate.csv`: - -- `csp_ATB_2024_*.csv`: - - `csp_ATB_2024_advanced.csv`: - - `csp_ATB_2024_conservative.csv`: - - `csp_ATB_2024_moderate.csv`: +Most files in this folder give the cost and performance trajectory for one technology +family, and are selected by the matching `plantchar_{tech}` switch in `cases.csv`, +which is set to the full file name without the extension +(e.g. `plantchar_coal = coal_ATB_2024_moderate`). + +The most common column schema is `i,t,capcost,fom,vom,heatrate`: + +| Column | Meaning | Units | +| --- | --- | --- | +| `i` / `type` / `tech` | Technology | | +| `t` | Year | | +| `capcost` | Overnight capital cost | $/kW | +| `capcost_energy` | Overnight capital cost of the energy component (storage) | $/kWh | +| `fom` | Fixed O&M cost | $/kW-yr | +| `fom_energy` | Fixed O&M cost of the energy component (storage) | $/kWh-yr | +| `vom` | Variable O&M cost | $/MWh | +| `heatrate` | Heat rate | MMBtu/MWh | +| `rte` | Round-trip efficiency | fraction | +| `cf_improvement` | Capacity factor improvement multiplier (UPV) | fraction | + +The wind and geothermal files instead carry their units in the column headers +(`Overnight Cap Cost $/kW`, `Fixed O&M $/(kW-yr)`, `Var O&M $/MWh`, +`Cap cost 1000$/MW`, `Fixed O&M 1000$/MW-yr`) and add a `CF_mult` capacity factor +multiplier; the offshore wind and geothermal-class files also add `rsc_mult`, a +supply-curve cost reduction multiplier. + +Costs are in the dollar year listed for that scenario in `dollaryear.csv` and are +deflated to 2004$ during input processing. `advanced`, `moderate`, and `conservative` +refer to the Annual Technology Baseline (ATB) cost scenarios of the same names. + +- `battery_ATB_2024_*.csv`: Battery cost and performance from the 2024 ATB, including separate power and energy cost components; selected by `plantchar_battery` + - `battery_ATB_2024_advanced.csv`: Advanced scenario + - `battery_ATB_2024_conservative.csv`: Conservative scenario + - `battery_ATB_2024_moderate.csv`: Moderate scenario + +- `beccs_BVRE_2021_*.csv`: BECCS cost and performance from the BVRE 2021 dataset; selected by `plantchar_beccs` + - `beccs_BVRE_2021_high.csv`: High cost scenario + - `beccs_BVRE_2021_low.csv`: Low cost scenario + - `beccs_BVRE_2021_mid.csv`: Mid cost scenario + +- `beccs_lowcost.csv`: BECCS cost and performance for a low-cost scenario + +- `beccs_reference.csv`: BECCS cost and performance for the reference scenario + +- `biopower_ATB_2024_moderate.csv`: Biopower cost and performance from the 2024 ATB, moderate scenario; selected by `plantchar_biopower` + +- `ccsflex_ATB_2020_*.csv`: Flexible CCS cost and performance based on the 2020 ATB; selected by `ccsflexscen` and used only when one of the `GSw_CCSFLEX_*` switches is on + - `ccsflex_ATB_2020_cost.csv`: Cost and heat rate trajectory for the flexible CCS technologies + - `ccsflex_ATB_2020_perf.csv`: Performance characteristics of the flexible CCS technologies: storage efficiency (`stor_eff`), storage duration (`stor_dur`) [hours], CO2 capture efficiency at 90% and 95% capture (`co2_eff_90`, `co2_eff_95`), and the power limit (`pow_lim`) + +- `coal-ccs_ATB_2024_*.csv`: Coal CCS cost and performance from the 2024 ATB; selected by `plantchar_coal_ccs` + - `coal-ccs_ATB_2024_advanced.csv`: Advanced scenario + - `coal-ccs_ATB_2024_conservative.csv`: Conservative scenario + - `coal-ccs_ATB_2024_moderate.csv`: Moderate scenario + +- `coal_ATB_2024_moderate.csv`: Coal cost and performance from the 2024 ATB, moderate scenario; selected by `plantchar_coal` + +- `cost_opres_*.csv`: Cost of providing operating reserves [$/MWh] by technology and reserve type (regulation, flexibility, spinning); used only when `GSw_OpRes` is on + - `cost_opres_default.csv`: Default costs, which charge only for regulation reserves + - `cost_opres_market.csv`: Market-based costs, which charge for all three reserve types + +- `csp_ATB_2023_*.csv`: CSP cost and performance from the 2023 ATB by CSP configuration; selected by `plantchar_csp` + - `csp_ATB_2023_advanced.csv`: Advanced scenario + - `csp_ATB_2023_conservative.csv`: Conservative scenario + - `csp_ATB_2023_moderate.csv`: Moderate scenario + +- `csp_ATB_2024_*.csv`: CSP cost and performance from the 2024 ATB by CSP configuration; selected by `plantchar_csp` + - `csp_ATB_2024_advanced.csv`: Advanced scenario + - `csp_ATB_2024_conservative.csv`: Conservative scenario + - `csp_ATB_2024_moderate.csv`: Moderate scenario - `csp_SunShot2030.csv`: CSP costs from the SunShot2030 cost scenario - `dollaryear.csv`: Dollar year mapping for each plant cost scenario -- `dr_shed_*_demo_data_January_2025.csv`: - - `dr_shed_capcost_scalars_demo_data_January_2025.csv`: - - `dr_shed_fom_demo_data_January_2025.csv`: - - `dr_shed_vom_demo_data_January_2025.csv`: +- `dr_shed_*_demo_data_January_2025.csv`: Shed demand response cost trajectories by technology, region, and year; selected by `dr_shedscen` and used only when `GSw_DRShed` is on + - `dr_shed_capcost_scalars_demo_data_January_2025.csv`: Capital cost scalar [unitless] applied over time + - `dr_shed_fom_demo_data_January_2025.csv`: Fixed O&M cost [$/kW-yr] + - `dr_shed_vom_demo_data_January_2025.csv`: Variable O&M cost [$/MWh] -- `evmc_*_Baseline.csv`: - - `evmc_shape_Baseline.csv`: - - `evmc_storage_Baseline.csv`: +- `evmc_*_Baseline.csv`: Electric vehicle managed charging (EVMC) cost and performance by year; selected by `evmcscen` + - `evmc_shape_Baseline.csv`: Load-shaping (shift) EVMC resources + - `evmc_storage_Baseline.csv`: Storage-like (bidirectional) EVMC resources -- `fuelcell_ATB_2024_*.csv`: - - `fuelcell_ATB_2024_advanced.csv`: - - `fuelcell_ATB_2024_moderate.csv`: +- `fuelcell_ATB_2024_*.csv`: Fuel cell cost and performance from the 2024 ATB; selected by `plantchar_fuelcell` + - `fuelcell_ATB_2024_advanced.csv`: Advanced scenario + - `fuelcell_ATB_2024_moderate.csv`: Moderate scenario -- `gas-ccs_ATB_2024_*.csv`: - - `gas-ccs_ATB_2024_advanced.csv`: - - `gas-ccs_ATB_2024_conservative.csv`: - - `gas-ccs_ATB_2024_moderate.csv`: +- `gas-ccs_ATB_2024_*.csv`: Natural gas CCS cost and performance from the 2024 ATB; selected by `plantchar_gas_ccs` + - `gas-ccs_ATB_2024_advanced.csv`: Advanced scenario + - `gas-ccs_ATB_2024_conservative.csv`: Conservative scenario + - `gas-ccs_ATB_2024_moderate.csv`: Moderate scenario -- `gas_ATB_2024_moderate.csv`: +- `gas_ATB_2024_moderate.csv`: Natural gas cost and performance from the 2024 ATB, moderate scenario; selected by `plantchar_gas` -- `geo_ATB_2023_*.csv`: - - `geo_ATB_2023_advanced.csv`: - - `geo_ATB_2023_conservative.csv`: - - `geo_ATB_2023_moderate.csv`: +- `geo_ATB_2023_*.csv`: Geothermal cost and performance from the 2023 ATB by technology, resource class, and depth; selected by `plantchar_geo` + - `geo_ATB_2023_advanced.csv`: Advanced scenario + - `geo_ATB_2023_conservative.csv`: Conservative scenario + - `geo_ATB_2023_moderate.csv`: Moderate scenario -- `geo_ATB_2024_*.csv`: - - `geo_ATB_2024_advanced.csv`: - - `geo_ATB_2024_conservative.csv`: - - `geo_ATB_2024_moderate.csv`: +- `geo_ATB_2024_*.csv`: Geothermal cost and performance from the 2024 ATB by technology, resource class, and depth; selected by `plantchar_geo` + - `geo_ATB_2024_advanced.csv`: Advanced scenario + - `geo_ATB_2024_conservative.csv`: Conservative scenario + - `geo_ATB_2024_moderate.csv`: Moderate scenario -- `h2-combustion_ATB_202*.csv`: - - `h2-combustion_ATB_2023.csv`: +- `h2-combustion_ATB_202*.csv`: Hydrogen CT and CC plant cost and performance, generated in preprocessing from ATB moderate case data; selected by `plantchar_h2combustion` and used only when `GSw_H2Combustion` is on + - `h2-combustion_ATB_2023.csv`: Generated from moderate case NREL ATB 2023 data - `h2-combustion_ATB_2024.csv`: Hydrogen CT and CC plant costs generated in preprocessing from moderate case NREL ATB 2024 data -- `heat_rate_adj.csv`: Heat rate adjustment multiplier by technology +- `h2fuelcell_*.csv`: Hydrogen fuel cell cost and performance; selected by `plantchar_h2fuelcell` + - `h2fuelcell_advanced.csv`: Advanced scenario + - `h2fuelcell_conservative.csv`: Conservative scenario + - `h2fuelcell_moderate.csv`: Moderate scenario + +- `heat_rate_adj.csv`: Partial-load heat rate adjustment multiplier [unitless] by technology, given separately for pre-2010 and post-2010 units + - Derived from historical EIA generation and fuel use data -- `heat_rate_penalty_spin.csv`: +- `heat_rate_penalty_spin.csv`: Heat rate penalty [fraction] incurred for providing spinning reserves, by technology -- `hydro_ATB_2019_*.csv`: +- `hydro_ATB_2019_*.csv`: Hydropower cost from the 2019 ATB; selected by `plantchar_hydro` - `hydro_ATB_2019_constant.csv`: Hydro costs from the 2019 ATB constant cost scenario - `hydro_ATB_2019_low.csv`: Hydro costs from the 2019 ATB low cost scenario - `hydro_ATB_2019_mid.csv`: Hydro costs from the 2019 ATB mid cost scenario -- `maxage.csv`: Maximum age allowed for each technology +- `maxage.csv`: Maximum age [years] allowed for each technology -- `maxdailycf.csv`: Maximum daily capacity factor - dr_shed input supply curves are based on one 4-hour event per day +- `maxdailycf.csv`: Maximum capacity factor [fraction] within each representative period, by technology + - Enforced per technology and zone when `Sw_MaxDailyCF` is on: fleet generation may not exceed + capacity x available hours x `maxdailycf` + - The shed demand response supply curves it is set for assume one 4-hour event per day -- `minCF.csv`: Minimum annual capacity factor for each tech fleet - applied to i-rto +- `minCF.csv`: Minimum annual capacity factor [fraction] for each technology fleet + - Enforced per technology and zone when `Sw_MinCF` is on: fleet generation must be at least + capacity x hours x `minCF` -- `min_retire_age.csv`: Minimum retirement age for given technology +- `min_retire_age.csv`: Minimum retirement age [years] for given technology -- `mingen_fixed.csv`: +- `mingen_fixed.csv`: Fixed minimum generation level [fraction of capacity] applied across all hours, by technology; used only when `GSw_MingenFixed` is on -- `minloadfrac0.csv`: Characteristics/minloadfrac0 database of minloadbed generator cs +- `minloadfrac0.csv`: Initial minimum loading fraction [fraction] by technology -- `mttr.csv`: +- `mttr.csv`: Mean time to repair [hours] by technology, used by the resource adequacy module to model outage duration -- `nuclear-smr_ATB_2024_*.csv`: - - `nuclear-smr_ATB_2024_advanced.csv`: - - `nuclear-smr_ATB_2024_conservative.csv`: - - `nuclear-smr_ATB_2024_moderate.csv`: +- `nuclear-smr_ATB_2024_*.csv`: Small modular reactor (SMR) cost and performance from the 2024 ATB; selected by `plantchar_nuclear_smr` + - `nuclear-smr_ATB_2024_advanced.csv`: Advanced scenario + - `nuclear-smr_ATB_2024_conservative.csv`: Conservative scenario + - `nuclear-smr_ATB_2024_moderate.csv`: Moderate scenario -- `nuclear_ATB_2024_*.csv`: - - `nuclear_ATB_2024_advanced.csv`: - - `nuclear_ATB_2024_conservative.csv`: - - `nuclear_ATB_2024_moderate.csv`: +- `nuclear_ATB_2024_*.csv`: Large nuclear cost and performance from the 2024 ATB; selected by `plantchar_nuclear` + - `nuclear_ATB_2024_advanced.csv`: Advanced scenario + - `nuclear_ATB_2024_conservative.csv`: Conservative scenario + - `nuclear_ATB_2024_moderate.csv`: Moderate scenario -- `ofs-wind_ATB_2023_*.csv`: +- `ofs-wind_ATB_2023_*.csv`: Offshore wind cost and performance from the 2023 ATB by resource class and year; selected by `plantchar_ofswind` and used only when `GSw_OfsWind` is on - `ofs-wind_ATB_2023_advanced.csv`: 2023 advanced ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2023_conservative.csv`: 2023 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2023_moderate.csv`: 2023 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2023_moderate_noFloating.csv`: 2023 moderate_noFloating ofs-wind capital (5x floating capital cost), fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year + - Generated by [`wind/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/wind) in the ReEDS_Input_Processing repo from ATB data and LBNL wind market reports -- `ofs-wind_ATB_2024_*.csv`: - - `ofs-wind_ATB_2024_advanced.csv`: +- `ofs-wind_ATB_2024_*.csv`: Offshore wind cost and performance from the 2024 ATB by resource class and year; selected by `plantchar_ofswind` and used only when `GSw_OfsWind` is on + - `ofs-wind_ATB_2024_advanced.csv`: 2024 advanced ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2024_conservative.csv`: 2024 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2024_moderate.csv`: 2024 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year - `ofs-wind_ATB_2024_moderate_noFloating.csv`: 2024 moderate_noFloating ofs-wind capital (5x floating capital cost), fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year + - Generated by [`wind/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/wind) in the ReEDS_Input_Processing repo from ATB data and LBNL wind market reports -- `ons-wind_ATB_2023_*.csv`: +- `ons-wind_ATB_2023_*.csv`: Land-based wind cost and performance from the 2023 ATB by resource class and year; selected by `plantchar_onswind` - `ons-wind_ATB_2023_advanced.csv`: Advanced cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind - `ons-wind_ATB_2023_conservative.csv`: Conservative cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind - `ons-wind_ATB_2023_moderate.csv`: Moderate cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind + - Generated by [`wind/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/wind) in the ReEDS_Input_Processing repo from ATB data and LBNL wind market reports -- `ons-wind_ATB_2024_*.csv`: +- `ons-wind_ATB_2024_*.csv`: Land-based wind cost and performance from the 2024 ATB by resource class and year; selected by `plantchar_onswind` - `ons-wind_ATB_2024_advanced.csv`: Advanced cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind - `ons-wind_ATB_2024_conservative.csv`: Conservative cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind - `ons-wind_ATB_2024_moderate.csv`: Moderate cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind + - Generated by [`wind/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/wind) in the ReEDS_Input_Processing repo from ATB data and LBNL wind market reports -- `other_plantchar.csv`: +- `other_plantchar.csv`: Cost and performance for the landfill gas (`lfill-gas`) and oil-gas-steam (`o-g-s`) technologies; selected by `plantchar_other` -- `outage_forced_*.csv`: +- `outage_forced_*.csv`: Forced outage rate [fraction] by technology - `outage_forced_static.csv`: Forced outage rates by technology - - `outage_forced_temperature_murphy2019.csv`: + - `outage_forced_temperature_murphy2019.csv`: Temperature-dependent forced outage rate [fraction] by prime mover and temperature [deg C], from [Murphy et al. 2019](https://doi.org/10.1016/j.apenergy.2019.113513); selected by `GSw_OutageScen` -- `outage_scheduled_*.csv`: - - `outage_scheduled_monthly.csv`: +- `outage_scheduled_*.csv`: Scheduled (planned maintenance) outage rate [fraction] by technology + - `outage_scheduled_monthly.csv`: Scheduled outage rate [fraction] by prime mover and month - `outage_scheduled_static.csv`: Scheduled outage rate by technology -- `pcm_defaults.json`: +- `pcm_defaults.json`: Default unit-level operating parameters by technology - average, minimum, and maximum unit capacity [MW], forced outage and maintenance rates, mean time to repair [hours], ramp limits, minimum up/down time, minimum stable level, and start cost [$/MW] -- `pvb_benchmark2020.csv`: +- `pvb_benchmark2020.csv`: Benchmark PV+battery system cost components used to derive hybrid PV+battery costs, with the units, dollar year, and source given per row -- `ramprate.csv`: Generator ramp rates by technology +- `ramprate.csv`: Ramp rate [fraction/min] of dispatchable generators, by technology -- `startcost.csv`: +- `startcost.csv`: Linearized generator startup cost [2004$/MW] by technology - Most startup costs are taken from Lew et al 2013 - Western Wind and Solar Integration Study Phase 2 [(NREL/TP-5500-55588)](https://www.nlr.gov/docs/fy13osti/55588.pdf) - The original data, reported in $2011/MW in Table 7 are: - Coal: 124 @@ -161,16 +206,16 @@ - RPM uses 466 $2011/MW for nuclear, but we don't have a citable source for that number - CCS startup costs are assumed to be the startup cost for the non-CCS version multiplied by the ratio of 2035 VOM costs between gas-cc-ccs and gas-cc in ATB 2023 -- `unitsize_atb.csv`: +- `unitsize_atb.csv`: Representative unit size [MW] by technology with the source given per row, used by the resource adequacy module when `pras_unitsize_source = atb` -- `upv_ATB_2023_*.csv`: - - `upv_ATB_2023_advanced.csv`: - - `upv_ATB_2023_conservative.csv`: - - `upv_ATB_2023_moderate.csv`: +- `upv_ATB_2023_*.csv`: Utility-scale PV cost and performance from the 2023 ATB; selected by `plantchar_upv` + - `upv_ATB_2023_advanced.csv`: Advanced scenario + - `upv_ATB_2023_conservative.csv`: Conservative scenario + - `upv_ATB_2023_moderate.csv`: Moderate scenario -- `upv_ATB_2024_*.csv`: - - `upv_ATB_2024_advanced.csv`: - - `upv_ATB_2024_conservative.csv`: - - `upv_ATB_2024_moderate.csv`: +- `upv_ATB_2024_*.csv`: Utility-scale PV cost and performance from the 2024 ATB; selected by `plantchar_upv` + - `upv_ATB_2024_advanced.csv`: Advanced scenario + - `upv_ATB_2024_conservative.csv`: Conservative scenario + - `upv_ATB_2024_moderate.csv`: Moderate scenario -- `years_until_endogenous.csv`: \ No newline at end of file +- `years_until_endogenous.csv`: Number of years from the current year until each technology becomes available for endogenous (model-chosen) builds, used to build the tech-specific `firstyear` parameter \ No newline at end of file diff --git a/inputs/reserves/README.md b/inputs/reserves/README.md index ff019091f..26b23602e 100644 --- a/inputs/reserves/README.md +++ b/inputs/reserves/README.md @@ -13,8 +13,9 @@ Values for MW_TotalDemand are for 2024 from [LTRA 2023](https://www.nerc.com/pa/ - `prm_annual.csv`: Annual planning reserve margin by NERC region - Taken from the 2023 NERC LTRA (specifically the "Reference Margin Level (%)" reported for each reliability region) -- `opres_periods.csv`: +- `opres_periods.csv`: Seasons within which the operating reserve constraint applies -- `orperc.csv`: +- `orperc.csv`: Operating reserve requirement [fraction] by reserve type (spinning, regulation, flexibility) and category + - `or_load` is a fraction of load; `or_wind` and `or_pv` are fractions of wind and PV generation -- `ramptime.csv`: +- `ramptime.csv`: Ramping time [minutes] used in the operating reserve ramping limit constraint, by reserve type diff --git a/inputs/sets/README.md b/inputs/sets/README.md index 2f19ec08b..4db9a5ded 100644 --- a/inputs/sets/README.md +++ b/inputs/sets/README.md @@ -52,7 +52,9 @@ - `allt.csv`: set of all potential years -- `bioclass.csv`: set of bio tech classes +- `bioclass.csv`: set of biomass supply curve classes, each a price/quantity bin of biomass feedstock supply + +- `captype.csv`: set of capacity types (existing and prescribed) - `c.csv`: set of renewable resource classes @@ -80,7 +82,7 @@ - `fuelbin.csv`: set of gas usage brackets -- `gb.csv`: set of gas price bins +- `gb.csv`: set of gas price bins; must have an odd number of bins (e.g. gb1*gb15) - `gbin.csv`: set of growth bins @@ -106,9 +108,11 @@ - `i_water_nocooling.csv`: set of technologies that use water, but are not differentiated by cooling tech and water source +- `jtype.csv`: set of job types used in the model (construction and O&M) + - `lcclike.csv`: set of transmission capacity types where lines are bundled with AC/DC converters -- `month.csv`: +- `month.csv`: Calendar months in a year - `noretire.csv`: set of technologies that will never be retired @@ -126,19 +130,19 @@ - `plantcat.csv`: set of categories for plant characteristics -- `prepost.csv`: - -- `prescriptivelink0.csv`: initial set of prescribed categories and their technologies - used in assigning prescribed builds +- `prepost.csv`: Defines pre-2010 versus post-2010 years - `pvb_agg.csv`: crosswalk between hybrid pv+battery configurations and technology options - `pvb_config.csv`: set of hybrid pv+battery configurations -- `quarter.csv`: +- `quarter.csv`: four quarters (listed as seasons) of the year + +- `resourceclass.csv`: set of renewable resource classes -- `sdbin.csv`: set of storage durage bins +- `sdbin.csv`: set of storage duration bins [hours] -- `tg.csv`: set of technology groups +- `tg.csv`: set of technology groups used for growth constraints - `tg_rsc_cspagg.csv`: set of csp technologies that belong to the same class @@ -156,6 +160,6 @@ - `wst_climate.csv`: set of water sources affected by climate change -- `wst_surface.csv`: +- `wst_surface.csv`: Surface water types, for which access is based on consumption rather than withdrawal - `yearafter.csv`: set to loop over for the final year calculation diff --git a/inputs/shapefiles/README.md b/inputs/shapefiles/README.md index d45653b26..dbc6c7357 100644 --- a/inputs/shapefiles/README.md +++ b/inputs/shapefiles/README.md @@ -1,13 +1,16 @@ ## Shapefiles Input Files -- `ctus_cs_polygons.gpkg`: +- `ctus_cs_polygons.gpkg`: Polygons of the CO2 storage formations (geologic sinks) used for carbon transport and storage + - The `cs` attribute names each formation and corresponds to `inputs/ctus/cs.csv` + - Also carries formation deposition, depth [ft], thickness [ft], basin, lithology, centroid state, and CO2 storage capacity [MMT CO2] -- `greatlakes.gpkg`: +- `greatlakes.gpkg`: Great Lakes polygons, used for cartographic masking in mapping and plotting scripts -- `h2_storage_sites.gpkg`: +- `h2_storage_sites.gpkg`: Polygons of candidate geologic hydrogen storage sites + - Contains a separate layer for each storage type (`salt` and `hardrock`) -- `offshore_zones.gpkg`: +- `offshore_zones.gpkg`: Polygons of the offshore wind zones - `state_fips_codes.csv`: Mapping of states to FIPS codes and postal code abbreviations -- `timezones.gpkg`: +- `timezones.gpkg`: Time zone polygons, used to assign a time zone to each ReEDS region diff --git a/inputs/state_policies/README.md b/inputs/state_policies/README.md index 04236ccc3..d3e968526 100644 --- a/inputs/state_policies/README.md +++ b/inputs/state_policies/README.md @@ -1,18 +1,22 @@ ## State Policies Input Files -- `acp_disallowed.csv`: List of states which do not allow alternative compliance payments in place of meeting RPS or CES requirements +- `acp_disallowed.csv`: Flag [1 = disallowed, 0 = allowed] for alternative compliance payments, by state and RPS/CES category -- `acp_prices.csv`: +- `acp_prices.csv`: Alternative compliance payment price [2004$/REC] for the RPS constraint, by state and year + - Sets the effective ceiling on the RPS penalty; used only when `GSw_StateRPS` is on -- `ces_fraction.csv`: Annual compliance for states with a CES policy +- `ces_fraction.csv`: Annual compliance fraction [fraction] for states with a CES policy + - Generated by [`state_policies/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/state_policies) in the ReEDS_Input_Processing repo -- `forced_retirements.csv`: List of regions with mandatory retirement policies for certain technologies +- `forced_retirements.csv`: Year by which certain technologies must be retired, by technology and state -- `hydrofrac_policy.csv`: +- `hydrofrac_policy.csv`: Fraction [fraction] of hydropower RPS or CES credits that can count toward policy targets, by state + - Given separately for the `RPS_All` and `CES` policies + - Generated by [`state_policies/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/state_policies) in the ReEDS_Input_Processing repo -- `ng_crf_penalty_st.csv`: Cost adjustment for NG techs in states where all NG techs must be retired by a certain year +- `ng_crf_penalty_st.csv`: Cost adjustment [unitless] for NG techs in states where all NG techs must be retired by a certain year -- `nuclear_subsidies.csv`: +- `nuclear_subsidies.csv`: Year in which an existing nuclear subsidy ends, by state - `offshore_req_default.csv`: Default state mandates of offshore wind capacity [MW], updated in November 2025 @@ -20,20 +24,27 @@ - `recstyle.csv`: Indication for how to apply state requirement (0 = end-use sales, 1 = bus-bar sales, 2 = generation). Default is 0. -- `rectable.csv`: Table defining which states are allowed to trade RECs +- `rectable.csv`: Allowed REC trade from each state to each other state + - `1` = unbundled trade allowed, `2` = only bundled trade allowed, `0` = no trade -- `rps_fraction.csv`: Indicates what fraction of sales or generation (based on recstyle.csv) must be from renewable energy +- `rps_fraction.csv`: Fraction [fraction] of sales or generation (based on `recstyle.csv`) that must be from renewable energy, by state and year + - Given separately for the overall requirement and any solar- or wind-specific carve-outs (`rps_all`, `rps_solar`, `rps_wind`) + - Generated by [`state_policies/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/state_policies) in the ReEDS_Input_Processing repo -- `storage_mandates.csv`: Energy storage mandates by region +- `storage_mandates.csv`: Cumulative battery storage mandate [MW] by state and year -- `techs_banned_ces.csv`: Indicates which technologies are not eligible to contribute to CES +- `techs_banned_ces.csv`: Technologies that may not contribute to a state's CES, by state + - A value of 1 marks the technology as ineligible for that state's CES -- `techs_banned_imports_rps.csv`: +- `techs_banned_imports_rps.csv`: Technologies that may not be imported into a state to meet its RPS, by state + - A value of 1 marks the technology as banned for imports in that state -- `techs_banned_rps.csv`: Indicates which technologies are not eligible to contribute to RPS +- `techs_banned_rps.csv`: Technologies that may not contribute to a state's RPS, by state + - A value of 1 marks the technology as ineligible for all of that state's RPS categories (`RPS_All`, `RPS_Wind`, `RPS_Solar`) -- `techs_banned.yaml`: +- `techs_banned.yaml`: Technologies banned from being built in specific regions + - The only allowed regional scopes (nested keys) are `st` and `county` -- `unbundled_limit_ces.csv`: Limit on fraction of credits towards CES which can be purchased unbundled from other states +- `unbundled_limit_ces.csv`: Limit [fraction] on credits towards CES which can be purchased unbundled from other states -- `unbundled_limit_rps.csv`: Limit on fraction of credits towards RPS which can be purchased unbundled from other states +- `unbundled_limit_rps.csv`: Limit [fraction] on credits towards RPS which can be purchased unbundled from other states diff --git a/inputs/storage/README.md b/inputs/storage/README.md index c34d918bd..9314d3ac4 100644 --- a/inputs/storage/README.md +++ b/inputs/storage/README.md @@ -1,7 +1,11 @@ ## Storage Input Files - `cap_existing_psh.csv`: County-wide PSH operational capacity [MW/MWh], pump capacity, and max energy, based on plant-level data from [https://www.hydropower.org/hydropower-pumped-storage-tool](https://www.hydropower.org/hydropower-pumped-storage-tool) + - Generated by [`psh/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/psh) in the ReEDS_Input_Processing repo -- `PSH_supply_curves_durations.csv`: PSH storage duration corresponding to each PSH supply curve, used to define the storage duration of new PSH capacity as well as existing PSH capacity if not explicitly defined. +- `PSH_supply_curves_durations.csv`: Storage duration [hours] of each pumped storage hydropower (PSH) supply curve + - The duration of the supply curve named by the `pshsupplycurve` switch is applied to all PSH capacity, overriding the PSH values in `storage_duration.csv` + - When `GSw_HydroPSHDurData` is on, region- and vintage-specific durations derived from `cap_existing_psh.csv` override this value for the existing PSH fleet -- `storage_duration.csv`: +- `storage_duration.csv`: Storage duration [hours] by technology + - Covers the PSH, CSP, and EVMC storage technologies diff --git a/inputs/supply_curve/README.md b/inputs/supply_curve/README.md index ce2b20575..3b8057a5c 100644 --- a/inputs/supply_curve/README.md +++ b/inputs/supply_curve/README.md @@ -12,28 +12,64 @@ - `reference`: all open exclusions [plus] ephemeral streams, critical habitat for endangered species, railway, pipelines, and major roads with 30m buffer, and National Wetlands Inventory (including small, localized riparian environments aroud rivers, streams, and lakes) - `limited`: all reference exclusions [plus] agricultural easements, farm trust conservation land, and nationally important agricultural land, mature forests on federal lands, **all** building footprints, and Census and BIA-defined "tribal areas" (reservations and off-reservation trust land) -- `bio_supplycurve.csv`: Regional biomass supply and costs by resource class +- `bio_supplycurve.csv`: Biomass supply [million dry tons] and cost [$/dry ton] by USDA region and biomass supply curve class - Dollar year: 2015 -- `dollaryear.csv`: +- `dollaryear.csv`: U.S. dollar year for each cost-related input file in this folder -- `dr_shed_cap_demo_data_January_2025.csv`: +- `classification_geothermal.csv`: Geothermal resource class definitions, giving the reV mean resource temperature range [deg C] spanned by each class for each siting access case -- `dr_shed_cost_demo_data_January_2025.csv`: +- `dr_shed_cap_demo_data_January_2025.csv`: Available shed demand response capacity [MW] by technology and state + - Selected by the `dr_shedscen` switch -- `hyd_add_upg_cap.csv`: +- `dr_shed_cost_demo_data_January_2025.csv`: Cost [$/MW] of shed demand response supply by technology and state + - Deflated to 2004$ during input processing using the `dr_shed` dollar year in `dollaryear.csv` + - Selected by the `dr_shedscen` switch; deflated to 2004$ during input processing using the `dr_shed` dollar year in `dollaryear.csv` -- `hydcap.csv`: +- `hyd_add_upg_cap.csv`: Cumulative increase in available hydropower upgrade capacity [MW] relative to the base year, by region, technology, resource bin, and year -- `hydcost.csv`: +- `hydcap.csv`: Available hydropower supply curve capacity [MW] by technology, resource class, and region -- `interconnection_land.h5`: +- `hydcost.csv`: Hydropower supply curve capital cost [$/kW] by technology, resource class, and region + - Converted to $/MW when loaded into `rsc_dat`; the dollar year is given in `dollaryear.csv` -- `interconnection_offshore.h5`: +- `interconnection_land.h5`: Land-based interconnection characteristics for each reV supply curve point (`sc_point_gid`): site and point-of-interconnection coordinates, FIPS code, transmission line GID and type, spur line and network reinforcement distances [km], and spur, POI, reinforcement, and total transmission costs [$/MW] + - Written by `hourlize/write_site_parameters.py`; the dollar year is stored as an attribute on the h5 file -- `rev_paths.csv`: +- `interconnection_offshore.h5`: Offshore interconnection characteristics for each reV supply curve point, in the same format as `interconnection_land.h5` + - Written by `hourlize/write_site_parameters.py` -- `sc_point_gid_old2new.csv`: +- `PSH_supply_curves_capacity_*.csv`: Pumped storage hydropower supply curve capacity as used in 2025 Annual Technology Baseline. Citation: [https://www.nlr.gov/gis/psh-supply-curves](https://www.nlr.gov/gis/psh-supply-curves) + - `PSH_supply_curves_capacity_10hr_ref_apr2025.csv`: supply curve capacity assuming 10 hour duration and reference exclusions + - `PSH_supply_curves_capacity_10hr_wEph_apr2025.csv`: supply curve capacity assuming 10 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_capacity_10hr_wExist_apr2025.csv`: supply curve capacity assuming 10 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_capacity_10hr_wExist_wEph_apr2025.csv`: supply curve capacity assuming 10 hour duration and allowing sites using existing reservoirs and on ephemeral streams + - `PSH_supply_curves_capacity_12hr_ref_apr2025.csv`: supply curve capacity assuming 12 hour duration and reference exclusions + - `PSH_supply_curves_capacity_12hr_wEph_apr2025.csv`: supply curve capacity assuming 12 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_capacity_12hr_wExist_apr2025.csv`: supply curve capacity assuming 12 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_capacity_12hr_wExist_wEph_apr2025.csv`: supply curve capacity assuming 12 hour duration and allowing sites using existing reservoirs and on ephemeral streams + - `PSH_supply_curves_capacity_8hr_ref_apr2025.csv`: supply curve capacity assuming 8 hour duration and reference exclusions + - `PSH_supply_curves_capacity_8hr_wEph_apr2025.csv`: supply curve capacity assuming 8 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_capacity_8hr_wExist_apr2025.csv`: supply curve capacity assuming 8 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_capacity_8hr_wExist_wEph_apr2025.csv`: supply curve capacity assuming 8 hour duration and allowing sites using existing reservoirs and on ephemeral streams + +- `PSH_supply_curves_cost_*.csv`: Pumped storage hydropower supply curve cost as used in 2025 Annual Technology Baseline. Citation: [https://www.nlr.gov/gis/psh-supply-curves](https://www.nlr.gov/gis/psh-supply-curves) + - `PSH_supply_curves_cost_10hr_ref_apr2025.csv`: assuming 10 hour duration and reference exclusions + - `PSH_supply_curves_cost_10hr_wEph_apr2025.csv`: assuming 10 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_cost_10hr_wExist_apr2025.csv`: assuming 10 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_cost_10hr_wExist_wEph_apr2025.csv`: assuming 10 hour duration and allowing sites using existing reservoirs and on ephemeral streams + - `PSH_supply_curves_cost_12hr_ref_apr2025.csv`: assuming 12 hour duration and reference exclusions + - `PSH_supply_curves_cost_12hr_wEph_apr2025.csv`: assuming 12 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_cost_12hr_wExist_apr2025.csv`: assuming 12 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_cost_12hr_wExist_wEph_apr2025.csv`: assuming 12 hour duration and allowing sites using existing reservoirs and on ephemeral streams + - `PSH_supply_curves_cost_8hr_ref_apr2025.csv`: assuming 8 hour duration and reference exclusions + - `PSH_supply_curves_cost_8hr_wEph_apr2025.csv`: assuming 8 hour duration and allowing sites on ephemeral streams + - `PSH_supply_curves_cost_8hr_wExist_apr2025.csv`: assuming 8 hour duration and allowing sites using existing reservoirs + - `PSH_supply_curves_cost_8hr_wExist_wEph_apr2025.csv`: assuming 8 hour duration and allowing sites using existing reservoirs and on ephemeral streams + +- `rev_paths.csv`: Paths to the reV supply curve and generation runs backing each technology, giving the siting access switch and case, supply curve and capacity factor paths, and the original reV case and supply curve file names + +- `supplycurve_egs-reference.csv`: Enhanced geothermal (EGS) supply curve by reV supply curve point: resource class, available capacity [MW], capital cost adder [$/MW], capacity factor, and mean resource temperature [deg C] - `sitemap.h5`: @@ -65,4 +101,4 @@ - `supplycurve_wind-ons-open.csv`: open siting scenario - `supplycurve_wind-ons-reference.csv`: reference siting scenario -- `trans_intra_cost_adder.csv`: +- `trans_intra_cost_adder.csv`: Intra-zone transmission cost adder [2004$/kW] by technology diff --git a/inputs/techs/README.md b/inputs/techs/README.md index 2f0eef1b5..19fb96961 100644 --- a/inputs/techs/README.md +++ b/inputs/techs/README.md @@ -1,6 +1,6 @@ ## Techs -- `tech_resourceclass.csv`: +- `tech_resourceclass.csv`: Map from CSP and DUPV technologies to their resource classes - `techs_default.csv`: List of technologies to be used in the model diff --git a/inputs/temporal/README.md b/inputs/temporal/README.md index 7cb6cfc79..9e9ba8f51 100644 --- a/inputs/temporal/README.md +++ b/inputs/temporal/README.md @@ -1,7 +1,9 @@ ## Temporal Input Files -- `month2quarter.csv`: +- `month2quarter.csv`: Map from calendar month to quarter (winter, spring, summer, fall) -- `period_szn_user.csv`: +- `period_szn_user.csv`: User-specified assignment of each actual period to the representative period that stands in for it + - Used when the `GSw_HourlyClusterAlgorithm` switch contains the substring `user`, which bypasses the clustering algorithms -- `stressperiods_user.csv`: +- `stressperiods_user.csv`: User-specified stress periods by model year + - Used when the `GSw_PRM_StressModel` switch starts with `user`, which bypasses stress period identification by PRAS diff --git a/inputs/transmission/README.md b/inputs/transmission/README.md index 14c3a1e0a..0fa6d4e88 100644 --- a/inputs/transmission/README.md +++ b/inputs/transmission/README.md @@ -1,6 +1,6 @@ ## Transmission Input Files -- `b2b_converters.csv`: Power capacity and location of back-to-back (B2B) AC/DC/AC converters in the USA. +- `b2b_converters.csv`: Power capacity [MW] and location of back-to-back (B2B) AC/DC/AC converters in the USA. - Power capacities are from [Brinkman et al. 2020](https://docs.nlr.gov/docs/fy21osti/78161.pdf) for converters at the eastern/western interface and [ERCOT 2020](https://www.ercot.com/files/docs/2020/07/30/ERCOT_DC_Tie_Operations_Document.docx) for converters at the eastern/texas interface. - Locations are from the [Open Infrastructure Map](https://openinframap.org/#4.1/40.65/-97.11). The `osm_id` column gives the OpenStreetMap ID of the converter; for example, the "Miles City, MT" converter (with `osm_id = 137835349`) can be found at . @@ -16,7 +16,7 @@ - `cost_hurdle_intra.csv`: Hurdle rate for transmission flows [\$/MWh] between ReEDS spatial hierarchy levels. -- `dollaryear.scv`: U.S. dollar year for cost-related input files +- `dollaryear.csv`: U.S. dollar year for cost-related input files - `hvdc_existing.csv`: Power capacity and start/end locations of [high-voltage direct current (HVDC) lines](https://en.wikipedia.org/wiki/List_of_HVDC_projects#North_America) in the USA. These lines are mapped to ReEDS zone interfaces during input processing. diff --git a/inputs/upgrades/README.md b/inputs/upgrades/README.md index a596060d3..40e942b79 100644 --- a/inputs/upgrades/README.md +++ b/inputs/upgrades/README.md @@ -4,9 +4,11 @@ - `i_coolingtech_watersource_upgrades_link.csv`: List of cooling technologies for water sources that can be upgraded + their to, from, ctt (cooling technology type) and wst (water source type) -- `upgrade_costs_ccs_coal.csv`: +- `upgrade_costs_ccs_coal.csv`: Exogenous capital cost [$/kW] to upgrade a coal plant to coal-CCS, by year and cost scenario + - Used only when the `upgradescen` switch is not `default`; `default` instead derives the cost from the difference between the from- and to-technology -- `upgrade_costs_ccs_gas.csv`: +- `upgrade_costs_ccs_gas.csv`: Exogenous capital cost [$/kW] to upgrade a gas plant to gas-CCS, by year and cost scenario + - Used only when the `upgradescen` switch is not `default`; `default` instead derives the cost from the difference between the from- and to-technology - `upgrade_link.csv`: Techs that can be upgraded including the original technology, the technology it is upgrading to, and the delta. diff --git a/inputs/userinput/README.md b/inputs/userinput/README.md index 13b42d66a..1a842cc59 100644 --- a/inputs/userinput/README.md +++ b/inputs/userinput/README.md @@ -1,24 +1,27 @@ ## User Input Files -- `futurefiles.csv`: +- `futurefiles.csv`: Registry describing how each file copied into `inputs_case/` is projected forward in time by `reeds/input_processing/forecast.py` + - Columns give the file format (`filetype`, `wide`, `header`), the index columns to hold fixed (`key`, `year_col`, `fix_cols`), the extrapolation method (`forecast_fit`), and optional bounds (`clip_min`, `clip_max`) + - Every new file written into `inputs_case/` needs an entry here; `forecast.py` prints a warning for files it does not find -- `ivt_default.csv`: +- `ivt_default.csv`: Default vintage bin assignment: the vintage number each technology belongs to in each year + - Selected by the `ivt_suffix` switch; the model converts these bin numbers into the `ivt(i,v,t)` mapping set -- `ivt_small.csv`: +- `ivt_small.csv`: Reduced vintage bin assignment used for smaller and faster test runs, with fewer distinct vintages than `ivt_default.csv` + - Selected with `ivt_suffix = small` - `ivt_step.csv`: ivt steps for endyears beyond 2050 -- `mcs_distribution_rules.yaml`: +- `mcs_distribution_rules.yaml`: Validation rules for the distribution specifications used in Monte Carlo Sampling (MCS) + - Each top-level key is a valid distribution name; the rules are checked against the user's `mcs_distributions_*.yaml` file by `reeds/input_processing/mcs_sampler.py` -- `mcs_distributions_default.yaml`: +- `mcs_distributions_default.yaml`: Default set of parameter distributions sampled for Monte Carlo Sampling (MCS) analysis + - The file is selected by the `MCS_dist` switch and is only used when `MCS_runs` is nonzero -- `modeled_regions.csv`: Sets of BA regions that a user can model in a run - - Each column is a different region option and can be specified in cases using `GSw_Region` +- `windows_2100.csv`: Start and stop years of each overlapping solve window used by the window solve method to 2100 -- `windows_2100.csv`: Window size for using window solve method to 2100 +- `windows_default.csv`: Start and stop years of each overlapping solve window used by the window solve method -- `windows_default.csv`: Window size for using window solve method +- `windows_step10.csv`: Start and stop years of each overlapping solve window used by the window solve method for `beyond2050step10` -- `windows_step10.csv`: Window size for beyond2050step10 - -- `windows_step5.csv`: Window size for beyond2050step5 +- `windows_step5.csv`: Start and stop years of each overlapping solve window used by the window solve method for `beyond2050step5` diff --git a/inputs/userinput/futurefiles.csv b/inputs/userinput/futurefiles.csv index b6559c777..0f718a1b0 100644 --- a/inputs/userinput/futurefiles.csv +++ b/inputs/userinput/futurefiles.csv @@ -427,7 +427,6 @@ storage_mandates.csv,.csv,0,None,t,*st,0,0,constant,None,None,done,constant storinmaxfrac.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant stressperiods_user.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new; check if we need to expand the input file, superpeak_hour_mapper.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant -supply_chain_adjust.csv,.csv,0,None,0,None,0,None,constant,None,None,new, supplycurve_egs.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new, supplycurve_geohydro.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new, supplycurve_psh.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new, diff --git a/inputs/valuestreams/README.md b/inputs/valuestreams/README.md index a8792f675..76fa4bfcf 100644 --- a/inputs/valuestreams/README.md +++ b/inputs/valuestreams/README.md @@ -1,3 +1,4 @@ ## Valuestreams Input Files -- `var_map.csv`: +- `var_map.csv`: Map from ReEDS optimization variable names to the position of each index (`i`, `v`, `r`, `h`, `t`) within that variable + - Used by `postprocessing/valuestreams.py` to parse variables out of the solution file; a blank entry means the variable does not carry that index diff --git a/inputs/waterclimate/README.md b/inputs/waterclimate/README.md index 15005b413..5b33adb41 100644 --- a/inputs/waterclimate/README.md +++ b/inputs/waterclimate/README.md @@ -1,21 +1,26 @@ ## Waterclimate Input Files -- `cost_cap_mult.csv`: +- `cost_cap_mult.csv`: Capital cost multiplier [fraction] used to differentiate cooling technology types, by technology and cooling technology type -- `cost_vom_mult.csv`: +- `cost_vom_mult.csv`: VOM cost multiplier [fraction] used to differentiate cooling technology types, by technology and cooling technology type -- `heat_rate_mult.csv`: +- `heat_rate_mult.csv`: Heat rate multiplier [fraction] used to differentiate cooling technology types, by technology and cooling technology type -- `i_coolingtech_watersource.csv`: +- `i_coolingtech_watersource.csv`: List of the expanded (non-numeraire) technologies formed by combining each technology with a cooling technology type and water source type + - Names follow the pattern `{tech}_{ctt}_{wst}`, e.g. `coal-CCS_mod_o_fsu` -- `i_coolingtech_watersource_link.csv`: +- `i_coolingtech_watersource_link.csv`: Link from each expanded cooling/water technology to its base technology (`ii`), cooling technology type (`ctt`), and water source type (`wst`) -- `tg_rsc_cspagg_tmp.csv`: +- `tg_rsc_cspagg_tmp.csv`: Expanded CSP resource aggregation mapping, extending `tg_rsc_cspagg(i,ii)` to include the non-numeraire CSP technologies -- `unapp_water_sea_distr.csv`: +- `unapp_water_sea_distr.csv`: Fractional quarterly allocation [fraction] of unappropriated water, by water source type and region + - Quarterly values sum to 1 for each water source type and region that has an allocation; regions with no unappropriated water of that type are all zero -- `wat_access_cap_cost.csv`: +- `wat_access_cap_cost.csv`: Water supply curve for post-2010 capacity, by water source type and region + - `sc_cat = cap` gives available supply [million gallons/year]; `sc_cat = cost` gives the access cost [$/(million gallons/year)] + - `sc_cat = cap` gives available supply [million gallons/year]; `sc_cat = cost` gives access cost [$ per million gallons/year] -- `water_req_psh_10h_1_51.csv`: +- `water_req_psh_10h_1_51.csv`: Water required to fill the reservoir during PSH construction [Mgal/MW/year], by region and resource bin + - Used only when both `GSw_PSHwatercon` and `GSw_WaterMain` are on -- `water_with_cons_rate.csv`: +- `water_with_cons_rate.csv`: Water withdrawal and consumption rate [gal/MWh] by technology, cooling technology type, and form of water use (`w` = `with` or `cons`) diff --git a/inputs/zones/README.md b/inputs/zones/README.md index 9ee7504e0..222bbbb65 100644 --- a/inputs/zones/README.md +++ b/inputs/zones/README.md @@ -1,5 +1,8 @@ ## Model Zone Definitions +Zone definition sets are maintained in [`zones/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/zones) of the ReEDS_Input_Processing repo, +one folder per set. + Model zones are defined by the following user-generated files: - `county2zone.csv`: Maps from counties (`FIPS` column) to ReEDS zones (`ba` column) diff --git a/postprocessing/git/20261021_all.txt b/postprocessing/git/20261021_all.txt new file mode 100644 index 000000000..ce1258b20 --- /dev/null +++ b/postprocessing/git/20261021_all.txt @@ -0,0 +1,30 @@ +Issues +-257: Update PRAS version; explore nonzero initial state of charge for storage (Patrick) +-256: Direct use load included, but existing commercial and industrial generators filtered out (Claire) +-254: `compare_cases.py`: Add support for groups of Monte Carlo and Random Vector runs (Patrick) +-250: Update H2 SMR efficiency (Brian) + +Open PRs +-255: Add regional capital cost multipliers for geothermal techs (Shashwat) +-251: Fix bugs in run_pcm.py () [Draft] +-249: Fix Monte Carlo random sampling for VRE siting availability (Patrick) +-243: Prompt user if too few representative periods, islanded zones, or failed RA (Patrick) +-237: Add ReEDS AI Policy (Kennedy) +-236: Add geothermal regional capital cost differences () [Draft] +-231: Overlaying county transmission adjustments (Max.Vanatta) [Draft] +-230: Compare run inputs (Claire) [Draft] +-222: Fix `cases_small.csv` and merge it with `cases_test.csv` (Wesley) +-220: Add generic demand response resources () [Draft] +-216: Interconnection Queue 2025 Update (Yunzhi) +-214: ATB 2025 Update (Yunzhi) +-205: Update inputs documentation (Wesley) +-201: Add CVAR and NCVAR resource adequacy reporting () +-193: Add EER high data center load profiles (Claire) [Draft] + +Recently closed PRs +-259: Update geopandas and cluster environment variables for RHEL9 on kestrel (Patrick) +-258: Fix region label fontsize in `compare_cases.py` (Claire) +-253: [FEATURE] Add region-level class binning procedure to resource.py (Vincent) +-252: Fix/wind rsc pandas3 () +-248: Remove supply curve cost clipping (Brian) + diff --git a/postprocessing/git/issues.json b/postprocessing/git/issues.json new file mode 100644 index 000000000..b0f46f00a --- /dev/null +++ b/postprocessing/git/issues.json @@ -0,0 +1 @@ +[{"author":{"id":"MDQ6VXNlcjI1MTI1MjEx","is_bot":false,"login":"patrickbrown4","name":"Patrick Brown"},"closedAt":null,"createdAt":"2026-10-16T21:54:53Z","number":257,"title":"Update PRAS version; explore nonzero initial state of charge for storage","url":"https://github.com/ReEDS-Model/ReEDS/issues/257"},{"author":{"id":"MDQ6VXNlcjczMjIzNDAz","is_bot":false,"login":"clairehalloran","name":"Claire Halloran"},"closedAt":null,"createdAt":"2026-10-14T18:25:24Z","number":256,"title":"Direct use load included, but existing 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--git a/reeds/input_processing/runfiles.csv b/reeds/input_processing/runfiles.csv index eabee9e8d..4e4e6572f 100644 --- a/reeds/input_processing/runfiles.csv +++ b/reeds/input_processing/runfiles.csv @@ -220,7 +220,6 @@ state_cap.csv,inputs/emission_constraints/state_cap.csv,int(sw.GSw_StateCap) != storage_duration.csv,inputs/storage/storage_duration.csv,int(sw.GSw_Storage) != 0,ignore,ignore,,,,,0,,,parameter,,--hours-- storage duration by tech, storage_mandates.csv,inputs/state_policies/storage_mandates.csv,int(sw.GSw_BatteryMandate) != 0,ignore,ignore,*st,t,,0,0,,,,,, stressperiods_user.csv,inputs/temporal/stressperiods_{GSw_PRM_StressModel}.csv,sw.GSw_PRM_StressModel == 'user*',ignore,ignore,,,,,,,,,,, -supply_chain_adjust.csv,inputs/financials/supply_chain_adjust.csv,1,ignore,ignore,,,,,0,,,,,, supplycurve_egs.csv,inputs/supply_curve/supplycurve_egs-reference.csv,int(sw.GSw_Geothermal) != 0,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file supplycurve_psh.csv,inputs/supply_curve/supplycurve_psh-{pshsupplycurve}.csv,int(sw.GSw_Storage) != 0,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file supplycurve_upv.csv,inputs/supply_curve/supplycurve_upv-{GSw_SitingUPV}.csv,1,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file