Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-4977-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4977-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
31 Aug 2023
Model description paper |
| 31 Aug 2023
| 31 Aug 2023
Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Falko Ueckerdt
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Robert Pietzcker
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Adrian Odenweller
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany
Wolf-Peter Schill
German Institute for Economic Research (DIW Berlin), Berlin, Germany
Martin Kittel
German Institute for Economic Research (DIW Berlin), Berlin, Germany
Gunnar Luderer
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany
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Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
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Thi Lan Anh Dinh and Filipe Aires
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We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
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GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
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Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
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Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Alimou, Y., Maïzi, N., Bourmaud, J.-Y., and Li, M.: Assessing the
security of electricity supply through multi-scale modeling: The
TIMES-ANTARES linking approach, Appl. Energ., 279, 115717,
https://doi.org/10.1016/j.apenergy.2020.115717, 2020.
Aryanpur, V., O'Gallachoir, B., Dai, H., Chen, W., and Glynn, J.: A review
of spatial resolution and regionalisation in national-scale energy systems
optimisation models, Energy Strateg. Rev., 37, 100702,
https://doi.org/10.1016/j.esr.2021.100702, 2021.
Azevedo, I., Bataille, C., Bistline, J., Clarke, L., and Davis, S.: Net-zero
emissions energy systems: What we know and do not know, Energy Clim. Change,
2, 100049, https://doi.org/10.1016/j.egycc.2021.100049, 2021.
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J.,
Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., Sytze de Boer, H., van den
Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat,
D., Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T.,
Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D.
P.: Shared Socio-Economic Pathways of the Energy Sector – Quantifying the
Narratives, Global Environ. Chang., 42, 316–330,
https://doi.org/10.1016/j.gloenvcha.2016.07.006, 2017.
Baumstark, L., Bauer, N., Benke, F., Bertram, C., Bi, S., Gong, C. C., Dietrich, J. P., Dirnaichner, A., Giannousakis, A., Hilaire, J., Klein, D., Koch, J., Leimbach, M., Levesque, A., Madeddu, S., Malik, A., Merfort, A., Merfort, L., Odenweller, A., Pehl, M., Pietzcker, R. C., Piontek, F., Rauner, S., Rodrigues, R., Rottoli, M., Schreyer, F., Schultes, A., Soergel, B., Soergel, D., Strefler, J., Ueckerdt, F., Kriegler, E., and Luderer, G.: REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits, Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, 2021.
Bhaskar, A., Assadi, M., and Nikpey Somehsaraei, H.: Decarbonization of the
Iron and Steel Industry with Direct Reduction of Iron Ore with Green
Hydrogen, Energies, 13, 758, https://doi.org/10.3390/en13030758, 2020.
Bistline, J. E. T.: The importance of temporal resolution in modeling deep
decarbonization of the electric power sector, Environ. Res. Lett., 16,
084005, https://doi.org/10.1088/1748-9326/ac10df, 2021.
Blanford, G. J. and Weissbart, C.: A Framework for Modeling the Dynamics of
Power Markets – The EU-REGEN Model, ifo Working Paper Series, ifo Institute
– Leibniz Institute for Economic Research at the University of Munich, https://www.ifo.de/en/publications/2019/working-paper/framework-modeling-dynamics-power-markets-eu-regen-model (last access: 22 January 2022), 2019.
Böttger, D. and Härtel, P.: On wholesale electricity prices and
market values in a carbon-neutral energy system, Energy Econ., 106, 105709,
https://doi.org/10.1016/j.eneco.2021.105709, 2022.
Brinkerink, M.: Assessing 1.5–2 ∘C scenarios of integrated
assessment models from a power system perspective – Linkage with a detailed
hourly global electricity model, Monograph, IIASA, Laxenburg, Austria, https://pure.iiasa.ac.at/id/eprint/16957/ (last access: 22 January 2022), 2020.
Brinkerink, M., Zakeri, B., Huppmann, D., Glynn, J., Ó Gallachóir,
B., and Deane, P.: Assessing global climate change mitigation scenarios from
a power system perspective using a novel multi-model framework, Environ.
Modell. Softw., 150,
105336, https://doi.org/10.1016/j.envsoft.2022.105336, 2022.
Brown, T. and Reichenberg, L.: Decreasing market value of variable
renewables can be avoided by policy action, Energy Econ., 100, 105354,
https://doi.org/10.1016/j.eneco.2021.105354, 2021.
Brown, T., Hörsch, J., and Schlachtberger, D.: PyPSA: Python for Power
System Analysis, J. Open Res. Softw., 6, 4,
https://doi.org/10.5334/jors.188, 2018a.
Brown, T., Schlachtberger, D., Kies, A., Schramm, S., and Greiner, M.:
Synergies of sector coupling and transmission reinforcement in a
cost-optimised, highly renewable European energy system, Energy, 160,
720–739, https://doi.org/10.1016/j.energy.2018.06.222, 2018b.
Brunner, C., Deac, G., Braun, S., and Zöphel, C.: The future need for
flexibility and the impact of fluctuating renewable power generation, Renew.
Energy, 149, 1314–1324, https://doi.org/10.1016/j.renene.2019.10.128,
2020.
Butnar, I., Li, P.-H., Strachan, N., Portugal Pereira, J., Gambhir, A.,
and Smith, P.: A deep dive into the modelling assumptions for biomass with
carbon capture and storage (BECCS): A transparency exercise, Environ. Res.
Lett., 15, 084008, https://doi.org/10.1088/1748-9326/ab5c3e, 2019.
Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C.,
Kim, S., Kyle, P., Link, R., Moss, R., McJeon, H., Patel, P., Smith, S.,
Waldhoff, S., and Wise, M.: The SSP4: A world of deepening inequality, Global
Environ. Chang., 42, 284–296,
https://doi.org/10.1016/j.gloenvcha.2016.06.010, 2017.
Chang, M., Thellufsen, J. Z., Zakeri, B., Pickering, B., Pfenninger, S.,
Lund, H., and Østergaard, P. A.: Trends in tools and approaches for
modelling the energy transition, Appl. Energ., 290, 116731,
https://doi.org/10.1016/j.apenergy.2021.116731, 2021.
Cherp, A., Vinichenko, V., Tosun, J., Gordon, J. A., and Jewell, J.:
National growth dynamics of wind and solar power compared to the growth
required for global climate targets, Nat. Energy, 6, 742–754,
https://doi.org/10.1038/s41560-021-00863-0, 2021.
Clarke, L., Wei, Y.-M., De La Vega Navarro, A., Garg, A., Hahmann, A. N.,
Khennas, S., Azevedo, I. M. L., Löschel, A., Singh, A. K., Steg, L.,
Strbac, G., and Wada, K.: Energy Systems, in: IPCC, 2022: Climate Change
2022: Mitigation of Climate Change, Contribution of Working Group III to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change,
219, https://doi.org/10.1017/9781009157926.008, 2022.
Conejo, A. J., Castillo, E., Mínguez R., and García-Bertrand, R.:
Decomposition techniques in mathematical programming, Springer-Verlag Berlin
Heidelberg, https://doi.org/10.1007/3-540-27686-6, 2006.
Creutzig, F., Agoston, P., Goldschmidt, J. C., Luderer, G., Nemet, G., and
Pietzcker, R. C.: The underestimated potential of solar energy to mitigate
climate change, Nat. Energy, 2, 17140,
https://doi.org/10.1038/nenergy.2017.140, 2017.
Deane, J. P., Chiodi, A., Gargiulo, M., and Ó Gallachóir, B. P.:
Soft-linking of a power systems model to an energy systems model, Energy,
42, 303–312, https://doi.org/10.1016/j.energy.2012.03.052, 2012,
E3MLab, PRIMES Model Version 2018 – detailed model description,
http://www.e3mlab.ntua.gr/e3mlab/PRIMES (last access: 4 May 2023), 2018.
Ellenbeck, S. and Lilliestam, J.: How modelers construct energy costs:
Discursive elements in Energy System and Integrated Assessment Models,
Energy Res. Soc. Sci., 47, 69–770,
https://doi.org/10.1016/j.erss.2018.08.021, 2019.
Figueiredo, N. C. and da Silva, P. P.: The price of wind power generation in
Iberia and the merit-order effect, International Journal of Sustainable Energy Planning and Management, 15,
21–30, https://doi.org/10.5278/ijsepm.2018.15.4, 2018.
Frysztacki, M. M., Hörsch, J., Hagenmeyer, V., and Brown, T.: The strong
effect of network resolution on electricity system models with high shares
of wind and solar, Appl. Energ., 291, 116726,
https://doi.org/10.1016/j.apenergy.2021.116726, 2021.
Fuso Nerini, F., Keppo, I., and Strachan, N.: Myopic decision making in energy
system decarbonisation pathways. A UK case study, Energy Strateg. Rev., 17,
19–26, https://doi.org/10.1016/j.esr.2017.06.001, 2017.
Gaete-Morales, C., Kittel, M., Roth, A., and Schill, W.-P.: DIETERpy: A
Python framework for the Dispatch and Investment Evaluation Tool with
Endogenous Renewables, SoftwareX, 15, 100784,
https://doi.org/10.1016/j.softx.2021.100784, 2021.
Gan, D., Feng, D., and Xie, J.: Electricity Markets and Power System
Economics, CRC Press, Boca Raton, 220 pp., https://doi.org/10.1201/b15550,
2013.
Geels, F., Berkhout, F. and van Vuuren, D.: Bridging analytical approaches
for low-carbon transitions, Nat. Clim. Change, 6, 576–583,
https://doi.org/10.1038/nclimate2980, 2016.
Giarola, S., Mittal, S., Vielle, M., Perdana, S., Campagnolo, L., Delpiazzo,
E., Bui, H., Kraavi, A. A., Kolpakov, A., Sognnaes, I., Peters, G., Hawkes,
A., Köberle, A. C., Grant, N., Gambhir, A., Nikas, A., Doukas, H.,
Moreno, J., and van de Ven, D.-J.: Challenges in the harmonisation of global
integrated assessment models: A comprehensive methodology to reduce model
response heterogeneity, Sci. Total Environ., 783, 146861,
https://doi.org/10.1016/j.scitotenv.2021.146861, 2021.
Gils, H. C., Gardian, H., Kittel, M., Schill, W.-P., Zerrahn, A., Murmann,
A., Launer, J., Fehler, A., Gaumnitz, F., van Ouwerkerk, J., Bußar, C.,
Mikurda, J., Torralba-Díaz, L., Janßen, T., and Krüger, C.:
Modeling flexibility in energy systems – comparison of power sector models
based on simplified test cases, Renew. Sust. Energ. Rev., 158, 111995,
https://doi.org/10.1016/j.rser.2021.111995, 2022a.
Gils, H. C., Gardian, H., Kittel, M., Schill, W.-P., Murmann, A., Launer,
J., Gaumnitz, F., van Ouwerkerk, J., Mikurda, J., and Torralba-Díaz,
L.: Model-related outcome differences in power system models with sector
coupling – Quantification and drivers, Renew. Sust. Energ. Rev., 159,
112177, https://doi.org/10.1016/j.rser.2022.112177, 2022b.
Gong, C. C.: remind-coupling-dieter[code], REMIND – REgional Model of INvestments and Development, GitHub [code], https://github.com/cchrisgong/remind-coupling-dieter/tree/couple (last access: 1 September 2022), 2022a.
Gong, C. C.: dieter-coupling-remind[code], GAMS, REMIND – REgional Model of INvestments and Development, GitHub [code], https://github.com/cchrisgong/dieter-coupling-remind (last access: 1 September 2022), 2022b.
Gong, C. C.: REMIND-DIETER – code, reportings, scripts,
Zenodo [code], https://doi.org/10.5281/zenodo.7072625, 2022c.
Griffiths, S., Sovacool, B. K., Kim, J., Bazilian, M., and Uratani, J. M.:
Industrial decarbonization via hydrogen: A critical and systematic review of
developments, socio-technical systems and policy options, Energy Res. Soc.
Sci., 80, 102208, https://doi.org/10.1016/j.erss.2021.102208, 2021.
Guivarch, C., Kriegler, E., Portugal-Pereira, J., and Bosetti, V.: IPCC,
2022: Annex III: Scenarios and modelling methods, in: IPCC, 2022:
Climate Change 2022: Mitigation of Climate Change. Contribution of Working
Group III to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, https://doi.org/10.1017/9781009157926.022, 2022.
Günther, C., Schill, W.-P., and Zerrahn, A.: Prosumage of solar
electricity: Tariff design, capacity investments, and power sector effects,
Energ. Policy, 152, 112168, https://doi.org/10.1016/j.enpol.2021.112168,
2021.
Guo, F., van Ruijven, B. J., Zakeri, B., Zhang, S., Chen, X., Liu, C., Yang,
F., Krey, V., Riahi, K., Huang, H., and Zhou, Y.: Implications of
intercontinental renewable electricity trade for energy systems and
emissions, Nat. Energy, 7, 1144–1156,
https://doi.org/10.1038/s41560-022-01136-0, 2022.
Haydt, G., Leal, V., Pina, A., and Silva, C. A.: The relevance of the energy
resource dynamics in the mid/long-term energy planning models, Renew.
Energy, 36, 3068–3074, 2011.
Hildmann, M., Ulbig, A., and Andersson, G.: Empirical Analysis of the
Merit-Order Effect and the Missing Money Problem in Power Markets With High
RES Shares, IEEE T. Power Syst., 30, 1560–1570,
https://doi.org/10.1109/TPWRS.2015.2412376, 2015.
Hirth, L.: The market value of variable renewables: The effect of solar wind
power variability on their relative price, Energy Econ., 38, 218–236,
https://doi.org/10.1016/j.eneco.2013.02.004, 2013.
Hirth, L.: What caused the drop in European electricity prices? A factor
decomposition analysis, Energy J., 39, 1,
https://doi.org/10.5547/01956574.39.1.lhir, 2018.
Hirth, L. and Ueckerdt, F.: Redistribution effects of energy and climate
policy: The electricity market, Energ. Policy, 62, 934–947,
https://doi.org/10.1016/j.enpol.2013.07.055, 2013.
Huppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M.,
Kushin, N., Vinca, A., Mastrucci, A., Riahi, K., and Krey, V.: The MESSAGEix
Integrated Assessment Model and the ix modeling platform (ixmp): An open
framework for integrated and cross-cutting analysis of energy, climate, the
environment, and sustainable development, Environ. Modell. Softw., 112,
143–156, https://doi.org/10.1016/j.envsoft.2018.11.012, 2019.
ICCSD Tsinghua University: Power Sector, in: China's Long-Term Low-Carbon
Development Strategies and Pathways: Comprehensive Report, edited by:
Institute of Climate Change and Sustainable Development of Tsinghua
University, Springer, Singapore, 109–130,
https://doi.org/10.1007/978-981-16-2524-4_4, 2022.
IEA: World Energy Outlook 2021,
https://www.iea.org/reports/world-energy-outlook-2021 (last access: 21 January 2022), 2021.
IPCC: Climate change 2014: mitigation of climate change: Working Group III
contribution to the Fifth assessment report of the Intergovernmental Panel
on Climate Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y.,
Farahani, E., Kadner, S., Seyboth, K., Alder, A., Baum, I., Brunner, S.,
Eikemeier, P., Kriemann, B., Salolainen, J., Schlömer, S., Stechow, C.
von, Zwickel, T., and Minx, J. C., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, https://doi.org/10.1017/CBO9781107415416, 2014.
IPCC: Climate Change 2022: Mitigation of Climate Change, Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Shukla, P. R., Skea, J.,
Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M., Hasija, A., Lisboa, G., Luz, S., and
Malley, J., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157926, 2022.
IRENA: Renewable power generation costs in 2019, International Renewable
Energy Agency, ISBN 978-92-9260-244-4, 2020.
Kannan, R. and Turton, H.: A Long-Term Electricity Dispatch Model with the
TIMES Framework, Environ. Model. Assess., 18, 325–343,
https://doi.org/10.1007/s10666-012-9346-y, 2013.
Karush, W.: Minima of functions of several variables with inequalities as side conditions (William Karush), Master's thesis, Department of Mathematics, University of Chicago, 1939.
Keppo, I., Butnar, I., Bauer, N., Caspani, M., Edelenbosch, O., Emmerling,
J., Fragkos, P., Guivarch, C., Harmsen, M., Lefèvre, J., Le Gallic, T.,
Leimbach, M., McDowall, W., Mercure, J.-F., Schaeffer, R., Trutnevyte, E.,
and Wagner, F.: Exploring the possibility space: taking stock of the diverse
capabilities and gaps in integrated assessment models, Environ. Res. Lett.,
16, 053006, https://doi.org/10.1088/1748-9326/abe5d8, 2021.
Koch, J. and Leimbach, M.: Update of Ssp GDP Projections: Capturing Recent Changes in National Accounting, PPP Conversion and Covid 19 Impacts, Ecol. Econ., 206,
https://doi.org/10.2139/ssrn.4011838, 2023.
Koutstaal, P. R. and va. Hout, M.: Integration costs and market value of
variable renewables: A study for the Dutch power market, ECN, Petten, http://resolver.tudelft.nl/uuid:a36bc05a-6a36-428f-9ff6-e33141fcf167 (last access: 22 January 2022),
2017.
Kuhn, H. W. and Tucker, A. W.: Nonlinear Programming, in: Traces and Emergence of Nonlinear Programming, edited by: Giorgi, G. and Kjeldsen, T.,
Birkhäuser, Basel, https://doi.org/10.1007/978-3-0348-0439-4_11, 1951.
Lazard: Lazard's Levelized Cost of Energy Analysis – Version 15.0, https://www.lazard.com/media/sptlfats/lazards-levelized-cost-of-energy-version-150-vf.pdf
(last access: 21 January 2022),
2021.
Levesque, A., Pietzcker, R. C., Baumstark, L., De Stercke, S., Grübler,
A., Luderer, G.: How much energy will buildings consume in 2100? A global
perspective within a scenario framework, Energy, 148, 514–527,
https://doi.org/10.1016/j.energy.2018.01.139, 2018.
Leimbach, M., Bauer, N., Baumstark, L., Luken, M., and Edenhofer, O.:
Technological Change and International Trade – Insights from REMIND-R,
Energy J., 31, 109–136,
https://doi.org/10.5547/ISSN0195-6574-EJ-Vol31-NoSI-5, 2010.
Li, P.-H. and Pye, S.: Assessing the benefits of demand-side flexibility in
residential and transport sectors from an integrated energy systems
perspective, Appl. Energ., 228, 965–979,
https://doi.org/10.1016/j.apenergy.2018.06.153, 2018.
López Prol, J. and Schill, W.-P.: The Economics of Variable Renewable
Energy and Electricity Storage, Annu. Rev. Resour. Econ., 13, 443–467,
https://doi.org/10.1146/annurev-resource-101620-081246, 2021.
Luderer, G., Pietzcker, R. C., Carrara, S., de Boer, H. S., Fujimori, S.,
Johnson, N., Mima, S., and Arent, D.: Assessment of wind and solar power in
global low-carbon energy scenarios: An introduction, Energy Econ., 64,
542–551, https://doi.org/10.1016/j.eneco.2017.03.027, 2017.
Luderer, G., Vrontisi, Z., Bertram, C., Edelenbosch, O., Pietzcker, R. C.,
Rogelj, J., De Boer, H. S., Drouet, L., Emmerling, J., Fricko, O., Fujimori,
S., Havlik, P., Iyer, G., Keramidas, K., Kitous, A., Pehl, M., Krey, V.,
Riahi, K., Saveyn, B., Tavoni, M., Van Vuuren, D. P., and Kriegler, E.:
Residual fossil CO2 emissions in 1.5–2∘C pathways, Nat. Clim.
Change, 8, 626–633, https://doi.org/10.1038/s41558-018-0198-6, 2018.
Luderer, G., Auer, C., Bauer, N., Baumstark, L., Bertram, C., Bi, S., Dirnaichner, A., Giannousakis, A., Hilaire, J., Klein, D., Koch, J., Leimbach, M., Levesque, A., Malik, A., Merfort, L., Pehl, M., Pietzker, R., Piontek, F., Rauner, S., Rodrigues, R., Rottoli, M., Schreyer, F., Sörgel, B., Strefler, J., and Ueckerdt, F.: REMIND v2.1.3 – Model documentation, Zenodo, https://doi.org/10.5281/zenodo.4268254, 2020.
Luderer, G., Madeddu, S., Merfort, L., Ueckerdt, F., Pehl, M., Pietzcker,
R., Rottoli, M., Schreyer, F., Bauer, N., Baumstark, L., Bertram, C.,
Dirnaichner, A., Humpenöder, F., Levesque, A., Popp, A., Rodrigues, R.,
Strefler, J., and Kriegler, E.: Impact of declining renewable energy costs
on electrification in low-emission scenarios, Nat. Energy, 7, 32–42,
https://doi.org/10.1038/s41560-021-00937-z, 2022a.
Luderer, G., Bauer, N., Baumstark, L., Bertram, C., Leimbach, M., Pietzcker,
R., Strefler, J., Aboumahboub, T., Abrahão, G., Auer, C., Benke, F., Bi,
S., Dietrich, J., Dirnaichner, A., Giannousakis, A., Gong, C. C., Haller,
M., Hasse, R., Hilaire, J., Hoppe, J., Klein, D., Koch, J., Körner, A.,
Kowalczyk, K., Kriegler, E., Levesque, A., Lorenz, A., Ludig, S., Lüken,
M., Malik, A., Manger, S., Merfort, A., Merfort, L., Moreno-Leiva, S.,
Mouratiadou, I., Odenweller, A., Pehl, M., Piontek, F., Popin, L., Rauner,
S., Richters, O., Rodrigues, R., Roming, N., Rottoli, M., Schmidt, E.,
Schötz, C., Schreyer, F., Schultes, A., Sörgel, B., Ueckerdt, F.,
Verpoort, P., and Weigmann, P.: REMIND – REgional Model of INvestments and
Development, Zenodo [code], https://doi.org/10.5281/zenodo.6794920, 2022b.
Luderer, G., Bauer, N., Gong, C. C., Odenweller, A., Baumstark, L., Bertram,
C., Leimbach, M., Pietzcker, R., Strefler, J., Aboumahboub, T., Abrahão,
G., Auer, C., Benke, F., Bi, S., Dietrich, J., Dirnaichner, A.,
Giannousakis, A., Haller, M., Hasse, R., Hilaire, J., Hoppe, J., Klein, D.,
Koch, J., Kowalczyk, K., Kriegler, E., Levesque, A., Ludig, S., Malik, A.,
Merfort, A., Merfort, L., Moreno, S., Mouratiadou, I., Pehl, M., Piontek,
F., Popin, L., Rauner, S., Richters, O., Schötz, C., Rodrigues, R.,
Ueckerdt, F., Zerrahn, A., Schreyer, F., Sörgel, B., Weigmann, P.,
Schill, W.-P., Verpoort, P., and Rottoli, M.: REMIND – DIETER coupling,
Zenodo [code], https://doi.org/10.5281/zenodo.7053246, 2022c.
Ludig, S., Haller, M., Schmid, E., and Bauer, N.: Fluctuating renewables in
a long-term climate change mitigation strategy, Energy, 36, 6674–6685,
https://doi.org/10.1016/j.energy.2011.08.021, 2011.
Martínez-Gordón, R., Morales-España, G., Sijm, J., and Faaij,
A. P. C.: A review of the role of spatial resolution in energy systems
modelling: Lessons learned and applicability to the North Sea region, Renew.
Sust. Energ. Rev., 141, 110857,
https://doi.org/10.1016/j.rser.2021.110857, 2021.
Mills, A. D. and Wiser, R. H.: Strategies to mitigate declines in the
economic value of wind and solar at high penetration in California, Appl.
Energ., 147, 269–278, https://doi.org/10.1016/j.apenergy.2015.03.014, 2015.
Mowers, M., Mignone, B. K. and Steinberg, D. C.: Quantifying value and
representing competitiveness of electricity system technologies in economic
models, Appl. Energ., 329, 120132,
https://doi.org/10.1016/j.apenergy.2022.120132, 2023.
National
long-term strategies:
https://ec.europa.eu/info/energy-climate-change-environment/implementation-eu-countries/energy-and-climate-governance-and-reporting/national-long-term-strategies_en, last access: 15 January 2022.
NGFS: NGFS Climate Scenarios for central banks and supervisors, Network for
Greening the Financial System, https://www.ngfs.net/en/ngfs-climate-scenarios-central-banks-and-supervisors-september-2022 (last access: 23 August 2023), 2022.
openmod – Open Energy Modelling Initiative: https://openmod-initiative.org/,
last access: 21 January 2022.
Padhy, N. P.: Unit commitment-a bibliographical survey, IEEE T. Power
Syst., 19, 1196–1205, https://doi.org/10.1109/TPWRS.2003.821611, 2004.
Pahle, M., Tietjen, O., Osorio, S., Egli, F., Steffen, B., Schmidt, T. S.
and Edenhofer O.: Safeguarding the energy transition against political
backlash to carbon markets, Nat. Energy, 7, 290–296,
https://doi.org/10.1038/s41560-022-00984-0, 2022.
Palzer, A. and Henning, H.-M.: A Future German Energy System with a
Dominating Contribution from Renewable Energies: A Holistic Model Based on
Hourly Simulation, Energy Technol., 2, 13–28,
https://doi.org/10.1002/ente.201300083, 2014.
Parra, D., Valverde, L., Pino, F. J., and Patel, M. K.: A review on the
role, cost and value of hydrogen energy systems for deep decarbonisation,
Renew. Sust. Energ. Rev., 101, 279–294,
https://doi.org/10.1016/j.rser.2018.11.010, 2019.
Pietzcker, R. C., Ueckerdt, F., Carrara, S., de Boer, H. S., Després,
J., Fujimori, S., Johnson, N., Kitous, A., Scholz, Y., Sullivan, P., and
Luderer, G.: System integration of wind and solar power in integrated
assessment models: A cross-model evaluation of new approaches, Energy Econ.,
64, 583–599, https://doi.org/10.1016/j.eneco.2016.11.018, 2017.
Pina, A., Silva, C., and Ferrão, P.: Modeling hourly electricity
dynamics for policy making in long-term scenarios, Energ. Policy, 39,
4692–4702, https://doi.org/10.1016/j.enpol.2011.06.062, 2011.
Prina, M. G., Manzolini, G., Moser, D., Nastasi, B., and Sparber, W.:
Classification and challenges of bottom-up energy system models – A review,
Renew. Sust. Energ. Rev., 129, 109917,
https://doi.org/10.1016/j.rser.2020.109917, 2020.
Prol, J. L. and Schill, W.-P.: The Economics of Variable Renewable Energy and Electricity Storage, Annual Review of Resource Economics, 13, 443–467, https://doi.org/10.1146/annurev-resource-101620-081246, 2021.
Ram, M., Bogdanov, D., Aghahosseini, A., Gulagi, A., Oyewo, S., Child, M.,
Caldera, U., Sadovskaia, K., Farfan Orozco, F., Noel, L., Fasihi, M.,
Maybodi, S., and Fell, H.-J.: Global Energy System based on 100 %
Renewable Energy: Energy Transition in Europe Across Power, Heat, Transport
and Desalination Sectors, Technical report, https://doi.org/10.13140/RG.2.2.10143.00160, 2018.
Ramsebner, J., Haas, R., Ajanovic, A., and Wietschel, M.: The sector
coupling concept: A critical review, WIREs Energy Environ., 10, e396,
https://doi.org/10.1002/wene.396, 2021.
Release REMIND v3.0.0 ⋅ remindmodel/remind:
https://github.com/remindmodel/remind/releases/tag/v3.0.0, last access: 11
August 2022.
Rechsteiner, R.: German energy transition (Energiewende) and what
politicians can learn for environmental and climate policy, Clean Technol.
Envir., 23, 305–342, https://doi.org/10.1007/s10098-020-01939-3,
2021.
Ringkjøb, H.-K., Haugan, P. M., and Solbrekke, I. M.: A review of
modelling tools for energy and electricity systems with large shares of
variable renewables, Renew. Sust. Energ. Rev., 96, 440-459,
https://doi.org/10.1016/j.rser.2018.08.002, 2018.
Rodrigues, R., Pietzcker, R., Fragkos, P., Price, J., McDowall, W., Siskos,
P., Fotiou, T., Luderer, G., and Capros, P.: Narrative-driven alternative
roads to achieve mid-century CO2 net neutrality in Europe, Energy, 239,
121908, https://doi.org/10.1016/j.energy.2021.121908, 2022.
Rogelj, J., Shindell, D., Jiang, K., Fifita, S., Forster, P., Ginzburg, V.,
Handa, C., Kheshgi, H., Kobayashi, S., Kriegler, E., Mundaca, L.,
Séférian, R., and Vilariño, M. V.: Mitigation pathways
compatible with 1.5 ∘C in the context of sustainable development,
in: Special Report on the impacts of global warming of 1.5 ∘C,
Intergovernmental Panel on Climate Change, Geneva, https://pure.iiasa.ac.at/id/eprint/15515/ (last access: 22 January 2022), 2018.
Rotmans, J. and van Asselt, M. B. A.: Uncertainty in Integrated Assessment
Modelling: A Labyrinthic Path, Integr. Assess., 2, 43–55,
https://doi.org/10.1023/A:1011588816469, 2001.
Ruhnau, O.: How flexible electricity demand stabilizes wind and solar market
values: The case of hydrogen electrolyzers, Appl. Energ., 307, 118194,
https://doi.org/10.1016/j.apenergy.2021.118194, 2022.
Say, K., Schill, W.-P., and John, M.: Degrees of displacement: The impact of
household PV battery prosumage on utility generation and storage, Appl.
Energ., 276, 115466, https://doi.org/10.1016/j.apenergy.2020.115466, 2020.
Schill, W.-P. and Zerrahn, A.: Long-run power storage requirements for high
shares of renewables: Results and sensitivities, Renew. Sust. Energ.
Rev., 83, 156–171, https://doi.org/10.1016/j.rser.2017.05.205, 2018.
Schill, W.-P. and Zerrahn, A.: Flexible electricity use for heating in
markets with renewable energy, Appl. Energ., 266, 114571,
https://doi.org/10.1016/j.apenergy.2020.114571, 2020.
Schill, W.-P., Pahle, M., and Gambardella, C.: Start-up costs of thermal
power plants in markets with increasing shares of variable renewable
generation, Nat. Energy, 2, 1–6, https://doi.org/10.1038/nenergy.2017.50,
2017.
Schill, W.-P., Roth, A., and Guéret, A.: Ampel-Monitor Energiewende Shows
the Pace of the Energy Transition Must Be Accelerated Significantly, DIW Weekly Report 26/27/28/2022, 171–179,
https://doi.org/10.18723/diw_dwr:2022-26-1, 2022.
Seljom, P., Rosenberg, E., Schäffer, L. E., and Fodstad, M.:
Bidirectional linkage between a long-term energy system and a short-term
power market model, Energy, 198, 117311,
https://doi.org/10.1016/j.energy.2020.117311, 2020.
Sensfuß, F.: Assessment of the impact of renewable electricity
generation on the German electricity sector: An agent-based simulation
approach, Universität Karlsruhe (TH), https://doi.org/10.5445/IR/1000007777, 2007.
Sensfuß, F., Ragwitz, M., and Genoese, M.: The merit-order effect: A
detailed analysis of the price effect of renewable electricity generation on
spot market prices in Germany, Energ. Policy, 36, 3076–3084, 2008.
Sepulveda, N. A., Jenkins, J. D., de Sisternes, F. J., and Lester, R. K.:
The Role of Firm Low-Carbon Electricity Resources in Deep Decarbonization of
Power Generation, Joule, 2, 2403–2420,
https://doi.org/10.1016/j.joule.2018.08.006, 2018.
Sitarz, J., Pahle, M., Osorio, S., Luderer, G., and Pietzcker, R.: EU carbon
prices signal high policy credibility and farsighted actors, Research Square [preprint],
https://doi.org/10.21203/rs.3.rs-2761645/v1, 2023.
Staub-Kaminski, I., Zimmer, A., Jakob, M. and Marschinski, R.: Climate
policy in practice: a typology of obstacles and implications for integrated
assessment modeling, Clim. Change Econ., 05, 1440004,
https://doi.org/10.1142/S2010007814400041, 2014.
Stehfest, E., van Vuuren, D., Bouwman, L., and Kram, T.: Integrated
assessment of global environmental change with IMAGE 3.0: Model description
and policy applications, Netherlands Environmental Assessment Agency (PBL), ISBN 978-94-91506-71-0,
2014.
Stöckl, F., Schill, W.-P., and Zerrahn, A.: Optimal supply chains and
power sector benefits of green hydrogen, Sci. Rep., 11, 14191,
https://doi.org/10.1038/s41598-021-92511-6, 2021.
Sullivan, P., Krey, V., and Riahi, K.: Impacts of considering electric
sector variability and reliability in the MESSAGE model, Energy Strateg.
Rev., 1, 157–163, https://doi.org/10.1016/j.esr.2013.01.001, 2013.
The White House: The Long-Term Strategy of the United States: Pathways to
Net-Zero Greenhouse Gas Emissions by 2050, United States Department of State
and the United States Executive Office of the President, Washington DC, https://unfccc.int/documents/308100 (last access: 22 January 2022),
2021.
Ueckerdt, F., Brecha, R., Luderer, G., Sullivan, P., Schmid, E., Bauer, N.,
Böttger, D., and Pietzcker, R.: Representing power sector variability
and the integration of variable renewables in long-term energy-economy
models using residual load duration curves, Energy, 90, Part 2, 1799–1814,
https://doi.org/10.1016/j.energy.2015.07.006, 2015.
Ueckerdt, F., Pietzcker, R., Scholz, Y., Stetter, D., Giannousakis, A., and
Luderer, G.: Decarbonizing global power supply under region-specific
consideration of challenges and options of integrating variable renewables
in the REMIND model, Energy Econ., 64, 665–684,
https://doi.org/10.1016/j.eneco.2016.05.012, 2017.
UNEP: The Emissions Gap Report 2019, UNEP, Nairobi, Kenya, ISBN
978-92-807-3766-0,
2019.
van Ouwerkerk, J., Gils, H. C., Gardian, H., Kittel, M., Schill, W.-P.,
Zerrahn, A., Murmann, A., Launer, J., Torralba-Díaz, L., and Bußar,
C.: Impacts of power sector model features on optimal capacity expansion: A
comparative study, Renew. Sust. Energ. Rev., 157, 112004,
https://doi.org/10.1016/j.rser.2021.112004, 2022.
Welsch, M., Mentis, D., and Howells, M.: Chapter 17 – Long-Term Energy
Systems Planning: Accounting for Short-Term Variability and Flexibility, in:
Renewable Energy Integration, edited by: Jones, L. E., Academic Press,
Boston, 215–225, https://doi.org/10.1016/B978-0-12-407910-6.00017-X, 2014.
Weyant, J.: Some Contributions of Integrated Assessment Models of Global
Climate Change, Rev. Env. Econ. Policy, 11, 115–137,
https://doi.org/10.1093/reep/rew018, 2017.
Wilson, C., Guivarch, C., Kriegler, E., van Ruijven, B., van Vuuren, D. P.,
Krey, V., Schwanitz, V. J., and Thompson, E. L.: Evaluating process-based
integrated assessment models of climate change mitigation, Climatic Change,
166, 3, https://doi.org/10.1007/s10584-021-03099-9, 2021.
Younis, A., Benders, R., Ramírez, J., de Wolf, M., and Faaij, A.:
Scrutinizing the Intermittency of Renewable Energy in a Long-Term Planning
Model via Combining Direct Integration and Soft-Linking Methods for
Colombia's Power System, Energies, 15, 7604,
https://doi.org/10.3390/en15207604, 2022.
Zerrahn, A. and Schill, W.-P.: Long-run power storage requirements for high
shares of renewables: review and a new model, Renew. Sust. Energ. Rev.,
79, 1518–1534, https://doi.org/10.1016/j.rser.2016.11.098, 2017.
Zerrahn, A., Schill, W.-P., and Kemfert, C.: On the economics of electrical
storage for variable renewable energy sources, Eur. Econ. Rev., 108,
259–279, https://doi.org/10.1016/j.euroecorev.2018.07.004, 2018.
Short summary
To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
To mitigate climate change, the global economy must drastically reduce its greenhouse gas...
