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'' * for ''joint production processes;'' '''<sup>§</sup>''' ''nuclear reactors with thermal efficiency of 33%; <sup>#</sup> technologies with exogenously improving efficiencies. 2005 values are represented by the lower end of the range. Long-term efficiencies (reached after 2045) are represented by high-end ranges.''
'' * for ''joint production processes;'' '''<sup>§</sup>''' ''nuclear reactors with thermal efficiency of 33%; <sup>#</sup> technologies with exogenously improving efficiencies. 2005 values are represented by the lower end of the range. Long-term efficiencies (reached after 2045) are represented by high-end ranges.''


For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section Grid and Infrastructure. To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan <ref>Sullivan et al. (2013)</ref>.
For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section Grid and Infrastructure. To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan <ref>Sullivan P, Krey V, Riahi K (2013) Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1:157–163. doi: 10.1016/j.esr.2013.01.001</ref>.


The techno-economic parameters of power technologies used in the model are given in <xr id="tab:REMINDtable_5"/> for fuel-based technologies and <xr id="tab:REMINDtable_6"/> for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section Non-biomass renewables.
The techno-economic parameters of power technologies used in the model are given in <xr id="tab:REMINDtable_5"/> for fuel-based technologies and <xr id="tab:REMINDtable_6"/> for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section Non-biomass renewables.


'''Table 3'''. Techno-economic characteristics of technologies based on non-biomass renewable energy sources <ref>Neij et al. 2003</ref>; <ref>Nitsch et al. 2004</ref>; <ref>IEA 2007a</ref>; <ref>Junginger et al. 2008</ref>; <ref>Pietzcker et al. 2014</ref>.
'''Table 3'''. Techno-economic characteristics of technologies based on non-biomass renewable energy sources <ref>Neij L, Andersen PD, Durstewitz M, et al (2003) Experience Curves: A Tool for Energy Policy Assessment (Extool Final Report). Lund University, Risø National Laboratory, ISET</ref>; <ref>Nitsch J, Krewitt W, Nast M, et al (2004) Ökologisch optimierter Ausbau der Nutzung erneuerbarer Energien in Deutschland (Kurzfassung). BMU, DLR, ifeu, Wuppertal Institut, Stuttgart, Heidelberg, Wuppertal</ref>; <ref>IEA (2007a) Energy Balances of OECD Countries. International Energy Agency, Paris</ref>; <ref>Junginger HM, Lako P, Lensink S, et al (2008) Technological learning in the energy sector. MNP</ref>; <ref>Pietzcker et al. 2014</ref>.


<figtable id="tab:REMINDtable_6">
<figtable id="tab:REMINDtable_6">
[[File:Remind Table 6.PNG]]
[[File:Remind Table 6.PNG]]
</figtable>
</figtable>

Revision as of 14:32, 3 February 2017

Model Documentation - REMIND-MAgPIE

Corresponding documentation
Previous versions
Model information
Model link
Institution Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de.
Solution concept General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector;
Solution method OptimizationMAgPIE: cost minimization;
Anticipation

Around twenty electricity generation technologies are represented in REMIND, see <xr id="tab:REMIND_electricity_technologies"/>, with several low-carbon (CCS) and zero carbon options (nuclear and renewables).


Table 1. Energy Conversion Technologies for Electricity (Note: † indicates that technologies can be combined with CCS). <figtable id="tab:REMIND_electricity_technologies">

Energy Conversion Technologies for Electricity
Energy Carrier Technology
Primary exhaustible resource
Coal
  • Conventional coal power plant
  • Integrated coal gasification combined cycle†
  • Coal combined heat and power plant
Oil
  • Diesel oil turbine
Gas
  • Gas turbine
  • Natural gas combined cycle†
  • Gas combined heat and power plant
Uranium
  • Light water reactor
Primary renewable resource
Solar
  • Solar photovoltaic
  • Concentrating solar power
Wind
  • Wind turbine
Hydropower
  • Hydropower
Biomass
  • Integrated biomass gasification combined cycle†
  • Biomass combined heat and power plant
Geothermal
  • Hot dry rock
Secondary energy type
Hydrogen
  • Hydrogen turbine

</figtable>

<figure id="fig:REMINDtable_4"> 54067596.jpg </figure>

Table 2. Techno-economic characteristics of technologies based on exhaustible energy sources and biomass [1]; [2]; [3]; [4]; [5]; [6]; [7]; [8]; [9]; [10]; [11]; [12]; [13].

<figtable id="tab:REMINDtable_5"> Remind Table 5.PNG </figtable>

Abbreviations: PC - pulverized coal, IGCC - integrated coal gasification combined cycle, CHP - coal combined heat and power plant, C2H2 - coal to hydrogen, C2L - coal to liquids, C2G - coal gasification, NGT - natural gas turbine, NGCC - natural gas combined cycle, SMR - steam methane reforming, BIGCC – Biomass IGCC, BioCHP – biomass combined heat and power, B2H2 – biomass to hydrogen, B2L – biomass to liquids, B2G – biogas, TNR - thermo-nuclear reactor; * for joint production processes; § nuclear reactors with thermal efficiency of 33%; # technologies with exogenously improving efficiencies. 2005 values are represented by the lower end of the range. Long-term efficiencies (reached after 2045) are represented by high-end ranges.

For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section Grid and Infrastructure. To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan [14].

The techno-economic parameters of power technologies used in the model are given in <xr id="tab:REMINDtable_5"/> for fuel-based technologies and <xr id="tab:REMINDtable_6"/> for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section Non-biomass renewables.

Table 3. Techno-economic characteristics of technologies based on non-biomass renewable energy sources [15]; [16]; [17]; [18]; [19].

<figtable id="tab:REMINDtable_6"> Remind Table 6.PNG </figtable>

  1. ↑ Iwasaki W (2003) A consideration of the economic efficiency of hydrogen production from biomass. International Journal of Hydrogen Energy 28:939–944
  2. ↑ Hamelinck C (2004) Outlook for advanced biofuels. Ph.D. Thesis, University of Utrecht
  3. ↑ Bauer N (2005) Carbon capture and sequestration: An option to buy time? Ph.D. Thesis, University of Potsdam
  4. ↑ Ansolabehere S, Beer J, Deutch J, et al (2007) The Future of Coal: An Interdisciplinary MIT Study. Massachusetts Institute of Technology, Cambridge, Massachusetts
  5. ↑ Gül T, Kypreos S, Barreto L (2007) Hydrogen and Biofuels – A Modelling Analysis of Competing Energy Carriers for Western Europe. In: Proceedings of the World Energy Congress “Energy Future in an Interdependent World”. 11–15 November 2007, Rome, Italy
  6. ↑ Ragettli M (2007) Cost outlook for the production of biofuels. Diploma Thesis, Swiss Federal Institute of Technology
  7. ↑ Schulz T (2007) Intermediate steps towards the 2000-Watt society in Switzerland: an energy-economic scenario analysis. PhD Thesis, Swiss Federal Institute of Technology (ETH)
  8. ↑ Uddin SN, Barreto L (2007) Biomass-fired cogeneration systems with CO2 capture and storage. Renewable Energy 32:1006–1019. doi: 10.1016/j.renene.2006.04.009
  9. ↑ Rubin ES, Chen C, Rao AB (2007) Cost and performance of fossil fuel power plants with CO2 capture and storage. Energy Policy 35:4444–4454. doi: 10.1016/j.enpol.2007.03.009
  10. ↑ Takeshita T, Yamaji K (2008) Important roles of Fischer-Tropsch synfuels in the global energy future. Energy Policy 36:2773–2784. doi: http://dx.doi.org/10.1016/j.enpol.2008.02.044
  11. ↑ Brown D, Gassner M, Fuchino T, Marechal F (2009) Thermo-economic analysis for the optimal conceptual design of biomass gasification energy conversion systems. Applied Thermal Engineering
  12. ↑ Klimantos P, Koukouzas N, Katsiadakis A, Kakaras E (2009) Air-blown biomass gasification combined cycles: System analysis and economic assessment. Energy 34:708–714
  13. ↑ Chen C, Rubin ES (2009) CO2 control technology effects on IGCC plant performance and cost. Energy Policy 37:915–924. doi: 10.1016/j.enpol.2008.09.093
  14. ↑ Sullivan P, Krey V, Riahi K (2013) Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1:157–163. doi: 10.1016/j.esr.2013.01.001
  15. ↑ Neij L, Andersen PD, Durstewitz M, et al (2003) Experience Curves: A Tool for Energy Policy Assessment (Extool Final Report). Lund University, Risø National Laboratory, ISET
  16. ↑ Nitsch J, Krewitt W, Nast M, et al (2004) Ökologisch optimierter Ausbau der Nutzung erneuerbarer Energien in Deutschland (Kurzfassung). BMU, DLR, ifeu, Wuppertal Institut, Stuttgart, Heidelberg, Wuppertal
  17. ↑ IEA (2007a) Energy Balances of OECD Countries. International Energy Agency, Paris
  18. ↑ Junginger HM, Lako P, Lensink S, et al (2008) Technological learning in the energy sector. MNP
  19. ↑ Pietzcker et al. 2014
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