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{{ModelDocumentationTemplate
{{ModelDocumentationTemplate
|IsEmpty=No
|IsEmpty=No
|IsDocumentationOf=REMIND
|IsDocumentationOf=REMIND-MAgPIE
|DocumentationCategory=Electricity
|DocumentationCategory=Electricity
}}
}}
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).
Around twenty electricity generation technologies are represented in REMIND-MAgPIE, see <xr id="tab:REMIND-MAgPIE_electricity_technologies"/>, with several low-carbon (CCS) and zero carbon options (nuclear and renewables).






'''Table 4'''. Energy Conversion Technologies for Electricity (Note: &dagger; indicates that technologies can be combined with CCS).
'''Table 1'''. Energy Conversion Technologies for Electricity (Note: &dagger; indicates that technologies can be combined with CCS).
<figtable id="tab:REMIND_electricity_technologies">
<figtable id="tab:REMIND-MAgPIE_electricity_technologies">
{| class="wikitable"
{| class="wikitable"
|+<caption>Energy Conversion Technologies for Electricity</caption>
|+<caption>Energy Conversion Technologies for Electricity</caption>
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</figtable>
</figtable>


<figure id="fig:REMINDtable_4">
<figure id="fig:REMIND-MAgPIEtable_4">
[[File:54067596.jpg]]
[[File:54067596.jpg]]
</figure>
</figure>


'''Table 2'''. Techno-economic characteristics of technologies based on exhaustible energy sources and biomass (Iwasaki 2003; Hamelinck 2004; Bauer 2005; Ansolabehere et al. 2007; Gül et al. 2007; Ragettli 2007; Schulz 2007; Uddin and Barreto 2007; Rubin et al. 2007; Takeshita and Yamaji 2008; Brown et al. 2009; Klimantos et al. 2009; Chen and Rubin 2009).
'''Table 2'''. Techno-economic characteristics of technologies based on exhaustible energy sources and biomass <ref>Iwasaki W (2003) A consideration of the economic efficiency of hydrogen production from biomass. International Journal of Hydrogen Energy 28:939–944</ref>; <ref>Hamelinck C (2004) Outlook for advanced biofuels. Ph.D. Thesis, University of Utrecht</ref>; <ref>Bauer N (2005) Carbon capture and sequestration: An option to buy time? Ph.D. Thesis, University of Potsdam</ref>; <ref>Ansolabehere S, Beer J, Deutch J, et al (2007) The Future of Coal: An Interdisciplinary MIT Study. Massachusetts Institute of Technology, Cambridge, Massachusetts</ref>; <ref>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</ref>; <ref>Ragettli M (2007) Cost outlook for the production of biofuels. Diploma Thesis, Swiss Federal Institute of Technology</ref>; <ref>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)</ref>; <ref>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</ref>; <ref>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</ref>; <ref>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</ref>; <ref>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</ref>; <ref>Klimantos P, Koukouzas N, Katsiadakis A, Kakaras E (2009) Air-blown biomass gasification combined cycles: System analysis and economic assessment. Energy 34:708–714</ref>; <ref>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</ref>.


<figure id="fig:REMINDtable_5">
<figtable id="tab:REMIND-MAgPIEtable_5">
[[File:Remind Table 5.PNG]]
[[File:Remind Table 5.PNG]]
</figure>
</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  
''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  
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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 et al. (2013).
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="fig:REMINDtable_5"/> for fuel-based technologies and <xr id="fig: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:REMIND-MAgPIEtable_5"/> for fuel-based technologies and <xr id="tab:REMIND-MAgPIEtable_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 (Neij et al. 2003; Nitsch et al. 2004; IEA 2007a; Junginger et al. 2008; Pietzcker et al. 2014).
'''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>.


<figure id="fig:REMINDtable_6">
<figtable id="tab:REMIND-MAgPIEtable_6">
[[File:Remind Table 6.PNG]]
[[File:Remind Table 6.PNG]]
</figure>
</figtable>
 
 
 
 
 
 
 
 
 
 
 
 
 
<references />

Latest revision as of 14:34, 21 November 2021

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-MAgPIE, see <xr id="tab:REMIND-MAgPIE_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-MAgPIE_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:REMIND-MAgPIEtable_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:REMIND-MAgPIEtable_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:REMIND-MAgPIEtable_5"/> for fuel-based technologies and <xr id="tab:REMIND-MAgPIEtable_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:REMIND-MAgPIEtable_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