Electricity - REMIND-MAgPIE
Corresponding documentation | |
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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 Carrier | Technology |
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Primary exhaustible resource | |
Coal |
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Oil |
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Gas |
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Uranium |
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Primary renewable resource | |
Solar |
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Wind |
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Hydropower |
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Biomass |
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Geothermal |
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Secondary energy type | |
Hydrogen |
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</figtable>
<figure id="fig:REMIND-MAgPIEtable_4"> </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"> </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"> </figtable>
- ↑ Iwasaki W (2003) A consideration of the economic efficiency of hydrogen production from biomass. International Journal of Hydrogen Energy 28:939–944
- ↑ Hamelinck C (2004) Outlook for advanced biofuels. Ph.D. Thesis, University of Utrecht
- ↑ Bauer N (2005) Carbon capture and sequestration: An option to buy time? Ph.D. Thesis, University of Potsdam
- ↑ Ansolabehere S, Beer J, Deutch J, et al (2007) The Future of Coal: An Interdisciplinary MIT Study. Massachusetts Institute of Technology, Cambridge, Massachusetts
- ↑ 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
- ↑ Ragettli M (2007) Cost outlook for the production of biofuels. Diploma Thesis, Swiss Federal Institute of Technology
- ↑ 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)
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ Klimantos P, Koukouzas N, Katsiadakis A, Kakaras E (2009) Air-blown biomass gasification combined cycles: System analysis and economic assessment. Energy 34:708–714
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ IEA (2007a) Energy Balances of OECD Countries. International Energy Agency, Paris
- ↑ Junginger HM, Lako P, Lensink S, et al (2008) Technological learning in the energy sector. MNP
- ↑ Pietzcker et al. 2014