Electricity - REMIND-MAgPIE

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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 Table 1, 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).

    Table 1: 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
    54067596.jpg

    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].

    Remind Table 5.PNG

    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 Table 2 for fuel-based technologies and Table 3 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].

    Remind Table 6.PNG







    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