Energy resource endowments - 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 | |
The primary energy carriers in REMIND include both exhaustible and renewable resources. Exhaustible resources comprise uranium as well as three fossil resources, namely coal, oil, and gas. Renewable resources include hydro, wind, solar, geothermal, and biomass. It is possible to trade coal, oil, gas, uranium, and biomass across regions, but the trading of resources is subject to regional and resource-specific trade costs.
Bioenergy
REMIND models three types of bioenergy feedstocks:
- First-generation biomass produced from sugar, starch, and oilseeds (typically small in quantity, based on an exogenous scenario);
- Ligno-cellulosic residues from agriculture and forest; and
- Second-generation purpose-grown biomass from specialized ligno-cellulosic grassy and woody bioenergy crops, such as miscanthus, poplar, and eucalyptus.
To represent supply of purpose-grown bioenergy from the land-use sector, REMIND draws on an emulation of the land-use model MAgPIE (Model of Agricultural Production and its Impact on the Environment) (Lotze-Campen et al. 2008; Popp et al. 2010; Lotze-Campen et al. 2010). The emulator describes supply costs and total agricultural emissions as a function of bioenergy demand, as described in detail in Klein et al. (2014). The supply curves capture the time, scale and region dependent change of bioenergy production costs, as well as path dependencies resulting from past land conversions and induced technological changes in the land-use sector, as represented in MAgPIE. Ligno-cellulosic agricultural and forest residues bases on low-cost bioenergy supply options. Their potential is assumed to increase from 20 EJ/yr in 2005 to 70 EJ/yr in 2100 (Chum et al. 2011), based on Haberl et al. (2010).
In REMIND, we assume that the use of traditional biomass (supplied by residues) is phased out, as modern and less harmful fuels are increasingly used with rising incomes rise (Sims et al. 2010). We also assume that first generation modern biofuels are phased-out, reflecting their high costs and to account for concerns about land-use impacts, co-emissions, and competition with food production from first-generation biofuels (Fargione et al. 2008; Searchinger et al. 2008). As a concequence, the main sources of bioenergy in REMIND sceanrios are second-generation purpose-grown biomass and from ligno-cellulosic agricultural and forestry residues.
To further reflect concerns about the sustainability of large-scale deployment of lingo-cellulosic bioenergy, REMIND assumes an ad valorem tax on bioenergy. The tax increases linearly from 0 to 100% between 2030 and 2100 and is applied to the bioenergy price given by the emulator (see above). Based on the current public debate, we consider this tax to be a reflection of the potential institutional limitations on the widespread-use of bioenergy.
Non-Biomass Renewables
REMIND models resource potentials for non-biomass renewables (hydro, solar, wind, and geothermal) using region-specific potentials. For each renewable energy type, we classify the potentials into different grades, specified by capacity factors. Superior grades have higher capacity factors, which correspond to more full-load hours per year. This implies higher energy production for a given installed capacity. Therefore, the grade structure leads to a gradual expansion of renewable energy deployment over time as a result of optimization.
REMIND?s renewable energy potentials often appear higher than the potentials used in other models (Luderer et al. 2014). However, these models typically limit potentials to specific locations that are currently competitive or close to becoming competitive. REMIND?s grade structure allows for the inclusion of sites that are less attractive, but may become competitive in the long-term as the costs of other power-generation technologies increase. This choice is dependent on the model. The regionally aggregated potentials for solar PV and CSP used in REMIND was developed in Pietzcker et al. (2014b) in cooperation with German Aerospace Center DLR. In total, the solar potentials is almost unlimited, with a total amount of 6500 EJ/year for PV and 2000EJ/year for CSP. However, the resource quality differs strongly across regions, so that some regions have mostly sites with low full-load hours. To account for the competition between PV and CSP for the same sites with good irradiation, an additional constraint for the combined deployment of PV and CSP was introduced in REMIND (Pietzcker et al. 2014b). This implies that the sum of the area used by both technologies is smaller than the total available area.
The regionally aggregated wind potentials were developed based on a number of studies (Hoogwijk 2004; Brückl 2005; Hoogwijk and Graus 2008; EEA 2009). The technical potentials for combined on- and off-shore wind power amount to 370EJ/year (half of this amount is at sites with less than 1400 full-load hours). The total value is twice as large as the potential estimated by (WGBU 2003), but is less than one fifth of the potential in Lu et al. (2009).
The global potentials of hydropower amount to 50 EJ/year. These estimates are based on the technological potentials provided in WGBU (2003). The regional disaggregation is based on information from a background paper produced for this report (Horlacher 2003).