Energy resource endowments - IMAGE: Difference between revisions
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==Fossil fuels uranium and fissile resources== | ==Fossil fuels uranium and fissile resources== | ||
==Bioenergy== | ==Bioenergy== |
Revision as of 10:23, 25 October 2016
Corresponding documentation | |
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Model information | |
Model link | |
Institution | PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, https://www.pbl.nl/en. |
Solution concept | Partial equilibrium (price elastic demand) |
Solution method | Simulation |
Anticipation | Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations. |
Fossil fuels uranium and fissile resources
Bioenergy
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences IMG_Hoogwijk_2004:
- Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
- The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE crop model for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, maize and sugar cane) are calculated with a Cobb-Douglas production function using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data IMG_Hoogwijk_2004.
- The model describes the conversion of biomass (including residues, in addition to wood crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
- The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand. ' '
The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of learning by doing. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, O&M and energy use in this process. For both steps, the associated greenhouse gas emissions (related to deforestation, N2O from fertilisers, energy) are estimated, and are subject to carbon tax, where relevant.
Wind and solar energy
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows IMG_Hoogwijk_2004IMG_DeVries_2007:
- Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit IMG_New_1997.
- The model assesses the part of the grid cell that can be used for energy production, given its physical--geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/ availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
- Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
- The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.