Energy supply/Description: Difference between revisions
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A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade. | A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade. | ||
<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])<table class="pbltable"> | |||
<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])< | |||
<tr><th> | <tr><th></th><th>Oil | ||
</th><th>Oil | |||
</th><th>Natural gas | </th><th>Natural gas | ||
</th><th>Underground coal | </th><th>Underground coal | ||
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The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]): | The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[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. | * 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 [[Crops and grass|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 economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]). | * The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|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, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[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 model describes the conversion of biomass (including residues, in addition to wood crops, grassy 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 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, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to | 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, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant. | ||
<div class="version newv31"> | <div class="version newv31"> | ||
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid. | Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>. | ||
</div> | </div> | ||
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# 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. | # 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. | # 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. | ||
<references/> | <references /> | ||
</div> | </div> |
Revision as of 12:41, 27 February 2019
Parts of Energy supply/Description
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Model description of Energy supply
Model description of Energy supply
Fossil fuels and uranium
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.
A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.