Energy supply/Description: Difference between revisions
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{{ComponentDescriptionTemplate | {{ComponentDescriptionTemplate | ||
|Status=On hold | |Status=On hold | ||
|Description===Fossil fuels and uranium== | |||
===Long-term depletion=== | |||
The depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the basis of the assumption that their 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 submodels is that of fuel demand (i.e. fuel used in final energy and conversion processes). Additional input consists of conversion losses in refining, liquefaction, conversion, and energy use within the energy system. In the submodels, it is indicated how demand can be met by supply, both within a region and from other regions through interregional trade. | |||
{{Table ES|Table}} | |||
Table 4.1.3.2 provides an overview of the fossil-fuel categories, for illustrational purposes here aggregated into only 5 categories for each fuel. Each category (as indicated in the table) has its own typical production costs. The table indicates that the assumptions for oil and natural gas supply limit this supply to only 2 to 8 times the 1970–2005 production level, for all categories, up to current reserves of unconventional sources. Production estimates on other unconventional resources are much larger, albeit very speculative. For coal, even current reserves amount to almost 10 times the production level of the last three decades. For all fuels, the model assumes that, if prices would increase (or spectacular technology development takes place), the energy could be produced in the higher-cost resource categories. The values in Table 4.1.3.2. represent medium estimates in the model. The model can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined influence of resource depletion and learning-by-doing (for more information see backrgound information on [[Technical learning and depletion|resource depletion and 'learning by doing']]. | |||
==Trade== | |||
In the fuel trade model, each region imports fuels from other regions: the amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transportation costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre. To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate the existence of trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas (LNG) tanker, depending on transportation distances (for short distances, pipelines are more attractive). Finally, the model makes a comparison between the production costs with and without unrestricted trade. In cases where regions can supply at much lower costs than the average production costs in importing regions (a threshold of 60% is used), such regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although this rule is implemented in a generic form for all energy carriers, it is only effective in the case of oil, where the behaviour of the OPEC cartel is simulated, to some degree. | |||
==Bio-energy== | |||
The structure of the biomass sub-model is similar to that of the fossil-fuel supply models, but with a few important differences (Hoogwijk, 2004) (see Figure 4.1.3.1b). | |||
• Depletion in the bio-energy model is not governed by cumulative production, but by the degree to which available land is being used for commercial energy crops. | |||
• The total amount of potentially available bio-energy is derived from bio-energy crop yields calculated on a 0.5 by 0.5 degree grid with the IMAGE crop model (Section 6.1.2) for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, most importantly the condition that bio-energy is only allowed on abandoned agricultural land and on part of the natural grasslands. The costs of primary bio-energy crops (woody, maize and sugar cane) are calculated with a Cobb-Douglas production function using labour costs, land rent costs and capital costs as input. The costs of land 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 as mentioned (Hoogwijk, 2004). | |||
• Next, the biomass 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 and bio-liquid fuels. The solid fuel is used in industrial and power sectors and the liquid fuel mostly in the transport sector. | |||
• The trade and allocation of biofuel production among regions is determined by optimisation. In this way, 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 their demand. | |||
The production costs for bio-energy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion; conversion costs, in contrast, decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital. The costs of the conversion process include capital costs, O&M costs and the costs of energy use in this process. For both steps, the associated greenhouse gas emissions (related to deforestation, N2O from fertilisers, energy) are estimated (Chapter 5) and, where relevant, are subject to carbon tax. | |||
==Other renewable energy== | |||
The potential supply of renewable energy (wind, solar and bio-energy) is estimated in a generic way (Hoogwijk, 2004; De Vries et al., 2007): | |||
1. First, the relevant physical and geographical data for the regions considered are collected on a 0.5 by 0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit (New et al., 1997). Land use information for energy crops is taken from the IMAGE land use model. | |||
2. Subsequently, the model assesses which part of the grid cell can be used for energy production, given its physical–geographical (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 of biomass production from energy crops is estimated using suitability/availability factors accounting for competing land-use options and the harvested rain-fed yield of energy crops. | |||
3. Because only part of the energy can be extracted in the form of secondary energy carriers (fuel, electricity), due to limited conversion efficiency and maximum power density, part of the geographical potential cannot be used. This result of accounting for these conversion efficiencies is called the technical potential. | |||
4. The final step is to relate this technical potential to on-site production costs. Information at grid level is then 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. | |||
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Revision as of 16:33, 9 December 2013
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