Energy resource endowments - IMAGE

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Model Documentation - IMAGE

Corresponding documentation
Model information
Model link https://models.pbl.nl/image
Institution Utrecht University (UU), Netherlands, https://www.uu.nl/en., 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.

Introduction

A key factor in future energy supply is the availability (and depletion) of various resources. One aspect is that energy resources are unevenly spread across world regions and often, poorly matched with regional energy demand. This is directly related to energy security. In representation of energy supply, the IMAGE energy model, describes long-term dynamics based on the interplay between resource depletion (upward pressure on prices) and technology development (downward pressure on prices). In the model, technology development is introduced in the form of learning curves for most fuels and renewable options. Costs decrease endogenously as a function of the cumulative energy capacity, and in some cases, assumptions are made about exogenous technology change.

Depletion is a function of either cumulative production or annual production. For example, for fossil-fuel resources and nuclear feedstock, low-cost resources are slowly being depleted, and thus higher cost resources need to be used. In annual production, for example, of renewables, attractive production sites are used first. Higher annual production levels require use of less attractive sites with less wind or lower yields.

It is assumed that all demand is always met. Because regions are usually unable to meet all of their own demand, energy carriers, such as coal, oil and gas, are widely traded. The impact of depletion and technology development lead to changes in primary fuel prices, which influence investment decisions in the end-use and energy-conversion modules Linkages to other parts of IMAGE framework include available land for bioenergy production, emissions of greenhouse gases and air pollutants (partly related to supply), and the use of land for bioenergy production (land use for other energy forms are not taken into account). Several key assumptions determine the long-term behaviour of the various energy supply submodules and are mostly related to technology development and resource base. An overview of the general energy supply model structure is provided in Figure 1.

Figure 1: Flowchart Energy supply.

Fossil fuels, uranium and fissile resources

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 cost levels. The model assumes that the cheapest deposits will be exploited first taking into account trade costs between regions. 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.

Table 1: Main assumptions on fossil fuel resources12
Oil Natural gas Underground coal Surface coal
Cum. 1970-2005 production 4.4 2.1 1.6 1.1
Reserves 4.8 4.6 23.0 2.2
Other conventional resources 6.6 6.9 117.7 10.0
Unconventional resources (reserves) 46.2 498.6 1.3 23.0
Total 65 519.2 168.6 270.0

Fossil fuel resources are aggregated to five resource categories for each fuel (see table above). Each category has typical production costs. The resource estimates for oil and natural gas imply that for conventional resources supply is limited to only two to eight times the 1970--2005 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

Bioenergy

Supply of biomass and bioenergy is based on the availability of two resources: Energy crops, and residues. Thier supply is determined as follows:

  • Energy crops1:
    • Primary energy crops are: Sugar-cane, maize, oil-crops, woody, and grassy crops.
    • Thier potential follows similar rules as those of fossil fuels but with some small differences. Depletion of energy crops 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 energy 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 energy crops can only be on abandoned agricultural land and on part of the natural grassland.
    • The costs of primary energy crops 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 2.
  • Residues3:
    • These are a byproduct of agricultural and forestry production. These are calculated on a 0.5x0.5 degree grid based on scenario projections.
    • Theoretical potential is based on agricultural yields and crop types (agricultural residues), and forest type and management (forestry residues)
    • A certain portion of the theoretical potential has to remain on the land to maintain nvironmental functions. This results in the environmental potential.
    • furthermore, some residues are diverted for (i) fuel use in poor households, and (ii) Livestock feed. The volume diverted depends on the scenario projection
    • This results in an available potential.
    • Spatially explicit costs are based on the yield of available potential, labour costs, and overheads.


The model describes the conversion of biomass (energy crops and residues) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used in the transport, residential, and chemical sectors1. 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 estimated4, 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 25:

  1. 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 6.
  2. 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.
  3. 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.
  4. 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

  1. a b c d  | |  Vassilis Daioglou, Jonathan C Doelman, Birka Wicke, Andre Faaij, Detlef P van Vuuren (2019). Integrated assessment of biomass supply and demand in climate change mitigation scenarios. Global environmental change, 54 (), 88--101.
  2. a b  | |  Monique Maria Hoogwijk (2004). On the global and regional potential of renewable energy sources. Utrecht, The Netherlands: Utrecht University.PhD thesis. 
  3. a b  | |  Vassilis Daioglou, Elke Stehfest, Birka Wicke, Andre Faaij, Detlef P van Vuuren (2016). Projections of the availability and cost of residues from agriculture and forestry. Gcb Bioenergy, 8 (2), 456--470.
  4. a b  | |  Vassilis Daioglou, Jonathan C Doelman, Elke Stehfest, Christoph Muller, Birka Wicke, Andre Faaij, Detlef P van Vuuren (2017). Greenhouse gas emission curves for advanced biofuel supply chains. Nature Climate Change, 7 (12), 920--924.
  5. ^  | |  Bert J.M. de Vries, Detlef P. van Vuuren, Monique M. Hoogwijk (2007). Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach. Energy Policy, 35 (), 2590-2610. http://dx.doi.org/10.1016/j.enpol.2006.09.002
  6. ^  | |  M New, M Hulme, P Jones (1997). A 1961-1990 mean monthly climatology of global land areas. Climatic Research Unit, University of East Anglia, Norwich, UK, ().