Energy supply/Data uncertainties limitations

Component is implemented in:

TIMER model (version TIMER 3.2) Aggregated component: Energy supply and demand Components: Emissions Energy conversion Energy demand Energy supply

Related IMAGE components

Atmospheric composition and climate Climate policy Crops and grass Drivers Land cover and land use

Projects/Applications

ADVANCE project Roads from Rio+20 (2012) project

Key publications

De Vries et al., 2007 Van Vuuren et al., 2008 Van Vuuren et al., 2009

References

Hendriks et al., 2004b Hoogwijk, 2004 IPCC, 2005 Mulders et al., 2006 Van Vuuren et al., 2008 Van Vuuren et al., 2009 Van Vuuren et al., 2010 WEC, 2010

Flowchart Energy supply. See also the Input/Output Table on the introduction page.

Data, uncertainty and limitations

Data

Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).

Table: Main data sources for the TIMER energy supply module

Data input	Sources
Fossil-fuel resources and costs	[!CHANGE] [[IEA, 2017]], [[USGS, 2012]]; [[BGR, 2016]]; EDGAR database; [[Abundant Gas Project]]. Costs mainly based on Rogner et al. [[(2018, in prep.)]]
Nuclear fuel data (uranium and thorium)	WEC-Uranium (WEC, 2010)
Bio-energy potential and costs	PBL calculations (Van Vuuren et al., 2009; Van Vuuren et al., 2010)
Solar, wind, and hydropower potential	PBL calculations (Hoogwijk, 2004)
CCS potential	Based on (Hendriks et al., 2004b; IPCC, 2005)

Uncertainties

One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios (Van Vuuren et al., 2008) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO₂ emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

Limitations

The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in 26 global regions. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.