Gaseous fuels - GCAM

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Model Documentation - GCAM

Corresponding documentation
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Model information
Model link
Institution Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri.
Solution concept General equilibrium (closed economy)GCAM solves all energy, water, and land markets simultaneously
Solution method Recursive dynamic solution method
Anticipation GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.

Gas Processing

The three subsectors of the gas processing sector, and the downstream sectors are described below and in the gas processing documentation section. See gas processing details for an overview of the structure. Click on each heading to bring you to the corresponding section in the documentation.

Natural Gas

Natural gas accounts for almost 99% of the gaseous fuel production represented in GCAM’s calibration year (2015). The natural gas commodity in GCAM includes all gaseous fuels produced at gas wells, the gaseous co-products from oil production, and gas produced from coal mines and coal seams. The natural gas commodity excludes natural gas liquids, and it excludes gas that is vented, flared, or re-injected. Further information is available in Mapping the IEA Energy Balances and IEA (2011).[1] In the gas processing sector, the natural gas technology is assigned an input-output coefficient of 1, as natural gas plant fuel is not a disaggregated flow in the IEA energy balances.

Coal Gasification

The GCAM coal gasification technology in historical years represents gas works gas, or town gas, that is produced from coal. It does not include blast furnace gas, coke oven gas, and other coal-derived gaseous fuels that are by-products of other activities, and typically consumed on-site. Many regions produced no coal gas in 2010. In future periods, the technology represents a broader suite of coal gasification processes that are capable of producing a commodity that competes for market share with natural gas. See Linden et al. 1976[2] for a review of technologies for producing pipeline-grade gaseous fuels from coal.

Biomass Gasification

In historical years, biomass gasification, or biogas, is considered to be gases captured from landfills, sludge, and agricultural wastes, that are used to provide heat and power. As with coal gasification, in future periods, biomass gasification is intended to represent a suite of processes that convert biomass feedstocks into pipeline-grade gaseous fuels that can be used by a variety of end users. For a technical description see Zwart et al. 2006.[3]

Gas Pipeline, Delivered Gas, and Wholesale Gas

The gas pipeline sector explicitly represents the energy consumed by compressors for transmission and distribution of natural gas. Delivered gas and wholesale gas are differentiated in their consumers and therefore cost mark-ups; delivered gas refers to gas used by the buildings and transportation sectors, whereas wholesale gas is used by industrial and energy sector consumers. The historical input-output coefficient of the gas pipeline sector in any region is estimated as the sum of reported pipeline energy consumption, delivered gas, and wholesale gas, divided by the sum of delivered gas and wholesale gas.

Hydrogen

Hydrogen is represented as a commodity in future time periods that is available for various energy and industrial processes. Hydrogen is not treated as a fuel in the IEA Energy Balances,[4] or most other energy statistics. As such, the representation excludes the on-site production and use of hydrogen at oil refineries, ammonia plants, and other present-day industrial facilities. The representation of hydrogen in GCAM includes 10 “central” production technologies, as well as 2 “forecourt” (i.e. on-site) production technologies, which may have higher costs due to the economies of scale and higher capacity factors of central production, but the forecourt technologies avoid the costs and energy requirements of distribution. The hydrogen distribution representation differentiates a range of hydrogen commodities whose costs largely reflect the various temperatures and pressures at which hydrogen is transported and stored for different end-use applications. Production technology costs and energy intensities are from the U.S. Department of Energy’s Hydrogen Analysis (H2A) models (NREL 2018),[5] and the distribution costs and energy intensities are from Argonne’s Hydrogen Delivery Scenario Analysis Model (HDSAM).[6] See hydrogen details for more information.

  1. International Energy Agency, 2011, Energy Balances of OECD Countries: Documentation for Beyond 2020 Files, International Energy Agency, Paris, France.
  2. Linden, H.R., Bodle, W.W., Lee, B.S., and Vyas, K.C. 1976. Production of high-btu gas from coal. Annual Reviews of Energy 1, pp. 65-86.
  3. Zwart, R., Boerrigter, H., Deurwaarder, E.P., van der Meijden, C.M., and van Paasen, S.V.B. 2006. Production of Synthetic Natural Gas (SNG) from Biomass: Development and operation of an integrated bio-SNG system. Report ECN-E-06-018, Energy Research Centre of the Netherlands.
  4. International Energy Agency, 2019, Energy Balances of OECD Countries 1960-2017 and Energy Balances of Non-OECD Countries 1971-2017, International Energy Agency, Paris, France.
  5. National Renewable Energy Laboratory, 2018, H2A: Hydrogen Analysis Production Models, National Renewable Energy Laboratory.
  6. Argonne National Laboratory, 2015, Hydrogen delivery scenario analysis model (HDSAM), Argonne National Laboratory.