| layout | title | current-version |
|---|---|---|
index |
GCAM-USA |
v4.3 |
The GCAM model was expanded to include greater spatial detail in the USA region, referred to as GCAM-USA. In GCAM-USA the 50 U.S. states plus the District of Columbia are included as explicit regions that operate within the global GCAM model. Energy transformation (electricity generation and refined liquids production) and end-use demands (buildings, transportation, and industry) are modeled at the individual state level. All primary fossil fuels, biomass, and refined liquids can be freely traded across all states with one endogenously determined price.
Note, several aspects of the energy system are still modeled at the aggregate U.S. level. Most notably this applies to primary production of fossil resources including oil, gas, and coal. The supply of biomass energy feedstocks, which include residues and dedicated energy crops, is modeled at the level of 10 agro-ecological zones (AEZ) in the United States (Calvin et al 2014, Wise et al 2014). As with primary fossil resources, biomass feedstocks are assumed to be freely traded at one global price.
The first step to modeling at the 50 state level was to create an energy balance table by sector and state. This data was developed to the final historical year of 2010. While more recent data is available we are limited in that the entire global energy balance in GCAM must also be updated. Thus we are constrained that the GCAM-USA energy balance must still be consistent with the global GCAM data set, which is derived from International Energy Agency (IEA) energy balances. With this in mind we decided to use a “downscaling” approach where we will divide the aggregate US level IEA energy balance utilizing state level data sets from the US Energy Information Agency (EIA). The EIA SEDS (State Energy Data System) was used as the primary data set for this purpose (EIA 2014). Additional details for further subdividing key sectors follows in the subsequent sections.
We begin by describing the process for developing the energy balances for final energy demands including the buildings, transportation, and industry sectors. To get the aggregate sector energy use for buildings and transportation this process was straightforward as the sector requires no further disaggregation beyond what is available in the EIA SEDS database, and because the sector in GCAM is only a consumer of energy, not a producer.
Additional processing to differentiate energy for specific buildings services required additional data sets and assumptions as described in Zhou et al. (2013). Each energy service can be supplied by one or more technologies. Heating and cooling service demand depends on building envelope thermal efficiency, internal heat gains, and climate. Technology and building envelope efficiencies are assumed to increase over the century as described in Zhou et al (2014).
The transportation sector is disaggregated between passenger, domestic freight, and international shipping. The demand for transportation services depends on vehicle miles traveled, GDP and population by state. Each of the freight and passenger vehicle classes represent on-road vehicle options such as cars, trucks, and motorcycles, as well as off-road options such as trains. In addition, these vehicle options include various drivetrain technologies such as liquid, hybrid, and electric. These assumptions are consistent with the transporation sector in the global GCAM model: Energy System. The following mapping is used calibrate historical energy balances from the EIA SEDS. For EIA Fuel + Sector combinations that span multiple GCAM mode definitions, Air Domestic and International, the national level shares will be used for all states.
| Mode | GCAM fuel | EIA fuel used for disaggregation |
|---|---|---|
| Air Domestic | refined liquids | Jet fuel in transporation |
| Air International | refined liquids | Jet fuel in transporation |
| Bus | gas | Natural gas in vehicle fuel |
| Bus | refined liquids | Distillate fuel oil in transporation |
| HSR | electricity | Electricity in transporation |
| LDV_2W | electricity | Electricity in transporation |
| LDV_2W | refined liquids | Motor gasoline in transportation |
| LDV_4W | electricity | Electricity in transporation |
| LDV_4W | gas | Motor gasoline in transportation |
| LDV_4W | hydrogen | Motor gasoline in transportation |
| LDV_4W | refined liquids | Motor gasoline in transportation |
| Rail | coal | Distillate fuel oil in transporation |
| Rail | electricity | Electricity in transporation |
| Rail | refined liquids | Distillate fuel oil in transporation |
| Ship Domestic | refined liquids | Residual fuel oil in transportation |
| Ship International | refined liquids | Residual fuel oil in transportation |
| Truck | refined liquids | Distillate fuel oil in transporation |
| Truck | gas | Distillate fuel oil in transporation |
Mapping EIA fuels to GCAM transportation modes.
Industry was more difficult due to the refinery sector’s energy use being included in the industry sector of SEDS as well as the need to quantify electricity co-generation from industrial plants. The national estimates of cogeneration by fuel are from the IEA’s Autoproducer CHP Plants, defined as facilities whose power production is primarily in support of activities that are on-site. No state-and fuel-level inventory of co-generation was available, so fuel consumption by the whole industrial sector is used to derive the proportional allocation to states. Inputs to cogeneration are then calculated using exogenous fuel-specific input-output coefficients, which are taken from the GCAM aggregate US module.
In the EIA’s state-level data, refinery energy use is tracked under the industrial sector, with nothing to distinguish it from the remainder of the sector except that it is the only part of the industrial sector that consumes raw crude oil. The amount of crude oil consumed by the industrial sector in the EIA database is therefore used as the basis for disaggregation of the national estimates for inputs and outputs of crude oil refining. We assume the same refinery input-output coefficients on all inputs (gas, electricity, and crude oil) in all states; therefore, given the amount of crude oil consumed as energy by the industrial sector (refineries) in each state, we can calculate the amount of electricity and gas consumed, and the amount of refined liquid products produced. The calculated amounts of gas and electricity that are inputs to refineries in each state are then deducted from the industrial sector energy consumption.
Further processing for biomass liquids, first the energy output (biofuel production) for the whole USA is disaggregated among states according to each state’s share of total national ethanol production in 2008. The same state shares are used for biomass liquids-FT, as no state-level data is available on biodiesel production (production is small compared to ethanol). The whole-US input-output coefficient on biomass liquids production is multiplied by each state’s output in order to get the input of biomass, but note that no ensuing deduction from industrial energy use is applied here, as the energy content of the biofuel feedstocks (corn kernels, soybean oil) were not considered part of the industrial sector’s energy consumption in the first place. The only EIA fuels considered as biomass are wood and waste, neither of which describes the biofuel feedstocks.
Liquid fuels used as industrial feedstocks for the whole nation are then disaggregated to states on the basis of the sum of construction feedstocks and petrochemical feedstocks in each state. Gas used as feedstocks is not explicitly accounted here; gas used as feedstocks is classified with all other industrial sector gas consumption in the EIA SEDS database. This is not surprising as (a) unlike liquid fuels, there is nothing different about the natural gas used as feedstocks from that used for energy, and (b) the characterization of gas as “feedstock” as opposed to “energy” is a difficult one which is only done post-hoc based on product yields, and only in some inventories. Moreover, in many cases (e.g. ammonia production, hydrogen production), even the “feedstock” uses of natural gas entail 100% release of all carbon as CO2. In any case, the national estimate of gas feedstocks is disaggregated to states on the basis of each state’s share of the whole nation’s use of (liquid fuel) petrochemical feedstocks, used as a proxy for the size of the chemicals industry in each state. This amount of gas is then deducted from industrial energy use.
Next we will describe creating the 50 state energy balance for the electric sector, starting with electricity generation. The EIA SEDS database represents hydrocarbon-fueled electricity only by fuel inputs to the electricity sector (EI in the SEDS database), with all other technologies represented in terms of electricity output (EG). Estimates for electricity generation from hydrocarbon-fueled electricity must then be made by applying the national average generation efficiency by fuel to all states.
At this stage we have estimated total electricity generation from central production as well as CHP by state. Together these constitute the input to the GCAM “electricity net ownuse” sector, which includes net generation plus electricity consumed on-site. The next step is to subtract each state’s own use of electricity. Own use of electricity is disaggregated to states on the basis of the “Direct Use” by states in the EIA, 2008b, Table A1: Selected Electric Industry Summary Statistics by State, 2008. As with all other sectors, the GCAM USA region’s total for electricity ownuse is disaggregated to states according to each state’s share in the table cited above. By subtracting own use, we then have the output of the electricity_net_ownuse sector in each state, which is the net input into the electric grid.
Finally we estimate electricity losses with in each state. EIA SEDS presents estimates of energy losses in transmission and distribution. The method for calculating the coefficient for electricity is to first add up all of the demands of electricity by all sectors: refining, electricity, buildings, industry, and transportation. Then the electricity T&D energy losses are added in, with the total of all the state estimates scaled to match the IEA’s whole-US estimates of electricity distribution losses.
A number of assumptions regarding technology characteristics in the electric power sector need to be made. Generally speaking assumptions are carried from the USA region of GCAM and applied equally to all states. This includes generation efficiencies, costs, and technology availability. Regional variations in resource characteristics such as wind and solar potential and geothermal and carbon storage resources are accounted for.
For electricity trade between states we group states roughly into the 13 NEMS Electricity Market Module Regions (EIA 2010) plus Alaska and Hawaii. Whereby states within the same sub-region can trade freely within that sub-region, trade between regions may be limited.
Modeled electricity markets based on NEMS.
Renewable energy resource are modeled at the state level. Wind and residential rooftop PV technologies include resource costs that are also calculated from exogenous supply curves that represent marginal costs that increase with deployment, such as long-distance transmission line costs that would be required to produce power from remote wind resources and reducing capacity factors as most optimal locations are used first. Central station solar technologies are assumed to have constant marginal costs regardless of deployment levels. Wind resource curves are based on Zhou, et al. 13. Geothermal resources are from Lopez A, et al. 14. Supply curves for residential rooftop PV are obtained from Denholm and Margolis 15. Hydro power is held fixed at historical production levels.
The depiction of the regionally-specific graded CO2 transport storage cost curves reflect adjusted per-ton project costs for CO2 transport and geologic storage as based on the methodology developed by Dahowski et al. (2011); Dahowski et al. (2005); Dahowski et al. (2010). The costs shown in the figure include site characterization, capital and operations and maintenance costs associated with CO2 injection into suitable deep geologic reservoirs, and costs for required measurement, monitoring and verification technologies as well as other costs associated with regulatory compliance. These values do not include the cost of CO2 capture and compression to pipeline pressures which is accounted for at the technology level within GCAM-USA. CO2 storage is aggregated to the same sub-regional markets as electricity to allow for some cross state border trade in CO2.
In brief, the regionally-specific CO2 transport and storage cost curves for the U.S. were developed using a cost-optimized source-sink matching algorithm designed to model globally optimal CCS deployment (again, less the cost of CO2 capture and compression) across the modeled domain. Each point on the resulting cost curves represents a single source-sink pair with a single average per-ton cost over the first 20-year timestep of the analysis (see Dahowski et al., 2005; Dahowski et al., 2010 for the rationale for this 20-year time step and its significance for these CO2 transport and storage cost curves). Source-sink pairs were derived using both spatial and economic criteria based on a set of 2017 large anthropogenic CO2 point sources[1] and 326 individual geologic storage reservoirs within the US (Dahowski et al., 2011). As shown in this earlier published research, the average per ton cost of CO2 transport and storage would increase in the future as the capacity of low cost, value-added geologic CO2 storage reservoirs are preferentially consumed ahead of non-value added geologic storage reservoirs. The cost-curves are implemented within GCAM-USA as exhaustible resources, so once low-cost storage sites are used, higher cost sites must be used.
In keeping with the US electricity-specific CCS modeling presented in Wise et al. (2007), the CO2 transport and storage cost curves represented in this analysis have been adjusted to account for the fact that the cost of CO2 capture can vary by an order of magnitude for the different CO2 sources modeled to generate the raw region specific CO2 transport and storage cost (i.e., a natural gas processing plant generates a virtually pure stream of CO2 that can be captured – already dehydrated and compressed - for a cost of less than $10/tonCO2, while an older and relatively small pulverized coal plant could see capture costs well above $50/tonCO2) and which kinds of CO2 point sources get to access what kinds of geologic storage reservoirs is influenced strongly by the capture costs. Therefore, the raw region-specific CO2 transport and storage curves would present an unrealistically low representation of the net cost of CO2 transport and storage likely to be experienced by large stationary CO2 point sources like coal, natural gas, and biomass – fired power plants and refineries, which are the anthropogenic CO2 sources that are modeled specifically in GCAM-USA.
Carbon storage potential by electricity market.
The socio-economic projections were developed as part of the PNNL PRIMA initiative (Scott et al. 2014). We estimate state medium population projections from the 2005 US Census 2010-to-2030 state projections, and extended to 2100 with a combination of state growth factor predictions and regression to the national projection. We ratio the medium state projection to the national medium projection to obtain a state-to-national proportionality profile. We then apply this proportionality profile to a quantile of the national projection distribution to obtain a state’s corresponding population projection quantile (Scott et al. 2014). Historical GDP at the state level is from the Bureau of Economic Analysis (BEA, 2009). Future GDP is projected using the same aggregate productivity growth rate for all states from the GCAM Base scenario. U.S. per capita GDP in this scenario increases by 60% from 2005 to 2050 to $67,470/capita.
Texas and Florida dominate the economic landscape in the Gulf Coast (Figure SI-10) with continued rapid growth in population to mid century of 1.2% per year for Texas and 1.6% for Florida. As such they will continue to be the largest source of GDP in the Gulf Coast however on a GDP per-capita basis the difference between the states remain the same since all states grow with the same labor productivity.
Figure SI-: Projected socioeconomics.
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