Carbon capture, utilisation and storage • IDEEA

library(IDEEA)
# devtools::load_all(".")
library(tidyverse)
library(glue)
library(kableExtra)

Settings

nreg <- 5 
# nreg <- 32 # alternative number of regions

# region names where offshore regions (if any) associated with closest land-region:
regN <- glue("reg{nreg}") 
# offshore regions have distinct names:
regN_off <- glue("reg{nreg}_off")

The goal of carbon capture, utilization and storage (CCS or CCUS) technologies is to reduce carbon emissions from fuels combustion and industrial processes. The emissions are captured, transported, and stored in geological formations, or used in other industrial processes. The captured CO2 potentially can be used in enhanced oil recovery (EOR), or in the production of chemicals, fuels, and materials. However, the current status of utilization is limited and the main focus is on the storage of CO2 in geological formations.

Carbon sink potential

The potential of geological storage varies by region and depends on the availability of suitable geological formations, the distance to the sources of CO2, and the existing infrastructure. Here we use an estimated value [LINK] of CCS storage potential in saline aquifers and basalt formations by region.

# Data on the potential of CO2 storage in geological formations
# NOTE: the original data is by 5 regions, disaggregation is used for higher nreg
ccs_reserve <- get_ideea_data(name = "ccs_r5", nreg = nreg)

# Declaration of domestic carbon sink resources
# the objects is declared in `energy` document and saved in ideea_modules$energy
# here we import saved objects for the requested number of regions

# A commodity to represent the storage of CO2
CO2SINK <- ideea_modules$energy[[regN]]$CO2SINK

# Permanent geological carbon storage (saline aquifers and basalt) potential
RES_CO2SINK <- ideea_modules$energy[[regN]]$RES_CO2SINK
  
# Declaration of captured CO2 "commodity" 
CCSCO2 <- newCommodity(
  name = "CCSCO2", 
  desc = "Captured CO2, kt",
  unit = "kt", 
  #slice = "ANNUAL", deprecated
  timeframe = "ANNUAL"
)

# Declaration of composite GHG
GHG <- newCommodity(
  name = "GHG",
  desc = "Greenhouse gas emissions, Mt",
  unit = "Mt",
  agg = data.frame(
    comm = c("CO2", "CCSCO2"),
    unit = "kt",
    agg = c(1e3, -1e3)
  ),
  misc = list(
    info = c("GHG is a composite commodity that aggregates CO2 and CCSCO2 with",
             "specified weights (in @agg$agg)")
  )
)

The implementation of CCS technologies in energy models can be done in many ways with different levels of detail. CO2 emissions in electric power sector in IDEEA model are linked to the fuels use (combustion) by power plants. The emission factor is set for every fuel in @emis slot of every commodity. For example, the emis slot of coal commodity COA@emis is set to:

comm	unit	emis
CO2	kt/GWh	0.33

and for natural gas (GAS@emis):

comm	unit	emis
CO2	kt/GWh	0.18

In the current settings, the emis parameter in the @emis slot represents the kt of CO2 emissions per GWh of fuel use (by default, all energy is measured in GWh in IDEEA model).
When the fuels are used in power plants, the emissions are calculated based on the fuel use, the emission factors, and the combustion parameter in technology class (slot @input) which is set to 1 by default.

CCS with fixed capture rate

The simplest way to reduce CO2 emissions of a power generation in the model is not adjust the combustion parameter by the CCS capture rate. The efficiency and costs associated with CCS must be also adjusted accordingly. Bellow we “upgrade” a coal power plant from IDEEA model dataset with CCS technology.

Coal power plant with CCS

COA <- ideea_modules$energy[[regN]]$COA
ECOA_prototype <- ideea_modules$techs$ECOAULT@data$ECOAULT_2030
ECOA_prototype@input
#>   comm unit group combustion
#> 1  COA  GWh  <NA>         NA

If the combustion parameter is not set (NA or empty), the default value is used (1). For simplicity, we assume that the plant with CCS has 90% capture rate, 10% efficiency loss, and 50% capital, fixed, and variable costs increase. The prototype of the CCS technology is shown below.

# no CCS
ECOA_prototype@input 
#>   comm unit group combustion
#> 1  COA  GWh  <NA>         NA
# with CCS
ECOA_CCS_FX <- update(
  ECOA_prototype,
  name = "ECOA_CCS_FX",
  desc = "Coal power plant with fixed CCS",
  input = list(comm = "COA", combustion = 0.1, unit = "GWh")
  )

# Efficiency, no CCS
kableExtra::kable(
  select(ECOA_prototype@ceff, region, year, comm, cinp2use)
)

region	year	comm	cinp2use
NA	NA	COA	0.418


# Efficiency, with CCS (~10% loss)
ECOA_CCS_FX@ceff$cinp2use <- ECOA_prototype@ceff$cinp2use * 0.9

# Costs (~50% up)
ECOA_CCS_FX@invcost$invcost <- ECOA_prototype@invcost$invcost * 1.5
ECOA_CCS_FX@fixom$fixom <- ECOA_prototype@fixom$fixom * 1.5
ECOA_CCS_FX@varom$varom <- ECOA_prototype@varom$varom * 1.5

# extend the availability window of the technology on the market
ECOA_CCS_FX@end$end <- 2100

Finally, since the sequestration potential is also limited, we can track the utilization of carbon storage by specifying input commodity (CO2SINK) and in the aeff slot of the technology. The CO2SINK commodity is used to track the amount of CO2 stored in geological formations. We can also reduce the emissions of conventional pollutants (NOX, SOX, PM) by the same percentage as the CO2 emissions, specified in the auxiliary commodities and efficiency slots @aux and @aeff.

# no CCS
ECOA_prototype@aux
#>   acomm unit
#> 1   NOX   kt
#> 2   SOX   kt
#> 3    PM   kt
ECOA_prototype@aeff |> select(acomm, comm, cinp2aout, cinp2ainp)
#>   acomm comm cinp2aout cinp2ainp
#> 1   SOX  COA      0.10        NA
#> 2   NOX  COA      0.10        NA
#> 3    PM  COA      0.03        NA

# with CCS
ECOA_CCS_FX <- update(
  ECOA_CCS_FX,
  aux = data.frame(
    acomm = c("NOX", "SOX", "PM", "CO2SINK"),
    unit = c("kt", "kt", "kt", "kt")
  ),
  aeff = data.frame(
    acomm = c("CO2SINK", ECOA_prototype@aeff$acomm),
    comm = c("COA", "COA", "COA", "COA"),
    cinp2ainp = c(COA@emis$emis * 0.9, NA, NA, NA), # 90% capture rate
    cinp2aout = c(NA,  ECOA_prototype@aeff$cinp2aout * 0.1)
  ),
  varom = data.frame(
    acomm = "CO2SINK",
    cvarom = convert(120.1, "USD/t", "cr.₹/kt") # ~0.1 cr.₹/kt
  )
)

draw(ECOA_CCS_FX)

If CO2SINK is used, the commodity and it’s resource must be added to the model.

Gas power plant with CCS

Similarly we can add CCS to natural gas and biomass-powered plants. Instead of updating the existing technology without CCS, we design the technology from scratch to demonstrate a technology-building process.

GAS <- ideea_modules$energy[[regN]]$GAS
EGAS_prototype <- ideea_modules$techs$ENGCC$ENGCC_2030

EGAS_CCS_FX <- newTechnology(
  name = "EGAS_CCS_FX",
  desc = "Natural gas power plant with fixed CCS",
  input = list(
    comm = "GAS", 
    unit = "GWh", 
    combustion = .1 # 10% of emissions are not captured by CCS
  ),
  output = data.frame(
    comm = "ELC",
    unit = c("GWh")
  ), 
  aux = data.frame(
    acomm = c("NOX", "SOX", "PM", "CO2SINK", "CO2"),
    unit = c("kt", "kt", "kt", "kt", "kt")
  ),
  ceff = list(
    cinp2use = c(EGAS_prototype@ceff$cinp2use, NA), # 62% efficiency w/o CCS
    use2cact = c(NA, .9), # 10% efficiency loss with CCS
    comm = c("GAS", "ELC")
  ),
  aeff = data.frame(
    acomm = c("CO2SINK", "NOX", "SOX", "PM"),
    comm = c("GAS", rep("ELC", 3)),
    cinp2ainp = c(GAS@emis$emis[1], rep(NA, 3)),
    cout2aout = c(NA, .01, .01, .005) # arbitrary
  ),
  olife = list(olife = 40),
  invcost = data.frame(
    invcost = 1.5 * EGAS_prototype@invcost$invcost
  ),
  fixom = data.frame(
    fixom = 1.5 * EGAS_prototype@fixom$fixom
  ),
  # variable costs are assigned in the CO2SINK supply,
  # here is an alternative formulation with the same effect:
  # varom = data.frame(
  #   acomm = "CO2SINK",
  #   cvarom = convert(12.1, "USD/t", "cr.₹/kt") # ~0.1 cr.₹/kt
  # ),
  start = list(start = 2025)
  # end = list(end = 2060),
)
draw(EGAS_CCS_FX)

CCS with flexible capture rate

The example above assumes that once installed, CCS will be operated at maximum removal capacity. This setting will probably fit most decarbonization studies, but in some cases it might be helpful to have an optional use of CCS technology. For example, the technology can be designed to operate at a lower capture rate, depending on the strength of the carbon control policy, carbon price or the market.

The following case demonstrates an alternative way to design a CCS technology with a flexible capture rate. To make the operation rate flexible, we introduce an alternative fuel input (COA_CCS or GAS_CCS) and define a group of fuel inputs with different capture rates (set via combistion parameter as before).

# Alias commodity for coal (COA) with zero emissions
COA0 <- COA |>
  update(
    name = "COA0",
    desc = "Alias for COA"
  )

ALIAS_COA <- newTechnology(
  name = "ALIAS_COA",
  desc = "Artificial technology to create an alias for COA commodity",
  input = list(
    comm = "COA",
    unit = "GWh",
    combustion = 0
  ),
  output = data.frame(
    comm = "COA0",
    unit = "GWh"
  ), 
  cap2act = 1e12, # capable to convert GWh a year
  capacity = list(stock = 1), # pre-defined capacity to reduce the model dimension
  end = list(end = 2000) #
)
draw(ALIAS_COA)

This “alias” commodity does not require a new resource, it’s supply will be defined and bounded by the supply of the original commodity via the ALIAS_COA technology which has no costs, therefore will not affect the model’s objective, but will be responsible for the supply of the COA0 commodity, “made” of the COA commodity.

Coal power plant with flexible CCS

The key difference of the flexible CCS technology vs. fixed described above is the introduction of the group parameter in the input and ceff slots. The group parameter is used to define a group of commodities with different capture rates. The ceff slot is used to define the efficiency loss when the alternative fuel is used (cinp2ginp) and the share of each commodity in the group to define the maximum capture rate (share.up).

ECOA_CCS_FL <- ECOA_CCS_FX |>
  update(
    name = "ECOA_CCS_FL",
    desc = "Coal power plant with flexible CCS",
    input = list(
      comm = c("COA", "COA0"),
      unit = c("GWh", "GWh"),
      group = c("i", "i"), # any group name
      combustion = c(1, 0) # 10% of emissions are not captured by CCS
    ),
    geff = list(
      group = "i",
      ginp2use = ECOA_prototype@ceff$cinp2use[1]
    ),
    ceff = list(
      comm = c("COA", "COA0"),
      cinp2ginp = c(1, .9), # efficiency loss when COA is used
      share.up = c(1, .9) # max share of each commodity in the group
    ),
    # auxiliary inputs/outputs does not require adjustment for CCS
    # since it is defined by CCS utilization (COA vs COA0)
    aeff = data.frame(
      acomm = c("CO2SINK", ECOA_prototype@aeff$acomm),
      comm = c("COA0", "COA", "COA", "COA"),
      cinp2ainp = c(COA@emis$emis, NA, NA, NA), # 90% capture rate
      cinp2aout = c(NA,  ECOA_prototype@aeff$cinp2aout)
    )
  )

draw(ECOA_CCS_FL)

This technology is designed to operate from 0% to 90% capture rate instead of fixed 90% in the fixed rate example.

Gas power plant with flexible CCS

The same approach can be applied to the gas technology.

GAS0 <- GAS |>
  update(
    name = "GAS0",
    desc = "Alias for GAS"
  )

ALIAS_GAS <- newTechnology(
  name = "ALIAS_GAS",
  desc = "Artificial technology to create an alias for GAS commodity",
  input = list(
    comm = "GAS",
    unit = "GWh",
    combustion = 0
  ),
  output = data.frame(
    comm = "GAS0",
    unit = "GWh"
  ), 
  cap2act = 1e12, # capable to convert GWh a year
  capacity = list(stock = 1), # pre-defined capacity to reduce the model dimension
  end = list(end = 2000) #
)
draw(ALIAS_GAS)


EGAS_CCS_FL <- EGAS_CCS_FX |>
  update(
    name = "EGAS_CCS_FL",
    desc = "Gas power plant with flexible CCS",
    input = list(
      comm = c("GAS", "GAS0"),
      unit = c("GWh", "GWh"),
      group = c("i", "i"), # any group name
      combustion = c(1, 0) # 10% of emissions are not captured by CCS
    ),
    geff = list(
      group = "i",
      ginp2use = EGAS_prototype@ceff$cinp2use[1]
    ),
    ceff = list(
      comm = c("GAS", "GAS0"),
      cinp2ginp = c(1, .9), # efficiency loss when COA is used
      share.up = c(1, .9) # max share of each commodity in the group
    ),
    aeff = data.frame(
      acomm = c("CO2SINK", EGAS_prototype@aeff$acomm),
      comm = c("GAS", "GAS", "GAS", "GAS"),
      cinp2ainp = c(GAS@emis$emis, NA, NA, NA), # 90% capture rate
      cinp2aout = c(NA,  EGAS_prototype@aeff$cinp2aout)
    )
  )

draw(EGAS_CCS_FL)

References

Carbon Capture, Utilization and Storage (CCUS) Policy Framework and its Deployment Mechanism in India Utilization and Storage (CCUS)
https://www.niti.gov.in/sites/default/files/2022-11/CCUS-Report.pdf