UTOPIA I: building the model

2026-07-05

Overview

UTOPIA is a teaching model for energy-system optimization – a small Reference Energy System (RES) mapping primary resources through conversion technologies to electricity demand. This vignette builds every block explicitly, one constructor call per object, so you can see exactly how a model is assembled – and ends by packing the blocks into a repository and a solvable model. The companion vignette UTOPIA II: running scenarios loads a ready-made kit ([utopia_modules], built by these same steps) and solves it.

All inputs are deterministic and shipped with the package.

library(energyRt)
library(dplyr)
library(ggplot2)
have_sf <- requireNamespace("sf", quietly = TRUE)   # region maps need the sf package
set_default_solver(solver_options$glpk)   # used by levcost() screening below

Units. Capacity in GW, energy/activity in PJ, costs in MEUR, emissions in kt; cap2act = 31.536 (1 GW x 8760 h = 31.536 PJ). Cost figures are rounded, catalogue-range values (Danish Energy Agency / NREL ATB): capex in EUR/kW (= MEUR/GW via convert()), fixed O&M in EUR/kW/yr, fuel in EUR/GJ (= MEUR/PJ).

Conventions

A few naming rules keep a model portable and readable. They matter because the same model is exported to several backends with different case-sensitivity: GAMS is case-insensitive, while GLPK/MathProg, Python/Pyomo and Julia/JuMP are case-sensitive. A model that relies on case to distinguish names would solve on one backend and break (or silently merge names) on another.

• Set elements are UPPER-CASE. Every name that appears as a set element – regions, commodities, technologies, groups, slices you define – is written in capitals (R1, COA, ECOA, FUEL). This is the rule that guarantees identical behaviour across backends.

• Process names carry a type prefix, so the role is obvious at a glance and names never collide across process families:

  Prefix	Process family
  SUP_	supply of a fuel / primary energy (SUP_COA, SUP_GAS)
  RES_	supply of a renewable resource (RES_SOL, RES_WIN, RES_HYD)
  STG_	storage (STG_ELC)
  TRD_	inter-regional trade (uni-directional)
  TBD_	inter-regional trade (bi-directional, TBD_ELC_R1_R2)
  IMP_	import from the rest of the world
  EXP_	export to the rest of the world

  Technologies are the conversion processes and take no fixed prefix – just an upper-case mnemonic (ECOA = electricity from coal, ESOL = electricity from solar).

We build every object below to these conventions – including inter-regional transmission (TBD_) and rest-of-world fuel imports / electricity exports (IMP_/EXP_).

Regions and the map

UTOPIA is a small multi-region country. The package ships four 11-region map layouts in utopia$map (squares, honeycomb, island, continent) — all share the region names R1…R11, so a model built for one layout draws over any of them. Pick whichever suits the story:

library(sf)
op <- par(mfrow = c(2, 2), mar = c(0.5, 0.5, 2, 0.5))
for (nm in names(utopia$map)) {
  m <- utopia$map[[nm]]
  plot(st_geometry(m), col = hcl.colors(nrow(m), "Set 3"), border = "white", main = nm)
  text(m$x, m$y, m$region, cex = 0.75)
}
par(op)

This vignette builds a three-region model on the first three regions (R1, R2, R3) — we draw the transmission network over the honeycomb layout below.

Time resolution and inputs

We pick a ready calendar – utopia_s4h24, four seasons x 24 hours (96 slices); calendar construction is covered in the time-resolution article. Region names are upper-case (R1, R2, R3), matching the region names in the UTOPIA maps. Deterministic capacity-factor and load profiles come from [utopia_profiles()].

regions <- c("R1", "R2", "R3")
cal  <- calendars$utopia_s4h24
prof <- utopia_profiles(regions, calendar = "utopia_s4h24")
str(prof, max.level = 1)

Commodities

Energy carriers, the three renewable resource carriers (SOL/WIN/HYD) and the CO2 emission commodity. Fossil fuels carry an emission factor (emis); biomass is carbon-neutral. All names are upper-case set elements.

COA <- newCommodity(
  name = "COA",
  desc = "Steam coal",
  timeframe = "ANNUAL",
  emis = data.frame(comm = "CO2", unit = "kt/PJ", emis = 95)
)
GAS <- newCommodity(
  name = "GAS",
  desc = "Natural gas",
  timeframe = "ANNUAL",
  emis = data.frame(comm = "CO2", unit = "kt/PJ", emis = 56)
)
BIO <- newCommodity(
  name = "BIO",
  desc = "Solid biomass (carbon-neutral)",
  timeframe = "ANNUAL"
)
NUC <- newCommodity(
  name = "NUC",
  desc = "Nuclear fuel",
  timeframe = "ANNUAL"
)
SOL <- newCommodity(
  name = "SOL",
  desc = "Solar irradiation",
  timeframe = "HOUR"
)
WIN <- newCommodity(
  name = "WIN",
  desc = "Wind resource",
  timeframe = "HOUR"
)
HYD <- newCommodity(
  name = "HYD",
  desc = "Hydro inflow",
  timeframe = "HOUR"
)
ELC <- newCommodity(
  name = "ELC",
  desc = "Electricity",
  timeframe = "HOUR"
)
CO2 <- newCommodity(
  name = "CO2",
  desc = "Carbon dioxide emissions",
  timeframe = "ANNUAL"
)

The emission intensities we just declared, compared:

autoplot(COA, GAS)

Supply and resources

Fuel supplies (SUP_*) are priced in EUR/GJ (= MEUR/PJ); renewable resources (RES_*) are free and weather-limited.

Endowments differ by region – that is what makes a multi-region model interesting: R1 mines coal, R2 produces gas, R3 has the hydro. Regions without a domestic fuel must import it (next section) or buy electricity over the grid – regional diversity stimulates trade. The region column of availability is where an endowment lives. The first two supplies in full:

SUP_COA <- newSupply(
  name = "SUP_COA",
  desc = "Coal supply (mined in R1)",
  commodity = "COA",
  unit = "PJ",
  availability = data.frame(
    region = "R1",                        # coal exists only in R1
    cost = 2.5
  )
)
SUP_GAS <- newSupply(
  name = "SUP_GAS",
  desc = "Natural gas supply (produced in R2)",
  commodity = "GAS",
  unit = "PJ",
  availability = data.frame(
    region = "R2",                        # gas exists only in R2
    cost = 6.0
  )
)

The remaining five differ only in name, commodity, price and region set. When a family of similar objects is needed, wrapping the constructor in a small helper is the idiomatic advanced pattern (the packaged kit is built exactly this way):

sup <- function(nm, comm, cost, reg = regions) newSupply(nm, commodity = comm,
  availability = data.frame(region = reg, cost = cost))

SUP_BIO <- sup("SUP_BIO", "BIO", 8.0)              # biomass: everywhere
SUP_NUC <- sup("SUP_NUC", "NUC", 0.9)              # nuclear fuel: world market
RES_SOL <- sup("RES_SOL", "SOL", 0)                # sun shines everywhere...
RES_WIN <- sup("RES_WIN", "WIN", 0)                # ...wind blows everywhere
RES_HYD <- sup("RES_HYD", "HYD", 0, reg = "R3")    # hydro inflow: only R3

Rest-of-world trade: imports and exports

Any region can buy fuel from the rest of the world – at a premium over the domestic source. IMP_* objects add an external supply at a price; EXP_* objects add an external market that buys at a price (capped, so exports stay a side business, not the objective):

IMP_COA <- newImport(
  name = "IMP_COA",
  desc = "Coal import from the rest of the world",
  commodity = "COA",
  unit = "PJ",
  imp = data.frame(
    region = regions,                     # available to every region
    price = 3.5                           # vs 2.5 domestic in R1
  )
)
IMP_GAS <- update(IMP_COA,
  name = "IMP_GAS",
  desc = "LNG import from the rest of the world",
  commodity = "GAS",
  imp = data.frame(
    region = regions,
    price = 9.0                           # vs 6.0 domestic in R2
  )
)

EXP_ELC <- newExport(
  name = "EXP_ELC",
  desc = "Electricity export to the rest of the world",
  commodity = "ELC",
  unit = "PJ",
  exp = merge(                            # ~10 PJ/yr per region, paced by slice
    data.frame(region = regions, price = 5.0),
    data.frame(slice  = as.data.frame(cal@slice_share)$slice,
               exp.up = 10 * as.data.frame(cal@slice_share)$share)
  ),
  reserve = 300                           # plus a cumulative cap, PJ
)

The export price sits below every technology’s levelized cost, so the model never builds capacity just to export – it only sells genuine surplus. Two caps work together: exp.up paces exports (the annual 10 PJ is spread over the slices via their year-shares – a bare exp.up = 10 would be read as 10 per slice), and reserve bounds the horizon total. Without the pacing, the model front-loads the whole reserve into the base year, where the sunk fleet makes surplus cheapest.

Weather

newWeather objects hold a capacity factor per region and slice for solar, wind and hydro – the physical limits on the renewable technologies below. Solar in full, the siblings via a helper:

WSOL <- newWeather(
  name = "WSOL",
  desc = "Solar capacity factor",
  timeframe = "HOUR",
  weather = prof$weather[prof$weather$resource == "WSOL",
                         c("region", "slice", "wval")]
)

wobj <- function(res) newWeather(res, timeframe = "HOUR",
  weather = prof$weather[prof$weather$resource == res,
                         c("region", "slice", "wval")])
WWIN <- wobj("WWIN")
WHYD <- wobj("WHYD")

autoplot() shows the profile structure at a glance – the solar day/season shape. Custom season names (WIN, SPR, …) are defined by the calendar, so pass it to resolve the slice layout. Note the regional endowments (utopia_profiles() scales the profiles deterministically): R1 is the sunniest region, R2 the windiest – one more reason for the regions to trade:

autoplot(WSOL, type = "line", calendar = cal)

(UTOPIA’s profiles derive from the IDEEA model of India – note the monsoon signature: summer is the solar minimum, spring the maximum. Weather is data, not an assumption.)

Demand

Electricity demand follows the deterministic load shape, weighted by each slice’s share of the year and grown over the milestone years.

years  <- c(2020, 2030, 2040, 2050)
growth <- c(1, 1.2, 1.4, 1.6)
annual_demand <- 100                                   # base-year PJ per region

share <- as.data.frame(cal@slice_share)[, c("slice", "share")]
d0 <- merge(prof$demand, share, by = "slice")
d0$w <- d0$load * d0$share
dem_rows <- do.call(rbind, lapply(seq_along(years), function(i) {
  do.call(rbind, lapply(regions, function(r) {
    dr <- d0[d0$region == r, ]
    data.frame(region = r, year = years[i], slice = dr$slice,
               dem = annual_demand * growth[i] * dr$w / sum(dr$w))
  }))
}))

DEM_ELC <- newDemand(
  name = "DEM_ELC",
  desc = "Final electricity demand",
  commodity = "ELC",
  unit = "PJ",
  dem = dem_rows
)

Two views of the demand object: aggregated annual totals by region (type = "area", the default), and the within-year profiles by region and year (type = "line") – note the morning ramp and the evening peak:

autoplot(DEM_ELC, years = 2020:2050)

autoplot(DEM_ELC, type = "line", years = c(2020, 2050))

Technologies

Thermal plants combust a fuel (combustion = 1) and convert it to electricity at an efficiency; renewables convert a free resource capped by weather.

Two modeling choices run through all of them:

• Existing stock retires on a schedule. capacity$stock is declared not just for the base year but as a declining path: energyRt interpolates between the anchors, so the base-year fleet fades out naturally – faster for solar and wind (mid-life already), ~30 years for thermal plants, longer for nuclear, longest for hydro. optimizeRetirement = TRUE additionally lets the model retire capacity earlier when that saves money.
• Investment windows. start and end bound the years when new capacity can be built – coal is phase-limited, nuclear needs a lead time, hydro sites are nearly exhausted. We chart all the windows after assembling the model.

Coal power plant

ECOA <- newTechnology(
  name = "ECOA",
  desc = "Coal power plant",
  input = list(
    comm = "COA",
    unit = "PJ",
    combustion = 1
  ),
  output = list(
    comm = "ELC",
    unit = "PJ"
  ),
  cap2act = 31.536,
  ceff = data.frame(
    comm = "COA",
    cinp2use = 0.40                       # 40% efficiency
  ),
  invcost = list(
    invcost = convert("EUR/kW", "MEUR/GW", 2000)
  ),
  fixom = list(
    fixom = 55
  ),
  capacity = data.frame(                  # fleet clusters near the mines (R1)
    region = c("R1", "R2", "R3", "R1", "R2", "R3"),
    year   = c(2020, 2020, 2020, 2050, 2050, 2050),
    stock  = c(   8,    3,    3,    0,    0,    0)
  ),
  start = list(start = 2010),
  end   = list(end   = 2030),             # no new coal after 2030
  olife = list(olife = 30),
  optimizeRetirement = TRUE
)
draw(ECOA)

Gas power plant

The gas plant has the same structure – we spell it out once more (the last time; further siblings will use update()):

EGAS <- newTechnology(
  name = "EGAS",
  desc = "Gas power plant (CCGT)",
  input = list(
    comm = "GAS",
    unit = "PJ",
    combustion = 1
  ),
  output = list(
    comm = "ELC",
    unit = "PJ"
  ),
  cap2act = 31.536,
  ceff = data.frame(
    comm = "GAS",
    cinp2use = 0.58
  ),
  invcost = list(
    invcost = convert("EUR/kW", "MEUR/GW", 900)
  ),
  fixom = list(
    fixom = 25
  ),
  capacity = data.frame(                  # gas plants sit on the gas (R2)
    region = c("R1", "R2", "R3", "R1", "R2", "R3"),
    year   = c(2020, 2020, 2020, 2050, 2050, 2050),
    stock  = c(   1,    6,    1,    0,    0,    0)
  ),
  start = list(start = 2010),
  olife = list(olife = 30),
  optimizeRetirement = TRUE
)

Nuclear power plant

Nuclear is a must-run baseload (af.lo = 0.7), with a long-lived existing fleet (retiring by 2060) and a build window that opens only in 2025 (licensing and construction lead times):

ENUC <- newTechnology(
  name = "ENUC",
  desc = "Nuclear power plant",
  input = list(
    comm = "NUC",
    unit = "PJ"
  ),
  output = list(
    comm = "ELC",
    unit = "PJ"
  ),
  cap2act = 31.536,
  ceff = data.frame(
    comm = "NUC",
    cinp2use = 0.35
  ),
  af = data.frame(
    af.lo = 0.7                           # must-run baseload
  ),
  invcost = list(
    invcost = convert("EUR/kW", "MEUR/GW", 8000)   # recent Western builds
  ),
  fixom = list(
    fixom = 120
  ),
  capacity = data.frame(
    region = c("R1", "R2", "R3", "R1", "R2", "R3"),
    year   = c(2020, 2020, 2020, 2060, 2060, 2060),
    stock  = c(   2,    2,    2,    0,    0,    0)
  ),
  start = list(start = 2025),
  olife = list(olife = 50)
)

Renewables

VRE technologies draw a free resource and are bounded by the weather capacity factor (waf.up = 1 means output can reach the profile, never exceed it). Solar, in full:

ESOL <- newTechnology(
  name = "ESOL",
  desc = "Utility-scale solar PV",
  input = list(
    comm = "SOL",
    unit = "PJ"
  ),
  output = list(
    comm = "ELC",
    unit = "PJ"
  ),
  cap2act = 31.536,
  ceff = data.frame(
    comm = "SOL",
    cinp2use = 1
  ),
  weather = list(
    weather = "WSOL",
    comm = "SOL",
    waf.up = 1
  ),
  invcost = list(
    invcost = convert("EUR/kW", "MEUR/GW", 650)
  ),
  fixom = list(
    fixom = 12
  ),
  capacity = data.frame(                  # young but small fleet, out by 2040
    region = c("R1", "R2", "R3", "R1", "R2", "R3"),
    year   = c(2020, 2020, 2020, 2040, 2040, 2040),
    stock  = c(   1,    1,    1,    0,    0,    0)
  ),
  start = list(start = 2015),
  olife = list(olife = 25)
)

Wind and hydro share solar’s structure, so instead of retyping we derive them with update() – it takes an existing object and replaces only the slots you name. This is the package’s intended way to build families of similar processes:

EWIN <- update(ESOL,
  name = "EWIN",
  desc = "Onshore wind",
  input = list(comm = "WIN", unit = "PJ"),
  ceff = data.frame(comm = "WIN", cinp2use = 1),
  weather = list(weather = "WWIN", comm = "WIN", waf.up = 1),
  invcost = list(invcost = convert("EUR/kW", "MEUR/GW", 1300)),
  fixom = list(fixom = 35)
)

EHYD <- update(EWIN,
  name = "EHYD",
  desc = "Hydro power (only in R3, no new sites)",
  input = list(comm = "HYD", unit = "PJ"),
  ceff = data.frame(comm = "HYD", cinp2use = 1),
  weather = list(weather = "WHYD", comm = "HYD", waf.up = 1),
  invcost = list(invcost = convert("EUR/kW", "MEUR/GW", 3000)),
  fixom = list(fixom = 45),
  capacity = data.frame(                  # the hydro endowment sits in R3
    region = c("R3", "R3"),
    year   = c(2020, 2070),
    stock  = c(  12,    0)
  ),
  start = list(start = 2010),
  end   = list(end   = 2010),             # window closed: NO new hydro at all
  olife = list(olife = 60)
)
draw(EHYD)

Note how EWIN inherited solar’s capacity path and window untouched, while EHYD overrides them: the hydro fleet exists only in R3 (12 GW, operating into the 2070s) and its investment window is closed – the model can run the legacy dams but never build new ones. And since RES_HYD flows only in R3, the other regions can tap this cheap resource only over the transmission lines.

The biomass plant

EBIO burns biomass to make electricity. Biomass is carbon-neutral (the BIO commodity carries no emission factor), so EBIO is a dispatchable zero-carbon generator – more expensive to fuel than coal, but emitting nothing:

EBIO <- newTechnology(
  name = "EBIO",
  desc = "Biomass power plant (carbon-neutral)",
  input = list(
    comm = "BIO",
    unit = "PJ",
    combustion = 1
  ),
  output = list(
    comm = "ELC",
    unit = "PJ"
  ),
  ceff = data.frame(
    comm = "BIO",
    cinp2use = 0.35                       # 35% efficiency
  ),
  cap2act = 31.536,
  invcost = list(
    invcost = convert("EUR/kW", "MEUR/GW", 2200)
  ),
  fixom = list(
    fixom = 60
  ),
  start = list(start = 2025),             # a new option, available from 2025
  olife = list(olife = 30),
  optimizeRetirement = TRUE
)
draw(EBIO)

Its levelized cost (levcost() builds and solves a tiny single-technology model; annual commodities make the LCOE read in MEUR/PJ):

ELCa <- newCommodity("ELCa", timeframe = "ANNUAL")
EBIO_lc <- update(EBIO, output = list(comm = "ELCa"))
lc <- levcost(EBIO_lc, discount = 0.05, verbose = FALSE,
  repo = newRepository("r", BIO, CO2, ELCa),
  fuel_costs = c(BIO = 8.0))
lc$levcost_npv
autoplot(lc, type = "components")

Commodity groups (a fuel blend on the input side, or a product split on the output side) are covered structurally in the Model bricks article. They are left out of this solvable UTOPIA model pending a fix to how grouped technologies enforce their input-output balance.

Storage

A 4-hour battery (STG_*) shifts electricity between slices:

STG_ELC <- newStorage(
  name = "STG_ELC",
  desc = "Grid battery, 4-hour",
  commodity = "ELC",
  invcost = list(
    invcost = convert("EUR/kWh", "MEUR/PJ", 200)
  ),
  cap2stg = 4,                            # 4 hours of storage per GW
  seff = data.frame(
    inpeff = 0.95,
    outeff = 0.95                         # ~90% round trip
  ),
  olife = list(olife = 20)
)

Interregional trade

Trade objects open routes between regions for a commodity. UTOPIA’s three regions form a line, so two bi-directional transmission links (TBD_) connect them. Each newTrade lists its routes (both directions), the transfer efficiency (teff – losses), and an endogenous capacity with investment cost (capacityVariable = TRUE lets the model expand the line):

TBD_ELC_R1_R2 <- newTrade(
  name = "TBD_ELC_R1_R2",
  desc = "Bidirectional transmission line R1-R2",
  commodity = "ELC",
  routes = data.frame(
    src = c("R1", "R2"),
    dst = c("R2", "R1")
  ),
  trade = data.frame(
    src = c("R1", "R2"),
    dst = c("R2", "R1"),
    teff = 0.97                           # 3% losses per transfer
  ),
  capacity = data.frame(
    stock = 1                             # 1 GW existing interconnector
  ),
  capacityVariable = TRUE,                # the model may build more
  invcost = data.frame(
    region = c("R1", "R2"),
    invcost = 350                         # MEUR/GW (per line end)
  ),
  olife = list(olife = 50)
)

TBD_ELC_R2_R3 <- update(TBD_ELC_R1_R2,
  name = "TBD_ELC_R2_R3",
  desc = "Bidirectional transmission line R2-R3",
  routes = data.frame(
    src = c("R2", "R3"),
    dst = c("R3", "R2")
  ),
  trade = data.frame(
    src = c("R2", "R3"),
    dst = c("R3", "R2"),
    teff = 0.97
  ),
  invcost = data.frame(
    region = c("R2", "R3"),
    invcost = 350
  )
)
draw(TBD_ELC_R1_R2)

draw() shows one link’s schematic; autoplot() (or plot_trade_map()) puts the whole network on the map — pass the layout to draw over. Bidirectional links render as double-headed arrows:

plot_trade_map(list(TBD_ELC_R1_R2, TBD_ELC_R2_R3), map = utopia$map$honeycomb)

Scenario levers

Policies and pathways are pre-built objects layered onto the base model when running scenarios (mirroring IDEEA’s CO2_CAP etc.):

• CO2_CAP – a declining cap on CO2 emissions (newConstraint on vEmsFuelTot; starts near BASE emissions (~5,000 kt/region in 2020) and tightens to a deep cut by 2050);
• CT_CO2 – a rising carbon tax (newTax; costs are MEUR and emissions kt, so 20-80 EUR/t reads as 0.02-0.08 MEUR/kt – watch the units);
• RES_SHARE – a growing renewable-generation floor;
• NO_NEW_NUC – no new nuclear capacity;
• EARLY_RET – forced early retirement of the coal and gas fleets (a declining per-region ceiling on installed coal and gas capacity; optimizeRetirement = TRUE on the plants lets the model choose which units to close).

nreg <- length(regions)
CO2_CAP <- newConstraint(
  name = "CO2_CAP",
  eq = "<=",
  for.each = data.frame(year = years, comm = "CO2"),
  term1 = list(variable = "vEmsFuelTot"),
  rhs = data.frame(year = range(years), rhs = c(5000, 2500) * nreg),
  defVal = Inf
)

CT_CO2 <- newTax(
  name = "CT_CO2",
  comm = "CO2",
  tax = data.frame(year = range(years), bal = c(0.02, 0.08))  # 20-80 EUR/t
)

ren <- c("ESOL", "EWIN", "EHYD")
sh  <- seq(0.15, 0.50, length.out = length(years))
RES_SHARE <- newConstraint(
  name = "RES_SHARE",
  eq = ">=",
  for.each = data.frame(year = years, comm = "ELC"),
  term1 = list(variable = "vTechOut", for.sum = list(tech = ren)),
  rhs = data.frame(year = years, rhs = sh * annual_demand * growth * nreg),
  defVal = 0
)

NO_NEW_NUC <- newConstraint(
  name = "NO_NEW_NUC",
  eq = "<=",
  for.each = data.frame(year = years, tech = "ENUC"),
  term1 = list(variable = "vTechNewCap"),
  rhs = 0,
  defVal = 0
)

# vTechCap is indexed by region, so this ceiling applies region by region.
# for.each is the full year x tech grid (expand.grid, not a recycled
# data.frame); rhs sets a declining per-region ceiling. The binding effect is
# on GAS -- without it the model builds gas out as the flexible VRE backstop;
# coal already phases out on economics. A full fossil exit is infeasible here
# (must-run nuclear + limited transmission need firm capacity for peak), so the
# schedule keeps a backup floor. defVal = Inf leaves 2020 unbound.
EARLY_RET <- newConstraint(
  name = "EARLY_RET",
  eq = "<=",
  # scope to 2030+ only: adding the 2020 milestone (even unbound) makes the LP
  # infeasible through rhs interpolation.
  for.each = expand.grid(year = c(2030, 2040, 2050), tech = c("ECOA", "EGAS"),
                         stringsAsFactors = FALSE),
  term1 = list(variable = "vTechCap"),
  rhs = data.frame(
    tech = rep(c("ECOA", "EGAS"), each = 3),
    year = rep(c(2030, 2040, 2050), 2),
    rhs  = c(12, 10, 8,   7, 6, 5)     # coal & gas ceilings (GW/region), falling
  ),
  defVal = Inf
)

Assembling the model

Collect every block into a base repository, then bind it to a region set, a calendar and a horizon to get a solvable model:

repo <- newRepository("utopia",
  COA, GAS, BIO, NUC, SOL, WIN, HYD, ELC, CO2,          # commodities
  SUP_COA, SUP_GAS, SUP_BIO, SUP_NUC,                   # fuel supplies
  RES_SOL, RES_WIN, RES_HYD,                            # renewable resources
  IMP_COA, IMP_GAS, EXP_ELC,                            # rest-of-world trade
  WSOL, WWIN, WHYD,                                     # weather
  ECOA, EGAS, ENUC, ESOL, EWIN, EHYD, EBIO,             # technologies
  STG_ELC,                                              # storage
  TBD_ELC_R1_R2, TBD_ELC_R2_R3,                         # interregional trade
  DEM_ELC)                                              # demand
length(repo); names(repo)

hor <- newHorizon(period = 2020:2050, intervals = c(1, 10, 10, 10),
                  mid_is_end = TRUE, name = "base")
mod <- newModel("UTOPIA", data = repo, calendar = cal,
                region = regions, horizon = hor, discount = 0.05)
mod

Investment windows

The start/end choices above define when each process can be built; the translucent tails show how long the last vintage then operates (end + olife). One call charts the whole fleet:

autoplot(mod, type = "windows")

That is a complete UTOPIA model, ready for interpolate_model() + solve_scenario() – the subject of UTOPIA II.

Screening technology costs

With the model assembled we can compare technologies before solving. levcost(mod, name = ...) prices one technology by building and solving a tiny unit-demand model that inherits the model’s regions, calendar, discount rate and fuel supplies. By default it reports the annual levelized cost: a weather-driven technology’s profile is collapsed to its annual capacity factor, so capacity is sized to serve demand at that factor (textbook LCOE).

techs <- c("ECOA", "EGAS", "ENUC", "ESOL", "EWIN", "EHYD")
lcoe  <- sapply(techs, function(tn)
  as.numeric(levcost(mod, name = tn, verbose = FALSE)$levcost_npv))
round(sort(lcoe), 1)                       # MEUR/PJ  (x3.6 ~ EUR/MWh)

data.frame(tech = names(lcoe), lcoe = lcoe) |>
  ggplot(aes(reorder(tech, lcoe), lcoe, fill = tech)) +
  geom_col(show.legend = FALSE) + coord_flip() +
  labs(x = NULL, y = "levelized cost, MEUR/PJ",
       title = "A-priori technology cost (annual LCOE)") + theme_bw()

This a-priori screening ranks technologies by their stand-alone cost. It is not the same as their realized cost in a solved system – a plant that runs only a few hours spreads its fixed costs over little output, and a variable renewable’s value depends on when it produces. For that (timeframe = "native", or the ex-post cost from a solved scenario) see UTOPIA II.

Reports

Any technology, the whole model, or (after solving) a scenario can be rendered as a shareable HTML/PDF document – parameters, diagrams, cost breakdowns included. Not run here (reports open in the browser); try them in your session:

# one technology: datasheet with diagram, share frontier and levelized cost
report(EBIO, discount = 0.05,
       repo = newRepository("r", COA, BIO, CO2, ELCa),
       fuel_costs = c(COA = 2.5, BIO = 8.0))

# a single process, priced inside the assembled model
report(mod, name = "ENUC")

# the WHOLE model: configuration, inventory, every process one-by-one
report(mod)

# a solved scenario: results overview (see UTOPIA II)
# report(scen_BASE)

The packaged kit

The steps above are exactly what data-raw/utopia_modules.R runs for a few region layouts (1, 3 and 7 regions) to produce the shipped [utopia_modules] dataset – a ready base repository ($repo), the individual blocks, and the levers, alongside the calendars, horizons and maps:

names(utopia_modules)
names(utopia_modules$electricity)                 # kits by number of regions
um <- utopia_modules$electricity$reg3
identical(names(repo), names(um$repo))            # same blocks we just built

Continue with UTOPIA II: running scenarios, which loads this kit, assembles a model and solves the base case and policy scenarios.