def test_years_active_extend3(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
scen.add_horizon(year=years, firstmodelyear=2010)
scen.add_set("year", [1992])
scen.add_par("duration_period", "1992", 2, "y")
scen.add_par("duration_period", "1995", 3, "y")
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=[20],
year_vtg=1990,
),
),
)
obs = scen.years_active("foo", "bar", 1990)
assert obs == [1990, 1992, 1995, 2000, 2005]
def test_years_active(test_mp):
test_mp.add_unit('year')
scen = Scenario(test_mp, *msg_args, version='new')
scen.add_set('node', 'foo')
scen.add_set('technology', 'bar')
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
# First period length is immaterial
duration = [1900, 5, 5, 5, 5, 10, 10]
scen.add_horizon({'year': years, 'firstmodelyear': years[-1]})
scen.add_par('duration_period',
pd.DataFrame(zip(years, duration), columns=['year', 'value']))
# 'bar' built in period '1995' with 25-year lifetime:
# - is constructed in 1991-01-01.
# - by 1995-12-31, has operated 5 years.
# - operates until 2015-12-31. This is within the period '2020'.
scen.add_par('technical_lifetime', pd.DataFrame(dict(
node_loc='foo',
technology='bar',
unit='year',
value=25,
year_vtg=years[1]), index=[0]))
result = scen.years_active('foo', 'bar', years[1])
# Correct return type
assert isinstance(years, list)
assert isinstance(years[0], int)
# Years 1995 through 2020
npt.assert_array_equal(result, years[1:-1])
def test_vintage_and_active_years(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
scen.add_horizon({'year': ['2000', '2010', '2020'],
'firstmodelyear': '2010'})
obs = scen.vintage_and_active_years()
exp = pd.DataFrame({'year_vtg': (2000, 2000, 2010, 2010, 2020),
'year_act': (2010, 2020, 2010, 2020, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
def test_add_horizon_repeat(test_mp, caplog):
"""add_horizon() does not handle scenarios with existing years."""
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
# Create a base scenario
scen.add_horizon([2010, 2020, 2030])
npt.assert_array_equal([10, 10, 10], scen.par("duration_period")["value"])
with pytest.raises(
ValueError,
match=r"Scenario has year=\[2010, 2020, 2030\] and related values",
):
scen.add_horizon([2015, 2020, 2025], firstmodelyear=2010)
def test_add_horizon(test_mp, args, kwargs, exp):
scen = Scenario(test_mp, **SCENARIO['dantzig'], version='new')
# Call completes successfully
if isinstance(args[0], dict):
with pytest.warns(
DeprecationWarning,
match=(r"dict\(\) argument to add_horizon\(\); use year= and "
"firstmodelyear=")):
scen.add_horizon(*args, **kwargs)
else:
scen.add_horizon(*args, **kwargs)
# Sets and parameters have the expected contents
npt.assert_array_equal(exp["year"], scen.set("year"))
npt.assert_array_equal(exp["fmy"], scen.cat("year", "firstmodelyear"))
npt.assert_array_equal(exp["dp"], scen.par("duration_period")["value"])
def test_years_active_extended2(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
# First period length is immaterial
duration = [1900, 5, 5, 5, 5, 10, 10]
scen.add_horizon(year=years, firstmodelyear=years[-1])
scen.add_par(
"duration_period", pd.DataFrame(zip(years, duration), columns=["year", "value"])
)
# 'bar' built in period '2020' with 10-year lifetime:
# - is constructed in 2011-01-01.
# - by 2020-12-31, has operated 10 years.
# - operates until 2020-12-31. This is within the period '2020'.
# The test ensures that the correct lifetime value is retrieved,
# i.e. the lifetime for the vintage 2020.
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=[20, 20, 20, 20, 20, 10, 10],
year_vtg=years,
),
),
)
result = scen.years_active("foo", "bar", years[-2])
# Correct return type
assert isinstance(result, list)
assert isinstance(result[0], int)
# Years 2020
npt.assert_array_equal(result, years[-2])
def test_vintage_and_active_years_with_lifetime(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
years = ['2000', '2010', '2020']
scen.add_horizon({'year': years,
'firstmodelyear': '2010'})
scen.add_set('node', 'foo')
scen.add_set('technology', 'bar')
scen.add_par('duration_period', pd.DataFrame({
'unit': '???',
'value': 10,
'year': years
}))
scen.add_par('technical_lifetime', pd.DataFrame({
'node_loc': 'foo',
'technology': 'bar',
'unit': '???',
'value': 20,
'year_vtg': years,
}))
# part is before horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'))
exp = pd.DataFrame({'year_vtg': (2000,),
'year_act': (2010,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'),
in_horizon=False)
exp = pd.DataFrame({'year_vtg': (2000, 2000),
'year_act': (2000, 2010)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# fully in horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2010'))
exp = pd.DataFrame({'year_vtg': (2010, 2010),
'year_act': (2010, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# part after horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2020'))
exp = pd.DataFrame({'year_vtg': (2020,),
'year_act': (2020,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
def test_years_active(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
# First period length is immaterial
duration = [1900, 5, 5, 5, 5, 10, 10]
scen.add_horizon(year=years, firstmodelyear=years[-1])
scen.add_par(
"duration_period", pd.DataFrame(zip(years, duration), columns=["year", "value"])
)
# 'bar' built in period '1995' with 25-year lifetime:
# - is constructed in 1991-01-01.
# - by 1995-12-31, has operated 5 years.
# - operates until 2015-12-31. This is within the period '2020'.
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=25,
year_vtg=years[1],
),
index=[0],
),
)
result = scen.years_active("foo", "bar", years[1])
# Correct return type
assert isinstance(result, list)
assert isinstance(result[0], int)
# Years 1995 through 2020
npt.assert_array_equal(result, years[1:-1])
def test_clone(tmpdir):
# Two local platforms
mp1 = ixmp.Platform(tmpdir / 'mp1', dbtype='HSQLDB')
mp2 = ixmp.Platform(tmpdir / 'mp2', dbtype='HSQLDB')
# A minimal scenario
scen1 = Scenario(mp1, model='model', scenario='scenario', version='new')
scen1.add_spatial_sets({'country': 'Austria'})
scen1.add_set('technology', 'bar')
scen1.add_horizon({'year': [2010, 2020]})
scen1.commit('add minimal sets for testing')
assert len(mp1.scenario_list(default=False)) == 1
# Clone
scen2 = scen1.clone(platform=mp2)
# Return type of ixmp.Scenario.clone is message_ix.Scenario
assert isinstance(scen2, Scenario)
# Close and re-open both databases
mp1.close_db() # TODO this should be done automatically on del
mp2.close_db() # TODO this should be done automatically on del
del mp1, mp2
mp1 = ixmp.Platform(tmpdir / 'mp1', dbtype='HSQLDB')
mp2 = ixmp.Platform(tmpdir / 'mp2', dbtype='HSQLDB')
# Same scenarios present in each database
assert all(
mp1.scenario_list(default=False) == mp2.scenario_list(default=False))
# Load both scenarios
scen1 = Scenario(mp1, 'model', 'scenario')
scen2 = Scenario(mp2, 'model', 'scenario')
# Contents are identical
assert all(scen1.set('node') == scen2.set('node'))
assert all(scen1.set('year') == scen2.set('year'))
def test_clone(tmpdir):
# Two local platforms
mp1 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp1")
mp2 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp2")
# A minimal scenario
scen1 = Scenario(mp1, model="model", scenario="scenario", version="new")
scen1.add_spatial_sets({"country": "Austria"})
scen1.add_set("technology", "bar")
scen1.add_horizon(year=[2010, 2020])
scen1.commit("add minimal sets for testing")
assert len(mp1.scenario_list(default=False)) == 1
# Clone
scen2 = scen1.clone(platform=mp2)
# Return type of ixmp.Scenario.clone is message_ix.Scenario
assert isinstance(scen2, Scenario)
# Close and re-open both databases
mp1.close_db() # TODO this should be done automatically on del
mp2.close_db() # TODO this should be done automatically on del
del mp1, mp2
mp1 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp1")
mp2 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp2")
# Same scenarios present in each database
assert all(
mp1.scenario_list(default=False) == mp2.scenario_list(default=False))
# Load both scenarios
scen1 = Scenario(mp1, "model", "scenario")
scen2 = Scenario(mp2, "model", "scenario")
# Contents are identical
assert all(scen1.set("node") == scen2.set("node"))
assert all(scen1.set("year") == scen2.set("year"))
def test_vintage_and_active_years(test_mp):
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
years = [2000, 2010, 2020]
scen.add_horizon(year=years, firstmodelyear=2010)
obs = scen.vintage_and_active_years()
exp = pd.DataFrame(
{
"year_vtg": (2000, 2000, 2010, 2010, 2020),
"year_act": (2010, 2020, 2010, 2020, 2020),
}
)
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Add a technology, its lifetime, and period durations
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
scen.add_par(
"duration_period", pd.DataFrame({"unit": "???", "value": 10, "year": years})
)
scen.add_par(
"technical_lifetime",
pd.DataFrame(
{
"node_loc": "foo",
"technology": "bar",
"unit": "???",
"value": 20,
"year_vtg": years,
}
),
)
# part is before horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2000"))
exp = pd.DataFrame({"year_vtg": (2000,), "year_act": (2010,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
obs = scen.vintage_and_active_years(
ya_args=("foo", "bar", "2000"), in_horizon=False
)
exp = pd.DataFrame({"year_vtg": (2000, 2000), "year_act": (2000, 2010)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# fully in horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2010"))
exp = pd.DataFrame({"year_vtg": (2010, 2010), "year_act": (2010, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# part after horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2020"))
exp = pd.DataFrame({"year_vtg": (2020,), "year_act": (2020,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Advance the first model year
scen.add_cat("year", "firstmodelyear", years[-1], is_unique=True)
# Empty data frame: only 2000 and 2010 valid year_act for this node/tec;
# but both are before the first model year
obs = scen.vintage_and_active_years(
ya_args=("foo", "bar", years[0]), in_horizon=True
)
pdt.assert_frame_equal(pd.DataFrame(columns=["year_vtg", "year_act"]), obs)
# Exception is raised for incorrect arguments
with pytest.raises(ValueError, match="3 arguments are required if using `ya_args`"):
scen.vintage_and_active_years(ya_args=("foo", "bar"))
def make_westeros(mp, emissions=False, solve=False, quiet=True):
"""Return an :class:`message_ix.Scenario` for the Westeros model.
This is the same model used in the ``westeros_baseline.ipynb`` tutorial.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
emissions : bool, optional
If True, the ``emissions_factor`` parameter is also populated for CO2.
solve : bool, optional
If True, the scenario is solved.
"""
mp.add_unit("USD/kW")
mp.add_unit("tCO2/kWa")
scen = Scenario(mp, version="new", **SCENARIO["westeros"])
# Sets
history = [690]
model_horizon = [700, 710, 720]
scen.add_horizon(year=history + model_horizon,
firstmodelyear=model_horizon[0])
year_df = scen.vintage_and_active_years()
vintage_years, act_years = year_df["year_vtg"], year_df["year_act"]
country = "Westeros"
scen.add_spatial_sets({"country": country})
for name, values in (
("technology", ["coal_ppl", "wind_ppl", "grid", "bulb"]),
("mode", ["standard"]),
("level", ["secondary", "final", "useful"]),
("commodity", ["electricity", "light"]),
):
scen.add_set(name, values)
# Parameters — copy & paste from the tutorial notebook
common = dict(
mode="standard",
node_dest=country,
node_loc=country,
node_origin=country,
node=country,
time_dest="year",
time_origin="year",
time="year",
year_act=act_years,
year_vtg=vintage_years,
year=model_horizon,
)
gdp_profile = np.array([1.0, 1.5, 1.9])
demand_per_year = 40 * 12 * 1000 / 8760
scen.add_par(
"demand",
make_df(
"demand",
**common,
commodity="light",
level="useful",
# FIXME should use demand_per_year; requires adjustments elsewhere.
value=(100 * gdp_profile).round(),
unit="GWa",
),
)
grid_efficiency = 0.9
common.update(unit="-")
for name, tec, c, l, value in [
("input", "bulb", "electricity", "final", 1.0),
("output", "bulb", "light", "useful", 1.0),
("input", "grid", "electricity", "secondary", 1.0),
("output", "grid", "electricity", "final", grid_efficiency),
("output", "coal_ppl", "electricity", "secondary", 1.0),
("output", "wind_ppl", "electricity", "secondary", 1.0),
]:
scen.add_par(
name,
make_df(name,
**common,
technology=tec,
commodity=c,
level=l,
value=value),
)
# FIXME the value for wind_ppl should be 0.36; requires adjusting other tests.
name = "capacity_factor"
capacity_factor = dict(coal_ppl=1.0, wind_ppl=1.0, bulb=1.0)
for tec, value in capacity_factor.items():
scen.add_par(name, make_df(name, **common, technology=tec,
value=value))
name = "technical_lifetime"
common.update(year_vtg=model_horizon, unit="y")
for tec, value in dict(coal_ppl=20, wind_ppl=20, bulb=1).items():
scen.add_par(name, make_df(name, **common, technology=tec,
value=value))
name = "growth_activity_up"
common.update(year_act=model_horizon, unit="-")
for tec in "coal_ppl", "wind_ppl":
scen.add_par(name, make_df(name, **common, technology=tec, value=0.1))
historic_demand = 0.85 * demand_per_year
historic_generation = historic_demand / grid_efficiency
coal_fraction = 0.6
common.update(year_act=history, year_vtg=history, unit="GWa")
for tec, value in (
("coal_ppl", coal_fraction * historic_generation),
("wind_ppl", (1 - coal_fraction) * historic_generation),
):
name = "historical_activity"
scen.add_par(name, make_df(name, **common, technology=tec,
value=value))
# 20 year lifetime
name = "historical_new_capacity"
scen.add_par(
name,
make_df(
name,
**common,
technology=tec,
value=value / (2 * 10 * capacity_factor[tec]),
),
)
name = "interestrate"
scen.add_par(name, make_df(name, year=model_horizon, value=0.05, unit="-"))
for name, tec, value in [
("inv_cost", "coal_ppl", 500),
("inv_cost", "wind_ppl", 1500),
("inv_cost", "bulb", 5),
("fix_cost", "coal_ppl", 30),
("fix_cost", "wind_ppl", 10),
("var_cost", "coal_ppl", 30),
("var_cost", "grid", 50),
]:
common.update(
dict(year_vtg=model_horizon, unit="USD/kW") if name ==
"inv_cost" else dict(
year_vtg=vintage_years, year_act=act_years, unit="USD/kWa"))
scen.add_par(name, make_df(name, **common, technology=tec,
value=value))
scen.commit("basic model of Westerosi electrification")
scen.set_as_default()
if emissions:
scen.check_out()
# Introduce the emission species CO2 and the emission category GHG
scen.add_set("emission", "CO2")
scen.add_cat("emission", "GHG", "CO2")
# we now add CO2 emissions to the coal powerplant
name = "emission_factor"
common.update(year_vtg=vintage_years,
year_act=act_years,
unit="tCO2/kWa")
scen.add_par(
name,
make_df(name,
**common,
technology="coal_ppl",
emission="CO2",
value=100.0),
)
scen.commit("Added emissions sets/params to Westeros model.")
if solve:
scen.solve(quiet=quiet)
return scen
def make_dantzig(mp, solve=False, multi_year=False, **solve_opts):
"""Return an :class:`message_ix.Scenario` for Dantzig's canning problem.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
solve : bool, optional
If True, the scenario is solved.
multi_year : bool, optional
If True, the scenario has years 1963--1965 inclusive. Otherwise, the
scenario has the single year 1963.
"""
# add custom units and region for timeseries data
mp.add_unit("USD/case")
mp.add_unit("case")
mp.add_region("DantzigLand", "country")
# initialize a new (empty) instance of an `ixmp.Scenario`
scen = Scenario(
mp,
model=SCENARIO["dantzig"]["model"],
scenario="multi-year" if multi_year else "standard",
annotation="Dantzig's canning problem as a MESSAGE-scheme Scenario",
version="new",
)
# Sets
# NB commit() is refused if technology and year are not given
t = ["canning_plant", "transport_from_seattle", "transport_from_san-diego"]
sets = {
"technology": t,
"node": "seattle san-diego new-york chicago topeka".split(),
"mode": "production to_new-york to_chicago to_topeka".split(),
"level": "supply consumption".split(),
"commodity": ["cases"],
}
for name, values in sets.items():
scen.add_set(name, values)
scen.add_horizon(year=[1962, 1963], firstmodelyear=1963)
# Parameters
par = {}
# Common values
common = dict(
commodity="cases",
year=1963,
year_vtg=1963,
year_act=1963,
time="year",
time_dest="year",
time_origin="year",
)
par["demand"] = make_df(
"demand",
**common,
node=["new-york", "chicago", "topeka"],
level="consumption",
value=[325, 300, 275],
unit="case",
)
par["bound_activity_up"] = make_df(
"bound_activity_up",
**common,
node_loc=["seattle", "san-diego"],
mode="production",
technology="canning_plant",
value=[350, 600],
unit="case",
)
par["ref_activity"] = par["bound_activity_up"].copy()
input = pd.DataFrame(
[
["to_new-york", "seattle", "seattle", t[1]],
["to_chicago", "seattle", "seattle", t[1]],
["to_topeka", "seattle", "seattle", t[1]],
["to_new-york", "san-diego", "san-diego", t[2]],
["to_chicago", "san-diego", "san-diego", t[2]],
["to_topeka", "san-diego", "san-diego", t[2]],
],
columns=["mode", "node_loc", "node_origin", "technology"],
)
par["input"] = make_df(
"input",
**input,
**common,
level="supply",
value=1,
unit="case",
)
output = pd.DataFrame(
[
["supply", "production", "seattle", "seattle", t[0]],
["supply", "production", "san-diego", "san-diego", t[0]],
["consumption", "to_new-york", "new-york", "seattle", t[1]],
["consumption", "to_chicago", "chicago", "seattle", t[1]],
["consumption", "to_topeka", "topeka", "seattle", t[1]],
["consumption", "to_new-york", "new-york", "san-diego", t[2]],
["consumption", "to_chicago", "chicago", "san-diego", t[2]],
["consumption", "to_topeka", "topeka", "san-diego", t[2]],
],
columns=["level", "mode", "node_dest", "node_loc", "technology"],
)
par["output"] = make_df("output", **output, **common, value=1, unit="case")
# Variable cost: cost per kilometre × distance (neither parametrized
# explicitly)
var_cost = pd.DataFrame(
[
["to_new-york", "seattle", "transport_from_seattle", 0.225],
["to_chicago", "seattle", "transport_from_seattle", 0.153],
["to_topeka", "seattle", "transport_from_seattle", 0.162],
["to_new-york", "san-diego", "transport_from_san-diego", 0.225],
["to_chicago", "san-diego", "transport_from_san-diego", 0.162],
["to_topeka", "san-diego", "transport_from_san-diego", 0.126],
],
columns=["mode", "node_loc", "technology", "value"],
)
par["var_cost"] = make_df("var_cost",
**var_cost,
**common,
unit="USD/case")
for name, value in par.items():
scen.add_par(name, value)
if multi_year:
scen.add_set("year", [1964, 1965])
scen.add_par("technical_lifetime", ["seattle", "canning_plant", 1964],
3, "y")
if solve:
# Always read one equation. Used by test_core.test_year_int.
scen.init_equ("COMMODITY_BALANCE_GT",
["node", "commodity", "level", "year", "time"])
solve_opts["equ_list"] = solve_opts.get("equ_list",
[]) + ["COMMODITY_BALANCE_GT"]
scen.commit("Created a MESSAGE-scheme version of the transport problem.")
scen.set_as_default()
if solve:
solve_opts.setdefault("quiet", True)
scen.solve(**solve_opts)
scen.check_out(timeseries_only=True)
scen.add_timeseries(HIST_DF, meta=True)
scen.add_timeseries(INP_DF)
scen.commit("Import Dantzig's transport problem for testing.")
return scen
def test_vintage_and_active_years(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
scen.add_horizon({'year': ['2010', '2020']})
exp = (('2010', '2010', '2020'), ('2010', '2020', '2020'))
obs = scen.vintage_and_active_years()
assert obs == exp
def test_vintage_and_active_years(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
years = [2000, 2010, 2020]
scen.add_horizon({'year': years, 'firstmodelyear': 2010})
obs = scen.vintage_and_active_years()
exp = pd.DataFrame({'year_vtg': (2000, 2000, 2010, 2010, 2020),
'year_act': (2010, 2020, 2010, 2020, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Add a technology, its lifetime, and period durations
scen.add_set('node', 'foo')
scen.add_set('technology', 'bar')
scen.add_par('duration_period', pd.DataFrame({
'unit': '???',
'value': 10,
'year': years
}))
scen.add_par('technical_lifetime', pd.DataFrame({
'node_loc': 'foo',
'technology': 'bar',
'unit': '???',
'value': 20,
'year_vtg': years,
}))
# part is before horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'))
exp = pd.DataFrame({'year_vtg': (2000,),
'year_act': (2010,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'),
in_horizon=False)
exp = pd.DataFrame({'year_vtg': (2000, 2000),
'year_act': (2000, 2010)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# fully in horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2010'))
exp = pd.DataFrame({'year_vtg': (2010, 2010),
'year_act': (2010, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# part after horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2020'))
exp = pd.DataFrame({'year_vtg': (2020,),
'year_act': (2020,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Advance the first model year
scen.add_cat('year', 'firstmodelyear', years[-1], is_unique=True)
# Empty data frame: only 2000 and 2010 valid year_act for this node/tec;
# but both are before the first model year
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', years[0]),
in_horizon=True)
pdt.assert_frame_equal(
pd.DataFrame(columns=['year_vtg', 'year_act']),
obs)
# Exception is raised for incorrect arguments
with pytest.raises(ValueError,
match='3 arguments are required if using `ya_args`'):
scen.vintage_and_active_years(ya_args=('foo', 'bar'))
def make_westeros(mp, emissions=False, solve=False):
"""Return an :class:`message_ix.Scenario` for the Westeros model.
This is the same model used in the ``westeros_baseline.ipynb`` tutorial.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
emissions : bool, optional
If True, the ``emissions_factor`` parameter is also populated for CO2.
solve : bool, optional
If True, the scenario is solved.
"""
scen = Scenario(mp, version='new', **SCENARIO['westeros'])
# Sets
history = [690]
model_horizon = [700, 710, 720]
scen.add_horizon({
'year': history + model_horizon,
'firstmodelyear': model_horizon[0]
})
country = 'Westeros'
scen.add_spatial_sets({'country': country})
sets = {
'technology': 'coal_ppl wind_ppl grid bulb'.split(),
'mode': ['standard'],
'level': 'secondary final useful'.split(),
'commodity': 'electricity light'.split(),
}
for name, values in sets.items():
scen.add_set(name, values)
# Parameters — copy & paste from the tutorial notebook
gdp_profile = pd.Series([1., 1.5, 1.9],
index=pd.Index(model_horizon, name='Time'))
demand_per_year = 40 * 12 * 1000 / 8760
light_demand = pd.DataFrame({
'node': country,
'commodity': 'light',
'level': 'useful',
'year': model_horizon,
'time': 'year',
'value': (100 * gdp_profile).round(),
'unit': 'GWa',
})
scen.add_par("demand", light_demand)
year_df = scen.vintage_and_active_years()
vintage_years, act_years = year_df['year_vtg'], year_df['year_act']
base = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'time': 'year',
'unit': '-',
}
base_input = make_df(base, node_origin=country, time_origin='year')
base_output = make_df(base, node_dest=country, time_dest='year')
bulb_out = make_df(base_output,
technology='bulb',
commodity='light',
level='useful',
value=1.0)
scen.add_par('output', bulb_out)
bulb_in = make_df(base_input,
technology='bulb',
commodity='electricity',
level='final',
value=1.0)
scen.add_par('input', bulb_in)
grid_efficiency = 0.9
grid_out = make_df(base_output,
technology='grid',
commodity='electricity',
level='final',
value=grid_efficiency)
scen.add_par('output', grid_out)
grid_in = make_df(base_input,
technology='grid',
commodity='electricity',
level='secondary',
value=1.0)
scen.add_par('input', grid_in)
coal_out = make_df(base_output,
technology='coal_ppl',
commodity='electricity',
level='secondary',
value=1.)
scen.add_par('output', coal_out)
wind_out = make_df(base_output,
technology='wind_ppl',
commodity='electricity',
level='secondary',
value=1.)
scen.add_par('output', wind_out)
base_capacity_factor = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'time': 'year',
'unit': '-',
}
capacity_factor = {
'coal_ppl': 1,
'wind_ppl': 1,
'bulb': 1,
}
for tec, val in capacity_factor.items():
df = make_df(base_capacity_factor, technology=tec, value=val)
scen.add_par('capacity_factor', df)
base_technical_lifetime = {
'node_loc': country,
'year_vtg': model_horizon,
'unit': 'y',
}
lifetime = {
'coal_ppl': 20,
'wind_ppl': 20,
'bulb': 1,
}
for tec, val in lifetime.items():
df = make_df(base_technical_lifetime, technology=tec, value=val)
scen.add_par('technical_lifetime', df)
base_growth = {
'node_loc': country,
'year_act': model_horizon,
'time': 'year',
'unit': '-',
}
growth_technologies = [
"coal_ppl",
"wind_ppl",
]
for tec in growth_technologies:
df = make_df(base_growth, technology=tec, value=0.1)
scen.add_par('growth_activity_up', df)
historic_demand = 0.85 * demand_per_year
historic_generation = historic_demand / grid_efficiency
coal_fraction = 0.6
base_capacity = {
'node_loc': country,
'year_vtg': history,
'unit': 'GWa',
}
base_activity = {
'node_loc': country,
'year_act': history,
'mode': 'standard',
'time': 'year',
'unit': 'GWa',
}
old_activity = {
'coal_ppl': coal_fraction * historic_generation,
'wind_ppl': (1 - coal_fraction) * historic_generation,
}
for tec, val in old_activity.items():
df = make_df(base_activity, technology=tec, value=val)
scen.add_par('historical_activity', df)
act_to_cap = {
# 20 year lifetime
'coal_ppl': 1 / 10 / capacity_factor['coal_ppl'] / 2,
'wind_ppl': 1 / 10 / capacity_factor['wind_ppl'] / 2,
}
for tec in act_to_cap:
value = old_activity[tec] * act_to_cap[tec]
df = make_df(base_capacity, technology=tec, value=value)
scen.add_par('historical_new_capacity', df)
rate = [0.05] * len(model_horizon)
unit = ['-'] * len(model_horizon)
scen.add_par("interestrate", model_horizon, rate, unit)
base_inv_cost = {
'node_loc': country,
'year_vtg': model_horizon,
'unit': 'USD/GWa',
}
# in $ / kW
costs = {
'coal_ppl': 500,
'wind_ppl': 1500,
'bulb': 5,
}
for tec, val in costs.items():
df = make_df(base_inv_cost, technology=tec, value=val)
scen.add_par('inv_cost', df)
base_fix_cost = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'unit': 'USD/GWa',
}
# in $ / kW
costs = {
'coal_ppl': 30,
'wind_ppl': 10,
}
for tec, val in costs.items():
df = make_df(base_fix_cost, technology=tec, value=val)
scen.add_par('fix_cost', df)
base_var_cost = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'time': 'year',
'unit': 'USD/GWa',
}
# in $ / MWh
costs = {
'coal_ppl': 30,
'grid': 50,
}
for tec, val in costs.items():
df = make_df(base_var_cost, technology=tec, value=val)
scen.add_par('var_cost', df)
scen.commit('basic model of Westerosi electrification')
scen.set_as_default()
if emissions:
scen.check_out()
# Introduce the emission species CO2 and the emission category GHG
scen.add_set('emission', 'CO2')
scen.add_cat('emission', 'GHG', 'CO2')
# we now add CO2 emissions to the coal powerplant
base_emission_factor = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'unit': 'USD/GWa',
}
emission_factor = make_df(base_emission_factor,
technology='coal_ppl',
emission='CO2',
value=100.)
scen.add_par('emission_factor', emission_factor)
scen.commit('Added emissions sets/params to Westeros model.')
if solve:
scen.solve()
return scen
def make_dantzig(mp, solve=False, multi_year=False, **solve_opts):
"""Return an :class:`message_ix.Scenario` for Dantzig's canning problem.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
solve : bool, optional
If True, the scenario is solved.
multi_year : bool, optional
If True, the scenario has years 1963--1965 inclusive. Otherwise, the
scenario has the single year 1963.
"""
# add custom units and region for timeseries data
mp.add_unit('USD/case')
mp.add_unit('case')
mp.add_region('DantzigLand', 'country')
# initialize a new (empty) instance of an `ixmp.Scenario`
scen = Scenario(
mp,
model=SCENARIO['dantzig']['model'],
scenario='multi-year' if multi_year else 'standard',
annotation="Dantzig's canning problem as a MESSAGE-scheme Scenario",
version='new')
# Sets
# NB commit() is refused if technology and year are not given
t = ['canning_plant', 'transport_from_seattle', 'transport_from_san-diego']
sets = {
'technology': t,
'node': 'seattle san-diego new-york chicago topeka'.split(),
'mode': 'production to_new-york to_chicago to_topeka'.split(),
'level': 'supply consumption'.split(),
'commodity': ['cases'],
}
for name, values in sets.items():
scen.add_set(name, values)
scen.add_horizon({'year': [1962, 1963], 'firstmodelyear': 1963})
# Parameters
par = {}
demand = {
'node': 'new-york chicago topeka'.split(),
'value': [325, 300, 275]
}
par['demand'] = make_df(pd.DataFrame.from_dict(demand),
commodity='cases',
level='consumption',
time='year',
unit='case',
year=1963)
b_a_u = {'node_loc': ['seattle', 'san-diego'], 'value': [350, 600]}
par['bound_activity_up'] = make_df(pd.DataFrame.from_dict(b_a_u),
mode='production',
technology='canning_plant',
time='year',
unit='case',
year_act=1963)
par['ref_activity'] = par['bound_activity_up'].copy()
input = pd.DataFrame(
[
['to_new-york', 'seattle', 'seattle', t[1]],
['to_chicago', 'seattle', 'seattle', t[1]],
['to_topeka', 'seattle', 'seattle', t[1]],
['to_new-york', 'san-diego', 'san-diego', t[2]],
['to_chicago', 'san-diego', 'san-diego', t[2]],
['to_topeka', 'san-diego', 'san-diego', t[2]],
],
columns=['mode', 'node_loc', 'node_origin', 'technology'])
par['input'] = make_df(input,
commodity='cases',
level='supply',
time='year',
time_origin='year',
unit='case',
value=1,
year_act=1963,
year_vtg=1963)
output = pd.DataFrame(
[
['supply', 'production', 'seattle', 'seattle', t[0]],
['supply', 'production', 'san-diego', 'san-diego', t[0]],
['consumption', 'to_new-york', 'new-york', 'seattle', t[1]],
['consumption', 'to_chicago', 'chicago', 'seattle', t[1]],
['consumption', 'to_topeka', 'topeka', 'seattle', t[1]],
['consumption', 'to_new-york', 'new-york', 'san-diego', t[2]],
['consumption', 'to_chicago', 'chicago', 'san-diego', t[2]],
['consumption', 'to_topeka', 'topeka', 'san-diego', t[2]],
],
columns=['level', 'mode', 'node_dest', 'node_loc', 'technology'])
par['output'] = make_df(output,
commodity='cases',
time='year',
time_dest='year',
unit='case',
value=1,
year_act=1963,
year_vtg=1963)
# Variable cost: cost per kilometre × distance (neither parametrized
# explicitly)
var_cost = pd.DataFrame(
[
['to_new-york', 'seattle', 'transport_from_seattle', 0.225],
['to_chicago', 'seattle', 'transport_from_seattle', 0.153],
['to_topeka', 'seattle', 'transport_from_seattle', 0.162],
['to_new-york', 'san-diego', 'transport_from_san-diego', 0.225],
['to_chicago', 'san-diego', 'transport_from_san-diego', 0.162],
['to_topeka', 'san-diego', 'transport_from_san-diego', 0.126],
],
columns=['mode', 'node_loc', 'technology', 'value'])
par['var_cost'] = make_df(var_cost,
time='year',
unit='USD/case',
year_act=1963,
year_vtg=1963)
for name, value in par.items():
scen.add_par(name, value)
if multi_year:
scen.add_set('year', [1964, 1965])
scen.add_par('technical_lifetime', ['seattle', 'canning_plant', 1964],
3, 'y')
if solve:
# Always read one equation. Used by test_core.test_year_int.
scen.init_equ('COMMODITY_BALANCE_GT',
['node', 'commodity', 'level', 'year', 'time'])
solve_opts['equ_list'] = solve_opts.get('equ_list', []) \
+ ['COMMODITY_BALANCE_GT']
scen.commit('Created a MESSAGE-scheme version of the transport problem.')
scen.set_as_default()
if solve:
scen.solve(**solve_opts)
scen.check_out(timeseries_only=True)
scen.add_timeseries(HIST_DF, meta=True)
scen.add_timeseries(INP_DF)
scen.commit("Import Dantzig's transport problem for testing.")
return scen
def make_austria(mp, solve=False, quiet=True):
"""Return an :class:`message_ix.Scenario` for the Austrian energy system.
This is the same model used in the ``austria.ipynb`` tutorial.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
solve : bool, optional
If True, the scenario is solved.
"""
mp.add_unit("USD/kW")
mp.add_unit("MtCO2")
mp.add_unit("tCO2/kWa")
scen = Scenario(
mp,
version="new",
**SCENARIO["austria"],
annotation=
"A stylized energy system model for illustration and testing",
)
# Structure
year = dict(all=list(range(2010, 2041, 10)))
scen.add_horizon(year=year["all"])
year_df = scen.vintage_and_active_years()
year["vtg"] = year_df["year_vtg"]
year["act"] = year_df["year_act"]
country = "Austria"
scen.add_spatial_sets({"country": country})
sets = dict(
commodity=["electricity", "light", "other_electricity"],
emission=["CO2"],
level=["secondary", "final", "useful"],
mode=["standard"],
)
sets["technology"] = AUSTRIA_TECH.index.to_list()
plants = sets["technology"][:7]
lights = sets["technology"][10:]
for name, values in sets.items():
scen.add_set(name, values)
scen.add_cat("emission", "GHGs", "CO2")
# Parameters
name = "interestrate"
scen.add_par(name, make_df(name, year=year["all"], value=0.05, unit="-"))
common = dict(
mode="standard",
node_dest=country,
node_loc=country,
node_origin=country,
node=country,
time_dest="year",
time_origin="year",
time="year",
year_act=year["act"],
year_vtg=year["vtg"],
year=year["all"],
)
gdp_profile = np.array([1.0, 1.21631, 1.4108, 1.63746])
beta = 0.7
demand_profile = gdp_profile**beta
# From IEA statistics, in GW·h, converted to GW·a
base_annual_demand = dict(other_electricity=55209.0 / 8760,
light=6134.0 / 8760)
name = "demand"
common.update(level="useful", unit="GWa")
for c, base in base_annual_demand.items():
scen.add_par(
name,
make_df(name, **common, commodity=c, value=base * demand_profile))
common.pop("level")
# input, output
common.update(unit="-")
for name, (tec, info) in product(("input", "output"),
AUSTRIA_TECH.iterrows()):
value = info[f"{name}_value"]
if np.isnan(value):
continue
scen.add_par(
name,
make_df(
name,
**common,
technology=tec,
commodity=info[f"{name}_commodity"],
level=info[f"{name}_level"],
value=value,
),
)
data = AUSTRIA_PAR
# Convert GW·h to GW·a
data["activity"] = data["activity"] / 8760.0
# Convert USD / MW·h to USD / GW·a
data["var_cost"] = data["var_cost"] * 8760.0 / 1e3
# Convert t / MW·h to t / kw·a
data["emission_factor"] = data["emission_factor"] * 8760.0 / 1e3
def _add():
"""Add using values from the calling scope."""
scen.add_par(name, make_df(name, **common, technology=tec,
value=value))
name = "capacity_factor"
for tec, value in data[name].dropna().items():
_add()
name = "technical_lifetime"
common.update(year_vtg=year["all"], unit="y")
for tec, value in data[name].dropna().items():
_add()
name = "growth_activity_up"
common.update(year_act=year["all"][1:], unit="%")
value = 0.05
for tec in plants + lights:
_add()
name = "initial_activity_up"
common.update(year_act=year["all"][1:], unit="%")
value = 0.01 * base_annual_demand["light"] * demand_profile[1:]
for tec in lights:
_add()
# bound_activity_lo, bound_activity_up
common.update(year_act=year["all"][0], unit="GWa")
for (tec, value), kind in product(data["activity"].dropna().items(),
("up", "lo")):
name = f"bound_activity_{kind}"
_add()
name = "bound_activity_up"
common.update(year_act=year["all"][1:])
for tec in ("bio_ppl", "hydro_ppl", "import"):
value = data.loc[tec, "activity"]
_add()
name = "bound_new_capacity_up"
common.update(year_vtg=year["all"][0], unit="GW")
for tec, value in (data["activity"] /
data["capacity_factor"]).dropna().items():
_add()
name = "inv_cost"
common.update(dict(year_vtg=year["all"], unit="USD/kW"))
for tec, value in data[name].dropna().items():
_add()
# fix_cost, var_cost
common.update(
dict(year_vtg=year["vtg"], year_act=year["act"], unit="USD/kWa"))
for name in ("fix_cost", "var_cost"):
for tec, value in data[name].dropna().items():
_add()
name = "emission_factor"
common.update(year_vtg=year["vtg"],
year_act=year["act"],
unit="tCO2/kWa",
emission="CO2")
for tec, value in data[name].dropna().items():
_add()
scen.commit("Initial commit for Austria model")
scen.set_as_default()
if solve:
scen.solve(quiet=quiet)
return scen
