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_years_active_extend(test_mp): scen = Scenario(test_mp, *msg_multiyear_args) scen = scen.clone(keep_solution=False) scen.check_out() scen.add_set('year', ['2040', '2050']) scen.add_par('duration_period', '2040', 10, 'y') scen.add_par('duration_period', '2050', 10, 'y') df = scen.years_active('seattle', 'canning_plant', '2020') npt.assert_array_equal(df, [2020, 2030, 2040]) scen.discard_changes()
def test_commodity_price_equality(test_mp): scen = Scenario(test_mp, "test_commodity_price", "equality", version="new") model_setup(scen, var_cost=-1) scen.commit("initialize test model with negative variable costs") # negative variable costs and supply >= demand causes an unbounded ray pytest.raises(CalledProcessError, scen.solve) # use the commodity-balance equality feature scen.check_out() scen.add_set("balance_equality", ["comm", "level"]) scen.commit("set commodity-balance for `[comm, level]` as equality") scen.solve(case="price_commodity_equality") assert scen.var("OBJ")["lvl"] == -1 assert scen.var("PRICE_COMMODITY")["lvl"][0] == -1
def test_commodity_price_equality(test_mp): scen = Scenario(test_mp, 'test_commodity_price', 'equality', version='new') model_setup(scen, var_cost=-1) scen.commit('initialize test model with negative variable costs') # negative variable costs and supply >= demand causes an unbounded ray pytest.raises(CalledProcessError, scen.solve) # use the commodity-balance equality feature scen.check_out() scen.add_set('balance_equality', ['comm', 'level']) scen.commit('set commodity-balance for `[comm, level]` as equality') scen.solve(case='price_commodity_equality') assert scen.var('OBJ')['lvl'] == -1 assert scen.var('PRICE_COMMODITY')['lvl'][0] == -1
def test_excel_read_write(message_test_mp, tmp_path): # Path to temporary file tmp_path /= 'excel_read_write.xlsx' # Convert to string to ensure this can be handled fname = str(tmp_path) scen1 = Scenario(message_test_mp, **SCENARIO['dantzig']) scen1 = scen1.clone(keep_solution=False) scen1.check_out() scen1.init_set('new_set') scen1.add_set('new_set', 'member') scen1.init_par('new_par', idx_sets=['new_set']) scen1.add_par('new_par', 'member', 2, '-') scen1.commit('new set and parameter added.') # Writing to Excel without solving scen1.to_excel(fname) # Writing to Excel when scenario has a solution scen1.solve() scen1.to_excel(fname) scen2 = Scenario(message_test_mp, model='foo', scenario='bar', version='new') # Fails without init_items=True with pytest.raises(ValueError, match="no set 'new_set'"): scen2.read_excel(fname) # Succeeds with init_items=True scen2.read_excel(fname, init_items=True, commit_steps=True) exp = scen1.par('input') obs = scen2.par('input') pdt.assert_frame_equal(exp, obs) assert scen2.has_par('new_par') assert float(scen2.par('new_par')['value']) == 2 scen2.commit('foo') # must be checked in scen2.solve() assert np.isclose(scen2.var('OBJ')['lvl'], scen1.var('OBJ')['lvl'])
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_excel_read_write(message_test_mp, tmp_path): # Path to temporary file tmp_path /= "excel_read_write.xlsx" # Convert to string to ensure this can be handled fname = str(tmp_path) scen1 = Scenario(message_test_mp, **SCENARIO["dantzig"]) scen1 = scen1.clone(keep_solution=False) scen1.check_out() scen1.init_set("new_set") scen1.add_set("new_set", "member") scen1.init_par("new_par", idx_sets=["new_set"]) scen1.add_par("new_par", "member", 2, "-") scen1.commit("new set and parameter added.") # Writing to Excel without solving scen1.to_excel(fname) # Writing to Excel when scenario has a solution scen1.solve() scen1.to_excel(fname) scen2 = Scenario(message_test_mp, model="foo", scenario="bar", version="new") # Fails without init_items=True with pytest.raises(ValueError, match="no set 'new_set'"): scen2.read_excel(fname) # Succeeds with init_items=True scen2.read_excel(fname, init_items=True, commit_steps=True) exp = scen1.par("input") obs = scen2.par("input") pdt.assert_frame_equal(exp, obs) assert scen2.has_par("new_par") assert float(scen2.par("new_par")["value"]) == 2 scen2.solve() assert np.isclose(scen2.var("OBJ")["lvl"], scen1.var("OBJ")["lvl"])
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_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_extend(test_mp): scen = Scenario(test_mp, *msg_multiyear_args) # Existing time horizon years = [2010, 2020, 2030] result = scen.years_active('seattle', 'canning_plant', years[1]) npt.assert_array_equal(result, years[1:]) # Add years to the scenario years.extend([2040, 2050]) scen.check_out() scen.add_set('year', years[-2:]) scen.add_par('duration_period', '2040', 10, 'y') scen.add_par('duration_period', '2050', 10, 'y') # technical_lifetime of seattle/canning_plant/2020 is 30 years. # - constructed in 2011-01-01. # - by 2020-12-31, has operated 10 years. # - operates until 2040-12-31. # - is NOT active within the period '2050' (2041-01-01 to 2050-12-31) result = scen.years_active('seattle', 'canning_plant', '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_years_active_extend(message_test_mp): scen = Scenario(message_test_mp, **SCENARIO["dantzig multi-year"]) # Existing time horizon years = [1963, 1964, 1965] result = scen.years_active("seattle", "canning_plant", years[1]) npt.assert_array_equal(result, years[1:]) # Add years to the scenario years.extend([1993, 1995]) scen.check_out() scen.add_set("year", years[-2:]) scen.add_par("duration_period", "1993", 28, "y") scen.add_par("duration_period", "1995", 2, "y") # technical_lifetime of seattle/canning_plant/1964 is 30 years. # - constructed in 1964-01-01. # - by 1964-12-31, has operated 1 year. # - by 1965-12-31, has operated 2 years. # - operates until 1993-12-31. # - is NOT active within the period '1995' (1994-01-01 to 1995-12-31) result = scen.years_active("seattle", "canning_plant", 1964) npt.assert_array_equal(result, years[1:-1])
def test_identify_nodes1(test_context): mp = test_context.get_platform() scenario = Scenario(mp, model="identify_nodes", scenario="identify_nodes", version="new") scenario.add_set("technology", "t") scenario.add_set("year", 0) scenario.commit("") with scenario.transact(): scenario.add_set("node", "R99_ZZZ") with pytest.raises( ValueError, match=re.escape( "Couldn't identify node codelist from ['R99_ZZZ', 'World']"), ): identify_nodes(scenario)
def storage_setup(test_mp, time_duration, comment): # First, building a simple model and adding seasonality scen = Scenario(test_mp, "no_storage", "standard", version="new") model_setup(scen, [2020]) add_seasonality(scen, time_duration) # Fixed share for parameters that don't change across timesteps fixed_share = {"a": 1, "b": 1, "c": 1, "d": 1} year_to_time(scen, "output", fixed_share) year_to_time(scen, "var_cost", fixed_share) # Variable share for parameters that are changing in each timestep # share of demand in each season from annual demand demand_share = {"a": 0.15, "b": 0.2, "c": 0.4, "d": 0.25} year_to_time(scen, "demand", demand_share) scen.commit("initialized a model with timesteps") scen.solve(case="no_storage" + comment) # Second, adding upper bound on activity of the cheap technology (wind_ppl) scen.remove_solution() scen.check_out() for h in time_duration.keys(): scen.add_par( "bound_activity_up", ["node", "wind_ppl", 2020, "mode", h], 0.25, "GWa" ) scen.commit("activity bounded") scen.solve(case="no_storage_bounded" + comment) cost_no_stor = scen.var("OBJ")["lvl"] act_no_stor = scen.var("ACT", {"technology": "gas_ppl"})["lvl"].sum() # Third, adding storage technologies but with no input to storage device scen.remove_solution() scen.check_out() # Chronological order of timesteps in the year time_order = {"a": 1, "b": 2, "c": 3, "d": 4} add_storage_data(scen, time_order) scen.commit("storage data added") scen.solve(case="with_storage_no_input" + comment) act = scen.var("ACT") # Forth, adding storage technologies and providing input to storage device scen.remove_solution() scen.check_out() # Adding a new technology "cooler" to provide input of "cooling" to dam scen.add_set("technology", "cooler") df = scen.par("output", {"technology": "turbine"}) df["technology"] = "cooler" df["commodity"] = "cooling" scen.add_par("output", df) # Changing input of dam from 1 to 1.2 to test commodity balance df = scen.par("input", {"technology": "dam"}) df["value"] = 1.2 scen.add_par("input", df) scen.commit("storage needs no separate input") scen.solve(case="with_storage_and_input" + comment) cost_with_stor = scen.var("OBJ")["lvl"] act_with_stor = scen.var("ACT", {"technology": "gas_ppl"})["lvl"].sum() # Fifth. Tests for the functionality of storage # 1. Check that "dam" is not active if no "input" commodity is defined assert "dam" not in act[act["lvl"] > 0]["technology"].tolist() # 2. Testing functionality of storage # Check the contribution of storage to the system cost assert cost_with_stor < cost_no_stor # Activity of expensive technology should be lower with storage assert act_with_stor < act_no_stor # 3. Activity of discharger <= activity of charger + initial content act_pump = scen.var("ACT", {"technology": "pump"})["lvl"] act_turb = scen.var("ACT", {"technology": "turbine"})["lvl"] initial_content = float(scen.par("storage_initial")["value"]) assert act_turb.sum() <= act_pump.sum() + initial_content # 4. Activity of input provider to storage = act of storage * storage input for ts in time_duration.keys(): act_cooler = scen.var("ACT", {"technology": "cooler", "time": ts})["lvl"] inp = scen.par("input", {"technology": "dam", "time": ts})["value"] act_stor = scen.var("ACT", {"technology": "dam", "time": ts})["lvl"] assert float(act_cooler) == float(inp) * float(act_stor) # 5. Max activity of charger <= max activity of storage max_pump = max(act_pump) act_storage = scen.var("ACT", {"technology": "dam"})["lvl"] max_stor = max(act_storage) assert max_pump <= max_stor # 6. Max activity of discharger <= max storage act - self discharge losses max_turb = max(act_turb) loss = scen.par("storage_self_discharge")["value"][0] assert max_turb <= max_stor * (1 - loss) # Sixth, testing equations of storage (when added to ixmp variables) if scen.has_var("STORAGE"): # 1. Equality: storage content in the beginning and end is related storage_first = scen.var("STORAGE", {"time": "a"})["lvl"] storage_last = scen.var("STORAGE", {"time": "d"})["lvl"] relation = scen.par("relation_storage", {"time_first": "d", "time_last": "a"})[ "value" ][0] assert storage_last >= storage_first * relation # 2. Storage content should never exceed storage activity assert max(scen.var("STORAGE")["lvl"]) <= max_stor # 3. Commodity balance: charge - discharge - losses = 0 change = scen.var("STORAGE_CHARGE").set_index(["year_act", "time"])["lvl"] loss = scen.par("storage_self_discharge").set_index(["year", "time"])["value"] assert sum(change[change > 0] * (1 - loss)) == -sum(change[change < 0]) # 4. Energy balance: storage change + losses = storage content storage = scen.var("STORAGE").set_index(["year", "time"])["lvl"] assert storage[(2020, "b")] * (1 - loss[(2020, "b")]) == -change[(2020, "c")]
def base_scen_mp(test_mp): scen = Scenario(test_mp, 'model', 'standard', version='new') data = {2020: 1, 2030: 2, 2040: 3} years = sorted(list(set(data.keys()))) scen.add_set('node', 'node') scen.add_set('commodity', 'comm') scen.add_set('level', 'level') scen.add_set('year', years) scen.add_set('technology', 'tec') scen.add_set('mode', 'mode') output_specs = ['node', 'comm', 'level', 'year', 'year'] for (yr, value) in data.items(): scen.add_par('demand', ['node', 'comm', 'level', yr, 'year'], 1, 'GWa') scen.add_par('technical_lifetime', ['node', 'tec', yr], 10, 'y') tec_specs = ['node', 'tec', yr, yr, 'mode'] scen.add_par('output', tec_specs + output_specs, 1, '-') scen.add_par('var_cost', tec_specs + ['year'], value, 'USD/GWa') scen.commit('initialize test model') scen.solve(case='original_years') yield scen, test_mp
def base_scen_mp(test_mp): scen = Scenario(test_mp, "model", "standard", version="new") data = {2020: 1, 2030: 2, 2040: 3} years = sorted(list(set(data.keys()))) scen.add_set("node", "node") scen.add_set("commodity", "comm") scen.add_set("level", "level") scen.add_set("year", years) scen.add_set("technology", "tec") scen.add_set("mode", "mode") output_specs = ["node", "comm", "level", "year", "year"] for (yr, value) in data.items(): scen.add_par("demand", ["node", "comm", "level", yr, "year"], 1, "GWa") scen.add_par("technical_lifetime", ["node", "tec", yr], 10, "y") tec_specs = ["node", "tec", yr, yr, "mode"] scen.add_par("output", tec_specs + output_specs, 1, "-") scen.add_par("var_cost", tec_specs + ["year"], value, "USD/GWa") scen.commit("initialize test model") scen.solve(case="original_years", quiet=True) yield scen, test_mp
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 model_generator( test_mp, comment, tec_time, demand_time, time_steps, com_dict, yr=2020, ): """ Generates a simple model with a few technologies, and a flexible number of time slices. Parameters ---------- comment : string Annotation for saving different scenarios and comparing their results. tec_time : dict A dictionary for mapping a technology to its input/output temporal levels. demand_time : dict A dictionary for mapping the total "demand" specified at a temporal level. time_steps : list of tuples Information about each time slice, packed in a tuple with three elements, including: "temporal_lvl", number of time slices, and the parent time slice. com_dict : dict A dictionary for specifying "input" and "output" commodities. yr : int, optional Model year. The default is 2020. """ # Building an empty scenario scen = Scenario(test_mp, "test_duration_time", comment, version="new") # Adding required sets scen.add_set("node", "fairyland") for c in com_dict.values(): scen.add_set("commodity", [x for x in list(c.values()) if x]) scen.add_set("level", "final") scen.add_set("year", yr) scen.add_set("type_year", yr) scen.add_set("technology", list(tec_time.keys())) scen.add_set("mode", "standard") # Adding "time" related info to the model: "lvl_temporal", "time", # "map_temporal_hierarchy", and "duration_time" map_time = {} for [tmp_lvl, number, parent] in time_steps: scen.add_set("lvl_temporal", tmp_lvl) if parent == "year": times = [tmp_lvl[0] + "-" + str(x + 1) for x in range(number)] else: times = [ p + "_" + tmp_lvl[0] + "-" + str(x + 1) for (p, x) in product(map_time[parent], range(number)) ] map_time[tmp_lvl] = times scen.add_set("time", times) # Adding "map_temporal_hierarchy" and "duration_time" for h in times: if parent == "year": p = "year" else: p = h.split("_" + tmp_lvl[0])[0] # Temporal hierarchy (order: temporal level, time, parent time) scen.add_set("map_temporal_hierarchy", [tmp_lvl, h, p]) # Duration time is relative to the duration of the parent temporal level dur_parent = float(scen.par("duration_time", {"time": p})["value"]) scen.add_par("duration_time", [h], dur_parent / number, "-") # Adding "demand" at a temporal level (total demand divided by the number of # time slices in that temporal level) for tmp_lvl, value in demand_time.items(): times = scen.set("map_temporal_hierarchy", {"lvl_temporal": tmp_lvl})["time"] for h in times: scen.add_par( "demand", ["fairyland", "electr", "final", yr, h], value / len(times), "GWa", ) # Adding "input" and "output" parameters of technologies for tec, [tmp_lvl_in, tmp_lvl_out] in tec_time.items(): times_in = scen.set("map_temporal_hierarchy", {"lvl_temporal": tmp_lvl_in})[ "time" ] times_out = scen.set("map_temporal_hierarchy", {"lvl_temporal": tmp_lvl_out})[ "time" ] # If technology is linking two different temporal levels if tmp_lvl_in != tmp_lvl_out: time_pairs = product(times_in, times_out) else: time_pairs = zip(times_in, times_out) # Configuring data for "time_origin" and "time" in "input" for (h_in, h_act) in time_pairs: # "input" inp = com_dict[tec]["input"] if inp: inp_spec = [yr, yr, "standard", "fairyland", inp, "final", h_act, h_in] scen.add_par("input", ["fairyland", tec] + inp_spec, 1, "-") # "output" for h in times_out: out = com_dict[tec]["output"] out_spec = [yr, yr, "standard", "fairyland", out, "final", h, h] scen.add_par("output", ["fairyland", tec] + out_spec, 1, "-") # Committing scen.commit("scenario was set up.") # Testing if the model solves in GAMS scen.solve(case=comment) # Testing if sum of "duration_time" is almost 1 for tmp_lvl in scen.set("lvl_temporal"): times = scen.set("map_temporal_hierarchy", {"lvl_temporal": tmp_lvl})[ "time" ].to_list() assert ( abs(sum(scen.par("duration_time", {"time": times})["value"]) - 1.0) < 1e-12 )
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
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') 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_subannual( request, tec_dict, time_steps, demand, time_relative=[], com_dict={"gas_ppl": { "input": "fuel", "output": "electr" }}, capacity={"gas_ppl": { "inv_cost": 0.1, "technical_lifetime": 5 }}, capacity_factor={}, var_cost={}, ): """Return an :class:`message_ix.Scenario` with subannual time resolution. The scenario contains a simple model with two technologies, and a number of time slices. Parameters ---------- request : The pytest ``request`` fixture. tec_dict : dict A dictionary for a technology and required info for time-related parameters. (e.g., ``tec_dict = {"gas_ppl": {"time_origin": ["summer"], "time": ["summer"], "time_dest": ["summer"]}``) time_steps : list of tuples Information about each time slice, packed in a tuple with four elements, including: time slice name, duration relative to "year", "temporal_lvl", and parent time slice (e.g., ``time_steps = [("summer", 1, "season", "year")]``) demand : dict A dictionary for information of "demand" in each time slice. (e.g., 11demand = {"summer": 2.5}``) time_relative: list of str, optional List of parent "time" slices, for which a relative duration time is maintained. This will be used to specify parameter "duration_time_rel" for these "time"s. com_dict : dict, optional A dictionary for specifying "input" and "output" commodities. (e.g., ``com_dict = {"gas_ppl": {"input": "fuel", "output": "electr"}}``) capacity : dict, optional Data for "inv_cost" and "technical_lifetime" per technology. capacity_factor : dict, optional "capacity_factor" with technology as key and "time"/"value" pairs as value. var_cost : dict, optional "var_cost" with technology as key and "time"/"value" pairs as value. """ # Get the `test_mp` fixture for the requesting test function mp = request.getfixturevalue("test_mp") # Build an empty scenario scen = Scenario(mp, request.node.name, scenario="test", version="new") # Add required sets scen.add_set("node", "node") for c in com_dict.values(): scen.add_set("commodity", [x for x in list(c.values()) if x]) # Fixed values y = 2020 unit = "GWa" scen.add_set("level", "level") scen.add_set("year", y) scen.add_set("type_year", y) scen.add_set("mode", "mode") scen.add_set("technology", list(tec_dict.keys())) # Add "time" and "duration_time" to the model for (h, dur, tmp_lvl, parent) in time_steps: scen.add_set("time", h) scen.add_set("time", parent) scen.add_set("lvl_temporal", tmp_lvl) scen.add_set("map_temporal_hierarchy", [tmp_lvl, h, parent]) scen.add_par("duration_time", [h], dur, "-") scen.add_set("time_relative", time_relative) # Common dimensions for parameter data common = dict( node="node", node_loc="node", mode="mode", level="level", year=y, year_vtg=y, year_act=y, ) # Define demand; unpack (key, value) pairs into individual pd.DataFrame rows df = make_df( "demand", **common, commodity="electr", time=demand.keys(), value=demand.values(), unit=unit, ) scen.add_par("demand", df) # Add "input" and "output" parameters of technologies common.update(value=1.0, unit="-") base_output = make_df("output", **common, node_dest="node") base_input = make_df("input", **common, node_origin="node") for tec, times in tec_dict.items(): c = com_dict[tec] for h1, h2 in zip(times["time"], times.get("time_dest", [])): scen.add_par( "output", base_output.assign(technology=tec, commodity=c["output"], time=h1, time_dest=h2), ) for h1, h2 in zip(times["time"], times.get("time_origin", [])): scen.add_par( "input", base_input.assign(technology=tec, commodity=c["input"], time=h1, time_origin=h2), ) # Add capacity related parameters for year, tec in product([y], capacity.keys()): for parname, val in capacity[tec].items(): scen.add_par(parname, ["node", tec, year], val, "-") common.pop("value") # Add capacity factor and variable cost data, both optional for name, arg in [("capacity_factor", capacity_factor), ("var_cost", var_cost)]: for tec, data in arg.items(): df = make_df(name, **common, technology=tec, time=data.keys(), value=data.values()) scen.add_par(name, df) scen.commit( f"Scenario with subannual time resolution for {request.node.name}") scen.solve() return scen
def storage_setup(test_mp, time_duration, comment): # First, building a simple model and adding seasonality scen = Scenario(test_mp, 'no_storage', 'standard', version='new') model_setup(scen, [2020]) add_seasonality(scen, time_duration) # Fixed share for parameters that don't change across timesteps fixed_share = {'a': 1, 'b': 1, 'c': 1, 'd': 1} year_to_time(scen, 'output', fixed_share) year_to_time(scen, 'var_cost', fixed_share) # Variable share for parameters that are changing in each timestep # share of demand in each season from annual demand demand_share = {'a': 0.15, 'b': 0.2, 'c': 0.4, 'd': 0.25} year_to_time(scen, 'demand', demand_share) scen.commit('initialized a model with timesteps') scen.solve(case='no_storage' + comment) # Second, adding upper bound on activity of the cheap technology (wind_ppl) scen.remove_solution() scen.check_out() for h in time_duration.keys(): scen.add_par('bound_activity_up', ['node', 'wind_ppl', 2020, 'mode', h], 0.25, 'GWa') scen.commit('activity bounded') scen.solve(case='no_storage_bounded' + comment) cost_no_stor = scen.var('OBJ')['lvl'] act_no_stor = scen.var('ACT', {'technology': 'gas_ppl'})['lvl'].sum() # Third, adding storage technologies but with no input to storage device scen.remove_solution() scen.check_out() # Chronological order of timesteps in the year time_order = {'a': 1, 'b': 2, 'c': 3, 'd': 4} add_storage_data(scen, time_order) scen.commit('storage data added') scen.solve(case='with_storage_no_input' + comment) act = scen.var('ACT') # Forth, adding storage technologies and providing input to storage device scen.remove_solution() scen.check_out() # Adding a new technology "cooler" to provide input of "cooling" to dam scen.add_set('technology', 'cooler') df = scen.par('output', {'technology': 'turbine'}) df['technology'] = 'cooler' df['commodity'] = 'cooling' scen.add_par('output', df) # Changing input of dam from 1 to 1.2 to test commodity balance df = scen.par('input', {'technology': 'dam'}) df['value'] = 1.2 scen.add_par('input', df) scen.commit('storage needs no separate input') scen.solve(case='with_storage_and_input' + comment) cost_with_stor = scen.var('OBJ')['lvl'] act_with_stor = scen.var('ACT', {'technology': 'gas_ppl'})['lvl'].sum() # Fifth. Tests for the functionality of storage # 1. Check that "dam" is not active if no "input" commodity is defined assert 'dam' not in act[act['lvl'] > 0]['technology'].tolist() # 2. Testing functionality of storage # Check the contribution of storage to the system cost assert cost_with_stor < cost_no_stor # Activity of expensive technology should be lower with storage assert act_with_stor < act_no_stor # 3. Activity of discharger <= activity of charger + initial content act_pump = scen.var('ACT', {'technology': 'pump'})['lvl'] act_turb = scen.var('ACT', {'technology': 'turbine'})['lvl'] initial_content = float(scen.par('storage_initial')['value']) assert act_turb.sum() <= act_pump.sum() + initial_content # 4. Activity of input provider to storage = act of storage * storage input for ts in time_duration.keys(): act_cooler = scen.var('ACT', { 'technology': 'cooler', 'time': ts })['lvl'] inp = scen.par('input', {'technology': 'dam', 'time': ts})['value'] act_stor = scen.var('ACT', {'technology': 'dam', 'time': ts})['lvl'] assert float(act_cooler) == float(inp) * float(act_stor) # 5. Max activity of charger <= max activity of storage max_pump = max(act_pump) act_storage = scen.var('ACT', {'technology': 'dam'})['lvl'] max_stor = max(act_storage) assert max_pump <= max_stor # 6. Max activity of discharger <= max storage act - self discharge losses max_turb = max(act_turb) loss = scen.par('storage_self_discharge')['value'][0] assert max_turb <= max_stor * (1 - loss) # Sixth, testing equations of storage (when added to ixmp variables) if scen.has_var('STORAGE'): # 1. Equality: storage content in the beginning and end is related storage_first = scen.var('STORAGE', {'time': 'a'})['lvl'] storage_last = scen.var('STORAGE', {'time': 'd'})['lvl'] relation = scen.par('relation_storage', { 'time_first': 'd', 'time_last': 'a' })['value'][0] assert storage_last >= storage_first * relation # 2. Storage content should never exceed storage activity assert max(scen.var('STORAGE')['lvl']) <= max_stor # 3. Commodity balance: charge - discharge - losses = 0 change = scen.var('STORAGE_CHARGE').set_index(['year_act', 'time'])['lvl'] loss = scen.par('storage_self_discharge').set_index(['year', 'time'])['value'] assert sum(change[change > 0] * (1 - loss)) == -sum(change[change < 0]) # 4. Energy balance: storage change + losses = storage content storage = scen.var('STORAGE').set_index(['year', 'time'])['lvl'] assert storage[(2020, 'b')] * (1 - loss[(2020, 'b')]) == -change[(2020, 'c')]
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 apply_spec( scenario: Scenario, spec: Mapping[str, ScenarioInfo], data: Callable = None, **options, ): """Apply `spec` to `scenario`. Parameters ---------- spec A 'specification': :class:`dict` with 'require', 'remove', and 'add' keys and :class:`.ScenarioInfo` objects as values. data : callable, optional Function to add data to `scenario`. `data` can either manipulate the scenario directly, or return a :class:`dict` compatible with :func:`.add_par_data`. Other parameters ---------------- dry_run : bool Don't modify `scenario`; only show what would be done. Default :obj:`False`. Exceptions will still be raised if the elements from ``spec['required']`` are missing; this serves as a check that the scenario has the required features for applying the spec. fast : bool Do not remove existing parameter data; increases speed on large scenarios. quiet : bool Only show log messages at level ``ERROR`` and higher. If :obj:`False` (default), show log messages at level ``DEBUG`` and higher. message : str Commit message. See also -------- .add_par_data .strip_par_data .Code .ScenarioInfo """ dry_run = options.get("dry_run", False) log.setLevel(logging.ERROR if options.get("quiet", False) else logging.DEBUG) if not dry_run: try: scenario.remove_solution() except ValueError: pass maybe_check_out(scenario) dump: Dict[str, pd.DataFrame] = {} # Removed data for set_name in scenario.set_list(): # Check whether this set is mentioned at all in the spec if 0 == sum(map(lambda info: len(info.set[set_name]), spec.values())): # Not mentioned; don't do anything continue log.info(f"Set {repr(set_name)}") # Base contents of the set base_set = scenario.set(set_name) # Unpack a multi-dimensional/indexed set to a list of tuples base = ( list(base_set.itertuples(index=False)) if isinstance(base_set, pd.DataFrame) else base_set.tolist() ) log.info(f" {len(base)} elements") # log.debug(', '.join(map(repr, base))) # All elements; verbose # Check for required elements require = spec["require"].set[set_name] log.info(f" Check {len(require)} required elements") # Raise an exception about the first missing element missing = list(filter(lambda e: e not in base, require)) if len(missing): log.error(f" {len(missing)} elements not found: {repr(missing)}") raise ValueError # Remove elements and associated parameter values remove = spec["remove"].set[set_name] for element in remove: msg = f"{repr(element)} and associated parameter elements" if options.get("fast", False): log.info(f" Skip removing {msg} (fast=True)") continue log.info(f" Remove {msg}") strip_par_data(scenario, set_name, element, dry_run=dry_run, dump=dump) # Add elements add = [] if dry_run else spec["add"].set[set_name] for element in add: scenario.add_set( set_name, element.id if isinstance(element, Code) else element, ) if len(add): log.info(f" Add {len(add)} element(s)") log.debug(" " + ellipsize(add)) log.info(" ---") N_removed = sum(len(d) for d in dump.values()) log.info(f"{N_removed} parameter elements removed") # Add units to the Platform before adding data for unit in spec["add"].set["unit"]: unit = unit if isinstance(unit, Code) else Code(id=unit, name=unit) log.info(f"Add unit {repr(unit)}") scenario.platform.add_unit(unit.id, comment=str(unit.name)) # Add data if callable(data): result = data(scenario, dry_run=dry_run) if result: # `data` function returned some data; use add_par_data() add_par_data(scenario, result, dry_run=dry_run) # Finalize log.info("Commit results.") maybe_commit( scenario, condition=not dry_run, message=options.get("message", f"{__name__}.apply_spec()"), )