def setup_class(cls):
path = os.path.join(TEST_PATH, "de21_no-heat_csv")
sc = st.DeflexScenario()
sc.read_csv(path)
cls.tables = sc.input_data
tmp_tables = {}
name = "heat_demand_deflex"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "transformer_balance"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0, 1, 2], header=[0])
powerplants.scenario_powerplants = MagicMock(
return_value={
"volatile plants": cls.tables["volatile plants"],
"power plants": cls.tables["power plants"],
})
feedin.scenario_feedin = MagicMock(
return_value=cls.tables["volatile series"])
demand_table = {
"electricity demand series":
cls.tables["electricity demand series"]
}
demand.scenario_demand = MagicMock(return_value=demand_table)
my_parameter = {
"copperplate": True,
"group_transformer": False,
"heat": False,
"use_variable_costs": True,
"use_CO2_costs": True,
"map": "de21",
}
my_name = "deflex"
for k, v in my_parameter.items():
my_name += "_" + str(k) + "-" + str(v)
polygons = deflex_regions(rmap=my_parameter["map"], rtype="polygons")
lines = deflex_power_lines(my_parameter["map"]).index
base = os.path.join(os.path.expanduser("~"), ".tmp_x345234dE_deflex")
os.makedirs(base, exist_ok=True)
path = os.path.join(base, "deflex_test{0}")
name = "deflex_2014_de21_no-heat"
scenario_creator.create_basic_reegis_scenario(
name=name,
regions=polygons,
lines=lines,
parameter=my_parameter,
excel_path=path.format(".xlsx"),
csv_path=path.format("_csv"),
)
sc_new = st.DeflexScenario()
sc_new.read_csv(path.format("_csv"))
cls.input_data = sc_new.input_data
def test_02_create_deflex_powerplants():
de = geometries.deflex_regions("de21")
fn_in = os.path.join(cfg.get("paths", "powerplants"), "reegis_pp_test.h5")
fn_out = os.path.join(cfg.get("paths", "powerplants"), "deflex_pp_test.h5")
powerplants.pp_reegis2deflex(
de, "de21", filename_in=fn_in, filename_out=fn_out
)
def scenario_feedin_test():
"""Test scenario feed-in."""
cfg.tmp_set("init", "map", "de21")
regions = geometries.deflex_regions(rmap="de21")
f = basic_scenario.scenario_feedin(regions, 2014, "de21")
eq_(int(f["DE01"].sum()["wind"]), 2159)
eq_(int(f["DE01"].sum()["solar"]), 913)
eq_(int(f["DE16"].sum()["wind"]), 1753)
def test_prevent_mutable_region_object():
"""Make sure the region object is not mutated."""
reg = geometries.deflex_regions("de21")
eq_(reg.geometry.iloc[0].geom_type, "MultiPolygon")
eq_(
geometries.divide_off_and_onshore(reg).offshore,
["DE19", "DE20", "DE21"],
)
eq_(reg.geometry.iloc[0].geom_type, "MultiPolygon")
def test_03_not_existing_file():
old_value = cfg.get("paths", "powerplants")
cfg.tmp_set("paths", "powerplants", "/home/pet/")
de = geometries.deflex_regions("de22")
powerplants.pp_reegis2deflex = MagicMock(return_value="/home/pet/pp.h5")
with assert_raises_regexp(
Exception, "File /home/pet/pp.h5 does not exist"
):
powerplants.get_deflex_pp_by_year(de, 2012, "de22")
cfg.tmp_set("paths", "powerplants", old_value)
def test_scenario_creation():
data = {}
for name in ["volatile_series", "demand_series"]:
fn = os.path.join(
os.path.dirname(__file__),
"data",
"deflex_2014_de21_test_csv",
name + ".csv",
)
data[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "heat_demand_deflex"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
data[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "transformer_balance"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
data[name] = pd.read_csv(fn, index_col=[0, 1, 2], header=[0])
basic_scenario.scenario_feedin = MagicMock(
return_value=data["volatile_series"])
basic_scenario.scenario_demand = MagicMock(
return_value=data["demand_series"])
energy_balance.get_transformation_balance_by_region = MagicMock(
return_value=data["transformer_balance"])
demand.get_heat_profiles_deflex = MagicMock(
return_value=data["heat_demand_deflex"])
regions = geometries.deflex_regions(rmap="de21")
table_collection = basic_scenario.create_scenario(regions, 2014, "de21")
eq_(
sorted(list(table_collection.keys())),
sorted([
"storages",
"transformer",
"volatile_source",
"transmission",
"decentralised_heat",
"commodity_source",
"volatile_series",
"demand_series",
"mobility_energy_content",
"mobility_mileage",
"mobility_spec_demand",
]),
)
eq_(len(list(table_collection.keys())), 11)
def more_heat_pumps(sc, heat_pump_fraction, cop):
year = 2014
abs_decentr_heat = sc.table_collection["demand_series"]["DE_demand"].sum(
axis=1
)
heat_pump = abs_decentr_heat * heat_pump_fraction
sc.table_collection["demand_series"]["DE_demand"] *= 1 - heat_pump_fraction
deflex_regions = geometries.deflex_regions(rmap=sc.map)
name = "{0}_region".format(sc.map)
inhab = inhabitants.get_inhabitants_by_region(
year, deflex_regions, name=name
)
inhab_fraction = inhab.div(inhab.sum())
for region in inhab_fraction.index:
if inhab_fraction.loc[region] > 0:
sc.table_collection["demand_series"][
(region, "electrical_load")
] += inhab_fraction.loc[region] * heat_pump.div(cop)
Dataframe containing aggregated yearly power demand (households, CTS and industry) for region selection
-------
"""
if elc_data is None:
elc_data = get_demandregio_electricity_consumption_by_nuts3(year)
agg_power = pd.DataFrame(index=regions.index, columns=['households', 'CTS', 'industry'])
nuts3_list = get_nutslist_for_regions(regions)
for zone in regions.index:
idx = nuts3_list.loc[zone]['nuts']
agg_power.loc[zone]['households'] = elc_data['households'][idx].sum()
agg_power.loc[zone]['CTS'] = elc_data['CTS'][idx].sum()
agg_power.loc[zone]['industry'] = elc_data['industry'][idx].sum()
return agg_power
regions = geo_deflex.deflex_regions(rmap='de22')
#test_el = get_demandregio_electricity_consumption_by_nuts3(2016)
#test_heat = get_combined_heatload_for_region(2016)
aggtest1 = aggregate_power_by_region(regions, 2015)
aggtest2 = aggregate_power_by_region(regions, 2016)
aggtest3 = aggregate_heat_by_region(regions, 2015)
aggtest4 = aggregate_heat_by_region(regions, 2016)
#power_DE22 = aggregate_power_by_region(regions, 2015)
#heat_DE22 = aggregate_heat_by_region(regions, 2015)
def plot_power_lines(
data,
key,
cmap_lines=None,
cmap_bg=None,
direction=True,
vmax=None,
label_min=None,
label_max=None,
unit="GWh",
size=None,
ax=None,
legend=True,
unit_to_label=False,
divide=1,
decimal=0,
):
"""
Parameters
----------
data
key
cmap_lines
cmap_bg
direction
vmax
label_min
label_max
unit
size
ax
legend
unit_to_label
divide
decimal
Returns
-------
"""
if size is None and ax is None:
ax = plt.figure(figsize=(5, 5)).add_subplot(1, 1, 1)
elif size is not None and ax is None:
ax = plt.figure(figsize=size).add_subplot(1, 1, 1)
if unit_to_label is True:
label_unit = unit
else:
label_unit = ""
lines = reegis.geometries.load(
cfg.get("paths", "geometry"), cfg.get("geometry", "de21_power_lines")
)
polygons = d_geometries.deflex_regions(rmap="de21", rtype="polygons")
lines = lines.merge(data.div(divide), left_index=True, right_index=True)
lines["centroid"] = lines.centroid
if cmap_bg is None:
cmap_bg = LinearSegmentedColormap.from_list(
"mycmap", [(0, "#aed8b4"), (1, "#bddce5")]
)
if cmap_lines is None:
cmap_lines = LinearSegmentedColormap.from_list(
"mycmap",
[(0, "#aaaaaa"), (0.0001, "green"), (0.5, "yellow"), (1, "red")],
)
offshore = d_geometries.divide_off_and_onshore(polygons).offshore
polygons["color"] = 0
polygons.loc[offshore, "color"] = 1
lines["reverse"] = lines[key] < 0
# if direction is False:
lines.loc[lines["reverse"], key] = lines.loc[lines["reverse"], key] * -1
if vmax is None:
vmax = lines[key].max()
if label_min is None:
label_min = vmax * 0.5
if label_max is None:
label_max = float("inf")
ax = polygons.plot(
edgecolor="#9aa1a9",
cmap=cmap_bg,
column="color",
ax=ax,
aspect="equal",
)
ax = lines.plot(
cmap=cmap_lines,
legend=legend,
ax=ax,
column=key,
vmin=0,
vmax=vmax,
aspect="equal",
)
for i, v in lines.iterrows():
x1 = v["geometry"].coords[0][0]
y1 = v["geometry"].coords[0][1]
x2 = v["geometry"].coords[1][0]
y2 = v["geometry"].coords[1][1]
value_relative = v[key] / vmax
mc = cmap_lines(value_relative)
orient = math.atan(abs(x1 - x2) / abs(y1 - y2))
if (y1 > y2) & (x1 > x2) or (y1 < y2) & (x1 < x2):
orient *= -1
if v["reverse"]:
orient += math.pi
if v[key] == 0 or not direction:
polygon = patches.RegularPolygon(
(v["centroid"].x, v["centroid"].y),
4,
0.15,
orientation=orient,
color=(0, 0, 0, 0),
zorder=10,
)
else:
polygon = patches.RegularPolygon(
(v["centroid"].x, v["centroid"].y),
3,
0.15,
orientation=orient,
color=mc,
zorder=10,
)
ax.add_patch(polygon)
if decimal == 0:
value = int(round(v[key]))
else:
value = round(v[key], decimal)
if label_min <= value <= label_max:
if v["reverse"] is True and direction is False:
value *= -1
ax.text(
v["centroid"].x,
v["centroid"].y,
"{0} {1}".format(value, label_unit),
color="#000000",
fontsize=9.5,
zorder=15,
path_effects=[
path_effects.withStroke(linewidth=3, foreground="w")
],
)
for spine in plt.gca().spines.values():
spine.set_visible(False)
ax.axis("off")
polygons.apply(
lambda x: ax.annotate(
x.name, xy=x.geometry.centroid.coords[0], ha="center"
),
axis=1,
)
return ax
def setup_class(cls):
path = os.path.join(TEST_PATH, "de22_heat_transmission_csv")
sc = st.DeflexScenario()
sc.read_csv(path)
cls.tables = sc.input_data
tmp_tables = {}
parameter = {
"costs_source": "ewi",
"downtime_bioenergy": 0.1,
"limited_transformer": "bioenergy",
"local_fuels": "district heating",
"map": "de22",
"mobility_other": "petrol",
"round": 1,
"separate_heat_regions": "de22",
"copperplate": False,
"default_transmission_efficiency": 0.9,
"group_transformer": False,
"heat": True,
"use_CO2_costs": True,
"use_downtime_factor": True,
"use_variable_costs": False,
"year": 2014,
}
config.init(paths=[os.path.dirname(dfile)])
for option, value in parameter.items():
cfg.tmp_set("creator", option, str(value))
config.tmp_set("creator", option, str(value))
name = "heat_demand_deflex"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "transformer_balance"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0, 1, 2], header=[0])
powerplants.scenario_powerplants = MagicMock(
return_value={
"volatile plants": cls.tables["volatile plants"],
"power plants": cls.tables["power plants"],
})
powerplants.scenario_chp = MagicMock(
return_value={
"heat-chp plants": cls.tables["heat-chp plants"],
"power plants": cls.tables["power plants"],
})
feedin.scenario_feedin = MagicMock(
return_value=cls.tables["volatile series"])
demand_table = {
"electricity demand series":
cls.tables["electricity demand series"],
"heat demand series": cls.tables["heat demand series"],
}
demand.scenario_demand = MagicMock(return_value=demand_table)
name = "deflex_2014_de22_heat_transmission"
polygons = deflex_regions(rmap=parameter["map"], rtype="polygons")
lines = deflex_power_lines(parameter["map"]).index
cls.input_data = scenario_creator.create_scenario(
polygons, 2014, name, lines)
# Define installable capacity per square meter in MW
p_per_qm_wind = 8 / 1e6 # 8 W/m² Fläche
p_per_qm_pv = 200 / 1e6 # 200 W/m² Fläche -> eta=20%
# Calculate maximum installable capacity for onshore wind and rooftop-PV
P_max_wind = suitable_area['wind_area'] * p_per_qm_wind
P_max_pv = suitable_area['pv_area'] * p_per_qm_pv
# Load NUTS3-mixed-COPS
nuts3_cops = pd.read_csv('/home/dbeier/Daten/COP_NUTS3.csv')
nuts3_cops.drop('Unnamed: 0', axis='columns', inplace=True)
nuts3_cops.set_index(pd.date_range('1/1/2014', periods=8760, freq='H'),
inplace=True)
# Get indices for zones of interest
de22_list = geo_deflex.deflex_regions(rmap='de22', rtype='polygons').index
de17_list = geo_reegis.get_federal_states_polygon().index
# Aggregate values for de17 and de22 regions to prepare for
# Create empty Dataframes
dflx_input = pd.DataFrame(
index=de22_list, columns=['power', 'lt-heat', 'ht-heat', 'P_wind', 'P_pv'])
dflx_input_fedstates = pd.DataFrame(
index=de17_list, columns=['power', 'lt-heat', 'ht-heat', 'P_wind', 'P_pv'])
dflx_cop_de17_heat = pd.DataFrame(index=pd.date_range('1/1/2014',
periods=8760,
freq='H'),
columns=de17_list)
dflx_cop_de22_heat = pd.DataFrame(index=pd.date_range('1/1/2014',
periods=8760,
def create_basic_scenario(
year,
rmap=None,
path=None,
csv_dir=None,
xls_name=None,
round_values=None,
only_out=None,
):
"""
Create a basic scenario for a given year and region-set and store it into
an excel-file or csv-collection.
Parameters
----------
year : int
rmap : str
path : str
csv_dir : str
xls_name : str
round_values : bool
only_out : str
Returns
-------
namedtuple : Path
Examples
--------
>>> year=2014 # doctest: +SKIP
>>> my_rmap='de21' # doctest: +SKIP
>>> p=create_basic_scenario(year, rmap=my_rmap) # doctest: +SKIP
>>> print("Xls path: {0}".format(p.xls)) # doctest: +SKIP
>>> print("Csv path: {0}".format(p.csv)) # doctest: +SKIP
"""
paths = namedtuple("paths", "xls, csv")
if rmap is not None:
cfg.tmp_set("init", "map", rmap)
name = cfg.get("init", "map")
regions = geometries.deflex_regions(rmap=cfg.get("init", "map"))
table_collection = create_scenario(regions, year, name, round_values)
table_collection = clean_time_series(table_collection)
name = "{0}_{1}_{2}".format("deflex", year, cfg.get("init", "map"))
sce = scenario_tools.Scenario(table_collection=table_collection,
name=name,
year=year)
if path is None:
path = os.path.join(cfg.get("paths", "scenario"), "deflex", str(year))
if only_out == "xls":
csv_path = None
elif csv_dir is None:
csv_path = os.path.join(path, "{0}_csv".format(name))
else:
csv_path = os.path.join(path, csv_dir)
if only_out == "csv":
xls_path = None
elif xls_name is None:
xls_path = os.path.join(path, name + ".xls")
else:
xls_path = os.path.join(path, xls_name)
fullpath = paths(xls=xls_path, csv=csv_path)
if not only_out == "csv":
os.makedirs(path, exist_ok=True)
sce.to_excel(fullpath.xls)
if not only_out == "xls":
os.makedirs(csv_path, exist_ok=True)
sce.to_csv(fullpath.csv)
return fullpath
P_max = pd.read_csv(fn)
P_max.set_index('nuts3', drop=True, inplace=True)
agg_capacity = pd.DataFrame(index=regions.index,
columns=["P_wind", "P_pv"])
nuts3_list = integrate_demandregio.get_nutslist_for_regions(regions)
for zone in regions.index:
idx = nuts3_list.loc[zone]['nuts']
agg_capacity.loc[zone]['P_wind'] = P_max['P_wind'][idx].sum()
agg_capacity.loc[zone]['P_pv'] = P_max['P_pv'][idx].sum()
return agg_capacity
regions = geo_deflex.deflex_regions(rmap='de17', rtype='polygons')
P_wind_pv = aggregate_capacity_by_region(regions)
#suitable_area = get_pv_wind_areas_by_nuts3()
#path = os.path.join(cfg.get("paths", "GLAES"), 'nuts3_geojson')
#test = calc_wind_pv_areas(path)
#de_area, ecWind = calculate_wind_area(DE)
#(de_area/1e6) / 357000
#wind_pv_area = get_pv_wind_areas_by_nuts3('/home/dbeier/git-projects/db_test_repo/nuts3_geojson/', create_geojson=True)
# Define path where nuts3 regions are stored oder should be stored
#path = '/home/dbeier/git-projects/test_repo/nuts3_geojson/'
# Only necessary if nuts3 vektor files are not created yet
def setUpClass(cls):
cls.regions = geometries.deflex_regions(rmap="de21")
cls.pp = basic_scenario.scenario_powerplants(dict(), cls.regions, 2014,
"de21", 1)
### Within this script releant energy system data for a mid-term scenario is fetched and processed
from disaggregator import data
from scenario_builder import cop_precalc, snippets, heatload_scenario_calculator
from deflex import geometries as geo_deflex
from reegis import land_availability_glaes, demand_disaggregator, entsoe, demand_heat
from scenario_builder import emobpy_processing
import pandas as pd
import os
# Set parameters and get data needed for all scenarios
nuts3_index = data.database_shapes().index
de21 = geo_deflex.deflex_regions(rmap='de21')
year = 2015
# Excel findet sich auch hier: SeaDrive/Für meine Gruppen/QUARREE 100/02_Modellierung/09_Szenarien Q100
path_to_data = '/home/dbeier/reegis/data/scenario_data/commodity_sources_costs.xls'
# Get ENTSO-E load profile from reegis
profile = entsoe.get_entsoe_load(2014).reset_index(drop=True)["DE_load_"]
norm_profile = profile.div(profile.sum())
heat_profiles_reegis = demand_heat.get_heat_profiles_by_region(de21,
2014,
name='test')
profile_lt = snippets.return_normalized_domestic_profiles(
de21, heat_profiles_reegis)
profile_ht = snippets.return_normalized_industrial_profiles(
de21, heat_profiles_reegis)
# Fetch costs and emission applicable for scenario (sheet1)
costs = snippets.get_cost_emission_scenario_data(path_to_data)
def scenario_demand_test():
"""Test scenario demand."""
regions = geometries.deflex_regions(rmap="de21")
d = basic_scenario.scenario_demand(regions, 2014, "de21")
eq_(int(d["DE01", "district heating"].sum()), 18639262)
eq_(int(d["DE05", "electrical_load"].sum()), 10069304)
