def test_capacity_based_roof_model_sums_to_one(self,
roof_model_area_based):
roof_model_capacity_based = area_to_capacity(roof_model_area_based,
power_density_flat=1,
power_density_tilted=2)
assert roof_model_capacity_based["share_of_roof_areas"].sum(
) == pytest.approx(1.0)
def test_weight_of_flat_increased_for_higher_power_density(
self, roof_model_area_based):
roof_model_capacity_based = area_to_capacity(roof_model_area_based,
power_density_flat=2,
power_density_tilted=1)
capacity_weight = float(roof_model_capacity_based.loc[("flat", 0.0)])
area_weight = float(roof_model_area_based.loc[("flat", 0.0)])
assert capacity_weight > area_weight
def test_weight_of_flat_reduced_for_lower_power_density(
self, roof_model_area_based):
roof_model_capacity_based = area_to_capacity(roof_model_area_based,
power_density_flat=1,
power_density_tilted=2)
capacity_weight = float(roof_model_capacity_based.loc[("flat", 0.0)])
area_weight = float(roof_model_area_based.loc[("flat", 0.0)])
assert capacity_weight < area_weight
def pv_simulation_parameters(path_to_shapes_of_land_surface,
path_to_roof_categories, bounds, ninja,
maximum_power_density, path_to_output):
"""Create PV simulation input for renewables.ninja."""
points = point_raster_on_shapes(
bounds_wgs84=bounds,
shapes=gpd.read_file(path_to_shapes_of_land_surface),
resolution_km2=ninja["resolution-grid"])
roof_categories = pd.read_csv(path_to_roof_categories, index_col=[0, 1])
roof_categories = area_to_capacity(
roof_categories,
power_density_flat=maximum_power_density["pv-on-flat-areas"],
power_density_tilted=maximum_power_density["pv-on-tilted-roofs"]
).reset_index()
lat_long = pd.DataFrame(
data={
"lat": [point.y for point in points.geometry],
"long": [point.x for point in points.geometry]
})
index = pd.MultiIndex.from_product((points.index, roof_categories.index),
names=["id", "roof_cat_id"])
data = pd.DataFrame(index=index).reset_index()
data = data.merge(
roof_categories, left_on="roof_cat_id",
right_index=True).rename(columns={"share_of_roof_areas": "weight"})
data = data.merge(lat_long, left_on="id", right_index=True)
data["azim"] = data["orientation"].map(orientation_to_azimuth)
data["site_id"] = data.id
data["sim_id"] = data.apply(lambda row: "{}_{}_{}".format(
row.id, row.orientation, round(row.average_tilt)),
axis=1)
flat_mask = data["orientation"] == "flat"
data.loc[flat_mask, "average_tilt"] = data.loc[flat_mask,
"lat"].map(optimal_tilt)
data["pr"] = ninja["pv-performance-ratio"]
data[[
"sim_id", "weight", "site_id", "lat", "long", "average_tilt",
"orientation", "azim", "pr"
]].sort_index().to_csv(path_to_output, header=True, index=False)
def test_capacity_based_roof_model_equals_area_based_for_equal_power_density(
self, roof_model_area_based):
roof_model_capacity_based = area_to_capacity(roof_model_area_based,
power_density_flat=1,
power_density_tilted=1)
assert_frame_equal(roof_model_capacity_based, roof_model_area_based)