def plot_map(network,
components=["links", "stores", "storage_units", "generators"],
bus_size_factor=1.7e10,
transmission=False):
n = network.copy()
assign_location(n)
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
costs = pd.DataFrame(index=n.buses.index)
for comp in components:
df_c = getattr(n, comp)
df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
costs_c = ((df_c.capital_cost * df_c[attr]).groupby(
[df_c.location, df_c.nice_group]).sum().unstack().fillna(0.))
costs = pd.concat([costs, costs_c], axis=1)
print(comp, costs)
costs = costs.groupby(costs.columns, axis=1).sum()
costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
new_columns = (preferred_order.intersection(costs.columns).append(
costs.columns.difference(preferred_order)))
costs = costs[new_columns]
for item in new_columns:
if item not in snakemake.config['plotting']['tech_colors']:
print("Warning!", item, "not in config/plotting/tech_colors")
costs = costs.stack() # .sort_index()
# hack because impossible to drop buses...
n.buses.loc["EU gas", ["x", "y"]] = n.buses.loc["DE0 0", ["x", "y"]]
n.links.drop(n.links.index[(n.links.carrier != "DC")
& (n.links.carrier != "B2B")],
inplace=True)
# drop non-bus
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
if len(to_drop) != 0:
print("dropping non-buses", to_drop)
costs.drop(to_drop, level=0, inplace=True, axis=0)
# make sure they are removed from index
costs.index = pd.MultiIndex.from_tuples(costs.index.values)
# PDF has minimum width, so set these to zero
line_lower_threshold = 500.
line_upper_threshold = 1e4
linewidth_factor = 2e3
ac_color = "gray"
dc_color = "m"
if snakemake.wildcards["lv"] == "1.0":
# should be zero
line_widths = n.lines.s_nom_opt - n.lines.s_nom
link_widths = n.links.p_nom_opt - n.links.p_nom
title = "Transmission reinforcement"
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.
title = "Today's transmission"
else:
line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
link_widths = n.links.p_nom_opt - n.links.p_nom_min
title = "Transmission reinforcement"
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
title = "Total transmission"
line_widths[line_widths < line_lower_threshold] = 0.
link_widths[link_widths < line_lower_threshold] = 0.
line_widths[line_widths > line_upper_threshold] = line_upper_threshold
link_widths[link_widths > line_upper_threshold] = line_upper_threshold
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
fig.set_size_inches(7, 6)
n.plot(bus_sizes=costs / bus_size_factor,
bus_colors=snakemake.config['plotting']['tech_colors'],
line_colors=ac_color,
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax,
**map_opts)
handles = make_legend_circles_for([5e9, 1e9],
scale=bus_size_factor,
facecolor="gray")
labels = ["{} bEUR/a".format(s) for s in (5, 1)]
l2 = ax.legend(handles,
labels,
loc="upper left",
bbox_to_anchor=(0.01, 1.01),
labelspacing=1.0,
frameon=False,
title='System cost',
handler_map=make_handler_map_to_scale_circles_as_in(ax))
ax.add_artist(l2)
handles = []
labels = []
for s in (10, 5):
handles.append(
plt.Line2D([0], [0],
color=ac_color,
linewidth=s * 1e3 / linewidth_factor))
labels.append("{} GW".format(s))
l1_1 = ax.legend(handles,
labels,
loc="upper left",
bbox_to_anchor=(0.22, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=1.5,
title=title)
ax.add_artist(l1_1)
fig.savefig(snakemake.output.map, transparent=True, bbox_inches="tight")
def plot_series(network, carrier="AC", name="test"):
n = network.copy()
assign_location(n)
assign_carriers(n)
buses = n.buses.index[n.buses.carrier.str.contains(carrier)]
supply = pd.DataFrame(index=n.snapshots)
for c in n.iterate_components(n.branch_components):
for i in range(2):
supply = pd.concat(
(supply, (-1) * c.pnl["p" + str(i)].
loc[:, c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(
c.df.carrier, axis=1).sum()),
axis=1)
for c in n.iterate_components(n.one_port_components):
comps = c.df.index[c.df.bus.isin(buses)]
supply = pd.concat((supply, ((c.pnl["p"].loc[:, comps]).multiply(
c.df.loc[comps, "sign"])).groupby(c.df.carrier, axis=1).sum()),
axis=1)
supply = supply.groupby(rename_techs_tyndp, axis=1).sum()
both = supply.columns[(supply < 0.).any() & (supply > 0.).any()]
positive_supply = supply[both]
negative_supply = supply[both]
positive_supply[positive_supply < 0.] = 0.
negative_supply[negative_supply > 0.] = 0.
supply[both] = positive_supply
suffix = " charging"
negative_supply.columns = negative_supply.columns + suffix
supply = pd.concat((supply, negative_supply), axis=1)
# 14-21.2 for flaute
# 19-26.1 for flaute
start = "2013-02-19"
stop = "2013-02-26"
threshold = 10e3
to_drop = supply.columns[(abs(supply) < threshold).all()]
if len(to_drop) != 0:
print("dropping", to_drop)
supply.drop(columns=to_drop, inplace=True)
supply.index.name = None
supply = supply / 1e3
supply.rename(columns={
"electricity": "electric demand",
"heat": "heat demand"
},
inplace=True)
supply.columns = supply.columns.str.replace("residential ", "")
supply.columns = supply.columns.str.replace("services ", "")
supply.columns = supply.columns.str.replace("urban decentral ",
"decentral ")
preferred_order = pd.Index([
"electric demand", "transmission lines", "hydroelectricity",
"hydro reservoir", "run of river", "pumped hydro storage", "CHP",
"onshore wind", "offshore wind", "solar PV", "solar thermal",
"building retrofitting", "ground heat pump", "air heat pump",
"resistive heater", "OCGT", "gas boiler", "gas", "natural gas",
"methanation", "hydrogen storage", "battery storage",
"hot water storage"
])
new_columns = (preferred_order.intersection(supply.columns).append(
supply.columns.difference(preferred_order)))
supply = supply.groupby(supply.columns, axis=1).sum()
fig, ax = plt.subplots()
fig.set_size_inches((8, 5))
(supply.loc[start:stop, new_columns].plot(
ax=ax,
kind="area",
stacked=True,
linewidth=0.,
color=[
snakemake.config['plotting']['tech_colors'][i.replace(suffix, "")]
for i in new_columns
]))
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
new_handles = []
new_labels = []
for i, item in enumerate(labels):
if "charging" not in item:
new_handles.append(handles[i])
new_labels.append(labels[i])
ax.legend(new_handles, new_labels, ncol=3, loc="upper left", frameon=False)
ax.set_xlim([start, stop])
ax.set_ylim([-1300, 1900])
ax.grid(True)
ax.set_ylabel("Power [GW]")
fig.tight_layout()
fig.savefig("{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
snakemake.config['results_dir'], snakemake.config['run'],
snakemake.wildcards["lv"], carrier, start, stop, name),
transparent=True)