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diagnostic_plots.py
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diagnostic_plots.py
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#%%### Imports
import os
import site
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import patheffects as pe
from glob import glob
import traceback
import gdxpds
import cmocean
pd.options.display.max_rows = 20
pd.options.display.max_columns = 200
### Local imports
try:
import ReEDS_Augur.functions as functions
except ModuleNotFoundError:
import functions
#%%### Fixed inputs
dpi = None
interactive = False
savefig = True
#%%### Functions
def group_techs(dfin, dfs, technamecol='i'):
"""
Aggregate techs using bokehpivot tables
"""
## Get the colors
tech_map = dfs['tech_map']
tech_map.index = tech_map.index.str.lower()
tech_map = tech_map.str.lower()
## Aggregate by type
dfout = dfin.copy()
if technamecol in ['column', 'columns', 'wide', None, 0, False]:
def renamer(x):
out = (x if x[0].startswith('battery')
else (x[0].strip('_01234567890*').lower(), x[1]))
return out
dfout.columns = dfout.columns.map(renamer)
dfout.columns = (
dfout.columns
.map(lambda x: ([i for i in tech_map.index if x[0].startswith(i)][0], x[1]))
.map(lambda x: (tech_map.get(x[0],x[0]), x[1]))
)
else:
def renamer(x):
out = x if x.startswith('battery') else x.strip('_01234567890*').lower()
return out
dfout[technamecol] = dfout[technamecol].map(renamer)
dfout[technamecol] = (
dfout[technamecol]
.map(lambda x: ([i for i in tech_map.drop('hyd').index if x.startswith(i)]+[x])[0])
.map(lambda x: tech_map.get(x,x))
)
return dfout
### Input-formatting functions
def get_pras_system(sw, verbose=0):
"""
Read a .pras .h5 file and return a dict of dataframes
"""
import h5py
###### Read all the tables in the .pras file
infile = os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS',
f"PRAS_{sw.t}i{sw.iteration}.pras")
pras = {}
with h5py.File(infile,'r') as f:
keys = list(f)
vals = {}
for key in keys:
vals[key] = list(f[key])
if verbose:
print(f"{key}:\n {','.join(vals[key])}\n")
for val in vals[key]:
pras[key,val] = pd.DataFrame(f[key][val][...])
if verbose:
print(f"{key}/{val}: {pras[key,val].shape}")
if verbose:
print('\n')
###### Combine into more easily-usable dataframes
dfpras = {}
keys = {
## our name: [pras key, pras capacity table name]
'storcap': ['storages', 'dischargecapacity'],
'gencap': ['generators', 'capacity'],
'genfailrate': ['generators', 'failureprobability'],
'genrepairrate': ['generators', 'repairprobability'],
# 'genstorcap': ['generatorstorages', 'gridinjectioncapacity'],
}
for key, val in keys.items():
dfpras[key] = pras[val[0], val[1]]
dfpras[key].columns = pd.MultiIndex.from_arrays(
[pras[val[0],'_core'].category.str.decode('UTF-8'),
pras[val[0],'_core'].region.str.decode('UTF-8')],
names=['i','r'],
)
if verbose:
print(key)
print(dfpras[key].columns.get_level_values('i').unique())
### Load
dfpras['load'] = pras['regions','load'].rename(
columns=pras['regions','_core'].name.str.decode('UTF-8'))
return dfpras
#%% Input-loading function
def get_inputs(sw):
### Make savepath
import plots
sw['savepath'] = os.path.join(sw['casedir'], 'outputs', 'Augur_plots')
os.makedirs(sw['savepath'], exist_ok=True)
##### Load shared parameters
fulltimeindex = functions.make_fulltimeindex()
if len(sw['resource_adequacy_years']) == 1:
timeindex = pd.date_range(
f"{sw['resource_adequacy_years'][0]}-01-01",
f"{sw['resource_adequacy_years'][0]+1}-01-01",
freq='H', inclusive='left', tz='EST',
)[:8760]
else:
timeindex = np.ravel([
pd.date_range(
'{}-01-01'.format(y),
'{}-01-01'.format(y+1),
freq='H', inclusive='left', tz='EST',
)[:8760]
for y in sw['resource_adequacy_years']
])
h_dt_szn = (
pd.read_csv(os.path.join(sw['casedir'],'inputs_case','h_dt_szn.csv'))
.assign(hr=(['hr{:>03}'.format(i+1) for i in range(sw['hoursperperiod'])]
* sw['periodsperyear'] * 7))
.assign(datetime=fulltimeindex)
)
h_dt_szn['d'] = h_dt_szn.datetime.dt.strftime('sy%Yd%j')
gdxreeds = gdxpds.to_dataframes(
os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f'reeds_data_{sw["t"]}.gdx'))
techs = gdxreeds['i_subsets'].pivot(columns='i_subtech',index='i',values='Value')
h2dac = techs['CONSUME'].dropna().index
tech_map = pd.read_csv(
os.path.join(sw['reeds_path'],'postprocessing','bokehpivot','in','reeds2','tech_map.csv'))
tech_map.raw = tech_map.raw.map(
lambda x: x if x.startswith('battery') else x.strip('_01234567890*')).str.lower()
tech_map = tech_map.drop_duplicates().set_index('raw').display.str.lower()
existinghydrotechs = [
'hyded','hydend','hydend_hyded',
]
newhydrotechs = [
'hydd','hydnd','hydnpd','hydnpnd','hydsd','hydsnd','hydud','hydund',
]
tech_map = pd.concat([
tech_map,
pd.Series(dict(zip(existinghydrotechs,['hydro_exist']*len(existinghydrotechs)))),
pd.Series(dict(zip(newhydrotechs,['hydro_new']*len(newhydrotechs)))),
]).reset_index().drop_duplicates().set_index('index')[0].rename(None)
tech_style = pd.read_csv(
os.path.join(sw['reeds_path'],'postprocessing','bokehpivot','in','reeds2','tech_style.csv'),
index_col='order',
).squeeze(1)
tech_style.index = tech_style.index.str.lower()
tech_style['dropped'] = '#d62728'
for i in ['hydro_exist', 'hydro_new']:
tech_style[i] = tech_style['hydro']
# ## Add some techs
# for i in [1,2]:
# tech_style[f'csp{i}'] = tech_style['csp']
# for i in [1,2,3]:
# tech_style[f'pvb{i}'] = tech_style['pvb']
hierarchy = functions.get_hierarchy(sw.casedir)
resources = pd.read_csv(
os.path.join(sw['casedir'],'inputs_case','resources.csv')
).set_index('resource')
resources['tech'] = (
resources.i.map(lambda x: x.split('|')[0])
.map(lambda x: x if x.startswith('battery') else x.strip('_01234567890*')))
resources['rb'] = resources.r
##### Hourly dispatch by month
### Load and aggregate the VRE generation profiles by tech group
try:
vre_gen = pd.read_hdf(
os.path.join(
sw['casedir'],'ReEDS_Augur','augur_data',f'pras_vre_gen_{sw.t}.h5'))
except FileNotFoundError:
vre_gen = None
### Get vre_gen by tech (only resource_adequacy_years)
vre_gen_usa = (
vre_gen
.rename(columns=resources.tech)
.groupby(axis=1, level=0).sum()
.set_index(fulltimeindex)
)
if len(sw['resource_adequacy_years']) == 1:
vre_gen_usa = vre_gen_usa.xs(int(sw['resource_adequacy_years'][0]), level='year', axis=0)
### Get vre_gen summed over tech by BA (full 7 years)
vre_gen_r = (
vre_gen
.rename(columns=resources.rb.to_dict())
.groupby(axis=1, level=0).sum()
)
### Load hourly demand
try:
load_r = pd.read_hdf(
os.path.join(
sw['casedir'],'ReEDS_Augur','augur_data',f'load_{sw.t}.h5')
)
except FileNotFoundError:
load_r = None
### Load PRAS load
try:
pras_load = pd.read_hdf(
os.path.join(
sw['casedir'],'ReEDS_Augur','augur_data',f'pras_load_{sw.t}.h5')
)
except FileNotFoundError:
pras_load = None
pras_load.index = fulltimeindex
try:
pras_h2dac_load = pd.read_hdf(
os.path.join(
sw['casedir'],'ReEDS_Augur','augur_data',
f"pras_h2dac_load_{sw['t']}.h5"))
except FileNotFoundError:
pras_h2dac_load = pd.DataFrame(columns=pras_load.columns)
pras_h2dac_load.index = fulltimeindex
### Load input capacity to PRAS
try:
max_cap = pd.read_csv(
os.path.join(
sw['casedir'],'ReEDS_Augur','augur_data',f"max_cap_{sw['t']}.csv"))
except FileNotFoundError:
max_cap = pd.DataFrame(columns=['i','v','r','MW'])
### Load LOLE/EUE/NEUE from PRAS
try:
pras = functions.read_pras_results(
os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS',
f"PRAS_{sw.t}i{sw.iteration}.h5")
)
pras.index = fulltimeindex
except FileNotFoundError as err:
print(f"Failed to load PRAS outputs: {err}")
pras = pd.DataFrame({'USA_EUE':9999}, index=fulltimeindex)
### Load the PRAS system
try:
pras_system = get_pras_system(sw)
for key in pras_system:
pras_system[key].index = fulltimeindex
except FileNotFoundError as err:
print(f"Failed to load .pras system: {err}")
pras_system = dict()
###### Get net load profiles
### Get net load by BA
net_load_r = load_r - vre_gen_r
### Get net load by ccreg
net_load_ccreg = net_load_r.rename(columns=hierarchy.ccreg).groupby(axis=1, level=0).sum()
### Get net load for the USA
net_load_usa = net_load_r.set_index(fulltimeindex).sum(axis=1)
###### Make combined dataframes for plotting
### Get top load hours by ccseason
hour2datetime = h_dt_szn.set_index('hour').datetime.copy()
### Use seasons appropriate to resolution
ccseasons = net_load_ccreg.index.get_level_values('ccseason').unique()
ccregs = sorted(net_load_ccreg.columns)
peakhours = {}
for ccseason in ccseasons:
for ccreg in ccregs:
peakhours[ccseason,ccreg] = (
net_load_ccreg.loc[ccseason][ccreg]
.nlargest(int(sw['GSw_PRM_CapCreditHours'])).index.get_level_values('hour'))
dfpeak = (
pd.DataFrame(peakhours)
.stack(level=0)
.reorder_levels([1,0], axis=0)
.stack().map(hour2datetime).rename('datetime').to_frame()
.assign(peak=1)
.reset_index(level=2).rename(columns={'level_2':'ccreg'})
.pivot(index='datetime', columns='ccreg', values='peak')
.reindex(fulltimeindex)
.fillna(0).astype(int)
)
peakcolors = plots.rainbowmapper(ccregs, plt.cm.tab20)
###### Store them for later
dfs = {}
dfs['dfpeak'] = dfpeak
dfs['fulltimeindex'] = fulltimeindex
dfs['gdxreeds'] = gdxreeds
dfs['h_dt_szn'] = h_dt_szn
dfs['h2dac'] = h2dac
dfs['hierarchy'] = hierarchy
dfs['load_r'] = load_r
dfs['max_cap'] = max_cap
dfs['net_load_r'] = net_load_r
dfs['net_load_usa'] = net_load_usa
dfs['peakcolors'] = peakcolors
dfs['pras_h2dac_load'] = pras_h2dac_load
dfs['pras_load'] = pras_load
dfs['pras_system'] = pras_system
dfs['pras'] = pras
dfs['resources'] = resources
dfs['tech_map'] = tech_map
dfs['tech_style'] = tech_style
dfs['timeindex'] = timeindex
dfs['vre_gen_usa'] = vre_gen_usa
dfs['vre_gen'] = vre_gen
return dfs
#%%### Plotting functions
def plot_netload_profile(sw, dfs):
"""
Net load profile
"""
import plots
for y in sw['resource_adequacy_years']:
savename = f"A-netload-profile-w{y}-{sw['t']}.png"
dfpos = dfs['net_load_usa'].loc[str(y)].clip(lower=0)
dfneg = dfs['net_load_usa'].loc[str(y)].clip(upper=0)
plt.close()
f,ax = plots.plotyearbymonth(
dfpos, colors=['C3'], dpi=dpi,
)
plots.plotyearbymonth(
dfneg, colors=['C0'],
f=f, ax=ax,
)
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_dropped_load_timeseries_full(sw, dfs):
"""
Dropped load timeseries
"""
import plots
dropped = dfs['pras']['USA_EUE'].copy()
timeindex_y = pd.date_range(
f"{sw['t']}-01-01", f"{sw['t']+1}-01-01", inclusive='left', freq='H',
tz='EST')[:8760]
savename = f"dropped_load-timeseries-wfull-{sw['t']}.png"
weatheryears = sw['resource_adequacy_years']
plt.close()
f,ax = plt.subplots(len(weatheryears), 1, sharex=True, sharey=True, figsize=(13.33,5))
for row, y in enumerate(weatheryears):
ax[row].fill_between(
timeindex_y, dropped.loc[str(y)].values/1e3,
lw=0.2, color='C3')
### Formatting
ax[row].annotate(
y, (0.01,1), xycoords='axes fraction',
fontsize=14, weight='bold', va='top')
### Formatting
ax[0].set_xlim(
pd.Timestamp(f"{sw['t']}-01-01 00:00-05:00"),
pd.Timestamp(f"{sw['t']}-12-31 23:59-05:00"))
ax[0].set_ylim(0)
ax[len(weatheryears)-1].set_ylabel('Dropped load [GW]', y=0, ha='left')
plots.despine(ax)
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_h2dac_load_timeseries(sw, dfs):
"""
H2 and DAC load timeseries
"""
import plots
dfplot = dfs['pras_h2dac_load'].sum(axis=1)
if not dfplot.sum():
return
for y in sw['resource_adequacy_years']:
savename = f"h2dac_load-timeseries-w{y}-{sw['t']}.png"
plt.close()
## DAC and H2 demand
f,ax = plots.plotyearbymonth(
dfplot.loc[str(y)].rename('H2/DAC\ndemand').abs().to_frame(),
colors=['C9'], dpi=dpi,
)
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_dropped_load_duration(sw, dfs):
"""
Dropped load duration
"""
import plots
dropped = dfs['pras']['USA_EUE'].copy()
savename = f"dropped_load-duration-{sw['t']}.png"
plt.close()
f,ax = plt.subplots(dpi=dpi)
## Dropped load
ax.fill_between(
range(len(dropped)),
dropped.rename('Dropped').sort_values(ascending=False).values/1e3,
color='C3', lw=0, label='Dropped',
)
## Mark the extrema
ax.plot(
[0], [dropped.max()/1000],
lw=0, marker=1, ms=5, c='C3',)
ax.plot(
[(dropped>0).sum()], [0],
lw=0, marker=2, ms=5, c='C3',)
## Formatting
ax.set_ylabel('Demand [GW]')
ax.set_xlabel(
f"Hours of dispatch period ({min(sw['resource_adequacy_years'])}–"
f"{max(sw['resource_adequacy_years'])}) [h]")
ax.set_xlim(0, max((dropped>0).sum(), 1))
ax.set_ylim(0)
ax.legend(
# loc='upper left', bbox_to_anchor=(1,1.25),
fontsize=11, frameon=True, ncol=1,
columnspacing=0.5, handletextpad=0.3, handlelength=0.7,
)
plots.despine(ax)
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def map_dropped_load(sw, dfs, level='r'):
"""
Annual EUE and NEUE by ReEDS zone
"""
import reedsplots
### Get the inputs
dfmap = reedsplots.get_dfmap(sw['casedir'])
dfba = dfmap['r']
dropped = dfs['pras'][
[c for c in dfs['pras'] if c.endswith('_EUE') and (not c.startswith('USA'))]
].copy()
dropped.columns = dropped.columns.map(lambda x: x.split('_')[0])
units = {
('EUE','max'): ('MW',1), ('EUE','mean'): ('MW',1), ('EUE','sum'): ('GWh',1e-3),
('NEUE','max'): ('%',1e2), ('NEUE','sum'): ('ppm',1e6),
}
load = dfs['pras_load']
### Plot it
for metric in ['EUE','NEUE']:
## Aggregate if necessary
if level not in ['r','rb','ba']:
dropped = (
dropped.rename(columns=dfs['hierarchy'][level])
.groupby(level=0, axis=1).sum().copy()
)
load = (
load.rename(columns=dfs['hierarchy'][level])
.groupby(level=0, axis=1).sum().copy()
)
for agg in ['max','sum','mean']:
if (metric,agg) not in units:
continue
savename = f"dropped_load-map-{metric}_{agg}-{level}-{sw['t']}.png"
dfplot = dfba.copy()
if level not in ['r','rb','ba']:
dfplot[level] = dfs['hierarchy'][level]
dfplot = dfplot.dissolve(level)
dfplot['centroid_x'] = dfplot.geometry.centroid.x
dfplot['centroid_y'] = dfplot.geometry.centroid.y
if metric == 'EUE':
dfplot['val'] = dropped.agg(agg)
elif (metric == 'NEUE') and (agg == 'max'):
dfplot['val'] = (dropped / load).agg(agg)
elif (metric == 'NEUE'):
dfplot['val'] = dropped.agg(agg) / load.agg(agg)
dfplot.val = (dfplot.val * units[metric,agg][1]).replace(0, np.nan)
plt.close()
f,ax = plt.subplots(figsize=(8,8/1.45), dpi=150)
### Background
dfba.plot(ax=ax, facecolor='none', edgecolor='k', lw=0.2)
### Data
dfplot.plot(ax=ax, column='val', cmap=cmocean.cm.rain)
for r, row in dfplot.iterrows():
if row.val > 0:
ax.annotate(
f'{row.val:,.0f} {units[metric,agg][0]}',
(row.centroid_x, row.centroid_y),
color='r', ha='center', va='top', fontsize=6, weight='bold')
### Formatting
if level in ['r','rb','ba']:
for r, row in dfba.iterrows():
ax.annotate(r, (row.centroid_x, row.centroid_y),
ha='center', va='bottom', fontsize=6, color='C7')
ax.axis('off')
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_pras_ICAP(sw, dfs):
"""
Plot the available capacity used in PRAS without any outages
"""
if not len(dfs['pras_system']):
print('PRAS system was not loaded')
return
### Collect the PRAS system capacities
gencap = dfs['pras_system']['gencap'].groupby(axis=1, level=0).sum()
storcap = dfs['pras_system']['storcap'].groupby(axis=1, level=0).sum()
# genstorcap = dfs['pras_system']['genstorcap'].groupby(axis=1, level=0).sum()
cap = pd.concat([
gencap,
storcap,
# genstorcap,
], axis=1)
## Drop any empties
cap = cap.replace(0,np.nan).dropna(axis=1, how='all').fillna(0).astype(int)
## Get the colors
tech_map = dfs['tech_map']
tech_map.index = tech_map.index.str.lower()
tech_map = tech_map.str.lower()
tech_style = dfs['tech_style']
## Aggregate by type
cap.columns = cap.columns.map(
lambda x: x if x.startswith('battery') else x.strip('_01234567890*')).str.lower()
cap.columns = (
cap.columns
.map(lambda x: [i for i in tech_map.index if x.startswith(i)][0])
.map(lambda x: tech_map.get(x,x))
)
cap.columns
cap = cap.groupby(axis=1, level=0).sum()
order = [c for c in tech_style.index if c in cap]
cap = cap[order]
if cap.shape[1] != len(order):
raise Exception(f"missing colors: {cap.columns}, {tech_style}")
## Cumulate for plot
cumcap = cap.cumsum(axis=1)[order[::-1]]
load = dfs['pras_system']['load'].sum(axis=1)
### Plot it
years = list(range(2007,2014))
for y in years:
savename = f"PRAS-ICAP-w{y}-{sw['t']}.png"
plt.close()
f,ax = plots.plotyearbymonth(
load.loc[str(y)],
colors=['k'], style='line', lwforline=1,
)
plots.plotyearbymonth(
cumcap.loc[str(y)], colors=[tech_style[c] for c in cumcap],
f=f, ax=ax,
)
## Legend
leg = ax[0].legend(
loc='upper left', bbox_to_anchor=(1,1.25),
fontsize=11, frameon=False, ncol=1,
columnspacing=0.5, handletextpad=0.3, handlelength=0.7,
)
leg.set_title(f'ICAP {y}', prop={'size':'large'})
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_augur_pras_capacity(sw, dfs):
"""
Plot the nameplate capacity from Augur and PRAS to check consistency
"""
if not len(dfs['pras_system']):
print('PRAS system was not loaded')
return
savename = f"PRAS-Augur-capacity-{sw['t']}.png"
### Get the colors
tech_style = dfs['tech_style']
### Collect the PRAS system capacities
gencap = dfs['pras_system']['gencap']
storcap = dfs['pras_system']['storcap']
# genstorcap = dfs['pras_system']['genstorcap']
# load = dfs['pras_system']['load'] / 1e3
cap = {}
cap['pras'] = pd.concat([
gencap,
storcap,
# genstorcap,
], axis=1) / 1e3
## Drop any empties
cap['pras'] = cap['pras'].replace(0,np.nan).dropna(axis=1, how='all').fillna(0)
## Aggregate by type
cap['pras'] = (
group_techs(cap['pras'], dfs, technamecol='wide')
.groupby(axis=1, level=[1,0]).sum().max().rename('MW')
)
### Collect the Augur capacities
cap['augur'] = dfs['max_cap'].groupby(['i','r'], as_index=False).MW.sum()
## Convert from s to p regions
cap['augur'].r = cap['augur'].r
## Aggregate by type
cap['augur'] = (
group_techs(cap['augur'], dfs, technamecol='i')
.replace({'i':{'hydro_new':'hydro', 'hydro_exist':'hydro'}})
.groupby(['r','i']).MW.sum() / 1e3
)
### Drop VRE since its capacity is handled differently
for datum in cap:
cap[datum] = (
cap[datum].loc[
~cap[datum].index.get_level_values('i').isin(
dfs['vre_gen_usa'].columns.tolist())
]
)
### Get coordinates
zones = dfs['hierarchy'].index
ncols = int(np.around(np.sqrt(len(zones)) * 1.618, 0))
nrows = len(zones) // ncols + int(bool(len(zones) % ncols))
coords = dict(zip(zones, [(row,col) for row in range(nrows) for col in range(ncols)]))
### Plot the capacities
plt.close()
f,ax = plt.subplots(
nrows, ncols, figsize=(1.2*ncols, 1.2*nrows), sharex=True,
gridspec_kw={'wspace':0.6, 'hspace':0.5}
)
alltechs = set()
for r in zones:
df = pd.concat({'A':cap['augur'].get(r,pd.Series()), 'P':cap['pras'].get(r,pd.Series())}, axis=1).T
order = [c for c in tech_style.index if c in df]
missing = [c for c in df if c not in order]
if len(missing):
print(f'WARNING: Missing colors for these techs: {missing}')
df = df[order].copy()
alltechs.update(df.columns.tolist())
plots.stackbar(df=df, ax=ax[coords[r]], colors=tech_style, net=False, width=0.8)
### Formatting
ax[coords[r]].set_title(r)
ax[coords[r]].set_xticks([0,1])
ax[coords[r]].set_xticklabels(['A','P'])
### Legend
if r == zones[-1]:
handles = [
mpl.patches.Patch(facecolor=tech_style[i], edgecolor='none', label=i)
for i in [j for j in tech_style.index if j in alltechs]
][::-1]
ax[coords[r]].legend(
handles=handles, loc='center left', bbox_to_anchor=(1,0.5),
frameon=False, columnspacing=0.5, handletextpad=0.3, handlelength=0.7,
ncol=len(alltechs)//3,
)
### Formatting
plots.trim_subplots(ax=ax, nrows=nrows, ncols=ncols, nsubplots=len(zones))
ax[-1, 0].set_ylabel('Nameplate capacity [GW]', y=0, ha='left')
plots.despine(ax)
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_pras_ICAP_regional(sw, dfs, numdays=5):
"""
Plot the available capacity used in PRAS without any outages
"""
if not len(dfs['pras_system']):
print('PRAS system was not loaded')
return
### Get the peak dropped-load periods
import plots
dropped = dfs['pras']['USA_EUE'].copy()
dropped = dropped.groupby(
[dropped.index.year, dropped.index.month, dropped.index.day]
).sum().nlargest(numdays).replace(0,np.nan).dropna()
### Collect the PRAS system capacities
gencap = dfs['pras_system']['gencap']
storcap = dfs['pras_system']['storcap']
# genstorcap = dfs['pras_system']['genstorcap']
load = dfs['pras_system']['load'] / 1e3
cap = pd.concat([
gencap,
storcap,
# genstorcap,
], axis=1) / 1e3
## Drop any empties
cap = cap.replace(0,np.nan).dropna(axis=1, how='all').fillna(0)
## Get the colors
tech_map = dfs['tech_map']
tech_map.index = tech_map.index.str.lower()
tech_map = tech_map.str.lower()
tech_style = dfs['tech_style']
## Aggregate by type
def renamer(x):
out = (x if x[0].startswith('battery')
else (x[0].strip('_01234567890*').lower(), x[1]))
return out
cap.columns = cap.columns.map(renamer)
cap.columns = (
cap.columns
.map(lambda x: ([i for i in tech_map.index if x[0].startswith(i)][0], x[1]))
.map(lambda x: (tech_map.get(x[0],x[0]), x[1]))
)
cap = cap.groupby(axis=1, level=[1,0]).sum()
### Get coordinates
zones = dfs['hierarchy'].index
ncols = int(np.around(np.sqrt(len(zones)) * 1.618, 0))
nrows = len(zones) // ncols + int(bool(len(zones) % ncols))
coords = dict(zip(zones, [(row,col) for row in range(nrows) for col in range(ncols)]))
### Plot the highest-EUE days
for day in range(len(dropped)):
date = '{}-{:>02}-{:>02}'.format(*dropped.index[day])
savename = f"PRAS-ICAP-{sw['t']}-{date.replace('-','')}.png"
plt.close()
f,ax = plt.subplots(
nrows, ncols, figsize=(1.2*ncols, 1.2*nrows), sharex=True,
gridspec_kw={'wspace':0.6, 'hspace':0.5}
)
for r in zones:
df = cap.loc[date][r].copy()
### Cumulate for plot
order = [c for c in tech_style.index if c in df]
df = df[order]
### Plot it
plots.stackbar(
df=df, ax=ax[coords[r]], colors=tech_style, net=False, align='edge')
ax[coords[r]].plot(range(len(df)), load.loc[date][r].values, c='k', lw=1)
### Formatting
ax[coords[r]].set_title(r)
### Formatting
plots.trim_subplots(ax=ax, nrows=nrows, ncols=ncols, nsubplots=len(zones))
ax[coords[zones[0]]].set_xlim(0,24)
ax[coords[zones[0]]].set_xticks([])
ax[-1, 0].set_xlabel(date, x=0, ha='left', labelpad=10)
ax[-1, 0].set_ylabel('ICAP [GW]', y=0, ha='left')
plots.despine(ax)
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_pras_unitsize_distribution(sw, dfs):
"""
Distribution of PRAS unit sizes by tech
"""
if not len(dfs['pras_system']):
print('PRAS system was not loaded')
return
savename = f"PRAS-unitcap-{sw['t']}.png"
import plots
# import reedsplots
gencap = dfs['pras_system']['gencap']
storcap = dfs['pras_system']['storcap']
# genstorcap = dfs['pras_system']['genstorcap']
cap = (
pd.concat([
gencap,
storcap,
# genstorcap,
], axis=1)
.max().rename('MW').reset_index()
)
## Get the colors
tech_map = dfs['tech_map'].copy()
tech_map.index = tech_map.index.str.lower()
tech_map = tech_map.str.lower()
tech_style = dfs['tech_style'].copy()
toadd = tech_style.loc[tech_style.index.str.endswith('_mod')]
toadd.index = toadd.index.str.replace('_mod','')
tech_style = pd.concat([tech_style,toadd])
## Aggregate by type
cap.i = cap.i.map(
lambda x: x if x.startswith('battery') else (x.strip('_01234567890*').lower()))
cap.i = (
cap.i
.map(lambda x: tech_map.get(x,x))
.str.replace('_upgrade','')
.str.replace('_mod','')
)
if 'new_blank_genstor' in cap.i.values:
if (cap.loc[cap.i=='new_blank_genstor','MW'] == 0).all():
cap = cap.loc[cap.i != 'new_blank_genstor'].copy()
techs = cap.i.unique()
nondisaggtechs = (
dfs['vre_gen_usa'].columns.tolist()
+ ['hydro_exist']
+ [i for i in techs if i.startswith('battery')]
+ [i for i in techs if (('canada' in i.lower()) or ('can-imports' in i.lower()))]
)
order = [i for i in tech_style.index if i in techs]
others = [i for i in techs if ((i not in order) and (i not in nondisaggtechs))]
# for i in others:
# tech_style[i] = 'k'
ylabel = {0: {'scale':1, 'units':'MW'}, 1: {'scale':1e-3, 'units':'GW'}}
plt.close()
f,ax = plt.subplots(1, 2, figsize=(7,3.75), gridspec_kw={'wspace':0.4})
for i in (order+others)[::-1]:
df = cap.loc[cap.i==i].copy()
col = 1 if i in nondisaggtechs else 0
df.MW = df.MW * ylabel[col]['scale']
ax[col].plot(
range(len(df)), df.MW.sort_values().values,
c=tech_style.get(i,'k'), label=i)
ax[col].annotate(
f' {i}', (len(df), df.MW.max()),
fontsize=10, color=tech_style.get(i,'k'),
path_effects=[pe.withStroke(linewidth=1.5, foreground='w', alpha=0.7)],
)
for col in range(2):
ax[col].set_ylabel(f"Unit size [{ylabel[col]['units']}]")
ax[col].set_xlabel('Number of units')
ax[col].set_xlim(0)
ax[col].set_ylim(0)
ax[col].legend(
ncol=1, columnspacing=0.5, frameon=False,
handletextpad=0.3, handlelength=0.7,
loc='upper center', bbox_to_anchor=(0.5,-0.2))
ax[0].set_title('Disaggregated techs')
ax[1].set_title('Aggregated techs (PRAS FOR=0)')
plots.despine(ax)
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_pras_augur_load(sw, dfs):
"""PRAS load against Augur load"""
import plots
dfpras = dfs['pras_system']['load'].sum(axis=1).rename('PRAS')
dfaugur = dfs['load_r'].set_axis(dfpras.index).sum(axis=1).rename('Augur')
years = dfpras.index.year.unique()
linecolors = {'Augur':'C0', 'PRAS':'C3'}
for year in years:
savename = f"demand_USA-Augur-PRAS-w{year}-{sw['t']}.png"
plt.close()
f,ax = plots.plotyearbymonth(
dfaugur.loc[str(year)], style='line', colors=linecolors['Augur'])
plots.plotyearbymonth(
dfpras.loc[str(year)], style='line', colors=linecolors['PRAS'], f=f, ax=ax)
## Legend
handles = [
mpl.lines.Line2D([], [], color=linecolors[i], label=i, lw=2)
for i in linecolors
]
ax[0].legend(
handles=handles, ncol=2, frameon=False,
loc='lower right', bbox_to_anchor=(1,0.5),
handlelength=1.0, handletextpad=0.3, labelspacing=0.5, columnspacing=0.5,
)
ax[0].annotate(year, (0.85, 1), xycoords='axes fraction', ha='right')
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def plot_pras_load(sw, dfs):
"""PRAS load over all weather years"""
import plots
dfpras = dfs['pras_system']['load'].sum(axis=1).rename('PRAS')
years = dfpras.index.year.unique()
linecolors = plots.rainbowmapper(years)
savename = f"demand_USA-PRAS-{sw['t']}.png"
plt.close()
f,ax = plots.plotyearbymonth(
dfpras.loc[str(years[0])], style='line', colors=linecolors[years[0]])
for year in years[1:]:
plots.plotyearbymonth(
dfpras.loc[str(year)], style='line', colors=linecolors[year], f=f, ax=ax)
## Legend
handles = [
mpl.lines.Line2D([], [], color=linecolors[y], label=y, lw=2)
for y in years
]
ax[0].legend(
handles=handles, ncol=len(years), frameon=False,
loc='lower right', bbox_to_anchor=(1,0.5),
handlelength=1.0, handletextpad=0.3, labelspacing=0.5, columnspacing=0.5,
)
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
def map_pras_failure_rate(sw, dfs, aggfunc='mean', repair=False):
"""Failure rates from PRAS"""
import plots
import reedsplots
dfmap = reedsplots.get_dfmap(sw['casedir'])
dfzones = dfmap['r']
failrate = (
dfs['pras_system']['genfailrate']
## Only keep one copy of each (i,r)
.T.reset_index().drop_duplicates().set_index(['i','r']).T
* 100
)
failrate.index = dfs['pras_system']['genfailrate'].index
failsum = failrate.sum()
plottechs = failsum.loc[failsum != 0].index.get_level_values('i').unique()
for tech in plottechs:
savename = f"hourly_failure_rate-year,month-{aggfunc}-{tech.replace('-','')}-{sw['t']}"
plt.close()
f, ax = plots.map_years_months(
dfzones=dfzones, dfdata=failrate[tech],
title=f"Monthly {aggfunc}\nhourly failure rate,\n{tech} [%]",
)
## Save it
if savefig:
plt.savefig(os.path.join(sw['savepath'],savename))
if interactive:
plt.show()
plt.close()
if repair:
repairrate = (
dfs['pras_system']['genrepairrate']
.T.reset_index().drop_duplicates().set_index(['i','r']).T
* 100
)
repairrate.index = dfs['pras_system']['genrepairrate'].index
for tech in plottechs:
savename = f"hourly_repair_rate-year,month-{aggfunc}-{tech.replace('-','')}-{sw['t']}"
plt.close()
f, ax = plots.map_years_months(
dfzones=dfzones, dfdata=repairrate[tech],
title=f"Monthly {aggfunc}\nhourly repair rate,\n{tech} [%]",
)
## Save it
if savefig: