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Background option of Elec Price
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@theodoreln
theodoreln committed Feb 3, 2025
1 parent 38a5adf commit aca897d
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2 changes: 1 addition & 1 deletion docs/postprocessing/map_plotting.md
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Expand Up @@ -66,7 +66,7 @@ fig, ax = res.plot_map(scenario='SC3', year=2050, commodity='Hydrogen', generati

## Background information

Using the option **background**, it is possible to plot an additional information that colors the countries. For now *H2 Storage*, *Elec Storage*, *Elec Net Export* and *H2 Net Export* have been implemented. \
Using the option **background**, it is possible to plot an additional information that colors the countries. For now *H2 Storage*, *Elec Storage*, *Elec Net Export*, *H2 Net Export* and *Elec Price* have been implemented. \
It is possible to personalize the background scale with the option **background_scale** and **background_scale_tick** as in the examples below (both can be used at the same time).


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202 changes: 93 additions & 109 deletions examples/MapPlotting.ipynb
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63 changes: 46 additions & 17 deletions src/pybalmorel/plotting/maps_balmorel.py
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Expand Up @@ -197,10 +197,11 @@ def plot_map(path_to_result: str,
generation_exclude_H2Storage = kwargs.get('generation_exclude_H2Storage', True) #do not plot the capacities of the H2 storage
generation_exclude_ElectricStorage = kwargs.get('generation_exclude_ElectricStorage', True) #do not plot the production of Electric storag, only works with Show pie production
generation_exclude_Geothermal = kwargs.get('generation_exclude_Geothermal', True) #do not plot the production of Geothermal, only works with Show pie production -> Do we have ?
background_dict = {'H2 Storage': {'type': 'simpletransfo','var': 'G_STO_YCRAF', 'filters': [('COMMODITY','HYDROGEN')], 'transformation': [1/1000], 'colormap': (plt.cm.Blues,'Blues'), 'unit': 'TWh'},
'Elec Storage': {'type': 'simpletransfo', 'var': 'G_STO_YCRAF', 'filters': [('COMMODITY','ELECTRICITY')], 'transformation': [1/1000], 'colormap': (plt.cm.Oranges,'Oranges'), 'unit': 'TWh'},
'H2 Net Export' : {'type': 'netexport', 'var': 'XH2_FLOW_YCR', 'colormap' : (plt.cm.RdYlGn, 'RdYlGn'), 'unit': 'TWh'},
'Elec Net Export' : {'type': 'netexport', 'var': 'X_FLOW_YCR', 'colormap' : (plt.cm.RdYlGn, 'RdYlGn'), 'unit': 'TWh'}} # Dictionary of background options
background_dict = {'H2 Storage': {'type': 'simpletransfo','var': 'G_STO_YCRAF', 'filters': [('COMMODITY','HYDROGEN')], 'transformation': [1/1000], 'colormap': (plt.cm.Blues,'Blues'), 'unit': 'TWh', 'basescale': '0max'},
'Elec Storage': {'type': 'simpletransfo', 'var': 'G_STO_YCRAF', 'filters': [('COMMODITY','ELECTRICITY')], 'transformation': [1/1000], 'colormap': (plt.cm.Oranges,'Oranges'), 'unit': 'TWh', 'basescale': '0max'},
'Elec Price': {'type': 'simpletransfo', 'var': ['EL_PRICE_YCR', 'EL_PRICE_YCRST'], 'colormap': (plt.cm.RdYlGn_r, 'RdYlGn_r'), 'unit': 'Money/MWh', 'basescale': 'minmax'},
'H2 Net Export' : {'type': 'netexport', 'var': 'XH2_FLOW_YCR', 'colormap' : (plt.cm.RdYlGn, 'RdYlGn'), 'unit': 'TWh', 'basescale': '0max'},
'Elec Net Export' : {'type': 'netexport', 'var': 'X_FLOW_YCR', 'colormap' : (plt.cm.RdYlGn, 'RdYlGn'), 'unit': 'TWh', 'basescale': '0max'}} # Dictionary of background options
if background not in background_dict.keys() and background != None : # Check that it's a possible type of background
print('background set to None')
background = None
Expand Down Expand Up @@ -323,7 +324,7 @@ def plot_map(path_to_result: str,
'SOLAR-PV': '#d2a106',
'WIND-OFFSHORE': '#08bdba',
'INTRASEASONAL-ELECT-STORAGE': '#ba4e00',
'ELECTROLYZER': '#ADD8E6',
'ELECTROLYZER': '#ADD8E6', #7FBF7F
'H2-STORAGE': '#FFC0CB',
'FUELCELL': '#d4bbff',
'CHP' : '#E5D8D8'
Expand Down Expand Up @@ -536,7 +537,16 @@ def df_creation(gdx_file, varname, system_directory):
if generation.lower() == 'productiontime':
var_list += ['PRO_YCRAGFST']
if selected_background != None:
var_list = var_list + [selected_background['var']]
if type(selected_background['var']) == list :
if lines in ['FlowTime', 'UtilizationTime'] or generation == 'ProductionTime' :
background_var = selected_background['var'][1]
var_list = var_list + [background_var]
else :
background_var = selected_background['var'][0]
var_list = var_list + [background_var]
else :
background_var = selected_background['var']
var_list = var_list + [background_var]


## 1.4.3 - Use function to read inputs
Expand Down Expand Up @@ -600,7 +610,7 @@ def df_creation(gdx_file, varname, system_directory):

# Background data
if selected_background != None:
df_background = all_df[selected_background['var']]
df_background = all_df[background_var]

## 1.4.4 - Select relevant dataframe and rename columns
column_dict = {'Val':'Value', 'Y':'Year', 'C':'Country'}
Expand Down Expand Up @@ -711,6 +721,17 @@ def df_creation(gdx_file, varname, system_directory):
df_generation = df_generation.reset_index(drop = True)
if len(df_generation) == 0:
raise ValueError('No data for the selected season and time step')
if selected_background != None:
if type(selected_background['var']) == list :
if lines in ['FlowTime', 'UtilizationTime'] or generation == 'ProductionTime' :
if S == '' :
S = df_background['SSS'].iloc[0]
if T == '' :
T = df_background['TTT'].iloc[0]
df_background = df_background.loc[df_background['SSS'] == S]
df_background = df_background.loc[df_background['TTT'] == T]
if len(df_background) == 0:
raise ValueError('No data for the selected season and time step')


### 2.3 Calculate the utilization of the lines
Expand Down Expand Up @@ -1013,14 +1034,20 @@ def df_creation(gdx_file, varname, system_directory):

if selected_background != None:
if selected_background['type'] == 'simpletransfo' :
# Filter the data
filters = selected_background['filters']
for i in range(len(filters)):
df_background = df_background.loc[df_background[filters[i][0]] == filters[i][1]].reset_index(drop = True)
# Apply the transformation
transformation = selected_background['transformation']
for i in range(len(transformation)):
df_background["Value"] = df_background["Value"]*transformation[i]
try :
# Filter the data
filters = selected_background['filters']
for i in range(len(filters)):
df_background = df_background.loc[df_background[filters[i][0]] == filters[i][1]].reset_index(drop = True)
except :
pass
try :
# Apply the transformation
transformation = selected_background['transformation']
for i in range(len(transformation)):
df_background["Value"] = df_background["Value"]*transformation[i]
except :
pass
# Group by region RRR
df_background = pd.DataFrame(df_background.groupby(['RRR'])['Value'].sum().reset_index())
elif selected_background['type'] == 'netexport' :
Expand All @@ -1036,7 +1063,10 @@ def df_creation(gdx_file, varname, system_directory):

# Deal with the scale of the background
if background_scale == [0,0]:
background_scale[1] = df_background['Value'].max()
if selected_background['basescale'] in ['0max', 'minmax']:
background_scale[1] = df_background['Value'].max()
if selected_background['basescale'] == 'minmax' :
background_scale[0] = df_background['Value'].min()
if selected_background['type'] == 'netexport' :
if -df_background['Value'].max() < df_background['Value'].min() :
background_scale[0] = -df_background['Value'].max()
Expand Down Expand Up @@ -1517,7 +1547,6 @@ def find_nearest(array, value):

if selected_background != None:
# Ticks label
print(background_scale, background_scale_tick)
bg_lower = np.ceil(background_scale[0] / background_scale_tick) * background_scale_tick
bg_upper = np.floor(background_scale[1] / background_scale_tick) * background_scale_tick
ticks = list(np.arange(bg_lower, bg_upper+background_scale_tick, background_scale_tick))
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