Notebook

In [2]:

import xarray as xr
import pandas as pd
import numpy as np

import calliope

calliope.set_log_verbosity('INFO', include_solver_output=False)

Model input¶

In [3]:

# Initialise the model with the Urban Scale example model
m = calliope.examples.urban_scale()

[2023-01-18 13:20:45] INFO     Model: initialising
[2023-01-18 13:20:46] INFO     Model: preprocessing stage 1 (model_run)
[2023-01-18 13:20:47] INFO     Model: preprocessing stage 2 (model_data)
[2023-01-18 13:20:47] INFO     Model: preprocessing complete

In [4]:

# Get information on the model
m.info()

Out[4]:

'Model name:   Urban-scale example model\nModel size:   4 locations, 9 technologies, 48 timesteps'

model_run¶

m._model_run is a python dictionary. The underscore before the method indicates that it defaults to being hidden (i.e. you wouldn't see it by trying a tab auto-complete and it isn't documented)

In [5]:

# Model run holds all the data from the YAML and CSV files, restructured into one dictionary
m._model_run.keys()

Out[5]:

dict_keys(['scenario', 'applied_overrides', 'techs', 'tech_groups', 'locations', 'timeseries_data', 'run', 'model', 'group_constraints', 'sets', 'constraint_sets'])

In [6]:

# All locations now hold all information about a technology at that location

m._model_run['locations']['X2']['techs']['pv']

Out[6]:

{'costs': {'monetary': {'export': -0.0491,
   'om_prod': -0.0203,
   'depreciation_rate': 0.11016807219002081,
   'energy_cap': 1350}},
 'constraints': {'energy_cap_max': 250,
  'energy_prod': True,
  'export_carrier': 'electricity',
  'force_resource': True,
  'lifetime': 25,
  'parasitic_eff': 0.85,
  'resource': 'file=pv_resource.csv:per_area',
  'resource_area_max': 1500,
  'resource_area_per_energy_cap': 7,
  'resource_eff': 1.0,
  'resource_unit': 'energy_per_area'}}

In [7]:

# This includes location-specific overrides, such as energy_cap_max of 50 for the pv technology at location X3

m._model_run['locations']['X3']['techs']['pv']

Out[7]:

{'constraints': {'energy_cap_max': 50,
  'energy_prod': True,
  'export_carrier': 'electricity',
  'force_resource': True,
  'lifetime': 25,
  'parasitic_eff': 0.85,
  'resource': 'file=pv_resource.csv:per_area',
  'resource_area_max': 1500,
  'resource_area_per_energy_cap': 7,
  'resource_eff': 1.0,
  'resource_unit': 'energy_per_area'},
 'costs': {'monetary': {'om_annual': -80.5,
   'depreciation_rate': 0.11016807219002081,
   'energy_cap': 1350}}}

In [8]:

# All sets have also been collated.
# locations and technologies are concatenated into loc::tech sets, 
# to create a dense matrix and smaller overall model size

m._model_run['sets']['loc_techs']

Out[8]:

['X3::power_lines:X1',
 'X1::supply_gas',
 'X3::boiler',
 'X3::pv',
 'X2::pv',
 'X1::supply_grid_power',
 'X1::chp',
 'X2::power_lines:X1',
 'X1::power_lines:X2',
 'X3::demand_heat',
 'X2::heat_pipes:N1',
 'X3::heat_pipes:N1',
 'X1::pv',
 'X1::power_lines:X3',
 'X1::demand_heat',
 'N1::heat_pipes:X3',
 'X2::supply_gas',
 'X2::demand_electricity',
 'N1::heat_pipes:X2',
 'X2::demand_heat',
 'X1::demand_electricity',
 'N1::heat_pipes:X1',
 'X2::boiler',
 'X1::heat_pipes:N1',
 'X3::supply_gas',
 'X3::demand_electricity']

In [9]:

# For every constraint, a set of loc_techs (or loc_tech_carriers) is prepared, 
# so we only build the constraint over that set

m._model_run['constraint_sets']['loc_techs_energy_capacity_constraint']

Out[9]:

['X3::power_lines:X1',
 'X1::supply_gas',
 'X3::boiler',
 'X3::pv',
 'X2::pv',
 'X1::supply_grid_power',
 'X1::chp',
 'X2::power_lines:X1',
 'X1::power_lines:X2',
 'X3::demand_heat',
 'X2::heat_pipes:N1',
 'X3::heat_pipes:N1',
 'X1::pv',
 'X1::power_lines:X3',
 'X1::demand_heat',
 'N1::heat_pipes:X3',
 'X2::supply_gas',
 'X2::demand_electricity',
 'N1::heat_pipes:X2',
 'X2::demand_heat',
 'X1::demand_electricity',
 'N1::heat_pipes:X1',
 'X2::boiler',
 'X1::heat_pipes:N1',
 'X3::supply_gas',
 'X3::demand_electricity']

In [10]:

m._model_run['constraint_sets']['loc_techs_resource_area_constraint']

Out[10]:

['X2::pv', 'X3::pv', 'X1::pv']

In [11]:

# timeseries data is stored as dataframes, having been loaded from CSV
m._model_run['timeseries_data']['pv_resource.csv'].head()

Out[11]:

	per_area	per_cap
2005-07-01 00:00:00	0.0	0.0
2005-07-01 01:00:00	0.0	0.0
2005-07-01 02:00:00	0.0	0.0
2005-07-01 03:00:00	0.0	0.0
2005-07-01 04:00:00	0.0	0.0

model_data¶

m._model_data is an xarray Dataset

In [15]:

# We can find the same PV energy_cap_max data as seen in m._model_run
m._model_data.energy_cap_max.loc[{'loc_techs': 'X2::pv'}]

Out[15]:

<xarray.DataArray 'energy_cap_max' ()>
array(250.)
Coordinates:
    loc_techs  <U6 'X2::pv'
Attributes:
    is_result:  0

In [16]:

m._model_data.energy_cap_max.loc[{'loc_techs': 'X3::pv'}]

Out[16]:

<xarray.DataArray 'energy_cap_max' ()>
array(50.)
Coordinates:
    loc_techs  <U6 'X3::pv'
Attributes:
    is_result:  0

In [17]:

# We can also see the constraint-specific set of loc::techs for setting the energy capacity constraint
m._model_data.loc_techs_energy_capacity_constraint

Out[17]:

<xarray.DataArray 'loc_techs_energy_capacity_constraint' (
                                                          loc_techs_energy_capacity_constraint: 26)>
array(['X3::power_lines:X1', 'X1::supply_gas', 'X3::boiler', 'X3::pv',
       'X2::pv', 'X1::supply_grid_power', 'X1::chp', 'X2::power_lines:X1',
       'X1::power_lines:X2', 'X3::demand_heat', 'X2::heat_pipes:N1',
       'X3::heat_pipes:N1', 'X1::pv', 'X1::power_lines:X3', 'X1::demand_heat',
       'N1::heat_pipes:X3', 'X2::supply_gas', 'X2::demand_electricity',
       'N1::heat_pipes:X2', 'X2::demand_heat', 'X1::demand_electricity',
       'N1::heat_pipes:X1', 'X2::boiler', 'X1::heat_pipes:N1',