For more details on analysing input/output data, see the full urban scale example model
import calliope
# We increase logging verbosity
calliope.set_log_verbosity('INFO', include_solver_output=False)
model = calliope.examples.milp()
# Note, we see the overrides that we have applied printed here, thanks to inreasing logging verbosity
# We also see a warning that we are applying binary/integer decision variables in a model, to remind us that this
# model may take a while to run
[2021-07-29 16:03:10] INFO Model: initialising [2021-07-29 16:03:10] INFO Applying the following overrides from scenario definition: ['milp'] [2021-07-29 16:03:10] INFO Override applied to model.name: Urban-scale example model -> Urban-scale example model with MILP `run.solver_options.mipgap`:0.05 applied from override as new configuration `techs.boiler.costs.monetary.energy_cap`:35 applied from override as new configuration `techs.boiler.costs.monetary.purchase`:2000 applied from override as new configuration `techs.chp.constraints.energy_cap_min_use`:0.2 applied from override as new configuration `techs.chp.constraints.energy_cap_per_unit`:300 applied from override as new configuration `techs.chp.constraints.units_max`:4 applied from override as new configuration Override applied to techs.chp.costs.monetary.energy_cap: 750 -> 700 `techs.chp.costs.monetary.purchase`:40000 applied from override as new configuration `techs.heat_pipes.constraints.force_asynchronous_prod_con`:True applied from override as new configuration [2021-07-29 16:03:10] INFO Model: preprocessing stage 1 (model_run) [2021-07-29 16:03:11] INFO NumExpr defaulting to 8 threads. [2021-07-29 16:03:11] INFO Model: preprocessing stage 2 (model_data) [2021-07-29 16:03:11] WARNING /Users/brynmorp/Repos/calliope-project/calliope/calliope/exceptions.py:60: ModelWarning: Possible issues found during model processing: * Integer and / or binary decision variables are included in this model. This may adversely affect solution time, particularly if you are using a non-commercial solver. To improve solution time, consider changing MILP related solver options (e.g. `mipgap`) or removing MILP constraints. [2021-07-29 16:03:11] INFO Model: preprocessing complete
# Model inputs can be viewed at `model.inputs`.
# Variables are indexed over any combination of `techs`, `locs`, `carriers`, `costs` and `timesteps`,
# although `techs`, `locs`, and `carriers` are often concatenated.
# e.g. `chp`, `X1`, `heat` -> `X1::chp::heat`
model.inputs
<xarray.Dataset>
Dimensions: (carrier_tiers: 3, carriers: 3, coordinates: 2, costs: 1, loc_carriers: 10, loc_tech_carriers_conversion_plus: 3, loc_techs: 26, loc_techs_area: 3, loc_techs_conversion: 2, loc_techs_conversion_plus: 1, loc_techs_export: 4, loc_techs_finite_resource: 9, loc_techs_investment_cost: 20, loc_techs_milp: 1, loc_techs_non_conversion: 23, loc_techs_om_cost: 9, loc_techs_supply_plus: 3, loc_techs_transmission: 10, locs: 4, techs: 9, timesteps: 48)
Coordinates: (12/21)
* carrier_tiers (carrier_tiers) <U5 'in' 'out' 'out_2'
* carriers (carriers) <U11 'gas' ... 'heat'
* coordinates (coordinates) object 'y' 'x'
* costs (costs) object 'monetary'
* loc_carriers (loc_carriers) object 'X1::electric...
* loc_tech_carriers_conversion_plus (loc_tech_carriers_conversion_plus) object ...
... ...
* loc_techs_om_cost (loc_techs_om_cost) object 'X1::sup...
* loc_techs_supply_plus (loc_techs_supply_plus) object 'X2:...
* loc_techs_transmission (loc_techs_transmission) object 'N1...
* locs (locs) object 'X3' 'X1' 'N1' 'X2'
* techs (techs) object 'heat_pipes' ... 'chp'
* timesteps (timesteps) datetime64[ns] 2005-07-...
Data variables: (12/43)
energy_cap_max (loc_techs) float64 2e+03 ... 2e+03
energy_con (loc_techs) float64 1.0 nan ... 1.0
resource_area_max (loc_techs_area) int64 1500 1500 1500
units_max (loc_techs_milp) int64 4
resource_area_per_energy_cap (loc_techs_area) int64 7 7 7
resource_eff (loc_techs_finite_resource) float64 ...
... ...
lookup_loc_techs_conversion_plus (carrier_tiers, loc_techs_conversion_plus) object ...
lookup_loc_techs_export (loc_techs_export) object 'X2::pv::...
lookup_loc_techs_area (locs) object 'X3::pv' ... 'X2::pv'
timestep_resolution (timesteps) float64 1.0 1.0 ... 1.0
timestep_weights (timesteps) float64 1.0 1.0 ... 1.0
max_demand_timesteps (carriers) datetime64[ns] 2005-07-0...
Attributes:
calliope_version: 0.6.7-dev
applied_overrides: milp
scenario: milp
defaults: available_area: null\ncarrier_ratios: false\ncharge_...
allow_operate_mode: 1array(['in', 'out', 'out_2'], dtype='<U5')
array(['gas', 'electricity', 'heat'], dtype='<U11')
array(['y', 'x'], dtype=object)
array(['monetary'], dtype=object)
array(['X1::electricity', 'X3::electricity', 'N1::heat', 'X2::gas', 'X1::heat',
'X1::gas', 'X2::heat', 'X3::gas', 'X3::heat', 'X2::electricity'],
dtype=object)array(['X1::chp::heat', 'X1::chp::electricity', 'X1::chp::gas'], dtype=object)
array(['N1::heat_pipes:X2', 'X1::supply_grid_power', 'X3::power_lines:X1',
'X2::demand_heat', 'X1::demand_heat', 'X2::boiler',
'X2::demand_electricity', 'X1::heat_pipes:N1', 'X3::demand_electricity',
'X3::boiler', 'X1::pv', 'X1::supply_gas', 'X2::supply_gas',
'X2::heat_pipes:N1', 'X3::heat_pipes:N1', 'X2::pv', 'X3::pv',
'X3::demand_heat', 'N1::heat_pipes:X3', 'X1::chp', 'X3::supply_gas',
'N1::heat_pipes:X1', 'X1::power_lines:X2', 'X1::power_lines:X3',
'X1::demand_electricity', 'X2::power_lines:X1'], dtype=object)array(['X2::pv', 'X1::pv', 'X3::pv'], dtype=object)
array(['X3::boiler', 'X2::boiler'], dtype='<U10')
array(['X1::chp'], dtype='<U7')
array(['X2::pv', 'X1::pv', 'X3::pv', 'X1::chp'], dtype=object)
array(['X2::demand_heat', 'X1::demand_heat', 'X2::demand_electricity',
'X3::demand_electricity', 'X1::pv', 'X2::pv', 'X3::pv',
'X3::demand_heat', 'X1::demand_electricity'], dtype=object)array(['N1::heat_pipes:X2', 'X1::supply_grid_power', 'X3::power_lines:X1',
'X2::boiler', 'X1::heat_pipes:N1', 'X2::heat_pipes:N1', 'X3::boiler',
'X1::pv', 'X1::supply_gas', 'X2::supply_gas', 'X3::heat_pipes:N1',
'X2::pv', 'X3::pv', 'N1::heat_pipes:X3', 'X1::chp', 'X3::supply_gas',
'N1::heat_pipes:X1', 'X1::power_lines:X2', 'X1::power_lines:X3',
'X2::power_lines:X1'], dtype=object)array(['X1::chp'], dtype=object)
array(['N1::heat_pipes:X2', 'X1::supply_grid_power', 'X3::power_lines:X1',
'X2::demand_heat', 'X1::demand_heat', 'X2::demand_electricity',
'X1::heat_pipes:N1', 'X3::demand_electricity', 'X1::pv',
'X2::supply_gas', 'X1::supply_gas', 'X2::heat_pipes:N1',
'X3::heat_pipes:N1', 'X2::pv', 'X3::pv', 'X3::demand_heat',
'N1::heat_pipes:X3', 'X3::supply_gas', 'N1::heat_pipes:X1',
'X1::power_lines:X2', 'X1::power_lines:X3', 'X1::demand_electricity',
'X2::power_lines:X1'], dtype=object)array(['X1::supply_grid_power', 'X3::supply_gas', 'X2::boiler', 'X3::boiler',
'X2::supply_gas', 'X1::supply_gas', 'X2::pv', 'X3::pv', 'X1::chp'],
dtype=object)array(['X2::pv', 'X1::pv', 'X3::pv'], dtype=object)
array(['N1::heat_pipes:X2', 'X3::power_lines:X1', 'N1::heat_pipes:X1',
'X1::power_lines:X2', 'X1::heat_pipes:N1', 'X2::heat_pipes:N1',
'X3::heat_pipes:N1', 'X1::power_lines:X3', 'N1::heat_pipes:X3',
'X2::power_lines:X1'], dtype=object)array(['X3', 'X1', 'N1', 'X2'], dtype=object)
array(['heat_pipes', 'demand_heat', 'power_lines', 'demand_electricity',
'supply_grid_power', 'supply_gas', 'pv', 'boiler', 'chp'], dtype=object)array(['2005-07-01T00:00:00.000000000', '2005-07-01T01:00:00.000000000',
'2005-07-01T02:00:00.000000000', '2005-07-01T03:00:00.000000000',
'2005-07-01T04:00:00.000000000', '2005-07-01T05:00:00.000000000',
'2005-07-01T06:00:00.000000000', '2005-07-01T07:00:00.000000000',
'2005-07-01T08:00:00.000000000', '2005-07-01T09:00:00.000000000',
'2005-07-01T10:00:00.000000000', '2005-07-01T11:00:00.000000000',
'2005-07-01T12:00:00.000000000', '2005-07-01T13:00:00.000000000',
'2005-07-01T14:00:00.000000000', '2005-07-01T15:00:00.000000000',
'2005-07-01T16:00:00.000000000', '2005-07-01T17:00:00.000000000',
'2005-07-01T18:00:00.000000000', '2005-07-01T19:00:00.000000000',
'2005-07-01T20:00:00.000000000', '2005-07-01T21:00:00.000000000',
'2005-07-01T22:00:00.000000000', '2005-07-01T23:00:00.000000000',
'2005-07-02T00:00:00.000000000', '2005-07-02T01:00:00.000000000',
'2005-07-02T02:00:00.000000000', '2005-07-02T03:00:00.000000000',
'2005-07-02T04:00:00.000000000', '2005-07-02T05:00:00.000000000',
'2005-07-02T06:00:00.000000000', '2005-07-02T07:00:00.000000000',
'2005-07-02T08:00:00.000000000', '2005-07-02T09:00:00.000000000',
'2005-07-02T10:00:00.000000000', '2005-07-02T11:00:00.000000000',
'2005-07-02T12:00:00.000000000', '2005-07-02T13:00:00.000000000',
'2005-07-02T14:00:00.000000000', '2005-07-02T15:00:00.000000000',
'2005-07-02T16:00:00.000000000', '2005-07-02T17:00:00.000000000',
'2005-07-02T18:00:00.000000000', '2005-07-02T19:00:00.000000000',
'2005-07-02T20:00:00.000000000', '2005-07-02T21:00:00.000000000',
'2005-07-02T22:00:00.000000000', '2005-07-02T23:00:00.000000000'],
dtype='datetime64[ns]')array([2000., 2000., 2000., nan, nan, 600., nan, 2000., nan,
600., 250., 2000., 2000., 2000., 2000., 250., 50., nan,
2000., 1500., 2000., 2000., 2000., 2000., nan, 2000.])array([ 1., nan, 1., 1., 1., 1., 1., 1., 1., 1., nan, nan, nan,
1., 1., nan, nan, 1., 1., 1., nan, 1., 1., 1., 1., 1.])array([1500, 1500, 1500])
array([4])
array([7, 7, 7])
array([nan, nan, nan, nan, 1., 1., 1., nan, nan])
array(['energy', 'energy', 'energy', 'energy', 'energy_per_area',
'energy_per_area', 'energy_per_area', 'energy', 'energy'],
dtype=object)array([0.85, 0.85, 0.85])
array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, 0.2, nan, nan, nan, nan, nan, nan])array([ 1., 1., 1., nan, nan, 1., nan, 1., nan, 1., 1., 1., 1.,
1., 1., 1., 1., nan, 1., 1., 1., 1., 1., 1., nan, 1.])array([ 1., nan, nan, nan, nan, nan, nan, 1., nan, nan, nan, nan, nan,
1., 1., nan, nan, nan, 1., nan, nan, 1., nan, nan, nan, nan])array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, 300., nan, nan,
nan, nan, nan, nan])array([ True, True, True, True, True, True, True, True, True])
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...
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dtype=object)array([25., 25., 25., nan, nan, 25., nan, 25., nan, 25., 25., 25., 25.,
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nan, nan, nan, 0.92685937, 0.90368789,
nan, nan, nan, 0.90368789, 0.405 ,
nan, 0.92685937, 0.98 , 0.98 , nan,
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1.35e+03, 1.20e+00, 7.00e+02, 1.00e+00, 9.00e-01, 1.00e-01,
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nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan],
...
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nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
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array([[ nan, nan, nan, 2000., nan, nan, 2000., nan,
nan, nan, nan, nan, nan, nan, 40000., nan,
nan, nan, nan, nan]])array([ 3., 5., 3., 10., 3., 3., 4., 5., 4., 10.])
array(['X2::heat_pipes:N1', 'X1::power_lines:X3', 'X1::heat_pipes:N1',
'X2::power_lines:X1', 'N1::heat_pipes:X1', 'N1::heat_pipes:X2',
'N1::heat_pipes:X3', 'X3::power_lines:X1', 'X3::heat_pipes:N1',
'X1::power_lines:X2'], dtype=object)array([ 900., 500., nan, 1300.])
array([[3, 7, 7, 7],
[5, 2, 5, 8]])array(['#823739', '#660507', '#6783E3', '#072486', '#C5ABE3', '#C98AAD',
'#F9D956', '#8E2999', '#E4AB97'], dtype=object)array(['transmission', 'demand', 'transmission', 'demand', 'supply',
'supply', 'supply_power_plus.supply_plus', 'conversion',
'conversion_plus'], dtype=object)array(['District heat distribution', 'Heat demand',
'Electrical power distribution', 'Electrical demand',
'National grid import', 'Natural gas import',
'Solar photovoltaic power', 'Natural gas boiler',
'Combined heat and power'], dtype=object)array([[1. , 1. , 1. ],
[1. , 1. , 1. ],
[0.8, 1. , 1. ]])array(['X1::power_lines:X3::electricity,X1::power_lines:X2::electricity,X1::chp::electricity,X1::pv::electricity,X1::demand_electricity::electricity,X1::supply_grid_power::electricity',
'X3::pv::electricity,X3::power_lines:X1::electricity,X3::demand_electricity::electricity',
'N1::heat_pipes:X2::heat,N1::heat_pipes:X1::heat,N1::heat_pipes:X3::heat',
'X2::supply_gas::gas,X2::boiler::gas',
'X1::chp::heat,X1::demand_heat::heat,X1::heat_pipes:N1::heat',
'X1::supply_gas::gas,X1::chp::gas',
'X2::heat_pipes:N1::heat,X2::demand_heat::heat,X2::boiler::heat',
'X3::boiler::gas,X3::supply_gas::gas',
'X3::demand_heat::heat,X3::boiler::heat,X3::heat_pipes:N1::heat',
'X2::pv::electricity,X2::demand_electricity::electricity,X2::power_lines:X1::electricity'],
dtype=object)array(['N1::heat_pipes:X2::heat', 'X1::supply_grid_power::electricity',
'X3::power_lines:X1::electricity', 'X2::demand_heat::heat',
'X1::demand_heat::heat', 'X2::demand_electricity::electricity',
'X1::heat_pipes:N1::heat', 'X3::demand_electricity::electricity',
'X1::pv::electricity', 'X2::supply_gas::gas',
'X1::supply_gas::gas', 'X2::heat_pipes:N1::heat',
'X3::heat_pipes:N1::heat', 'X2::pv::electricity',
'X3::pv::electricity', 'X3::demand_heat::heat',
'N1::heat_pipes:X3::heat', 'X3::supply_gas::gas',
'N1::heat_pipes:X1::heat', 'X1::power_lines:X2::electricity',
'X1::power_lines:X3::electricity',
'X1::demand_electricity::electricity',
'X2::power_lines:X1::electricity'], dtype=object)array([['X3::boiler::gas', 'X2::boiler::gas'],
['X3::boiler::heat', 'X2::boiler::heat'],
[None, None]], dtype=object)array(['X1::chp::gas'], dtype=object)
array(['X1::chp::electricity'], dtype=object)
array([['X1::chp::gas'],
['X1::chp::electricity'],
['X1::chp::heat']], dtype=object)array(['X2::pv::electricity', 'X1::pv::electricity',
'X3::pv::electricity', 'X1::chp::electricity'], dtype=object)array(['X3::pv', 'X1::pv', '', 'X2::pv'], dtype=object)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])array(['2005-07-01T00:00:00.000000000', '2005-07-01T08:00:00.000000000',
'2005-07-01T07:00:00.000000000'], dtype='datetime64[ns]')# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
# Here we look at one of the MILP overrides that we have added, the fixed `purchase` cost
model.inputs.cost_purchase.to_pandas().dropna(axis=1)
| loc_techs_investment_cost | X2::boiler | X3::boiler | X1::chp |
|---|---|---|---|
| costs | |||
| monetary | 2000.0 | 2000.0 | 40000.0 |
# Solve the model. Results are loaded into `model.results`.
# By including logging (see package importing), we can see the timing of parts of the run, as well as the solver's log
model.run()
[2021-07-29 16:03:11] INFO Backend: starting model run [2021-07-29 16:03:12] INFO Loading sets [2021-07-29 16:03:12] INFO Loading parameters [2021-07-29 16:03:12] INFO constraints are loaded in the following order: ['capacity', 'dispatch', 'policy', 'energy_balance', 'costs', 'network', 'conversion', 'group', 'conversion_plus', 'export', 'milp'] [2021-07-29 16:03:12] INFO creating capacity constraints [2021-07-29 16:03:12] INFO creating dispatch constraints [2021-07-29 16:03:12] INFO creating policy constraints [2021-07-29 16:03:12] INFO creating energy_balance constraints [2021-07-29 16:03:12] INFO creating costs constraints [2021-07-29 16:03:12] INFO creating network constraints [2021-07-29 16:03:12] INFO creating conversion constraints [2021-07-29 16:03:12] INFO creating group constraints [2021-07-29 16:03:12] INFO creating conversion_plus constraints [2021-07-29 16:03:12] INFO creating export constraints [2021-07-29 16:03:12] INFO creating milp constraints [2021-07-29 16:03:12] INFO Backend: model generated. Time since start of model run: 0:00:00.463842 [2021-07-29 16:03:12] INFO Backend: sending model to solver [2021-07-29 16:03:14] INFO Backend: solver finished running. Time since start of model run: 0:00:02.825151 [2021-07-29 16:03:14] INFO Backend: loaded results [2021-07-29 16:03:14] INFO Backend: generated solution array. Time since start of model run: 0:00:02.930689 [2021-07-29 16:03:14] INFO Postprocessing: started [2021-07-29 16:03:15] INFO Postprocessing: All values < 1e-10 set to 0 in system_balance [2021-07-29 16:03:15] INFO Postprocessing: ended. Time since start of model run: 0:00:03.061067
# Model results are held in the same structure as model inputs.
# The results consist of the optimal values for all decision variables, including capacities and carrier flow
# There are also results, like system capacity factor and levelised costs, which are calculated in postprocessing
# before being added to the results Dataset
model.results
<xarray.Dataset>
Dimensions: (carriers: 3, costs: 1, loc_carriers: 10, loc_carriers_system_balance_constraint: 10, loc_tech_carriers_con: 19, loc_tech_carriers_export: 4, loc_tech_carriers_prod: 21, loc_techs: 26, loc_techs_area: 3, loc_techs_asynchronous_prod_con: 6, loc_techs_balance_demand_constraint: 6, loc_techs_cost: 20, loc_techs_cost_investment_constraint: 20, loc_techs_investment_cost: 20, loc_techs_milp: 1, loc_techs_om_cost: 9, loc_techs_purchase: 2, loc_techs_supply_plus: 3, techs: 16, timesteps: 48)
Coordinates: (12/20)
* carriers (carriers) <U11 'electricity' ......
* loc_carriers (loc_carriers) object 'N1::heat' ...
* loc_carriers_system_balance_constraint (loc_carriers_system_balance_constraint) object ...
* loc_tech_carriers_con (loc_tech_carriers_con) object 'N...
* loc_tech_carriers_export (loc_tech_carriers_export) object ...
* loc_tech_carriers_prod (loc_tech_carriers_prod) object '...
... ...
* costs (costs) object 'monetary'
* loc_techs (loc_techs) object 'N1::heat_pipe...
* loc_techs_area (loc_techs_area) object 'X2::pv' ...
* loc_techs_milp (loc_techs_milp) object 'X1::chp'
* loc_techs_purchase (loc_techs_purchase) object 'X3::...
* timesteps (timesteps) datetime64[ns] 2005-0...
Data variables: (12/23)
energy_cap (loc_techs) float64 214.8 ... 274.1
carrier_prod (loc_tech_carriers_prod, timesteps) float64 ...
carrier_con (loc_tech_carriers_con, timesteps) float64 ...
cost (costs, loc_techs_cost) float64 0...
resource_area (loc_techs_area) float64 994.1 .....
resource_con (loc_techs_supply_plus, timesteps) float64 ...
... ...
cost_investment_rhs (costs, loc_techs_cost_investment_constraint) float64 ...
cost_var_rhs (costs, loc_techs_om_cost, timesteps) float64 ...
capacity_factor (timesteps, loc_tech_carriers_prod) float64 ...
systemwide_capacity_factor (carriers, techs) float64 0.0 ......
systemwide_levelised_cost (carriers, costs, techs) float64 ...
total_levelised_cost (carriers, costs) float64 0.08171...
Attributes:
termination_condition: optimal
objective_function_value: 900.2250130640398
solution_time: 2.930689
time_finished: 2021-07-29 16:03:14
calliope_version: 0.6.7-dev
applied_overrides: milp
scenario: milp
defaults: available_area: null\ncarrier_ratios: false\nc...
allow_operate_mode: 1
model_config: calliope_version: 0.6.7\nname: Urban-scale exa...
run_config: backend: pyomo\nbigM: 1000000.0\ncyclic_storag...array(['electricity', 'gas', 'heat'], dtype='<U11')
array(['N1::heat', 'X1::electricity', 'X1::gas', 'X1::heat', 'X2::electricity',
'X2::gas', 'X2::heat', 'X3::electricity', 'X3::gas', 'X3::heat'],
dtype=object)array(['N1::heat', 'X1::electricity', 'X1::gas', 'X1::heat', 'X2::electricity',
'X2::gas', 'X2::heat', 'X3::electricity', 'X3::gas', 'X3::heat'],
dtype=object)array(['N1::heat_pipes:X1::heat', 'N1::heat_pipes:X2::heat',
'N1::heat_pipes:X3::heat', 'X1::chp::gas',
'X1::demand_electricity::electricity', 'X1::demand_heat::heat',
'X1::heat_pipes:N1::heat', 'X1::power_lines:X2::electricity',
'X1::power_lines:X3::electricity', 'X2::boiler::gas',
'X2::demand_electricity::electricity', 'X2::demand_heat::heat',
'X2::heat_pipes:N1::heat', 'X2::power_lines:X1::electricity',
'X3::boiler::gas', 'X3::demand_electricity::electricity',
'X3::demand_heat::heat', 'X3::heat_pipes:N1::heat',
'X3::power_lines:X1::electricity'], dtype=object)array(['X1::chp::electricity', 'X1::pv::electricity', 'X2::pv::electricity',
'X3::pv::electricity'], dtype=object)array(['N1::heat_pipes:X1::heat', 'N1::heat_pipes:X2::heat',
'N1::heat_pipes:X3::heat', 'X1::chp::electricity', 'X1::chp::heat',
'X1::heat_pipes:N1::heat', 'X1::power_lines:X2::electricity',
'X1::power_lines:X3::electricity', 'X1::pv::electricity',
'X1::supply_gas::gas', 'X1::supply_grid_power::electricity',
'X2::boiler::heat', 'X2::heat_pipes:N1::heat',
'X2::power_lines:X1::electricity', 'X2::pv::electricity',
'X2::supply_gas::gas', 'X3::boiler::heat', 'X3::heat_pipes:N1::heat',
'X3::power_lines:X1::electricity', 'X3::pv::electricity',
'X3::supply_gas::gas'], dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::heat_pipes:N1', 'X2::heat_pipes:N1', 'X3::heat_pipes:N1'],
dtype=object)array(['X1::demand_electricity', 'X1::demand_heat', 'X2::demand_electricity',
'X2::demand_heat', 'X3::demand_electricity', 'X3::demand_heat'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['N1::heat_pipes:X1', 'N1::heat_pipes:X2', 'N1::heat_pipes:X3',
'X1::chp', 'X1::heat_pipes:N1', 'X1::power_lines:X2',
'X1::power_lines:X3', 'X1::pv', 'X1::supply_gas',
'X1::supply_grid_power', 'X2::boiler', 'X2::heat_pipes:N1',
'X2::power_lines:X1', 'X2::pv', 'X2::supply_gas', 'X3::boiler',
'X3::heat_pipes:N1', 'X3::power_lines:X1', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['X1::chp', 'X1::supply_gas', 'X1::supply_grid_power', 'X2::boiler',
'X2::pv', 'X2::supply_gas', 'X3::boiler', 'X3::pv', 'X3::supply_gas'],
dtype=object)array(['X1::pv', 'X2::pv', 'X3::pv'], dtype=object)
array(['boiler', 'chp', 'demand_electricity', 'demand_heat', 'heat_pipes',
'heat_pipes:N1', 'heat_pipes:X1', 'heat_pipes:X2', 'heat_pipes:X3',
'power_lines', 'power_lines:X1', 'power_lines:X2', 'power_lines:X3',
'pv', 'supply_gas', 'supply_grid_power'], dtype=object)array(['monetary'], dtype=object)
array(['N1::heat_pipes:X2', 'X1::supply_grid_power', 'X3::power_lines:X1',
'X2::demand_heat', 'X1::demand_heat', 'X2::boiler',
'X2::demand_electricity', 'X1::heat_pipes:N1', 'X3::demand_electricity',
'X3::boiler', 'X1::pv', 'X1::supply_gas', 'X2::supply_gas',
'X2::heat_pipes:N1', 'X3::heat_pipes:N1', 'X2::pv', 'X3::pv',
'X3::demand_heat', 'N1::heat_pipes:X3', 'X1::chp', 'X3::supply_gas',
'N1::heat_pipes:X1', 'X1::power_lines:X2', 'X1::power_lines:X3',
'X1::demand_electricity', 'X2::power_lines:X1'], dtype=object)array(['X2::pv', 'X1::pv', 'X3::pv'], dtype=object)
array(['X1::chp'], dtype=object)
array(['X3::boiler', 'X2::boiler'], dtype=object)
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...,
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0.26285701, 0.55131856, 0. , 0. , nan,
0. , 0. , 0. , 0. , 0. ,
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nan, inf, inf, inf,
inf, nan, 1.00180312e-06, inf,
1.29274001e-05, 3.84387146e-02, inf, 1.08372629e-01]],
[[ inf, inf, nan, nan,
nan, inf, inf, inf,
inf, nan, inf, inf,
inf, inf, 2.50248671e-02, inf]],
[[8.81617200e-03, 2.67869848e-02, nan, nan,
nan, 2.16908962e-05, 1.02650610e-05, inf,
inf, nan, inf, inf,
inf, inf, inf, inf]]])array([[0.08170905],
[0.03961771],
[0.10986101]])# We can sum operating units of CHP over all locations and turn the result into a pandas DataFrame
df_units = model.get_formatted_array('operating_units').sum('locs').to_pandas().T
#The information about the dataframe tells us about the amount of data it holds in the index and in each column
df_units.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 48 entries, 2005-07-01 00:00:00 to 2005-07-02 23:00:00 Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 chp 48 non-null float64 dtypes: float64(1) memory usage: 1.8 KB
# Using .head() to see the first few rows of operating units
df_units.head()
| techs | chp |
|---|---|
| timesteps | |
| 2005-07-01 00:00:00 | 1.0 |
| 2005-07-01 01:00:00 | 1.0 |
| 2005-07-01 02:00:00 | 1.0 |
| 2005-07-01 03:00:00 | 1.0 |
| 2005-07-01 04:00:00 | 1.0 |
# We can plot this by using the timeseries plotting functionality.
# The top-left dropdown gives us the chance to scroll through other timeseries data too.
model.plot.timeseries()
[2021-07-29 16:03:15] WARNING /Users/brynmorp/Repos/calliope-project/calliope/calliope/postprocess/plotting/plotting.py:105: FutureWarning: Plotting will no longer be available as a method of the Calliope model object infuture versions of Calliope. In the meantime, as of v0.6.6, plotting is untested; this functionality should now be used with caution. We expect to reintroduce it as a seperate module in v0.7.0.