Calliope Urban Scale MILP Example Model

For more details on analysing input/output data, see the full urban scale example model

In [1]:
import calliope

# We increase logging verbosity
calliope.set_log_verbosity('INFO', include_solver_output=False)
In [2]:
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
In [3]:
# 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
Out[3]:
<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:  1
In [4]:
# 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)
Out[4]:
loc_techs_investment_cost X2::boiler X3::boiler X1::chp
costs
monetary 2000.0 2000.0 40000.0
In [5]:
# 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
In [6]:
# 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
Out[6]:
<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...
In [7]:
# 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
In [8]:
# Using .head() to see the first few rows of operating units

df_units.head()
Out[8]:
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
In [9]:
# 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.