Notebook

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

[2023-01-18 13:22:07] INFO     Model: initialising
[2023-01-18 13:22:07] INFO     Applying the following overrides from scenario definition: ['milp'] 
[2023-01-18 13:22:07] 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
[2023-01-18 13:22:07] INFO     Model: preprocessing stage 1 (model_run)
[2023-01-18 13:22:08] INFO     Model: preprocessing stage 2 (model_data)
[2023-01-18 13:22:08] 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.


[2023-01-18 13:22:08] INFO     Model: preprocessing complete

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	X1::chp	X2::boiler	X3::boiler
costs
monetary	40000.0	2000.0	2000.0

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()

[2023-01-18 13:22:09] INFO     Backend: starting model run
[2023-01-18 13:22:09] INFO     Loading sets
[2023-01-18 13:22:09] INFO     Loading parameters
[2023-01-18 13:22:09] INFO     constraints are loaded in the following order: ['capacity', 'dispatch', 'policy', 'energy_balance', 'costs', 'network', 'conversion', 'group', 'conversion_plus', 'export', 'milp']
[2023-01-18 13:22:09] INFO     creating capacity constraints
[2023-01-18 13:22:09] INFO     creating dispatch constraints
[2023-01-18 13:22:09] INFO     creating policy constraints
[2023-01-18 13:22:09] INFO     creating energy_balance constraints
[2023-01-18 13:22:09] INFO     creating costs constraints
[2023-01-18 13:22:09] INFO     creating network constraints
[2023-01-18 13:22:09] INFO     creating conversion constraints
[2023-01-18 13:22:09] INFO     creating group constraints
[2023-01-18 13:22:09] INFO     creating conversion_plus constraints
[2023-01-18 13:22:09] INFO     creating export constraints
[2023-01-18 13:22:09] INFO     creating milp constraints
[2023-01-18 13:22:09] INFO     Backend: model generated. Time since start of model run: 0:00:00.437858
[2023-01-18 13:22:09] INFO     Backend: sending model to solver
[2023-01-18 13:22:11] INFO     Backend: solver finished running. Time since start of model run: 0:00:01.991228
[2023-01-18 13:22:11] INFO     Backend: loaded results
[2023-01-18 13:22:11] INFO     Backend: generated solution array. Time since start of model run: 0:00:02.086582
[2023-01-18 13:22:11] INFO     Postprocessing: started
[2023-01-18 13:22:11] INFO     Postprocessing: All values < 1e-10 set to 0 in system_balance
[2023-01-18 13:22:11] INFO     Postprocessing: ended. Time since start of model run: 0:00:02.206912

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()

[2023-01-18 13:22:12] 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.