Notebook

Calliope 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.urban_scale()

[2023-01-18 13:24:40] INFO     Model: initialising
[2023-01-18 13:24:40] INFO     Model: preprocessing stage 1 (model_run)
[2023-01-18 13:24:43] INFO     Model: preprocessing stage 2 (model_data)
[2023-01-18 13:24:43] INFO     Model: preprocessing complete

In [4]:

# Individual data variables can be accessed easily, `to_pandas()` reformats the data to look nicer
model.inputs.resource.to_pandas()

Out[4]:

timesteps	2005-07-01 00:00:00	2005-07-01 01:00:00	2005-07-01 02:00:00	2005-07-01 03:00:00	2005-07-01 04:00:00	2005-07-01 05:00:00	2005-07-01 06:00:00	2005-07-01 07:00:00	2005-07-01 08:00:00	2005-07-01 09:00:00	...	2005-07-02 14:00:00	2005-07-02 15:00:00	2005-07-02 16:00:00	2005-07-02 17:00:00	2005-07-02 18:00:00	2005-07-02 19:00:00	2005-07-02 20:00:00	2005-07-02 21:00:00	2005-07-02 22:00:00	2005-07-02 23:00:00
loc_techs_finite_resource
X3::demand_electricity	-18.762912	-18.762912	-18.762912	-18.762912	-18.762912	-30.212425	-35.233307	-61.395269	-63.642962	-62.679665	...	-18.762912	-18.954418	-19.145924	-18.762912	-18.762912	-18.762912	-18.762912	-18.762912	-18.762912	-18.762912
X1::pv	0.000000	0.000000	0.000000	0.000000	0.000000	0.007857	0.029143	0.055571	0.078429	0.096000	...	0.057857	0.041143	0.027000	0.014714	0.006143	0.000857	0.000000	0.000000	0.000000	0.000000
X2::demand_heat	-64.731991	-70.453439	-77.192976	-104.556436	-123.228444	-167.668819	-264.887092	-365.137675	-258.172589	-190.585578	...	-105.261025	-84.614417	-104.549875	-122.646451	-166.442507	-161.099889	-166.931078	-240.034833	-143.576460	-86.082014
X3::demand_heat	-0.015600	-0.860322	-0.015600	-0.015600	-0.860407	-7.263327	-9.398229	-5.792842	-3.322585	-1.927264	...	-0.015600	-0.015600	-0.860335	-0.015600	-0.015600	-0.860291	-0.015600	-0.860300	-0.015600	-0.860290
X2::pv	0.000000	0.000000	0.000000	0.000000	0.000000	0.007857	0.029143	0.055571	0.078429	0.096000	...	0.057857	0.041143	0.027000	0.014714	0.006143	0.000857	0.000000	0.000000	0.000000	0.000000
X2::demand_electricity	-94.545801	-76.960619	-77.475750	-77.475750	-82.731496	-148.533479	-189.570817	-238.734711	-244.284493	-231.440181	...	-199.895515	-221.324570	-188.344877	-249.962248	-248.894106	-269.344347	-245.412357	-196.280957	-135.289242	-103.741556
X3::pv	0.000000	0.000000	0.000000	0.000000	0.000000	0.007857	0.029143	0.055571	0.078429	0.096000	...	0.057857	0.041143	0.027000	0.014714	0.006143	0.000857	0.000000	0.000000	0.000000	0.000000
X1::demand_electricity	-0.455564	-0.405798	-0.393291	-0.393992	-0.440085	-0.567821	-0.732535	-0.713803	-0.689992	-0.707650	...	-0.683060	-0.780370	-0.940634	-0.978388	-1.022063	-1.169519	-1.307938	-1.099334	-0.826212	-0.559499
X1::demand_heat	-0.215376	-0.200838	-0.207306	-0.318949	-0.650734	-1.039384	-1.181567	-1.285403	-1.209117	-1.219912	...	-0.491097	-0.540068	-0.641510	-0.741098	-0.761822	-0.707075	-0.634092	-0.523707	-0.400787	-0.272040

9 rows × 48 columns

In [5]:

# To reformat the array, deconcatenating loc_techs / loc_tech_carriers, you can use model.get_formatted_array()
# You can then apply loc/tech/carrier only operations, like summing information over locations: 
model.get_formatted_array('resource').sum('locs').to_pandas()

Out[5]:

timesteps	2005-07-01 00:00:00	2005-07-01 01:00:00	2005-07-01 02:00:00	2005-07-01 03:00:00	2005-07-01 04:00:00	2005-07-01 05:00:00	2005-07-01 06:00:00	2005-07-01 07:00:00	2005-07-01 08:00:00	2005-07-01 09:00:00	...	2005-07-02 14:00:00	2005-07-02 15:00:00	2005-07-02 16:00:00	2005-07-02 17:00:00	2005-07-02 18:00:00	2005-07-02 19:00:00	2005-07-02 20:00:00	2005-07-02 21:00:00	2005-07-02 22:00:00	2005-07-02 23:00:00
techs
demand_electricity	-113.764277	-96.129329	-96.631954	-96.632654	-101.934493	-179.313724	-225.536658	-300.843784	-308.617446	-294.827496	...	-219.341487	-241.059359	-208.431436	-269.703548	-268.679081	-289.276777	-265.483207	-216.143204	-154.878366	-123.063967
demand_heat	-64.962967	-71.514599	-77.415882	-104.890985	-124.739585	-175.971530	-275.466888	-372.215920	-262.704292	-193.732754	...	-105.767723	-85.170084	-106.051720	-123.403148	-167.219928	-162.667255	-167.580770	-241.418840	-143.992847	-87.214344
pv	0.000000	0.000000	0.000000	0.000000	0.000000	0.023571	0.087429	0.166714	0.235286	0.288000	...	0.173571	0.123429	0.081000	0.044143	0.018429	0.002571	0.000000	0.000000	0.000000	0.000000

3 rows × 48 columns

In [6]:

# 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 13:24:46] INFO     Backend: starting model run
[2023-01-18 13:24:46] INFO     Loading sets
[2023-01-18 13:24:46] INFO     Loading parameters
[2023-01-18 13:24:46] INFO     constraints are loaded in the following order: ['capacity', 'dispatch', 'policy', 'energy_balance', 'costs', 'network', 'conversion', 'group', 'conversion_plus', 'export', 'milp']
[2023-01-18 13:24:46] INFO     creating capacity constraints
[2023-01-18 13:24:46] INFO     creating dispatch constraints
[2023-01-18 13:24:46] INFO     creating policy constraints
[2023-01-18 13:24:46] INFO     creating energy_balance constraints
[2023-01-18 13:24:46] INFO     creating costs constraints
[2023-01-18 13:24:46] INFO     creating network constraints
[2023-01-18 13:24:46] INFO     creating conversion constraints
[2023-01-18 13:24:46] INFO     creating group constraints
[2023-01-18 13:24:46] INFO     creating conversion_plus constraints
[2023-01-18 13:24:46] INFO     creating export constraints
[2023-01-18 13:24:46] INFO     creating milp constraints
[2023-01-18 13:24:47] INFO     Backend: model generated. Time since start of model run: 0:00:00.944208
[2023-01-18 13:24:47] INFO     Backend: sending model to solver
[2023-01-18 13:24:47] INFO     Backend: solver finished running. Time since start of model run: 0:00:01.567531
[2023-01-18 13:24:47] INFO     Backend: loaded results
[2023-01-18 13:24:47] INFO     Backend: generated solution array. Time since start of model run: 0:00:01.694511
[2023-01-18 13:24:47] INFO     Postprocessing: started
[2023-01-18 13:24:47] INFO     Postprocessing: All values < 1e-10 set to 0 in system_balance
[2023-01-18 13:24:47] INFO     Postprocessing: ended. Time since start of model run: 0:00:01.874165

In [8]:

# We can sum heat output over all locations and turn the result into a pandas DataFrame
df_heat = model.get_formatted_array('carrier_prod').loc[{'carriers':'heat'}].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_heat.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 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   boiler             48 non-null     float64
 1   chp                48 non-null     float64
 2   heat_pipes:N1      48 non-null     float64
 3   heat_pipes:X1      48 non-null     float64
 4   heat_pipes:X2      48 non-null     float64
 5   heat_pipes:X3      48 non-null     float64
 6   power_lines:X1     48 non-null     float64
 7   power_lines:X2     48 non-null     float64
 8   power_lines:X3     48 non-null     float64
 9   pv                 48 non-null     float64
 10  supply_gas         48 non-null     float64
 11  supply_grid_power  48 non-null     float64
dtypes: float64(12)
memory usage: 5.9 KB

In [9]:

# Using .head() to see the first few rows of heat generation and demand

# Note: carrier production in heat_pipes:N1 is heat received by the heat network at any location connected to `N1`

df_heat.head()

Out[9]:

techs	boiler	chp	heat_pipes:N1	heat_pipes:X1	heat_pipes:X2	heat_pipes:X3	power_lines:X1	power_lines:X2	power_lines:X3	pv	supply_gas	supply_grid_power
timesteps
2005-07-01 00:00:00	0.000000	92.861360	170.055601	183.475520	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2005-07-01 01:00:00	4.100046	78.466297	67.213715	72.541074	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2005-07-01 02:00:00	9.626102	78.876806	67.582474	72.915564	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2005-07-01 03:00:00	37.084989	78.877367	67.487047	72.812606	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2005-07-01 04:00:00	53.191064	83.204646	70.897788	76.515868	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

In [10]:

# 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 13:24:49] 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.