Notebook

Calliope National 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.national_scale()

[2023-01-18 13:22:58] INFO     Model: initialising
[2023-01-18 13:22:58] INFO     Model: preprocessing stage 1 (model_run)
[2023-01-18 13:22:59] INFO     Model: preprocessing stage 2 (model_data)
[2023-01-18 13:22:59] 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	2005-01-01 00:00:00	2005-01-01 01:00:00	2005-01-01 02:00:00	2005-01-01 03:00:00	2005-01-01 04:00:00	2005-01-01 05:00:00	2005-01-01 06:00:00	2005-01-01 07:00:00	2005-01-01 08:00:00	2005-01-01 09:00:00	...	2005-01-05 14:00:00	2005-01-05 15:00:00	2005-01-05 16:00:00	2005-01-05 17:00:00	2005-01-05 18:00:00	2005-01-05 19:00:00	2005-01-05 20:00:00	2005-01-05 21:00:00	2005-01-05 22:00:00	2005-01-05 23:00:00
loc_techs_finite_resource
region1-1::csp	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.021060	0.263805	0.434037	...	0.322062	0.07927	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
region1::demand_power	-25284.480	-24387.440	-23730.656	-23123.040	-23119.600	-23683.280	-24364.720	-25249.968000	-26090.208000	-26870.464000	...	-37078.160000	-38203.12000	-39033.520	-38631.008	-36990.800	-35330.832	-33623.456	-31341.168	-29390.624	-28132.928
region1-3::csp	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000000	0.000000	0.026837	...	0.118691	0.00000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
region2::demand_power	-2254.098	-2131.148	-2090.164	-2131.148	-2172.132	-2172.132	-2213.114	-2295.082000	-2459.016000	-2459.016000	...	-2295.082000	-2459.01600	-2909.836	-2868.852	-2786.886	-2745.902	-2622.950	-2459.016	-2254.098	-2295.082
region1-2::csp	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.009056	0.096755	0.245351	...	0.000000	0.00000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

5 rows × 120 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	2005-01-01 00:00:00	2005-01-01 01:00:00	2005-01-01 02:00:00	2005-01-01 03:00:00	2005-01-01 04:00:00	2005-01-01 05:00:00	2005-01-01 06:00:00	2005-01-01 07:00:00	2005-01-01 08:00:00	2005-01-01 09:00:00	...	2005-01-05 14:00:00	2005-01-05 15:00:00	2005-01-05 16:00:00	2005-01-05 17:00:00	2005-01-05 18:00:00	2005-01-05 19:00:00	2005-01-05 20:00:00	2005-01-05 21:00:00	2005-01-05 22:00:00	2005-01-05 23:00:00
techs
csp	0.000	0.000	0.00	0.000	0.000	0.000	0.000	0.030116	0.36056	0.706225	...	0.440753	0.07927	0.000	0.00	0.000	0.000	0.000	0.000	0.000	0.00
demand_power	-27538.578	-26518.588	-25820.82	-25254.188	-25291.732	-25855.412	-26577.834	-27545.050000	-28549.22400	-29329.480000	...	-39373.242000	-40662.13600	-41943.356	-41499.86	-39777.686	-38076.734	-36246.406	-33800.184	-31644.722	-30428.01

2 rows × 120 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 13:23:00] INFO     Backend: starting model run
[2023-01-18 13:23:00] INFO     Loading sets
[2023-01-18 13:23:00] INFO     Loading parameters
[2023-01-18 13:23:00] INFO     constraints are loaded in the following order: ['capacity', 'dispatch', 'policy', 'energy_balance', 'costs', 'network', 'conversion', 'group', 'conversion_plus', 'export', 'milp']
[2023-01-18 13:23:00] INFO     creating capacity constraints
[2023-01-18 13:23:00] INFO     creating dispatch constraints
[2023-01-18 13:23:01] INFO     creating policy constraints
[2023-01-18 13:23:01] INFO     creating energy_balance constraints
[2023-01-18 13:23:01] INFO     creating costs constraints
[2023-01-18 13:23:01] INFO     creating network constraints
[2023-01-18 13:23:01] INFO     creating conversion constraints
[2023-01-18 13:23:01] INFO     creating group constraints
[2023-01-18 13:23:01] INFO     creating conversion_plus constraints
[2023-01-18 13:23:01] INFO     creating export constraints
[2023-01-18 13:23:01] INFO     creating milp constraints
[2023-01-18 13:23:01] INFO     Backend: model generated. Time since start of model run: 0:00:00.739055
[2023-01-18 13:23:01] INFO     Backend: sending model to solver
[2023-01-18 13:23:02] INFO     Backend: solver finished running. Time since start of model run: 0:00:01.449866
[2023-01-18 13:23:02] INFO     Backend: loaded results
[2023-01-18 13:23:02] INFO     Backend: generated solution array. Time since start of model run: 0:00:01.549066
[2023-01-18 13:23:02] INFO     Postprocessing: started
[2023-01-18 13:23:02] INFO     Postprocessing: All values < 1e-10 set to 0 in energy_cap, carrier_prod, carrier_con, cost, storage, cost_var, system_balance, cost_var_rhs
[2023-01-18 13:23:02] INFO     Postprocessing: ended. Time since start of model run: 0:00:01.650617

In [8]:

# We can sum power output over all locations and turn the result into a pandas DataFrame
df_power = model.get_formatted_array('carrier_prod').loc[{'carriers':'power'}].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_power.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 120 entries, 2005-01-01 00:00:00 to 2005-01-05 23:00:00
Data columns (total 9 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   ac_transmission:region1      120 non-null    float64
 1   ac_transmission:region2      120 non-null    float64
 2   battery                      120 non-null    float64
 3   ccgt                         120 non-null    float64
 4   csp                          120 non-null    float64
 5   free_transmission:region1    120 non-null    float64
 6   free_transmission:region1-1  120 non-null    float64
 7   free_transmission:region1-2  120 non-null    float64
 8   free_transmission:region1-3  120 non-null    float64
dtypes: float64(9)
memory usage: 13.4 KB

In [9]:

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

# Note: power output in ac_transmission:region1 is power received by the high voltage line at any location connected to `r1`

df_power.head()

Out[9]:

techs	ac_transmission:region1	ac_transmission:region2	battery	ccgt	csp	free_transmission:region1	free_transmission:region1-1	free_transmission:region1-2	free_transmission:region1-3
timesteps
2005-01-01 00:00:00	2254.098	0.0	0.0	27936.360	0.0	0.0	0.0	0.0	0.0
2005-01-01 01:00:00	2131.148	0.0	0.0	26894.673	0.0	0.0	0.0	0.0	0.0
2005-01-01 02:00:00	2090.164	0.0	0.0	26189.672	0.0	0.0	0.0	0.0	0.0
2005-01-01 03:00:00	2131.148	0.0	0.0	25630.273	0.0	0.0	0.0	0.0	0.0
2005-01-01 04:00:00	2172.132	0.0	0.0	25675.049	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 13:23:03] 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.