Note
You can download this example as a Jupyter notebook or start it in interactive mode.
Optimization with Linopy - Migrate Extra Functionalities
The extra funcionalities for the native pypsa optimization code are mostly using the function of the pypsa.linopt model. Here we show how you can recycle large parts of your code by using the compatibility functions from the pypsa.optimization.compat module.
These are
define_variablesdefine_constraintsget_varlinexprjoin_exprs
You might want to use them if you have extra_functionalities written for the native optimization code. However, expect some hick-ups, as some operations might behave differently.
Let’s import pypsa and the compat functions:
[1]:
import pypsa
from pypsa.optimization.compat import get_var, define_constraints, join_exprs, linexpr
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [1], line 1
----> 1 import pypsa
2 from pypsa.optimization.compat import get_var, define_constraints, join_exprs, linexpr
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/pypsa/__init__.py:10
1 # -*- coding: utf-8 -*-
4 """
5 Python for Power Systems Analysis (PyPSA)
6
7 Grid calculation library.
8 """
---> 10 from pypsa import (
11 components,
12 contingency,
13 descriptors,
14 examples,
15 geo,
16 io,
17 linopf,
18 linopt,
19 networkclustering,
20 opf,
21 opt,
22 optimization,
23 pf,
24 plot,
25 )
26 from pypsa.components import Network, SubNetwork
28 __version__ = "0.21.2"
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/pypsa/components.py:50
37 from pypsa.io import (
38 export_to_csv_folder,
39 export_to_hdf5,
(...)
47 import_series_from_dataframe,
48 )
49 from pypsa.opf import network_lopf, network_opf
---> 50 from pypsa.optimization.optimize import OptimizationAccessor
51 from pypsa.pf import (
52 calculate_B_H,
53 calculate_dependent_values,
(...)
62 sub_network_pf,
63 )
64 from pypsa.plot import iplot, plot
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/pypsa/optimization/__init__.py:7
1 #!/usr/bin/env python3
2 # -*- coding: utf-8 -*-
3 """
4 Build optimisation problems from PyPSA networks with Linopy.
5 """
----> 7 from pypsa.optimization import abstract, constraints, optimize, variables
8 from pypsa.optimization.optimize import create_model
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/pypsa/optimization/constraints.py:9
6 import logging
8 import pandas as pd
----> 9 from linopy.expressions import LinearExpression, merge
10 from numpy import arange, cumsum, inf, nan, roll
11 from scipy import sparse
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/linopy/__init__.py:9
1 #!/usr/bin/env python3
2 # -*- coding: utf-8 -*-
3 """
4 Created on Wed Mar 10 11:03:06 2021.
5
6 @author: fabulous
7 """
----> 9 from linopy import model, remote
10 from linopy.expressions import merge
11 from linopy.io import read_netcdf
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/linopy/model.py:22
20 from linopy import solvers
21 from linopy.common import best_int, replace_by_map
---> 22 from linopy.constraints import (
23 AnonymousConstraint,
24 AnonymousScalarConstraint,
25 Constraints,
26 )
27 from linopy.eval import Expr
28 from linopy.expressions import LinearExpression, ScalarLinearExpression
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/linopy/constraints.py:21
18 from scipy.sparse import coo_matrix
19 from xarray import DataArray, Dataset
---> 21 from linopy import expressions, variables
22 from linopy.common import (
23 _merge_inplace,
24 has_assigned_model,
(...)
27 replace_by_map,
28 )
31 class Constraint(DataArray):
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/linopy/expressions.py:23
20 from xarray.core.dataarray import DataArrayCoordinates
21 from xarray.core.groupby import _maybe_reorder, peek_at
---> 23 from linopy import constraints, variables
24 from linopy.common import as_dataarray
27 def exprwrap(method, *default_args, **new_default_kwargs):
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/site-packages/linopy/variables.py:398
393 roll = varwrap(DataArray.roll)
395 rolling = varwrap(DataArray.rolling)
--> 398 @dataclass(repr=False)
399 class Variables:
400 """
401 A variables container used for storing multiple variable arrays.
402 """
404 labels: Dataset = Dataset()
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/dataclasses.py:1211, in dataclass.<locals>.wrap(cls)
1210 def wrap(cls):
-> 1211 return _process_class(cls, init, repr, eq, order, unsafe_hash,
1212 frozen, match_args, kw_only, slots,
1213 weakref_slot)
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/dataclasses.py:959, in _process_class(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, weakref_slot)
956 kw_only = True
957 else:
958 # Otherwise it's a field of some type.
--> 959 cls_fields.append(_get_field(cls, name, type, kw_only))
961 for f in cls_fields:
962 fields[f.name] = f
File ~/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.21.2/lib/python3.11/dataclasses.py:816, in _get_field(cls, a_name, a_type, default_kw_only)
812 # For real fields, disallow mutable defaults. Use unhashable as a proxy
813 # indicator for mutability. Read the __hash__ attribute from the class,
814 # not the instance.
815 if f._field_type is _FIELD and f.default.__class__.__hash__ is None:
--> 816 raise ValueError(f'mutable default {type(f.default)} for field '
817 f'{f.name} is not allowed: use default_factory')
819 return f
ValueError: mutable default <class 'xarray.core.dataset.Dataset'> for field labels is not allowed: use default_factory
We load the same network from the previous section into memory:
[2]:
n = pypsa.examples.ac_dc_meshed(from_master=True)
n.generators.loc[n.generators.carrier == "gas", "p_nom_extendable"] = False
n.generators.loc[n.generators.carrier == "gas", "ramp_limit_down"] = 0.2
n.generators.loc[n.generators.carrier == "gas", "ramp_limit_up"] = 0.2
n.add(
"StorageUnit",
"su",
bus="Manchester",
marginal_cost=10,
inflow=50,
p_nom_extendable=True,
capital_cost=10,
p_nom=2000,
efficiency_dispatch=0.5,
cyclic_state_of_charge=True,
state_of_charge_initial=1000,
)
n.add(
"StorageUnit",
"su2",
bus="Manchester",
marginal_cost=10,
p_nom_extendable=True,
capital_cost=50,
p_nom=2000,
efficiency_dispatch=0.5,
carrier="gas",
cyclic_state_of_charge=False,
state_of_charge_initial=1000,
)
n.storage_units_t.state_of_charge_set.loc[n.snapshots[7], "su"] = 100
n.add("Bus", "storebus", carrier="hydro", x=-5, y=55)
n.madd(
"Link",
["battery_power", "battery_discharge"],
"",
bus0=["Manchester", "storebus"],
bus1=["storebus", "Manchester"],
p_nom=100,
efficiency=0.9,
p_nom_extendable=True,
p_nom_max=1000,
)
n.madd(
"Store",
["store"],
bus="storebus",
e_nom=2000,
e_nom_extendable=True,
marginal_cost=10,
capital_cost=10,
e_nom_max=5000,
e_initial=100,
e_cyclic=True,
);
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In [2], line 1
----> 1 n = pypsa.examples.ac_dc_meshed(from_master=True)
3 n.generators.loc[n.generators.carrier == "gas", "p_nom_extendable"] = False
4 n.generators.loc[n.generators.carrier == "gas", "ramp_limit_down"] = 0.2
NameError: name 'pypsa' is not defined
And define the extra functionalities as we defined them for the native code in here
[3]:
def minimal_state_of_charge(n, snapshots):
vars_soc = get_var(n, "StorageUnit", "state_of_charge")
lhs = linexpr((1, vars_soc))
define_constraints(n, lhs, ">", 50, "StorageUnit", "soc_lower_bound")
With the compat functions, this will work as expected. Let’s go on to the next one.
[4]:
def fix_link_cap_ratio(n, snapshots):
vars_link = get_var(n, "Link", "p_nom")
eff = n.links.at["battery_power", "efficiency"]
lhs = linexpr(
(1, vars_link["battery_power"]), (-eff, vars_link["battery_discharge"])
)
define_constraints(n, lhs, "=", 0, "battery_discharge", attr="fixratio")
This function as well should not make any problems. Let’s go on.
[5]:
def fix_bus_production(n, snapshots):
total_demand = n.loads_t.p_set.sum().sum()
prod_per_bus = (
linexpr((1, get_var(n, "Generator", "p")))
.groupby(n.generators.bus, axis=1)
.apply(join_exprs)
)
define_constraints(
n, prod_per_bus, ">=", total_demand / 5, "Bus", "production_share"
)
Here, we come into difficult terrain. The groupby function won’t work since the linexpr functions returns some sort of a xarray object (a LinearExpression object, derived from xarray.Dataset).
Instead, we have to rewrite parts:
use
groupby_sumfunction insteadgroupbyremove the
axisargumentexplicitly sum over all snapshots afterwards. This has nothing to do with groupby but with the fact that we want to limit to total production.
[6]:
def fix_bus_production(n, snapshots):
total_demand = n.loads_t.p_set.sum().sum()
prod_per_bus = (
linexpr((1, get_var(n, "Generator", "p")))
.groupby_sum(n.generators.bus)
.sum("snapshot")
)
define_constraints(
n, prod_per_bus, ">=", total_demand / 5, "Bus", "production_share"
)
[7]:
def extra_functionalities(n, snapshots):
minimal_state_of_charge(n, snapshots)
fix_link_cap_ratio(n, snapshots)
fix_bus_production(n, snapshots)
[8]:
n.optimize(
extra_functionality=extra_functionalities,
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In [8], line 1
----> 1 n.optimize(
2 extra_functionality=extra_functionalities,
3 )
NameError: name 'n' is not defined