Note
You can download this example as a Jupyter notebook or start it in interactive mode.
Redispatch Example with SciGRID network#
In this example, we compare a 2-stage market with an initial market clearing in two bidding zones with flow-based market coupling and a subsequent redispatch market (incl. curtailment) to an idealised nodal pricing scheme.
[1]:
import pypsa
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from pypsa.descriptors import get_switchable_as_dense as as_dense
[2]:
solver = "cbc"
Load example network#
[3]:
o = pypsa.examples.scigrid_de(from_master=True)
o.lines.s_max_pu = 0.7
o.lines.loc[["316", "527", "602"], "s_nom"] = 1715
o.set_snapshots([o.snapshots[12]])
WARNING:pypsa.io:Importing network from PyPSA version v0.17.1 while current version is v0.25.1. Read the release notes at https://pypsa.readthedocs.io/en/latest/release_notes.html to prepare your network for import.
INFO:pypsa.io:Imported network scigrid-de.nc has buses, generators, lines, loads, storage_units, transformers
[4]:
n = o.copy() # for redispatch model
m = o.copy() # for market model
[5]:
o.plot();
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.25.1/lib/python3.11/site-packages/cartopy/mpl/style.py:76: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
Solve original nodal market model o#
First, let us solve a nodal market using the original model o:
[6]:
o.optimize(solver_name=solver)
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
INFO:linopy.model: Solve linear problem using Cbc solver
INFO:linopy.io: Writing time: 0.11s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 2485 primals, 5957 duals
Objective: 3.01e+05
Solver model: not available
Solver message: Optimal - objective value 301209.38232509
INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-fix-p-lower, Generator-fix-p-upper, Line-fix-s-lower, Line-fix-s-upper, Transformer-fix-s-lower, Transformer-fix-s-upper, StorageUnit-fix-p_dispatch-lower, StorageUnit-fix-p_dispatch-upper, StorageUnit-fix-p_store-lower, StorageUnit-fix-p_store-upper, StorageUnit-fix-state_of_charge-lower, StorageUnit-fix-state_of_charge-upper, Kirchhoff-Voltage-Law, StorageUnit-energy_balance were not assigned to the network.
Welcome to the CBC MILP Solver
Version: 2.10.10
Build Date: Apr 19 2023
command line - cbc -printingOptions all -import /tmp/linopy-problem-8m5ilz21.lp -solve -solu /tmp/linopy-solve-cvw8y7mu.sol (default strategy 1)
Option for printingOptions changed from normal to all
Presolve 621 (-5336) rows, 1087 (-1398) columns and 3838 (-7083) elements
Perturbing problem by 0.001% of 2240.9252 - largest nonzero change 0.00099393761 ( 0.0086171445%) - largest zero change 0.00099021152
0 Obj -10.814696 Primal inf 1276398.7 (573)
87 Obj -10.814696 Primal inf 1046350 (542)
165 Obj -10.710198 Primal inf 1820085 (536)
232 Obj -9.2845977 Primal inf 516624.02 (454)
310 Obj -7.3741247 Primal inf 2198294.6 (468)
392 Obj 3997.3772 Primal inf 8486670.8 (450)
446 Obj 3998.6048 Primal inf 479358.77 (314)
513 Obj 4000.6529 Primal inf 267960.54 (192)
593 Obj 4004.0545 Primal inf 93434.682 (137)
680 Obj 251685.32 Primal inf 6235.1994 (71)
750 Obj 301212.17
Optimal - objective value 301209.38
After Postsolve, objective 301209.38, infeasibilities - dual 0 (0), primal 0 (0)
Optimal objective 301209.3823 - 750 iterations time 0.102, Presolve 0.02
Total time (CPU seconds): 0.15 (Wallclock seconds): 0.12
[6]:
('ok', 'optimal')
Costs are 301 k€.
Build market model m with two bidding zones#
For this example, we split the German transmission network into two market zones at latitude 51 degrees.
You can build any other market zones by providing an alternative mapping from bus to zone.
[7]:
zones = (n.buses.y > 51).map(lambda x: "North" if x else "South")
Next, we assign this mapping to the market model m.
We re-assign the buses of all generators and loads, and remove all transmission lines within each bidding zone.
Here, we assume that the bidding zones are coupled through the transmission lines that connect them.
[8]:
for c in m.iterate_components(m.one_port_components):
c.df.bus = c.df.bus.map(zones)
for c in m.iterate_components(m.branch_components):
c.df.bus0 = c.df.bus0.map(zones)
c.df.bus1 = c.df.bus1.map(zones)
internal = c.df.bus0 == c.df.bus1
m.mremove(c.name, c.df.loc[internal].index)
m.mremove("Bus", m.buses.index)
m.madd("Bus", ["North", "South"]);
Now, we can solve the coupled market with two bidding zones.
[9]:
m.optimize(solver_name=solver)
INFO:linopy.model: Solve linear problem using Cbc solver
INFO:linopy.io: Writing time: 0.08s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 1561 primals, 3185 duals
Objective: 2.14e+05
Solver model: not available
Solver message: Optimal - objective value 213988.68595810
INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-fix-p-lower, Generator-fix-p-upper, Line-fix-s-lower, Line-fix-s-upper, StorageUnit-fix-p_dispatch-lower, StorageUnit-fix-p_dispatch-upper, StorageUnit-fix-p_store-lower, StorageUnit-fix-p_store-upper, StorageUnit-fix-state_of_charge-lower, StorageUnit-fix-state_of_charge-upper, Kirchhoff-Voltage-Law, StorageUnit-energy_balance were not assigned to the network.
Welcome to the CBC MILP Solver
Version: 2.10.10
Build Date: Apr 19 2023
command line - cbc -printingOptions all -import /tmp/linopy-problem-9znw9a47.lp -solve -solu /tmp/linopy-solve-keril6at.sol (default strategy 1)
Option for printingOptions changed from normal to all
Presolve 40 (-3145) rows, 410 (-1151) columns and 487 (-4342) elements
Perturbing problem by 0.001% of 212.59539 - largest nonzero change 0.00017578427 ( 0.0036987348%) - largest zero change 0.00015445146
0 Obj 0 Primal inf 11285.222 (1)
48 Obj 184184.9 Primal inf 1700.1029 (24)
86 Obj 213988.73
Optimal - objective value 213988.69
After Postsolve, objective 213988.69, infeasibilities - dual 0 (0), primal 0 (0)
Optimal objective 213988.686 - 86 iterations time 0.012, Presolve 0.00
Total time (CPU seconds): 0.03 (Wallclock seconds): 0.03
[9]:
('ok', 'optimal')
Costs are 214 k€, which is much lower than the 301 k€ of the nodal market.
This is because network restrictions apart from the North/South division are not taken into account yet.
We can look at the market clearing prices of each zone:
[10]:
m.buses_t.marginal_price
[10]:
| Bus | North | South |
|---|---|---|
| snapshot | ||
| 2011-01-01 12:00:00 | 8.0 | 25.0 |
Build redispatch model n#
Next, based on the market outcome with two bidding zones m, we build a secondary redispatch market n that rectifies transmission constraints through curtailment and ramping up/down thermal generators.
First, we fix the dispatch of generators to the results from the market simulation. (For simplicity, this example disregards storage units.)
[11]:
p = m.generators_t.p / m.generators.p_nom
n.generators_t.p_min_pu = p
n.generators_t.p_max_pu = p
Then, we add generators bidding into redispatch market using the following assumptions:
All generators can reduce their dispatch to zero. This includes also curtailment of renewables.
All generators can increase their dispatch to their available/nominal capacity.
No changes to the marginal costs, i.e. reducing dispatch lowers costs.
With these settings, the 2-stage market should result in the same cost as the nodal market.
[12]:
g_up = n.generators.copy()
g_down = n.generators.copy()
g_up.index = g_up.index.map(lambda x: x + " ramp up")
g_down.index = g_down.index.map(lambda x: x + " ramp down")
up = (
as_dense(m, "Generator", "p_max_pu") * m.generators.p_nom - m.generators_t.p
).clip(0) / m.generators.p_nom
down = -m.generators_t.p / m.generators.p_nom
up.columns = up.columns.map(lambda x: x + " ramp up")
down.columns = down.columns.map(lambda x: x + " ramp down")
n.madd("Generator", g_up.index, p_max_pu=up, **g_up.drop("p_max_pu", axis=1))
n.madd(
"Generator",
g_down.index,
p_min_pu=down,
p_max_pu=0,
**g_down.drop(["p_max_pu", "p_min_pu"], axis=1)
);
Now, let’s solve the redispatch market:
[13]:
n.optimize(solver_name=solver)
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
INFO:linopy.model: Solve linear problem using Cbc solver
INFO:linopy.io: Writing time: 0.14s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 5331 primals, 11649 duals
Objective: 3.01e+05
Solver model: not available
Solver message: Optimal - objective value 301209.38114435
INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-fix-p-lower, Generator-fix-p-upper, Line-fix-s-lower, Line-fix-s-upper, Transformer-fix-s-lower, Transformer-fix-s-upper, StorageUnit-fix-p_dispatch-lower, StorageUnit-fix-p_dispatch-upper, StorageUnit-fix-p_store-lower, StorageUnit-fix-p_store-upper, StorageUnit-fix-state_of_charge-lower, StorageUnit-fix-state_of_charge-upper, Kirchhoff-Voltage-Law, StorageUnit-energy_balance were not assigned to the network.
Welcome to the CBC MILP Solver
Version: 2.10.10
Build Date: Apr 19 2023
command line - cbc -printingOptions all -import /tmp/linopy-problem-ockh4e_k.lp -solve -solu /tmp/linopy-solve-l57djxcj.sol (default strategy 1)
Option for printingOptions changed from normal to all
Presolve 623 (-11026) rows, 1315 (-4016) columns and 4089 (-15370) elements
Perturbing problem by 0.001% of 2240.9252 - largest nonzero change 0.00098805511 ( 0.0067054933%) - largest zero change 0.00097853979
0 Obj 194177.17 Primal inf 1290782.9 (574) Dual inf 7673.8313 (158)
87 Obj -10.303563 Primal inf 1123501.7 (544)
165 Obj -10.113865 Primal inf 842123.9 (519)
241 Obj -8.9199545 Primal inf 1255908.1 (499)
328 Obj -6.7182346 Primal inf 2297659.5 (443)
410 Obj -4.9082228 Primal inf 642106.46 (352)
474 Obj 3999.0994 Primal inf 305606.72 (280)
535 Obj 4001.4851 Primal inf 317102.38 (275)
612 Obj 4036.3279 Primal inf 45523.653 (101)
699 Obj 186249.32 Primal inf 14128.886 (57)
781 Obj 301212.1
781 Obj 301209.38 Dual inf 6.4709158e-05 (4)
785 Obj 301209.38
Optimal - objective value 301209.38
After Postsolve, objective 301209.38, infeasibilities - dual 1520.8358 (101), primal 2.2634163e-05 (95)
Presolved model was optimal, full model needs cleaning up
0 Obj 301209.38 Primal inf 7.8549888e-07 (4) Dual inf 4.0000001e+08 (105)
End of values pass after 106 iterations
106 Obj 301209.38
Optimal - objective value 301209.38
Optimal objective 301209.3811 - 891 iterations time 0.092, Presolve 0.02
Total time (CPU seconds): 0.19 (Wallclock seconds): 0.17
[13]:
('ok', 'optimal')
And, as expected, the costs are the same as for the nodal market: 301 k€.
Now, we can plot both the market results of the 2 bidding zone market and the redispatch results:
[14]:
fig, axs = plt.subplots(
1, 3, figsize=(20, 10), subplot_kw={"projection": ccrs.AlbersEqualArea()}
)
market = (
n.generators_t.p[m.generators.index]
.T.squeeze()
.groupby(n.generators.bus)
.sum()
.div(2e4)
)
n.plot(ax=axs[0], bus_sizes=market, title="2 bidding zones market simulation")
redispatch_up = (
n.generators_t.p.filter(like="ramp up")
.T.squeeze()
.groupby(n.generators.bus)
.sum()
.div(2e4)
)
n.plot(
ax=axs[1], bus_sizes=redispatch_up, bus_colors="blue", title="Redispatch: ramp up"
)
redispatch_down = (
n.generators_t.p.filter(like="ramp down")
.T.squeeze()
.groupby(n.generators.bus)
.sum()
.div(-2e4)
)
n.plot(
ax=axs[2],
bus_sizes=redispatch_down,
bus_colors="red",
title="Redispatch: ramp down / curtail",
);
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.25.1/lib/python3.11/site-packages/cartopy/mpl/style.py:76: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
We can also read out the final dispatch of each generator:
[15]:
grouper = n.generators.index.str.split(" ramp", expand=True).get_level_values(0)
n.generators_t.p.groupby(grouper, axis=1).sum().squeeze()
[15]:
1 Gas 0.000000
1 Hard Coal 0.000000
1 Solar 11.326192
1 Wind Onshore 1.754375
100_220kV Solar 14.913326
...
98 Wind Onshore 71.451646
99_220kV Gas 0.000000
99_220kV Hard Coal 0.000000
99_220kV Solar 8.246606
99_220kV Wind Onshore 3.432939
Name: 2011-01-01 12:00:00, Length: 1423, dtype: float64
Changing bidding strategies in redispatch market#
We can also formulate other bidding strategies or compensation mechanisms for the redispatch market.
For example, that ramping up a generator is twice as expensive.
[16]:
n.generators.loc[n.generators.index.str.contains("ramp up"), "marginal_cost"] *= 2
Or that generators need to be compensated for curtailing them or ramping them down at 50% of their marginal cost.
[17]:
n.generators.loc[n.generators.index.str.contains("ramp down"), "marginal_cost"] *= -0.5
In this way, the outcome should be more expensive than the ideal nodal market:
[18]:
n.optimize(solver_name=solver)
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
WARNING:pypsa.components:The following transformers have zero r, which could break the linear load flow:
Index(['2', '5', '10', '12', '13', '15', '18', '20', '22', '24', '26', '30',
'32', '37', '42', '46', '52', '56', '61', '68', '69', '74', '78', '86',
'87', '94', '95', '96', '99', '100', '104', '105', '106', '107', '117',
'120', '123', '124', '125', '128', '129', '138', '143', '156', '157',
'159', '160', '165', '184', '191', '195', '201', '220', '231', '232',
'233', '236', '247', '248', '250', '251', '252', '261', '263', '264',
'267', '272', '279', '281', '282', '292', '303', '307', '308', '312',
'315', '317', '322', '332', '334', '336', '338', '351', '353', '360',
'362', '382', '384', '385', '391', '403', '404', '413', '421', '450',
'458'],
dtype='object', name='Transformer')
INFO:linopy.model: Solve linear problem using Cbc solver
INFO:linopy.io: Writing time: 0.14s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 5331 primals, 11649 duals
Objective: 4.79e+05
Solver model: not available
Solver message: Optimal - objective value 479003.12190570
INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-fix-p-lower, Generator-fix-p-upper, Line-fix-s-lower, Line-fix-s-upper, Transformer-fix-s-lower, Transformer-fix-s-upper, StorageUnit-fix-p_dispatch-lower, StorageUnit-fix-p_dispatch-upper, StorageUnit-fix-p_store-lower, StorageUnit-fix-p_store-upper, StorageUnit-fix-state_of_charge-lower, StorageUnit-fix-state_of_charge-upper, Kirchhoff-Voltage-Law, StorageUnit-energy_balance were not assigned to the network.
Welcome to the CBC MILP Solver
Version: 2.10.10
Build Date: Apr 19 2023
command line - cbc -printingOptions all -import /tmp/linopy-problem-crmaraa8.lp -solve -solu /tmp/linopy-solve-xcnre53y.sol (default strategy 1)
Option for printingOptions changed from normal to all
Presolve 623 (-11026) rows, 1315 (-4016) columns and 4089 (-15370) elements
Perturbing problem by 0.001% of 4481.8503 - largest nonzero change 0.0005851649 ( 0.0020515391%) - largest zero change 0.00055543458
0 Obj 223876.24 Primal inf 1290782.9 (574)
87 Obj 223876.53 Primal inf 1044791.2 (540)
161 Obj 223876.53 Primal inf 1074145.1 (515)
248 Obj 223879.27 Primal inf 984093.41 (477)
335 Obj 223881.14 Primal inf 1654235.2 (439)
405 Obj 223883.31 Primal inf 11440305 (429)
485 Obj 231343.42 Primal inf 59440577 (408)
553 Obj 231795.91 Primal inf 552511.38 (203)
631 Obj 232960.56 Primal inf 42113192 (507)
713 Obj 248197.74 Primal inf 1569212.8 (70)
800 Obj 478895.81 Primal inf 3456.0113 (14)
812 Obj 479005.72
812 Obj 479003.13 Dual inf 0.00039334043 (10)
823 Obj 479003.12
Optimal - objective value 479003.12
After Postsolve, objective 479003.12, infeasibilities - dual 2828.1704 (90), primal 1.9845131e-05 (84)
Presolved model was optimal, full model needs cleaning up
0 Obj 479003.12 Primal inf 7.8549911e-07 (4) Dual inf 4.0000003e+08 (94)
End of values pass after 94 iterations
94 Obj 479003.12
Optimal - objective value 479003.12
Optimal objective 479003.1219 - 917 iterations time 0.112, Presolve 0.03
Total time (CPU seconds): 0.20 (Wallclock seconds): 0.17
[18]:
('ok', 'optimal')
Costs are now 502 k€ compared to 301 k€.