123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
|
|
#
|
|
# SPDX-License-Identifier: MIT
|
|
|
|
"""
|
|
Solves linear optimal dispatch in hourly resolution
|
|
using the capacities of previous capacity expansion in rule :mod:`solve_network`.
|
|
|
|
Relevant Settings
|
|
-----------------
|
|
|
|
.. code:: yaml
|
|
|
|
solving:
|
|
tmpdir:
|
|
options:
|
|
formulation:
|
|
clip_p_max_pu:
|
|
load_shedding:
|
|
noisy_costs:
|
|
nhours:
|
|
min_iterations:
|
|
max_iterations:
|
|
solver:
|
|
name:
|
|
(solveroptions):
|
|
|
|
.. seealso::
|
|
Documentation of the configuration file ``config.yaml`` at
|
|
:ref:`solving_cf`
|
|
|
|
Inputs
|
|
------
|
|
|
|
- ``networks/elec_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
|
|
- ``results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`solve`
|
|
|
|
Outputs
|
|
-------
|
|
|
|
- ``results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc``: Solved PyPSA network for optimal dispatch including optimisation results
|
|
|
|
Description
|
|
-----------
|
|
|
|
"""
|
|
|
|
import logging
|
|
from _helpers import configure_logging
|
|
|
|
import pypsa
|
|
import numpy as np
|
|
|
|
from pathlib import Path
|
|
from vresutils.benchmark import memory_logger
|
|
from solve_network import solve_network, prepare_network
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
def set_parameters_from_optimized(n, n_optim):
|
|
lines_typed_i = n.lines.index[n.lines.type != '']
|
|
n.lines.loc[lines_typed_i, 'num_parallel'] = \
|
|
n_optim.lines['num_parallel'].reindex(lines_typed_i, fill_value=0.)
|
|
n.lines.loc[lines_typed_i, 's_nom'] = (
|
|
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
|
|
n.lines.bus0.map(n.buses.v_nom) * n.lines.num_parallel)
|
|
|
|
lines_untyped_i = n.lines.index[n.lines.type == '']
|
|
for attr in ('s_nom', 'r', 'x'):
|
|
n.lines.loc[lines_untyped_i, attr] = \
|
|
n_optim.lines[attr].reindex(lines_untyped_i, fill_value=0.)
|
|
n.lines['s_nom_extendable'] = False
|
|
|
|
links_dc_i = n.links.index[n.links.p_nom_extendable]
|
|
n.links.loc[links_dc_i, 'p_nom'] = \
|
|
n_optim.links['p_nom_opt'].reindex(links_dc_i, fill_value=0.)
|
|
n.links.loc[links_dc_i, 'p_nom_extendable'] = False
|
|
|
|
gen_extend_i = n.generators.index[n.generators.p_nom_extendable]
|
|
n.generators.loc[gen_extend_i, 'p_nom'] = \
|
|
n_optim.generators['p_nom_opt'].reindex(gen_extend_i, fill_value=0.)
|
|
n.generators.loc[gen_extend_i, 'p_nom_extendable'] = False
|
|
|
|
stor_units_extend_i = n.storage_units.index[n.storage_units.p_nom_extendable]
|
|
n.storage_units.loc[stor_units_extend_i, 'p_nom'] = \
|
|
n_optim.storage_units['p_nom_opt'].reindex(stor_units_extend_i, fill_value=0.)
|
|
n.storage_units.loc[stor_units_extend_i, 'p_nom_extendable'] = False
|
|
|
|
stor_extend_i = n.stores.index[n.stores.e_nom_extendable]
|
|
n.stores.loc[stor_extend_i, 'e_nom'] = \
|
|
n_optim.stores['e_nom_opt'].reindex(stor_extend_i, fill_value=0.)
|
|
n.stores.loc[stor_extend_i, 'e_nom_extendable'] = False
|
|
|
|
return n
|
|
|
|
if __name__ == "__main__":
|
|
if 'snakemake' not in globals():
|
|
from _helpers import mock_snakemake
|
|
snakemake = mock_snakemake('solve_operations_network', network='elec',
|
|
simpl='', clusters='40', ll='v1.0', opts='Co2L-4H')
|
|
configure_logging(snakemake)
|
|
|
|
tmpdir = snakemake.config['solving'].get('tmpdir')
|
|
if tmpdir is not None:
|
|
Path(tmpdir).mkdir(parents=True, exist_ok=True)
|
|
|
|
n = pypsa.Network(snakemake.input.unprepared)
|
|
n_optim = pypsa.Network(snakemake.input.optimized)
|
|
n = set_parameters_from_optimized(n, n_optim)
|
|
del n_optim
|
|
|
|
opts = snakemake.wildcards.opts.split('-')
|
|
snakemake.config['solving']['options']['skip_iterations'] = False
|
|
|
|
fn = getattr(snakemake.log, 'memory', None)
|
|
with memory_logger(filename=fn, interval=30.) as mem:
|
|
n = prepare_network(n, snakemake.config['solving']['options'])
|
|
n = solve_network(n, snakemake.config, opts, solver_dir=tmpdir,
|
|
solver_logfile=snakemake.log.solver)
|
|
n.export_to_netcdf(snakemake.output[0])
|
|
|
|
logger.info("Maximum memory usage: {}".format(mem.mem_usage))
|