e0215bc5a9
* Propagate the solver log file name to the solver Previously, the PyPSA network solving functions were not told about the solver logfile specified in the Snakemake file. * Pass solver_logfile on as kwargs The `solve_network` function passes any additional arguments on to the pypsa `network_lopf` and `ilopf` functions. Now we also pass `solver_logfile` on as part of kwargs.
291 lines
11 KiB
Python
Executable File
291 lines
11 KiB
Python
Executable File
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: GPL-3.0-or-later
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"""
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Solves linear optimal power flow for a network iteratively while updating reactances.
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Relevant Settings
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-----------------
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.. code:: yaml
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solving:
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tmpdir:
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options:
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formulation:
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clip_p_max_pu:
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load_shedding:
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noisy_costs:
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nhours:
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min_iterations:
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max_iterations:
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skip_iterations:
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track_iterations:
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solver:
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name:
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.. seealso::
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Documentation of the configuration file ``config.yaml`` at
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:ref:`electricity_cf`, :ref:`solving_cf`, :ref:`plotting_cf`
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Inputs
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------
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- ``networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`prepare`
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Outputs
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-------
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- ``results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Solved PyPSA network including optimisation results
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.. image:: ../img/results.png
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:scale: 40 %
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Description
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-----------
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Total annual system costs are minimised with PyPSA. The full formulation of the
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linear optimal power flow (plus investment planning
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is provided in the
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`documentation of PyPSA <https://pypsa.readthedocs.io/en/latest/optimal_power_flow.html#linear-optimal-power-flow>`_.
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The optimization is based on the ``pyomo=False`` setting in the :func:`network.lopf` and :func:`pypsa.linopf.ilopf` function.
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Additionally, some extra constraints specified in :mod:`prepare_network` are added.
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Solving the network in multiple iterations is motivated through the dependence of transmission line capacities and impedances.
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As lines are expanded their electrical parameters change, which renders the optimisation bilinear even if the power flow
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equations are linearized.
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To retain the computational advantage of continuous linear programming, a sequential linear programming technique
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is used, where in between iterations the line impedances are updated.
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Details (and errors made through this heuristic) are discussed in the paper
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- Fabian Neumann and Tom Brown. `Heuristics for Transmission Expansion Planning in Low-Carbon Energy System Models <https://arxiv.org/abs/1907.10548>`_), *16th International Conference on the European Energy Market*, 2019. `arXiv:1907.10548 <https://arxiv.org/abs/1907.10548>`_.
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.. warning::
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Capital costs of existing network components are not included in the objective function,
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since for the optimisation problem they are just a constant term (no influence on optimal result).
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Therefore, these capital costs are not included in ``network.objective``!
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If you want to calculate the full total annual system costs add these to the objective value.
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.. tip::
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The rule :mod:`solve_all_networks` runs
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for all ``scenario`` s in the configuration file
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the rule :mod:`solve_network`.
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"""
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import logging
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from _helpers import configure_logging
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import numpy as np
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import pandas as pd
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import re
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import pypsa
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from pypsa.linopf import (get_var, define_constraints, linexpr, join_exprs,
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network_lopf, ilopf)
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from pathlib import Path
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from vresutils.benchmark import memory_logger
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logger = logging.getLogger(__name__)
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def prepare_network(n, solve_opts):
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if 'clip_p_max_pu' in solve_opts:
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for df in (n.generators_t.p_max_pu, n.storage_units_t.inflow):
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df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True)
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if solve_opts.get('load_shedding'):
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n.add("Carrier", "Load")
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n.madd("Generator", n.buses.index, " load",
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bus=n.buses.index,
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carrier='load',
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sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
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marginal_cost=1e2, # Eur/kWh
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# intersect between macroeconomic and surveybased
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# willingness to pay
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# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
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p_nom=1e9 # kW
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)
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if solve_opts.get('noisy_costs'):
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for t in n.iterate_components(n.one_port_components):
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#if 'capital_cost' in t.df:
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# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
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if 'marginal_cost' in t.df:
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t.df['marginal_cost'] += (1e-2 + 2e-3 *
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(np.random.random(len(t.df)) - 0.5))
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for t in n.iterate_components(['Line', 'Link']):
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t.df['capital_cost'] += (1e-1 +
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2e-2*(np.random.random(len(t.df)) - 0.5)) * t.df['length']
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if solve_opts.get('nhours'):
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nhours = solve_opts['nhours']
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n.set_snapshots(n.snapshots[:nhours])
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n.snapshot_weightings[:] = 8760./nhours
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return n
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def add_CCL_constraints(n, config):
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agg_p_nom_limits = config['electricity'].get('agg_p_nom_limits')
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try:
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agg_p_nom_minmax = pd.read_csv(agg_p_nom_limits,
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index_col=list(range(2)))
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except IOError:
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logger.exception("Need to specify the path to a .csv file containing "
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"aggregate capacity limits per country in "
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"config['electricity']['agg_p_nom_limit'].")
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logger.info("Adding per carrier generation capacity constraints for "
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"individual countries")
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gen_country = n.generators.bus.map(n.buses.country)
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# cc means country and carrier
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p_nom_per_cc = (pd.DataFrame(
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{'p_nom': linexpr((1, get_var(n, 'Generator', 'p_nom'))),
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'country': gen_country, 'carrier': n.generators.carrier})
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.dropna(subset=['p_nom'])
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.groupby(['country', 'carrier']).p_nom
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.apply(join_exprs))
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minimum = agg_p_nom_minmax['min'].dropna()
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if not minimum.empty:
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minconstraint = define_constraints(n, p_nom_per_cc[minimum.index],
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'>=', minimum, 'agg_p_nom', 'min')
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maximum = agg_p_nom_minmax['max'].dropna()
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if not maximum.empty:
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maxconstraint = define_constraints(n, p_nom_per_cc[maximum.index],
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'<=', maximum, 'agg_p_nom', 'max')
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def add_EQ_constraints(n, o, scaling=1e-1):
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float_regex = "[0-9]*\.?[0-9]+"
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level = float(re.findall(float_regex, o)[0])
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if o[-1] == 'c':
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ggrouper = n.generators.bus.map(n.buses.country)
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lgrouper = n.loads.bus.map(n.buses.country)
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sgrouper = n.storage_units.bus.map(n.buses.country)
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else:
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ggrouper = n.generators.bus
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lgrouper = n.loads.bus
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sgrouper = n.storage_units.bus
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load = n.snapshot_weightings @ \
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n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
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inflow = n.snapshot_weightings @ \
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n.storage_units_t.inflow.groupby(sgrouper, axis=1).sum()
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inflow = inflow.reindex(load.index).fillna(0.)
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rhs = scaling * ( level * load - inflow )
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lhs_gen = linexpr((n.snapshot_weightings * scaling,
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get_var(n, "Generator", "p").T)
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).T.groupby(ggrouper, axis=1).apply(join_exprs)
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lhs_spill = linexpr((-n.snapshot_weightings * scaling,
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get_var(n, "StorageUnit", "spill").T)
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).T.groupby(sgrouper, axis=1).apply(join_exprs)
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lhs_spill = lhs_spill.reindex(lhs_gen.index).fillna("")
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lhs = lhs_gen + lhs_spill
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define_constraints(n, lhs, ">=", rhs, "equity", "min")
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def add_BAU_constraints(n, config):
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mincaps = pd.Series(config['electricity']['BAU_mincapacities'])
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lhs = (linexpr((1, get_var(n, 'Generator', 'p_nom')))
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.groupby(n.generators.carrier).apply(join_exprs))
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define_constraints(n, lhs, '>=', mincaps[lhs.index], 'Carrier', 'bau_mincaps')
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def add_SAFE_constraints(n, config):
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peakdemand = (1. + config['electricity']['SAFE_reservemargin']) *\
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n.loads_t.p_set.sum(axis=1).max()
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conv_techs = config['plotting']['conv_techs']
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exist_conv_caps = n.generators.query('~p_nom_extendable & carrier in @conv_techs')\
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.p_nom.sum()
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ext_gens_i = n.generators.query('carrier in @conv_techs & p_nom_extendable').index
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lhs = linexpr((1, get_var(n, 'Generator', 'p_nom')[ext_gens_i])).sum()
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rhs = peakdemand - exist_conv_caps
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define_constraints(n, lhs, '>=', rhs, 'Safe', 'mintotalcap')
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def add_battery_constraints(n):
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nodes = n.buses.index[n.buses.carrier == "battery"]
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if nodes.empty or ('Link', 'p_nom') not in n.variables.index:
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return
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link_p_nom = get_var(n, "Link", "p_nom")
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lhs = linexpr((1,link_p_nom[nodes + " charger"]),
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(-n.links.loc[nodes + " discharger", "efficiency"].values,
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link_p_nom[nodes + " discharger"].values))
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define_constraints(n, lhs, "=", 0, 'Link', 'charger_ratio')
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def extra_functionality(n, snapshots):
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"""
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Collects supplementary constraints which will be passed to ``pypsa.linopf.network_lopf``.
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If you want to enforce additional custom constraints, this is a good location to add them.
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The arguments ``opts`` and ``snakemake.config`` are expected to be attached to the network.
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"""
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opts = n.opts
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config = n.config
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if 'BAU' in opts and n.generators.p_nom_extendable.any():
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add_BAU_constraints(n, config)
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if 'SAFE' in opts and n.generators.p_nom_extendable.any():
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add_SAFE_constraints(n, config)
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if 'CCL' in opts and n.generators.p_nom_extendable.any():
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add_CCL_constraints(n, config)
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for o in opts:
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if "EQ" in o:
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add_EQ_constraints(n, o)
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add_battery_constraints(n)
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def solve_network(n, config, opts='', **kwargs):
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solver_options = config['solving']['solver'].copy()
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solver_name = solver_options.pop('name')
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cf_solving = config['solving']['options']
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track_iterations = cf_solving.get('track_iterations', False)
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min_iterations = cf_solving.get('min_iterations', 4)
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max_iterations = cf_solving.get('max_iterations', 6)
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# add to network for extra_functionality
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n.config = config
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n.opts = opts
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if cf_solving.get('skip_iterations', False):
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network_lopf(n, solver_name=solver_name, solver_options=solver_options,
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extra_functionality=extra_functionality, **kwargs)
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else:
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ilopf(n, solver_name=solver_name, solver_options=solver_options,
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track_iterations=track_iterations,
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min_iterations=min_iterations,
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max_iterations=max_iterations,
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extra_functionality=extra_functionality, **kwargs)
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return n
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake('solve_network', network='elec', simpl='',
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clusters='5', ll='copt', opts='Co2L-BAU-CCL-24H')
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configure_logging(snakemake)
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tmpdir = snakemake.config['solving'].get('tmpdir')
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if tmpdir is not None:
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Path(tmpdir).mkdir(parents=True, exist_ok=True)
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opts = snakemake.wildcards.opts.split('-')
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solve_opts = snakemake.config['solving']['options']
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fn = getattr(snakemake.log, 'memory', None)
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with memory_logger(filename=fn, interval=30.) as mem:
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n = pypsa.Network(snakemake.input[0])
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n = prepare_network(n, solve_opts)
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n = solve_network(n, config=snakemake.config, opts=opts,
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solver_dir=tmpdir,
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solver_logfile=snakemake.log.solver)
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n.export_to_netcdf(snakemake.output[0])
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logger.info("Maximum memory usage: {}".format(mem.mem_usage))
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