354 lines
15 KiB
Python
354 lines
15 KiB
Python
"""
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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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(electricity:)
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(BAU_mincapacities:)
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(SAFE_reservemargin:)
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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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solver:
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name:
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(solveroptions):
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(plotting:)
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(conv_techs:)
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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/{network}_s{simpl}_{clusters}_l{ll}_{opts}.nc``: confer :ref:`prepare`
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Outputs
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-------
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- ``results/networks/{network}_s{simpl}_{clusters}_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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Additionaly some extra constraints from :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 numpy as np
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import pandas as pd
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import logging
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logger = logging.getLogger(__name__)
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import gc
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import os
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import pypsa
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from pypsa.descriptors import free_output_series_dataframes
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# Suppress logging of the slack bus choices
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pypsa.pf.logger.setLevel(logging.WARNING)
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from vresutils.benchmark import memory_logger
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def patch_pyomo_tmpdir(tmpdir):
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# PYOMO should write its lp files into tmp here
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import os
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if not os.path.isdir(tmpdir):
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os.mkdir(tmpdir)
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from pyutilib.services import TempfileManager
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TempfileManager.tempdir = tmpdir
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def prepare_network(n, solve_opts=None):
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if solve_opts is None:
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solve_opts = snakemake.config['solving']['options']
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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*(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 + 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_opts_constraints(n, opts=None):
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if opts is None:
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opts = snakemake.wildcards.opts.split('-')
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if 'BAU' in opts:
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mincaps = snakemake.config['electricity']['BAU_mincapacities']
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def bau_mincapacities_rule(model, carrier):
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gens = n.generators.index[n.generators.p_nom_extendable & (n.generators.carrier == carrier)]
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return sum(model.generator_p_nom[gen] for gen in gens) >= mincaps[carrier]
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n.model.bau_mincapacities = pypsa.opt.Constraint(list(mincaps), rule=bau_mincapacities_rule)
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if 'SAFE' in opts:
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peakdemand = (1. + snakemake.config['electricity']['SAFE_reservemargin']) * n.loads_t.p_set.sum(axis=1).max()
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conv_techs = snakemake.config['plotting']['conv_techs']
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exist_conv_caps = n.generators.loc[n.generators.carrier.isin(conv_techs) & ~n.generators.p_nom_extendable, 'p_nom'].sum()
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ext_gens_i = n.generators.index[n.generators.carrier.isin(conv_techs) & n.generators.p_nom_extendable]
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n.model.safe_peakdemand = pypsa.opt.Constraint(expr=sum(n.model.generator_p_nom[gen] for gen in ext_gens_i) >= peakdemand - exist_conv_caps)
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def add_lv_constraint(n):
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line_volume = getattr(n, 'line_volume_limit', None)
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if line_volume is not None and not np.isinf(line_volume):
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links_dc_ext_i = n.links.index[(n.links.carrier == 'DC') & n.links.p_nom_extendable] if not n.links.empty else pd.Index([])
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n.model.line_volume_constraint = pypsa.opt.Constraint(
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expr=((sum(n.model.passive_branch_s_nom["Line",line]*n.lines.at[line,"length"]
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for line in n.lines.index[n.lines.s_nom_extendable]) +
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sum(n.model.link_p_nom[link]*n.links.at[link,"length"]
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for link in links_dc_ext_i))
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<= line_volume)
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)
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def add_lc_constraint(n):
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line_cost = getattr(n, 'line_cost_limit', None)
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if line_cost is not None and not np.isinf(line_cost):
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links_dc_ext_i = n.links.index[(n.links.carrier == 'DC') & n.links.p_nom_extendable] if not n.links.empty else pd.Index([])
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n.model.line_cost_constraint = pypsa.opt.Constraint(
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expr=((sum(n.model.passive_branch_s_nom["Line",line]*n.lines.at[line,"capital_cost_lc"]
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for line in n.lines.index[n.lines.s_nom_extendable]) +
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sum(n.model.link_p_nom[link]*n.links.at[link,"capital_cost_lc"]
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for link in links_dc_ext_i))
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<= line_cost)
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)
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def add_eps_storage_constraint(n):
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if not hasattr(n, 'epsilon'):
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n.epsilon = 1e-5
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fix_sus_i = n.storage_units.index[~ n.storage_units.p_nom_extendable]
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n.model.objective.expr += sum(n.epsilon * n.model.state_of_charge[su, n.snapshots[0]] for su in fix_sus_i)
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def fix_branches(n, lines_s_nom=None, links_p_nom=None):
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if lines_s_nom is not None and len(lines_s_nom) > 0:
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for l, s_nom in lines_s_nom.iteritems():
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n.model.passive_branch_s_nom["Line", l].fix(s_nom)
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if isinstance(n.opt, pypsa.opf.PersistentSolver):
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n.opt.update_var(n.model.passive_branch_s_nom)
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if links_p_nom is not None and len(links_p_nom) > 0:
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for l, p_nom in links_p_nom.iteritems():
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n.model.link_p_nom[l].fix(p_nom)
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if isinstance(n.opt, pypsa.opf.PersistentSolver):
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n.opt.update_var(n.model.link_p_nom)
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def solve_network(n, config=None, solver_log=None, opts=None, callback=None):
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if config is None:
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config = snakemake.config['solving']
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solve_opts = config['options']
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solver_options = config['solver'].copy()
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if solver_log is None:
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solver_log = snakemake.log.solver
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solver_name = solver_options.pop('name')
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def extra_postprocessing(n, snapshots, duals):
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if hasattr(n, 'line_volume_limit') and hasattr(n.model, 'line_volume_constraint'):
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cdata = pd.Series(list(n.model.line_volume_constraint.values()),
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index=list(n.model.line_volume_constraint.keys()))
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n.line_volume_limit_dual = -cdata.map(duals).sum()
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if hasattr(n, 'line_cost_limit') and hasattr(n.model, 'line_cost_constraint'):
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cdata = pd.Series(list(n.model.line_cost_constraint.values()),
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index=list(n.model.line_cost_constraint.keys()))
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n.line_cost_limit_dual = -cdata.map(duals).sum()
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def run_lopf(n, allow_warning_status=False, fix_ext_lines=False):
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free_output_series_dataframes(n)
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pypsa.opf.network_lopf_build_model(n, formulation=solve_opts['formulation'])
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add_opts_constraints(n, opts)
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if not fix_ext_lines:
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add_lv_constraint(n)
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add_lc_constraint(n)
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pypsa.opf.network_lopf_prepare_solver(n, solver_name=solver_name)
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if fix_ext_lines:
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fix_branches(n,
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lines_s_nom=n.lines.loc[n.lines.s_nom_extendable, 's_nom_opt'],
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links_p_nom=n.links.loc[n.links.p_nom_extendable, 'p_nom_opt'])
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# Firing up solve will increase memory consumption tremendously, so
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# make sure we freed everything we can
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gc.collect()
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status, termination_condition = \
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pypsa.opf.network_lopf_solve(n,
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solver_logfile=solver_log,
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solver_options=solver_options,
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formulation=solve_opts['formulation'],
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extra_postprocessing=extra_postprocessing
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#free_memory={'pypsa'}
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)
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assert status == "ok" or allow_warning_status and status == 'warning', \
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("network_lopf did abort with status={} "
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"and termination_condition={}"
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.format(status, termination_condition))
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return status, termination_condition
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iteration = 0
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lines_ext_b = n.lines.s_nom_extendable
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if lines_ext_b.any():
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# puh: ok, we need to iterate, since there is a relation
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# between s/p_nom and r, x for branches.
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msq_threshold = 0.01
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lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']])
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lines['s_nom'] = (
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np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
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n.lines.bus0.map(n.buses.v_nom)
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).where(n.lines.type != '', n.lines['s_nom'])
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lines_ext_typed_b = (n.lines.type != '') & lines_ext_b
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lines_ext_untyped_b = (n.lines.type == '') & lines_ext_b
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def update_line_parameters(n, zero_lines_below=10):
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if zero_lines_below > 0:
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n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 0.
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n.links.loc[n.links.p_nom_opt < zero_lines_below, 'p_nom_opt'] = 0.
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if lines_ext_untyped_b.any():
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for attr in ('r', 'x'):
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n.lines.loc[lines_ext_untyped_b, attr] = (
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lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt'])
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)
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if lines_ext_typed_b.any():
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n.lines.loc[lines_ext_typed_b, 'num_parallel'] = (
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n.lines['s_nom_opt']/lines['s_nom']
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)
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logger.debug("lines.num_parallel={}".format(n.lines.loc[lines_ext_typed_b, 'num_parallel']))
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iteration += 1
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lines['s_nom_opt'] = lines['s_nom'] * n.lines['num_parallel'].where(n.lines.type != '', 1.)
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status, termination_condition = run_lopf(n, allow_warning_status=True)
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if callback is not None: callback(n, iteration, status)
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def msq_diff(n):
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lines_err = np.sqrt(((n.lines['s_nom_opt'] - lines['s_nom_opt'])**2).mean())/lines['s_nom_opt'].mean()
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logger.info("Mean square difference after iteration {} is {}".format(iteration, lines_err))
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return lines_err
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min_iterations = solve_opts.get('min_iterations', 2)
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max_iterations = solve_opts.get('max_iterations', 999)
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while msq_diff(n) > msq_threshold or iteration < min_iterations:
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if iteration >= max_iterations:
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logger.info("Iteration {} beyond max_iterations {}. Stopping ...".format(iteration, max_iterations))
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break
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update_line_parameters(n)
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lines['s_nom_opt'] = n.lines['s_nom_opt']
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iteration += 1
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status, termination_condition = run_lopf(n, allow_warning_status=True)
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if callback is not None: callback(n, iteration, status)
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update_line_parameters(n, zero_lines_below=100)
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logger.info("Starting last run with fixed extendable lines")
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iteration += 1
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status, termination_condition = run_lopf(n, fix_ext_lines=True)
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if callback is not None: callback(n, iteration, status)
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return n
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils.snakemake import MockSnakemake, Dict
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='45', lv='1.0', opts='Co2L-3H'),
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input=["networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc"],
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output=["results/networks/s{simpl}_{clusters}_lv{lv}_{opts}.nc"],
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log=dict(solver="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_solver.log",
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python="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_python.log")
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)
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tmpdir = snakemake.config['solving'].get('tmpdir')
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if tmpdir is not None:
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patch_pyomo_tmpdir(tmpdir)
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logging.basicConfig(filename=snakemake.log.python,
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level=snakemake.config['logging_level'])
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with memory_logger(filename=getattr(snakemake.log, 'memory', None), interval=30.) as mem:
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n = pypsa.Network(snakemake.input[0])
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n = prepare_network(n)
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n = solve_network(n)
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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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