solve.py: start converting extra_funcionalities
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@ -4,8 +4,10 @@ import pypsa
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import numpy as np
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import pandas as pd
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import xarray as xr
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from pypsa.linopt import get_var, linexpr, define_constraints
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from linopy import merge
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from pypsa.linopf import network_lopf, ilopf
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@ -107,110 +109,79 @@ def prepare_network(n, solve_opts=None):
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def add_battery_constraints(n):
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chargers_b = n.links.carrier.str.contains("battery charger")
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chargers = n.links.index[chargers_b & n.links.p_nom_extendable]
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dischargers = chargers.str.replace("charger", "discharger")
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if chargers.empty or ('Link', 'p_nom') not in n.variables.index:
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"""
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Add constraints to ensure that the ratio between the charger and
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discharger.
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1 * charger_size - efficiency * discharger_size = 0
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"""
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nodes = n.buses.index[n.buses.carrier == "battery"]
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if nodes.empty:
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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[chargers]),
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(-n.links.loc[dischargers, "efficiency"].values,
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link_p_nom[dischargers].values))
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define_constraints(n, lhs, "=", 0, 'Link', 'charger_ratio')
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link_p_nom = n.model["Link-p_nom"]
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eff = n.links.efficiency[nodes + " discharger"].values
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lhs = link_p_nom.loc[nodes + ' charger'] - link_p_nom.loc[nodes + ' discharger'] * eff
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n.model.add_constraints(lhs == 0, name="Link-charger_ratio")
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def add_chp_constraints(n):
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electric_bool = (n.links.index.str.contains("urban central")
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& n.links.index.str.contains("CHP")
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& n.links.index.str.contains("electric"))
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heat_bool = (n.links.index.str.contains("urban central")
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& n.links.index.str.contains("CHP")
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& n.links.index.str.contains("heat"))
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electric = (n.links.index.str.contains("urban central")
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& n.links.index.str.contains("CHP")
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& n.links.index.str.contains("electric"))
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heat = (n.links.index.str.contains("urban central")
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& n.links.index.str.contains("CHP")
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& n.links.index.str.contains("heat"))
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electric = n.links.index[electric_bool]
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heat = n.links.index[heat_bool]
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electric_ext = n.links[electric].query("p_nom_extendable").index
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heat_ext = n.links[heat].query("p_nom_extendable").index
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electric_ext = n.links.index[electric_bool & n.links.p_nom_extendable]
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heat_ext = n.links.index[heat_bool & n.links.p_nom_extendable]
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electric_fix = n.links[electric].query("~p_nom_extendable").index
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heat_fix = n.links[heat].query("~p_nom_extendable").index
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electric_fix = n.links.index[electric_bool & ~n.links.p_nom_extendable]
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heat_fix = n.links.index[heat_bool & ~n.links.p_nom_extendable]
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link_p = get_var(n, "Link", "p")
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p = n.model["Link-p"] # dimension: [time, link]
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# output ratio between heat and electricity and top_iso_fuel_line for extendable
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if not electric_ext.empty:
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p_nom = n.model["Link-p_nom"]
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link_p_nom = get_var(n, "Link", "p_nom")
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lhs = (p_nom.loc[electric_ext] * (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values -
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p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values)
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n.model.add_constraints(lhs == 0, name='chplink-fix_p_nom_ratio')
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#ratio of output heat to electricity set by p_nom_ratio
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lhs = linexpr((n.links.loc[electric_ext, "efficiency"]
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*n.links.loc[electric_ext, "p_nom_ratio"],
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link_p_nom[electric_ext]),
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(-n.links.loc[heat_ext, "efficiency"].values,
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link_p_nom[heat_ext].values))
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rename = {"Link-ext": "Link"}
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lhs = p.loc[:, electric_ext] + p.loc[:, heat_ext] - p_nom.rename(rename).loc[electric_ext]
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n.model.add_constraints(lhs <= 0, name='chplink-top_iso_fuel_line_ext')
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define_constraints(n, lhs, "=", 0, 'chplink', 'fix_p_nom_ratio')
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#top_iso_fuel_line for extendable
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lhs = linexpr((1,link_p[heat_ext]),
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(1,link_p[electric_ext].values),
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(-1,link_p_nom[electric_ext].values))
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define_constraints(n, lhs, "<=", 0, 'chplink', 'top_iso_fuel_line_ext')
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# top_iso_fuel_line for fixed
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if not electric_fix.empty:
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lhs = p.loc[:, electric_fix] + p.loc[:, heat_fix]
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rhs = n.links.p_nom[electric_fix]
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n.model.add_constraints(lhs <= rhs, name='chplink-top_iso_fuel_line_fix')
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#top_iso_fuel_line for fixed
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lhs = linexpr((1,link_p[heat_fix]),
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(1,link_p[electric_fix].values))
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rhs = n.links.loc[electric_fix, "p_nom"].values
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define_constraints(n, lhs, "<=", rhs, 'chplink', 'top_iso_fuel_line_fix')
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# back-pressure
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if not electric.empty:
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lhs = (p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values) -
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p.loc[:, electric] * n.links.efficiency[electric])
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n.model.add_constraints(lhs <= rhs, name='chplink-backpressure')
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#backpressure
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lhs = linexpr((n.links.loc[electric, "c_b"].values
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*n.links.loc[heat, "efficiency"],
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link_p[heat]),
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(-n.links.loc[electric, "efficiency"].values,
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link_p[electric].values))
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define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
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def basename(x):
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return x.split("-2")[0]
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def add_pipe_retrofit_constraint(n):
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"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
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gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
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h2_retrofitted_i = n.links.query("carrier == 'H2 pipeline retrofitted' and p_nom_extendable").index
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if h2_retrofitted_i.empty or gas_pipes_i.empty: return
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if h2_retrofitted_i.empty or gas_pipes_i.empty:
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return
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link_p_nom = get_var(n, "Link", "p_nom")
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p_nom = n.model["Link-p_nom"]
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CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
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fr = "H2 pipeline retrofitted"
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to = "gas pipeline"
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lhs = p_nom.loc[gas_pipes_i] + CH4_per_H2 * p_nom.loc[h2_retrofitted_i]
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rhs = n.links.p_nom[gas_pipes_i].rename_axis("Link-ext")
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pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom'].rename(basename)
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n.model.add_constraints(lhs == rhs, name='Link-pipe_retrofit')
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lhs = linexpr(
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(CH4_per_H2, link_p_nom.loc[h2_retrofitted_i].rename(index=lambda x: x.replace(fr, to))),
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(1, link_p_nom.loc[gas_pipes_i])
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)
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lhs.rename(basename, inplace=True)
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define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
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def add_co2_sequestration_limit(n, sns):
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@ -243,37 +214,48 @@ def add_co2_sequestration_limit(n, sns):
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def extra_functionality(n, snapshots):
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add_battery_constraints(n)
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add_pipe_retrofit_constraint(n)
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add_co2_sequestration_limit(n, snapshots)
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# add_co2_sequestration_limit(n, snapshots)
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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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keep_shadowprices = cf_solving.get('keep_shadowprices', True)
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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,
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keep_shadowprices=keep_shadowprices, **kwargs)
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skip_iterations = cf_solving.get("skip_iterations", False)
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if not n.lines.s_nom_extendable.any():
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skip_iterations = True
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logger.info("No expandable lines found. Skipping iterative solving.")
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if skip_iterations:
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n.optimize(
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solver_name=solver_name,
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solver_options=solver_options,
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extra_functionality=extra_functionality,
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**kwargs,
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)
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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,
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keep_shadowprices=keep_shadowprices,
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**kwargs)
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n.optimize.optimize_transmission_expansion_iteratively(
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solver_name=solver_name,
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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,
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**kwargs,
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)
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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 helper import mock_snakemake
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@ -281,10 +263,10 @@ if __name__ == "__main__":
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'solve_network',
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simpl='',
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opts="",
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clusters="37",
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clusters="45",
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lv=1.0,
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sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
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planning_horizons="2030",
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sector_opts='Co2L0-3H-T-H-B-I-A-solar+p3-dist1',
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planning_horizons="2050",
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)
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logging.basicConfig(filename=snakemake.log.python,
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@ -306,6 +288,7 @@ if __name__ == "__main__":
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n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
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n = prepare_network(n, solve_opts)
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n.snapshots = n.snapshots[:20]
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n = solve_network(n, config=snakemake.config, opts=opts,
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solver_dir=tmpdir,
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