86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
"""
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Iteratively solves expansion problem like the rule ``solve_network``, but additionally
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records intermediate branch capacity steps and values of the objective function.
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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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solver:
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name:
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{solveroptions}:
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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}_trace.nc``: Solved PyPSA network including optimisation results (with trace)
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Description
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-----------
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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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from solve_network import patch_pyomo_tmpdir, prepare_network, solve_network
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import pypsa
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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='1.25', 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}_trace.nc"],
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log=dict(python="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_python_trace.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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n = pypsa.Network(snakemake.input[0])
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solver_log = 'solver.log'
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config = snakemake.config['solving']
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opts = snakemake.wildcards.opts.split('-')
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def save_optimal_capacities(net, iteration, status):
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net.lines[f"s_nom_opt_{iteration}"] = net.lines["s_nom_opt"]
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net.links[f"p_nom_opt_{iteration}"] = net.links["p_nom_opt"]
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setattr(net, f"status_{iteration}", status)
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setattr(net, f"objective_{iteration}", net.objective)
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net.iteration = iteration
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net.export_to_netcdf(snakemake.output[0])
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config['options']['max_iterations'] = 12
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n = prepare_network(n, config['options'])
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n = solve_network(n, config, solver_log, opts, save_optimal_capacities)
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n.export_to_netcdf(snakemake.output[0])
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