344 lines
11 KiB
Python
Executable File
344 lines
11 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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# coding: utf-8
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"""
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Prepare PyPSA network for solving according to :ref:`opts` and :ref:`ll`, such
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as.
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- adding an annual **limit** of carbon-dioxide emissions,
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- adding an exogenous **price** per tonne emissions of carbon-dioxide (or other kinds),
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- setting an **N-1 security margin** factor for transmission line capacities,
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- specifying an expansion limit on the **cost** of transmission expansion,
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- specifying an expansion limit on the **volume** of transmission expansion, and
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- reducing the **temporal** resolution by averaging over multiple hours
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or segmenting time series into chunks of varying lengths using ``tsam``.
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Relevant Settings
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-----------------
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.. code:: yaml
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costs:
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year:
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version:
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fill_values:
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emission_prices:
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marginal_cost:
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capital_cost:
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electricity:
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co2limit:
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max_hours:
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.. seealso::
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Documentation of the configuration file ``config/config.yaml`` at
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:ref:`costs_cf`, :ref:`electricity_cf`
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Inputs
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------
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- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
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- ``networks/elec_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
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Outputs
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-------
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- ``networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Complete PyPSA network that will be handed to the ``solve_network`` rule.
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Description
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-----------
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.. tip::
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The rule :mod:`prepare_elec_networks` runs
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for all ``scenario`` s in the configuration file
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the rule :mod:`prepare_network`.
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"""
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import logging
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import re
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import numpy as np
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import pandas as pd
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import pypsa
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from _helpers import configure_logging
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from add_electricity import load_costs, update_transmission_costs
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idx = pd.IndexSlice
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logger = logging.getLogger(__name__)
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def add_co2limit(n, co2limit, Nyears=1.0):
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n.add(
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"GlobalConstraint",
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"CO2Limit",
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carrier_attribute="co2_emissions",
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sense="<=",
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constant=co2limit * Nyears,
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)
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def add_gaslimit(n, gaslimit, Nyears=1.0):
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sel = n.carriers.index.intersection(["OCGT", "CCGT", "CHP"])
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n.carriers.loc[sel, "gas_usage"] = 1.0
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n.add(
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"GlobalConstraint",
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"GasLimit",
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carrier_attribute="gas_usage",
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sense="<=",
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constant=gaslimit * Nyears,
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)
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def add_emission_prices(n, emission_prices={"co2": 0.0}, exclude_co2=False):
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if exclude_co2:
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emission_prices.pop("co2")
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ep = (
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pd.Series(emission_prices).rename(lambda x: x + "_emissions")
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* n.carriers.filter(like="_emissions")
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).sum(axis=1)
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gen_ep = n.generators.carrier.map(ep) / n.generators.efficiency
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n.generators["marginal_cost"] += gen_ep
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su_ep = n.storage_units.carrier.map(ep) / n.storage_units.efficiency_dispatch
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n.storage_units["marginal_cost"] += su_ep
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def set_line_s_max_pu(n, s_max_pu=0.7):
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n.lines["s_max_pu"] = s_max_pu
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logger.info(f"N-1 security margin of lines set to {s_max_pu}")
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def set_transmission_limit(n, ll_type, factor, costs, Nyears=1):
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links_dc_b = n.links.carrier == "DC" if not n.links.empty else pd.Series()
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_lines_s_nom = (
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np.sqrt(3)
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* n.lines.type.map(n.line_types.i_nom)
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* n.lines.num_parallel
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* n.lines.bus0.map(n.buses.v_nom)
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)
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lines_s_nom = n.lines.s_nom.where(n.lines.type == "", _lines_s_nom)
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col = "capital_cost" if ll_type == "c" else "length"
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ref = (
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lines_s_nom @ n.lines[col]
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+ n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col]
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)
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update_transmission_costs(n, costs)
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if factor == "opt" or float(factor) > 1.0:
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n.lines["s_nom_min"] = lines_s_nom
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n.lines["s_nom_extendable"] = True
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n.links.loc[links_dc_b, "p_nom_min"] = n.links.loc[links_dc_b, "p_nom"]
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n.links.loc[links_dc_b, "p_nom_extendable"] = True
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if factor != "opt":
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con_type = "expansion_cost" if ll_type == "c" else "volume_expansion"
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rhs = float(factor) * ref
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n.add(
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"GlobalConstraint",
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f"l{ll_type}_limit",
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type=f"transmission_{con_type}_limit",
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sense="<=",
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constant=rhs,
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carrier_attribute="AC, DC",
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)
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return n
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def average_every_nhours(n, offset):
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logger.info(f"Resampling the network to {offset}")
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m = n.copy(with_time=False)
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snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
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m.set_snapshots(snapshot_weightings.index)
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m.snapshot_weightings = snapshot_weightings
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for c in n.iterate_components():
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pnl = getattr(m, c.list_name + "_t")
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for k, df in c.pnl.items():
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if not df.empty:
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pnl[k] = df.resample(offset).mean()
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return m
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def apply_time_segmentation(n, segments, solver_name="cbc"):
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logger.info(f"Aggregating time series to {segments} segments.")
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try:
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import tsam.timeseriesaggregation as tsam
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except:
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raise ModuleNotFoundError(
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"Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
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)
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p_max_pu_norm = n.generators_t.p_max_pu.max()
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p_max_pu = n.generators_t.p_max_pu / p_max_pu_norm
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load_norm = n.loads_t.p_set.max()
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load = n.loads_t.p_set / load_norm
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inflow_norm = n.storage_units_t.inflow.max()
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inflow = n.storage_units_t.inflow / inflow_norm
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raw = pd.concat([p_max_pu, load, inflow], axis=1, sort=False)
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agg = tsam.TimeSeriesAggregation(
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raw,
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hoursPerPeriod=len(raw),
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noTypicalPeriods=1,
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noSegments=int(segments),
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segmentation=True,
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solver=solver_name,
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)
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segmented = agg.createTypicalPeriods()
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weightings = segmented.index.get_level_values("Segment Duration")
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offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
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snapshots = [n.snapshots[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
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n.set_snapshots(pd.DatetimeIndex(snapshots, name="name"))
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n.snapshot_weightings = pd.Series(
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weightings, index=snapshots, name="weightings", dtype="float64"
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)
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segmented.index = snapshots
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n.generators_t.p_max_pu = segmented[n.generators_t.p_max_pu.columns] * p_max_pu_norm
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n.loads_t.p_set = segmented[n.loads_t.p_set.columns] * load_norm
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n.storage_units_t.inflow = segmented[n.storage_units_t.inflow.columns] * inflow_norm
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return n
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def enforce_autarky(n, only_crossborder=False):
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if only_crossborder:
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lines_rm = n.lines.loc[
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n.lines.bus0.map(n.buses.country) != n.lines.bus1.map(n.buses.country)
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].index
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links_rm = n.links.loc[
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n.links.bus0.map(n.buses.country) != n.links.bus1.map(n.buses.country)
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].index
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else:
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lines_rm = n.lines.index
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links_rm = n.links.loc[n.links.carrier == "DC"].index
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n.mremove("Line", lines_rm)
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n.mremove("Link", links_rm)
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def set_line_nom_max(n, s_nom_max_set=np.inf, p_nom_max_set=np.inf):
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n.lines.s_nom_max.clip(upper=s_nom_max_set, inplace=True)
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n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
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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(
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"prepare_network", simpl="", clusters="40", ll="v0.3", opts="Co2L-24H"
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)
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configure_logging(snakemake)
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opts = snakemake.wildcards.opts.split("-")
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n = pypsa.Network(snakemake.input[0])
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Nyears = n.snapshot_weightings.objective.sum() / 8760.0
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costs = load_costs(
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snakemake.input.tech_costs,
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snakemake.config["costs"],
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snakemake.config["electricity"],
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Nyears,
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)
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set_line_s_max_pu(n, snakemake.config["lines"]["s_max_pu"])
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for o in opts:
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m = re.match(r"^\d+h$", o, re.IGNORECASE)
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if m is not None:
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n = average_every_nhours(n, m.group(0))
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break
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for o in opts:
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m = re.match(r"^\d+seg$", o, re.IGNORECASE)
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if m is not None:
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solver_name = snakemake.config["solving"]["solver"]["name"]
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n = apply_time_segmentation(n, m.group(0)[:-3], solver_name)
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break
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for o in opts:
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if "Co2L" in o:
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m = re.findall("[0-9]*\.?[0-9]+$", o)
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if len(m) > 0:
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co2limit = float(m[0]) * snakemake.config["electricity"]["co2base"]
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add_co2limit(n, co2limit, Nyears)
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logger.info("Setting CO2 limit according to wildcard value.")
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else:
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add_co2limit(n, snakemake.config["electricity"]["co2limit"], Nyears)
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logger.info("Setting CO2 limit according to config value.")
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break
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for o in opts:
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if "CH4L" in o:
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m = re.findall("[0-9]*\.?[0-9]+$", o)
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if len(m) > 0:
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limit = float(m[0]) * 1e6
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add_gaslimit(n, limit, Nyears)
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logger.info("Setting gas usage limit according to wildcard value.")
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else:
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add_gaslimit(n, snakemake.config["electricity"].get("gaslimit"), Nyears)
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logger.info("Setting gas usage limit according to config value.")
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break
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for o in opts:
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oo = o.split("+")
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suptechs = map(lambda c: c.split("-", 2)[0], n.carriers.index)
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if oo[0].startswith(tuple(suptechs)):
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carrier = oo[0]
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# handles only p_nom_max as stores and lines have no potentials
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attr_lookup = {"p": "p_nom_max", "c": "capital_cost", "m": "marginal_cost"}
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attr = attr_lookup[oo[1][0]]
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factor = float(oo[1][1:])
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if carrier == "AC": # lines do not have carrier
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n.lines[attr] *= factor
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else:
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comps = {"Generator", "Link", "StorageUnit", "Store"}
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for c in n.iterate_components(comps):
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sel = c.df.carrier.str.contains(carrier)
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c.df.loc[sel, attr] *= factor
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for o in opts:
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if "Ep" in o:
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m = re.findall("[0-9]*\.?[0-9]+$", o)
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if len(m) > 0:
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logger.info("Setting emission prices according to wildcard value.")
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add_emission_prices(n, dict(co2=float(m[0])))
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else:
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logger.info("Setting emission prices according to config value.")
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add_emission_prices(n, snakemake.config["costs"]["emission_prices"])
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break
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ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
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set_transmission_limit(n, ll_type, factor, costs, Nyears)
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set_line_nom_max(
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n,
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s_nom_max_set=snakemake.config["lines"].get("s_nom_max,", np.inf),
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p_nom_max_set=snakemake.config["links"].get("p_nom_max,", np.inf),
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)
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if "ATK" in opts:
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enforce_autarky(n)
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elif "ATKc" in opts:
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enforce_autarky(n, only_crossborder=True)
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n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
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n.export_to_netcdf(snakemake.output[0])
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