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