584 lines
17 KiB
Python
584 lines
17 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Creates summaries of aggregated energy and costs as ``.csv`` files.
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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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marginal_cost:
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capital_cost:
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electricity:
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max_hours:
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.. seealso::
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Documentation of the configuration file ``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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Outputs
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-------
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Description
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-----------
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The following rule can be used to summarize the results in separate .csv files:
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.. code:: bash
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snakemake results/summaries/elec_s_all_lall_Co2L-3H_all
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clusters
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line volume or cost cap
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- options
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- all countries
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the line volume/cost cap field can be set to one of the following:
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* ``lv1.25`` for a particular line volume extension by 25%
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* ``lc1.25`` for a line cost extension by 25 %
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* ``lall`` for all evaluated caps
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* ``lvall`` for all line volume caps
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* ``lcall`` for all line cost caps
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Replacing '/summaries/' with '/plots/' creates nice colored maps of the results.
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"""
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import logging
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import os
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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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opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
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def _add_indexed_rows(df, raw_index):
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new_index = df.index.union(pd.MultiIndex.from_product(raw_index))
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if isinstance(new_index, pd.Index):
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new_index = pd.MultiIndex.from_tuples(new_index)
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return df.reindex(new_index)
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def assign_carriers(n):
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if "carrier" not in n.loads:
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n.loads["carrier"] = "electricity"
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for carrier in ["transport", "heat", "urban heat"]:
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n.loads.loc[n.loads.index.str.contains(carrier), "carrier"] = carrier
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n.storage_units["carrier"].replace(
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{"hydro": "hydro+PHS", "PHS": "hydro+PHS"}, inplace=True
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)
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if "carrier" not in n.lines:
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n.lines["carrier"] = "AC"
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n.lines["carrier"].replace({"AC": "lines"}, inplace=True)
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if n.links.empty:
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n.links["carrier"] = pd.Series(dtype=str)
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n.links["carrier"].replace({"DC": "lines"}, inplace=True)
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if (
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"EU gas store" in n.stores.index
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and n.stores.loc["EU gas Store", "carrier"] == ""
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):
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n.stores.loc["EU gas Store", "carrier"] = "gas Store"
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def calculate_costs(n, label, costs):
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for c in n.iterate_components(
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n.branch_components | n.controllable_one_port_components ^ {"Load"}
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):
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capital_costs = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
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capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
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# Index tuple(s) indicating the newly to-be-added row(s)
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raw_index = tuple(
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[[c.list_name], ["capital"], list(capital_costs_grouped.index)]
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)
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costs = _add_indexed_rows(costs, raw_index)
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costs.loc[idx[raw_index], label] = capital_costs_grouped.values
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if c.name == "Link":
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p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
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elif c.name == "Line":
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continue
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elif c.name == "StorageUnit":
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p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
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p_all[p_all < 0.0] = 0.0
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p = p_all.sum()
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else:
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p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
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marginal_costs = p * c.df.marginal_cost
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marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
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costs = costs.reindex(
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costs.index.union(
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pd.MultiIndex.from_product(
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[[c.list_name], ["marginal"], marginal_costs_grouped.index]
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)
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)
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)
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costs.loc[
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idx[c.list_name, "marginal", list(marginal_costs_grouped.index)], label
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] = marginal_costs_grouped.values
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return costs
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def calculate_curtailment(n, label, curtailment):
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avail = (
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n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
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.sum()
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.groupby(n.generators.carrier)
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.sum()
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)
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used = n.generators_t.p.sum().groupby(n.generators.carrier).sum()
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curtailment[label] = (((avail - used) / avail) * 100).round(3)
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return curtailment
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def calculate_energy(n, label, energy):
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for c in n.iterate_components(n.one_port_components | n.branch_components):
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if c.name in {"Generator", "Load", "ShuntImpedance"}:
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c_energies = (
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c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
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.sum()
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.multiply(c.df.sign)
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.groupby(c.df.carrier)
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.sum()
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)
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elif c.name in {"StorageUnit", "Store"}:
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c_energies = (
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c.pnl.p.multiply(n.snapshot_weightings.stores, axis=0)
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.sum()
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.multiply(c.df.sign)
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.groupby(c.df.carrier)
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.sum()
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)
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else:
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c_energies = (
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(
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-c.pnl.p1.multiply(n.snapshot_weightings.generators, axis=0).sum()
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- c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
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)
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.groupby(c.df.carrier)
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.sum()
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)
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energy = include_in_summary(energy, [c.list_name], label, c_energies)
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return energy
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def include_in_summary(summary, multiindexprefix, label, item):
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# Index tuple(s) indicating the newly to-be-added row(s)
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raw_index = tuple([multiindexprefix, list(item.index)])
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summary = _add_indexed_rows(summary, raw_index)
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summary.loc[idx[raw_index], label] = item.values
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return summary
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def calculate_capacity(n, label, capacity):
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for c in n.iterate_components(n.one_port_components):
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if "p_nom_opt" in c.df.columns:
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c_capacities = (
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abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
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)
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capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
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elif "e_nom_opt" in c.df.columns:
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c_capacities = (
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abs(c.df.e_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
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)
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capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
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for c in n.iterate_components(n.passive_branch_components):
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c_capacities = c.df["s_nom_opt"].groupby(c.df.carrier).sum()
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capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
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for c in n.iterate_components(n.controllable_branch_components):
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c_capacities = c.df.p_nom_opt.groupby(c.df.carrier).sum()
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capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
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return capacity
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def calculate_supply(n, label, supply):
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"""
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calculate the max dispatch of each component at the buses where the loads
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are attached.
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"""
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load_types = n.buses.carrier.unique()
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for i in load_types:
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buses = n.buses.query("carrier == @i").index
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bus_map = pd.Series(False, index=n.buses.index)
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bus_map.loc[buses] = True
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for c in n.iterate_components(n.one_port_components):
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items = c.df.index[c.df.bus.map(bus_map)]
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if len(items) == 0 or c.pnl.p.empty:
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continue
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s = (
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c.pnl.p[items]
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.max()
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.multiply(c.df.loc[items, "sign"])
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.groupby(c.df.loc[items, "carrier"])
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.sum()
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)
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# Index tuple(s) indicating the newly to-be-added row(s)
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raw_index = tuple([[i], [c.list_name], list(s.index)])
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supply = _add_indexed_rows(supply, raw_index)
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supply.loc[idx[raw_index], label] = s.values
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for c in n.iterate_components(n.branch_components):
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for end in ["0", "1"]:
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items = c.df.index[c.df["bus" + end].map(bus_map)]
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if len(items) == 0 or c.pnl["p" + end].empty:
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continue
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# lots of sign compensation for direction and to do maximums
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s = (-1) ** (1 - int(end)) * (
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(-1) ** int(end) * c.pnl["p" + end][items]
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).max().groupby(c.df.loc[items, "carrier"]).sum()
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supply = supply.reindex(
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supply.index.union(
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pd.MultiIndex.from_product([[i], [c.list_name], s.index])
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)
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)
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supply.loc[idx[i, c.list_name, list(s.index)], label] = s.values
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return supply
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def calculate_supply_energy(n, label, supply_energy):
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"""
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calculate the total dispatch of each component at the buses where the loads
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are attached.
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"""
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load_types = n.buses.carrier.unique()
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for i in load_types:
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buses = n.buses.query("carrier == @i").index
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bus_map = pd.Series(False, index=n.buses.index)
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bus_map.loc[buses] = True
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for c in n.iterate_components(n.one_port_components):
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items = c.df.index[c.df.bus.map(bus_map)]
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if len(items) == 0 or c.pnl.p.empty:
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continue
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s = (
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c.pnl.p[items]
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.sum()
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.multiply(c.df.loc[items, "sign"])
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.groupby(c.df.loc[items, "carrier"])
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.sum()
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)
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# Index tuple(s) indicating the newly to-be-added row(s)
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raw_index = tuple([[i], [c.list_name], list(s.index)])
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supply_energy = _add_indexed_rows(supply_energy, raw_index)
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supply_energy.loc[idx[raw_index], label] = s.values
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for c in n.iterate_components(n.branch_components):
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for end in ["0", "1"]:
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items = c.df.index[c.df["bus" + end].map(bus_map)]
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if len(items) == 0 or c.pnl["p" + end].empty:
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continue
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s = (-1) * c.pnl["p" + end][items].sum().groupby(
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c.df.loc[items, "carrier"]
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).sum()
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supply_energy = supply_energy.reindex(
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supply_energy.index.union(
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pd.MultiIndex.from_product([[i], [c.list_name], s.index])
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)
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)
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supply_energy.loc[idx[i, c.list_name, list(s.index)], label] = s.values
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return supply_energy
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def calculate_metrics(n, label, metrics):
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metrics = metrics.reindex(
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metrics.index.union(
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pd.Index(
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[
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"line_volume",
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"line_volume_limit",
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"line_volume_AC",
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"line_volume_DC",
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"line_volume_shadow",
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"co2_shadow",
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]
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)
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)
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)
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metrics.at["line_volume_DC", label] = (n.links.length * n.links.p_nom_opt)[
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n.links.carrier == "DC"
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].sum()
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metrics.at["line_volume_AC", label] = (n.lines.length * n.lines.s_nom_opt).sum()
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metrics.at["line_volume", label] = metrics.loc[
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["line_volume_AC", "line_volume_DC"], label
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].sum()
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if hasattr(n, "line_volume_limit"):
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metrics.at["line_volume_limit", label] = n.line_volume_limit
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if hasattr(n, "line_volume_limit_dual"):
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metrics.at["line_volume_shadow", label] = n.line_volume_limit_dual
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if "CO2Limit" in n.global_constraints.index:
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metrics.at["co2_shadow", label] = n.global_constraints.at["CO2Limit", "mu"]
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return metrics
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def calculate_prices(n, label, prices):
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bus_type = pd.Series(n.buses.index.str[3:], n.buses.index).replace(
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"", "electricity"
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)
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prices = prices.reindex(prices.index.union(bus_type.value_counts().index))
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logger.warning("Prices are time-averaged, not load-weighted")
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prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
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return prices
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def calculate_weighted_prices(n, label, weighted_prices):
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logger.warning("Weighted prices don't include storage units as loads")
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weighted_prices = weighted_prices.reindex(
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pd.Index(
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[
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"electricity",
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"heat",
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"space heat",
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"urban heat",
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"space urban heat",
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"gas",
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"H2",
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]
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)
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)
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link_loads = {
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"electricity": [
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"heat pump",
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"resistive heater",
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"battery charger",
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"H2 Electrolysis",
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],
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"heat": ["water tanks charger"],
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"urban heat": ["water tanks charger"],
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"space heat": [],
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"space urban heat": [],
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"gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
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"H2": ["Sabatier", "H2 Fuel Cell"],
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}
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for carrier in link_loads:
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if carrier == "electricity":
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suffix = ""
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elif carrier[:5] == "space":
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suffix = carrier[5:]
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else:
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suffix = " " + carrier
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buses = n.buses.index[n.buses.index.str[2:] == suffix]
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if buses.empty:
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continue
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if carrier in ["H2", "gas"]:
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load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
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elif carrier[:5] == "space":
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load = heat_demand_df[buses.str[:2]].rename(
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columns=lambda i: str(i) + suffix
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)
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else:
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load = n.loads_t.p_set[buses]
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for tech in link_loads[carrier]:
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names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
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if names.empty:
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continue
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load += (
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n.links_t.p0[names]
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.groupby(n.links.loc[names, "bus0"], axis=1)
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.sum(axis=1)
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)
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# Add H2 Store when charging
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if carrier == "H2":
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stores = (
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n.stores_t.p[buses + " Store"]
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.groupby(n.stores.loc[buses + " Store", "bus"], axis=1)
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.sum(axis=1)
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)
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stores[stores > 0.0] = 0.0
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load += -stores
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weighted_prices.loc[carrier, label] = (
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load * n.buses_t.marginal_price[buses]
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).sum().sum() / load.sum().sum()
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if carrier[:5] == "space":
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print(load * n.buses_t.marginal_price[buses])
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return weighted_prices
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outputs = [
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"costs",
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"curtailment",
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"energy",
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"capacity",
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"supply",
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"supply_energy",
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"prices",
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"weighted_prices",
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"metrics",
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]
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def make_summaries(networks_dict, paths, config, country="all"):
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columns = pd.MultiIndex.from_tuples(
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networks_dict.keys(), names=["simpl", "clusters", "ll", "opts"]
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)
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dfs = {}
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for output in outputs:
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dfs[output] = pd.DataFrame(columns=columns, dtype=float)
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|
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for label, filename in networks_dict.items():
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print(label, filename)
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if not os.path.exists(filename):
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print("does not exist!!")
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continue
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try:
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n = pypsa.Network(filename)
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except OSError:
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logger.warning("Skipping {filename}".format(filename=filename))
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continue
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|
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if country != "all":
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n = n[n.buses.country == country]
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|
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Nyears = n.snapshot_weightings.objective.sum() / 8760.0
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costs = load_costs(paths[0], config["costs"], config["electricity"], Nyears)
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update_transmission_costs(n, costs)
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assign_carriers(n)
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for output in outputs:
|
|
dfs[output] = globals()["calculate_" + output](n, label, dfs[output])
|
|
|
|
return dfs
|
|
|
|
|
|
def to_csv(dfs, dir):
|
|
os.makedirs(dir, exist_ok=True)
|
|
for key, df in dfs.items():
|
|
df.to_csv(os.path.join(dir, f"{key}.csv"))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake(
|
|
"make_summary",
|
|
simpl="",
|
|
clusters="5",
|
|
ll="copt",
|
|
opts="Co2L-24H",
|
|
country="all",
|
|
)
|
|
network_dir = os.path.join(
|
|
"..", "results", "networks", snakemake.config["run"]["name"]
|
|
)
|
|
else:
|
|
network_dir = os.path.join(
|
|
"results", "networks", snakemake.config["run"]["name"]
|
|
)
|
|
configure_logging(snakemake)
|
|
|
|
config = snakemake.config
|
|
wildcards = snakemake.wildcards
|
|
|
|
def expand_from_wildcard(key, config):
|
|
w = getattr(wildcards, key)
|
|
return config["scenario"][key] if w == "all" else [w]
|
|
|
|
if wildcards.ll.endswith("all"):
|
|
ll = config["scenario"]["ll"]
|
|
if len(wildcards.ll) == 4:
|
|
ll = [l for l in ll if l[0] == wildcards.ll[0]]
|
|
else:
|
|
ll = [wildcards.ll]
|
|
|
|
networks_dict = {
|
|
(simpl, clusters, l, opts): os.path.join(
|
|
network_dir, f"elec_s{simpl}_" f"{clusters}_ec_l{l}_{opts}.nc"
|
|
)
|
|
for simpl in expand_from_wildcard("simpl", config)
|
|
for clusters in expand_from_wildcard("clusters", config)
|
|
for l in ll
|
|
for opts in expand_from_wildcard("opts", config)
|
|
}
|
|
|
|
dfs = make_summaries(
|
|
networks_dict, snakemake.input, config, country=wildcards.country
|
|
)
|
|
|
|
to_csv(dfs, snakemake.output[0])
|