[pre-commit.ci] auto fixes from pre-commit.com hooks
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@ -65,6 +65,7 @@ rule solve_sector_networks:
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**config["scenario"]
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),
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rule solve_sector_networks_perfect:
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input:
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expand(
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@ -73,6 +74,7 @@ rule solve_sector_networks_perfect:
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**config["scenario"]
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),
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rule plot_networks:
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input:
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expand(
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@ -172,10 +172,7 @@ rule make_summary_perfect:
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log:
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LOGS + "make_summary_perfect.log",
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benchmark:
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(
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BENCHMARKS
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+ "make_summary_perfect"
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)
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(BENCHMARKS + "make_summary_perfect")
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conda:
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"../envs/environment.yaml"
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script:
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@ -4,39 +4,33 @@
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# SPDX-License-Identifier: MIT
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"""
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Create summary CSV files for all scenario runs with perfect foresight
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including costs, capacities, capacity factors, curtailment, energy balances,
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prices and other metrics.
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Create summary CSV files for all scenario runs with perfect foresight including
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costs, capacities, capacity factors, curtailment, energy balances, prices and
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other metrics.
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"""
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from six import iteritems
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import pandas as pd
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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 pypsa.descriptors import (
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nominal_attrs,
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get_active_assets,
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)
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from _helpers import override_component_attrs
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from make_summary import (
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assign_carriers,
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assign_locations,
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calculate_cfs,
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calculate_nodal_cfs,
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calculate_nodal_costs,
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)
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from prepare_sector_network import prepare_costs
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from make_summary import (assign_carriers, assign_locations,
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calculate_cfs, calculate_nodal_cfs,
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calculate_nodal_costs)
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from pypsa.descriptors import get_active_assets, nominal_attrs
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from six import iteritems
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idx = pd.IndexSlice
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opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
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def calculate_costs(n, label, costs):
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def calculate_costs(n, label, costs):
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investments = n.investment_periods
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cols = pd.MultiIndex.from_product(
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[
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@ -65,9 +59,7 @@ def calculate_costs(n, label, costs):
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n.investment_period_weightings["objective"]
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/ n.investment_period_weightings["years"]
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)
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capital_costs_grouped = (
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capital_costs.groupby(c.df.carrier).sum().mul(discount)
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)
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capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum().mul(discount)
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capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
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capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
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@ -176,7 +168,6 @@ def calculate_nodal_capacities(n, label, nodal_capacities):
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def calculate_capacities(n, label, capacities):
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investments = n.investment_periods
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cols = pd.MultiIndex.from_product(
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[
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@ -202,9 +193,7 @@ def calculate_capacities(n, label, capacities):
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caps = c.df[opt_name.get(c.name, "p") + "_nom_opt"]
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caps = active.mul(caps, axis=0)
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capacities_grouped = (
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caps.groupby(c.df.carrier)
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.sum()
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.drop("load", errors="ignore")
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caps.groupby(c.df.carrier).sum().drop("load", errors="ignore")
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)
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capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
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@ -218,7 +207,6 @@ def calculate_capacities(n, label, capacities):
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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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@ -233,9 +221,7 @@ def calculate_curtailment(n, label, 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 n.one_port_components:
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c_energies = (
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c.pnl.p.multiply(n.snapshot_weightings, axis=0)
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@ -265,7 +251,10 @@ def calculate_energy(n, label, energy):
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def calculate_supply(n, label, supply):
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"""calculate the max dispatch of each component at the buses aggregated by carrier"""
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"""
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Calculate the max dispatch of each component at the buses aggregated by
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carrier.
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"""
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bus_carriers = n.buses.carrier.unique()
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@ -274,7 +263,6 @@ def calculate_supply(n, label, supply):
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bus_map.at[""] = False
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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).fillna(False)]
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if len(items) == 0:
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@ -294,9 +282,7 @@ def calculate_supply(n, label, supply):
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supply.loc[s.index, label] = s
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for c in n.iterate_components(n.branch_components):
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for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
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items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
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if len(items) == 0:
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@ -317,7 +303,10 @@ def calculate_supply(n, label, supply):
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def calculate_supply_energy(n, label, supply_energy):
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"""calculate the total energy supply/consuption of each component at the buses aggregated by carrier"""
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"""
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Calculate the total energy supply/consuption of each component at the buses
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aggregated by carrier.
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"""
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investments = n.investment_periods
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cols = pd.MultiIndex.from_product(
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@ -338,7 +327,6 @@ def calculate_supply_energy(n, label, supply_energy):
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bus_map.at[""] = False
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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).fillna(False)]
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if len(items) == 0:
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@ -350,9 +338,11 @@ def calculate_supply_energy(n, label, supply_energy):
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weightings = n.snapshot_weightings.stores
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if i in ["oil", "co2", "H2"]:
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if c.name=="Load":
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c.df.loc[items, "carrier"] = [load.split("-202")[0] for load in items]
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if i=="oil" and c.name=="Generator":
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if c.name == "Load":
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c.df.loc[items, "carrier"] = [
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load.split("-202")[0] for load in items
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]
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if i == "oil" and c.name == "Generator":
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c.df.loc[items, "carrier"] = "imported oil"
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s = (
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c.pnl.p[items]
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@ -373,9 +363,7 @@ def calculate_supply_energy(n, label, supply_energy):
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supply_energy.loc[s.index, label] = s.values
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for c in n.iterate_components(n.branch_components):
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for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
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items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
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if len(items) == 0:
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@ -405,7 +393,6 @@ def calculate_supply_energy(n, label, supply_energy):
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def calculate_metrics(n, label, metrics):
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metrics = metrics.reindex(
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pd.Index(
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[
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@ -438,15 +425,10 @@ def calculate_metrics(n, label, metrics):
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def calculate_prices(n, label, prices):
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prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
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# WARNING: this is time-averaged, see weighted_prices for load-weighted average
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prices[label] = (
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n.buses_t.marginal_price.mean()
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.groupby(n.buses.carrier)
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.mean()
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)
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prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
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return prices
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@ -484,7 +466,6 @@ def calculate_weighted_prices(n, label, weighted_prices):
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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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@ -503,7 +484,6 @@ def calculate_weighted_prices(n, label, weighted_prices):
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load = n.loads_t.p_set.reindex(buses, axis=1)
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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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@ -585,7 +565,6 @@ def calculate_market_values(n, label, market_values):
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def calculate_price_statistics(n, label, price_statistics):
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price_statistics = price_statistics.reindex(
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price_statistics.index.union(
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pd.Index(["zero_hours", "mean", "standard_deviation"])
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@ -604,9 +583,7 @@ def calculate_price_statistics(n, label, price_statistics):
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df.shape[0] * df.shape[1]
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)
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price_statistics.at["mean", label] = (
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n.buses_t.marginal_price[buses].mean().mean()
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)
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price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].mean().mean()
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price_statistics.at["standard_deviation", label] = (
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n.buses_t.marginal_price[buses].droplevel(0).unstack().std()
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@ -616,7 +593,6 @@ def calculate_price_statistics(n, label, price_statistics):
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def calculate_co2_emissions(n, label, df):
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carattr = "co2_emissions"
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emissions = n.carriers.query(f"{carattr} != 0")[carattr]
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@ -642,11 +618,7 @@ def calculate_co2_emissions(n, label, df):
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emitted = n.generators_t.p[gens.index].mul(em_pu)
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emitted_grouped = (
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emitted.groupby(level=0)
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.sum()
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.groupby(n.generators.carrier, axis=1)
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.sum()
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.T
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emitted.groupby(level=0).sum().groupby(n.generators.carrier, axis=1).sum().T
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)
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df = df.reindex(emitted_grouped.index.union(df.index))
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@ -681,7 +653,6 @@ outputs = [
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def make_summaries(networks_dict):
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columns = pd.MultiIndex.from_tuples(
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networks_dict.keys(), names=["cluster", "lv", "opt"]
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)
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@ -694,9 +665,7 @@ def make_summaries(networks_dict):
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for label, filename in iteritems(networks_dict):
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print(label, filename)
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try:
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n = pypsa.Network(
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filename, override_component_attrs=overrides
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)
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n = pypsa.Network(filename, override_component_attrs=overrides)
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except OSError:
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print(label, " not solved yet.")
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continue
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@ -715,33 +684,32 @@ def make_summaries(networks_dict):
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def to_csv(df):
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for key in df:
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df[key] = df[key].apply(lambda x: pd.to_numeric(x))
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df[key].to_csv(snakemake.output[key])
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#%%
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# %%
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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 _helpers import mock_snakemake
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snakemake = mock_snakemake('make_summary_perfect')
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snakemake = mock_snakemake("make_summary_perfect")
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networks_dict = {
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(clusters, lv, opts+sector_opts) :
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"results/" + snakemake.config['run']["name"] + f'postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc' \
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for simpl in snakemake.config['scenario']['simpl'] \
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for clusters in snakemake.config['scenario']['clusters'] \
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for opts in snakemake.config['scenario']['opts'] \
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for sector_opts in snakemake.config['scenario']['sector_opts'] \
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for lv in snakemake.config['scenario']['ll'] \
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(clusters, lv, opts + sector_opts): "results/"
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+ snakemake.config["run"]["name"]
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+ f"postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc"
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for simpl in snakemake.config["scenario"]["simpl"]
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for clusters in snakemake.config["scenario"]["clusters"]
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for opts in snakemake.config["scenario"]["opts"]
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for sector_opts in snakemake.config["scenario"]["sector_opts"]
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for lv in snakemake.config["scenario"]["ll"]
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}
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print(networks_dict)
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nyears = 1
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costs_db = prepare_costs(
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snakemake.input.costs,
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@ -44,7 +44,7 @@ def rename_techs(label):
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"land transport fuel cell",
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"land transport oil",
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"H2 for industry",
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"shipping oil"
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"shipping oil",
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]
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rename_if_contains_dict = {
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@ -277,7 +277,6 @@ def plot_balances():
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i for i in balances_df.index.levels[0] if i not in co2_carriers
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]
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for k, v in balances.items():
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df = balances_df.loc[v]
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df = df.groupby(df.index.get_level_values(2)).sum()
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@ -462,18 +461,21 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
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plt.rcParams["xtick.labelsize"] = 20
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plt.rcParams["ytick.labelsize"] = 20
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path_cb = "results/" + snakemake.params.RDIR + "csvs/"
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countries = snakemake.config["countries"]
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emissions_scope = snakemake.config["energy"]["emissions"]
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# this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
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report_year = snakemake.config["energy"]["eurostat_report_year"]
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e_1990 = co2_emissions_year(
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countries, input_eurostat, input_eea, opts, emissions_scope,
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report_year, year=1990
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countries,
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input_eurostat,
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input_eea,
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opts,
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emissions_scope,
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report_year,
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year=1990,
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)
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CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
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plt.figure(figsize=(10, 7))
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@ -483,7 +485,6 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
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ax1.set_ylim([0, 5])
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ax1.set_xlim([1990, snakemake.config["scenario"]["planning_horizons"][-1] + 1])
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ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
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emissions = historical_emissions(countries)
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@ -560,7 +561,8 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
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path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/"
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plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
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#%%
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# %%
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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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@ -581,5 +583,6 @@ if __name__ == "__main__":
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opts = sector_opts.split("-")
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for o in opts:
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if "cb" in o:
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plot_carbon_budget_distribution(snakemake.input.eurostat,
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snakemake.input.co2)
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plot_carbon_budget_distribution(
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snakemake.input.eurostat, snakemake.input.co2
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)
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@ -7,43 +7,50 @@
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Concats pypsa networks of single investment periods to one network.
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"""
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import pypsa
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import pandas as pd
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from _helpers import override_component_attrs, update_config_with_sector_opts
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from pypsa.io import import_components_from_dataframe
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from add_existing_baseyear import add_build_year_to_new_assets
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from six import iterkeys
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from pypsa.descriptors import expand_series
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import re
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import logging
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import re
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import pandas as pd
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import pypsa
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from _helpers import override_component_attrs, update_config_with_sector_opts
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from add_existing_baseyear import add_build_year_to_new_assets
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from pypsa.descriptors import expand_series
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from pypsa.io import import_components_from_dataframe
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from six import iterkeys
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logger = logging.getLogger(__name__)
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# helper functions ---------------------------------------------------
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def get_missing(df, n, c):
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"""Get in network n missing assets of df for component c.
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"""
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Get in network n missing assets of df for component c.
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Input:
|
||||
df: pandas DataFrame, static values of pypsa components
|
||||
n : pypsa Network to which new assets should be added
|
||||
c : string, pypsa component.list_name (e.g. "generators")
|
||||
Return:
|
||||
pd.DataFrame with static values of missing assets
|
||||
"""
|
||||
Input:
|
||||
df: pandas DataFrame, static values of pypsa components
|
||||
n : pypsa Network to which new assets should be added
|
||||
c : string, pypsa component.list_name (e.g. "generators")
|
||||
Return:
|
||||
pd.DataFrame with static values of missing assets
|
||||
"""
|
||||
df_final = getattr(n, c)
|
||||
missing_i = df.index.difference(df_final.index)
|
||||
return df.loc[missing_i]
|
||||
|
||||
|
||||
def get_social_discount(t, r=0.01):
|
||||
"""Calculate for a given time t the social discount."""
|
||||
"""
|
||||
Calculate for a given time t the social discount.
|
||||
"""
|
||||
return 1 / (1 + r) ** t
|
||||
|
||||
|
||||
def get_investment_weighting(time_weighting, r=0.01):
|
||||
"""Define cost weighting.
|
||||
"""
|
||||
Define cost weighting.
|
||||
|
||||
Returns cost weightings depending on the the time_weighting (pd.Series)
|
||||
and the social discountrate r
|
||||
Returns cost weightings depending on the the time_weighting
|
||||
(pd.Series) and the social discountrate r
|
||||
"""
|
||||
end = time_weighting.cumsum()
|
||||
start = time_weighting.cumsum().shift().fillna(0)
|
||||
@ -52,6 +59,7 @@ def get_investment_weighting(time_weighting, r=0.01):
|
||||
axis=1,
|
||||
)
|
||||
|
||||
|
||||
def add_year_to_constraints(n, baseyear):
|
||||
"""
|
||||
Parameters
|
||||
@ -62,28 +70,27 @@ def add_year_to_constraints(n, baseyear):
|
||||
"""
|
||||
|
||||
for c in n.iterate_components(["GlobalConstraint"]):
|
||||
|
||||
c.df["investment_period"] = baseyear
|
||||
c.df.rename(index=lambda x: x + "-" + str(baseyear), inplace=True)
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
def concat_networks(years):
|
||||
"""Concat given pypsa networks and adds build_year.
|
||||
"""
|
||||
Concat given pypsa networks and adds build_year.
|
||||
|
||||
Return:
|
||||
n : pypsa.Network for the whole planning horizon
|
||||
|
||||
"""
|
||||
Return:
|
||||
n : pypsa.Network for the whole planning horizon
|
||||
"""
|
||||
|
||||
# input paths of sector coupling networks
|
||||
network_paths = [snakemake.input.brownfield_network] + [
|
||||
snakemake.input[f"network_{year}"] for year in years[1:]]
|
||||
snakemake.input[f"network_{year}"] for year in years[1:]
|
||||
]
|
||||
# final concatenated network
|
||||
overrides = override_component_attrs(snakemake.input.overrides)
|
||||
n = pypsa.Network(override_component_attrs=overrides)
|
||||
|
||||
|
||||
# iterate over single year networks and concat to perfect foresight network
|
||||
for i, network_path in enumerate(network_paths):
|
||||
year = years[i]
|
||||
@ -102,7 +109,6 @@ def concat_networks(years):
|
||||
for component in network.iterate_components(
|
||||
["Generator", "Link", "Store", "Load", "Line", "StorageUnit"]
|
||||
):
|
||||
|
||||
df_year = component.df.copy()
|
||||
missing = get_missing(df_year, n, component.list_name)
|
||||
|
||||
@ -117,14 +123,14 @@ def concat_networks(years):
|
||||
pnl = getattr(n, component.list_name + "_t")
|
||||
for k in iterkeys(component.pnl):
|
||||
pnl_year = component.pnl[k].copy().reindex(snapshots, level=1)
|
||||
if pnl_year.empty and ~(component.name=="Load" and k=="p_set"): continue
|
||||
if pnl_year.empty and ~(component.name == "Load" and k == "p_set"):
|
||||
continue
|
||||
if component.name == "Load":
|
||||
static_load = network.loads.loc[network.loads.p_set != 0]
|
||||
static_load_t = expand_series(
|
||||
static_load.p_set, network_sns
|
||||
).T
|
||||
pnl_year = pd.concat([pnl_year.reindex(network_sns),
|
||||
static_load_t], axis=1)
|
||||
static_load_t = expand_series(static_load.p_set, network_sns).T
|
||||
pnl_year = pd.concat(
|
||||
[pnl_year.reindex(network_sns), static_load_t], axis=1
|
||||
)
|
||||
columns = (pnl[k].columns.union(pnl_year.columns)).unique()
|
||||
pnl[k] = pnl[k].reindex(columns=columns)
|
||||
pnl[k].loc[pnl_year.index, pnl_year.columns] = pnl_year
|
||||
@ -134,8 +140,7 @@ def concat_networks(years):
|
||||
cols = pnl_year.columns.difference(pnl[k].columns)
|
||||
pnl[k] = pd.concat([pnl[k], pnl_year[cols]], axis=1)
|
||||
|
||||
|
||||
n.snapshot_weightings.loc[year,:] = network.snapshot_weightings.values
|
||||
n.snapshot_weightings.loc[year, :] = network.snapshot_weightings.values
|
||||
|
||||
# (3) global constraints
|
||||
for component in network.iterate_components(["GlobalConstraint"]):
|
||||
@ -148,18 +153,22 @@ def concat_networks(years):
|
||||
time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
|
||||
n.investment_period_weightings["years"] = time_w
|
||||
# set objective weightings
|
||||
objective_w = get_investment_weighting(n.investment_period_weightings["years"],
|
||||
social_discountrate)
|
||||
objective_w = get_investment_weighting(
|
||||
n.investment_period_weightings["years"], social_discountrate
|
||||
)
|
||||
n.investment_period_weightings["objective"] = objective_w
|
||||
# all former static loads are now time-dependent -> set static = 0
|
||||
n.loads["p_set"] = 0
|
||||
n.loads_t.p_set.fillna(0,inplace=True)
|
||||
n.loads_t.p_set.fillna(0, inplace=True)
|
||||
|
||||
return n
|
||||
|
||||
|
||||
def adjust_stores(n):
|
||||
"""Make sure that stores still behave cyclic over one year and not whole
|
||||
modelling horizon."""
|
||||
"""
|
||||
Make sure that stores still behave cyclic over one year and not whole
|
||||
modelling horizon.
|
||||
"""
|
||||
# cylclic constraint
|
||||
cyclic_i = n.stores[n.stores.e_cyclic].index
|
||||
n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
|
||||
@ -177,42 +186,50 @@ def adjust_stores(n):
|
||||
|
||||
return n
|
||||
|
||||
|
||||
def set_phase_out(n, carrier, ct, phase_out_year):
|
||||
"""Set planned phase outs for given carrier,country (ct) and planned year
|
||||
of phase out (phase_out_year)."""
|
||||
df = n.links[(n.links.carrier.isin(carrier))& (n.links.bus1.str[:2]==ct)]
|
||||
"""
|
||||
Set planned phase outs for given carrier,country (ct) and planned year of
|
||||
phase out (phase_out_year).
|
||||
"""
|
||||
df = n.links[(n.links.carrier.isin(carrier)) & (n.links.bus1.str[:2] == ct)]
|
||||
# assets which are going to be phased out before end of their lifetime
|
||||
assets_i = df[df[["build_year", "lifetime"]].sum(axis=1) > phase_out_year].index
|
||||
build_year = n.links.loc[assets_i, "build_year"]
|
||||
# adjust lifetime
|
||||
n.links.loc[assets_i, "lifetime"] = (phase_out_year - build_year).astype(float)
|
||||
|
||||
|
||||
def set_all_phase_outs(n):
|
||||
# TODO move this to a csv or to the config
|
||||
planned= [(["nuclear"], "DE", 2022),
|
||||
(["nuclear"], "BE", 2025),
|
||||
(["nuclear"], "ES", 2027),
|
||||
(["coal", "lignite"], "DE", 2038),
|
||||
(["coal", "lignite"], "ES", 2027),
|
||||
(["coal", "lignite"], "FR", 2022),
|
||||
(["coal", "lignite"], "GB", 2024),
|
||||
(["coal", "lignite"], "IT", 2025)]
|
||||
planned = [
|
||||
(["nuclear"], "DE", 2022),
|
||||
(["nuclear"], "BE", 2025),
|
||||
(["nuclear"], "ES", 2027),
|
||||
(["coal", "lignite"], "DE", 2038),
|
||||
(["coal", "lignite"], "ES", 2027),
|
||||
(["coal", "lignite"], "FR", 2022),
|
||||
(["coal", "lignite"], "GB", 2024),
|
||||
(["coal", "lignite"], "IT", 2025),
|
||||
]
|
||||
for carrier, ct, phase_out_year in planned:
|
||||
set_phase_out(n, carrier,ct, phase_out_year)
|
||||
set_phase_out(n, carrier, ct, phase_out_year)
|
||||
# remove assets which are already phased out
|
||||
remove_i = n.links[n.links[["build_year", "lifetime"]].sum(axis=1)<years[0]].index
|
||||
remove_i = n.links[n.links[["build_year", "lifetime"]].sum(axis=1) < years[0]].index
|
||||
n.mremove("Link", remove_i)
|
||||
|
||||
|
||||
def set_carbon_constraints(n, opts):
|
||||
"""Add global constraints for carbon emissions."""
|
||||
"""
|
||||
Add global constraints for carbon emissions.
|
||||
"""
|
||||
budget = None
|
||||
for o in opts:
|
||||
# other budgets
|
||||
m = re.match(r"^\d+p\d$", o, re.IGNORECASE)
|
||||
if m is not None:
|
||||
budget = snakemake.config["co2_budget"][m.group(0)] * 1e9
|
||||
if budget!=None:
|
||||
if budget != None:
|
||||
logger.info("add carbon budget of {}".format(budget))
|
||||
n.add(
|
||||
"GlobalConstraint",
|
||||
@ -238,12 +255,14 @@ def set_carbon_constraints(n, opts):
|
||||
if "co2min" in opts:
|
||||
emissions_1990 = 4.53693
|
||||
emissions_2019 = 3.344096
|
||||
target_2030 = 0.45*emissions_1990
|
||||
annual_reduction = (emissions_2019-target_2030)/11
|
||||
target_2030 = 0.45 * emissions_1990
|
||||
annual_reduction = (emissions_2019 - target_2030) / 11
|
||||
first_year = n.snapshots.levels[0][0]
|
||||
time_weightings = n.investment_period_weightings.loc[first_year, "years"]
|
||||
co2min = emissions_2019-((first_year-2019)*annual_reduction)
|
||||
logger.info("add minimum emissions for {} of {} t CO2/a".format(first_year, co2min))
|
||||
co2min = emissions_2019 - ((first_year - 2019) * annual_reduction)
|
||||
logger.info(
|
||||
"add minimum emissions for {} of {} t CO2/a".format(first_year, co2min)
|
||||
)
|
||||
n.add(
|
||||
"GlobalConstraint",
|
||||
"Co2min",
|
||||
@ -251,34 +270,40 @@ def set_carbon_constraints(n, opts):
|
||||
carrier_attribute="co2_emissions",
|
||||
sense=">=",
|
||||
investment_period=first_year,
|
||||
constant=co2min*1e9*time_weightings,
|
||||
constant=co2min * 1e9 * time_weightings,
|
||||
)
|
||||
|
||||
return n
|
||||
#%%
|
||||
|
||||
|
||||
# %%
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
'prepare_perfect_foresight',
|
||||
simpl='',
|
||||
"prepare_perfect_foresight",
|
||||
simpl="",
|
||||
opts="",
|
||||
clusters="37",
|
||||
ll=1.0,
|
||||
sector_opts='cb40ex0-2190H-T-H-B-solar+p3-dist1',
|
||||
sector_opts="cb40ex0-2190H-T-H-B-solar+p3-dist1",
|
||||
)
|
||||
|
||||
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
||||
# parameters -----------------------------------------------------------
|
||||
years = snakemake.config["scenario"]["planning_horizons"]
|
||||
opts = snakemake.wildcards.sector_opts.split('-')
|
||||
opts = snakemake.wildcards.sector_opts.split("-")
|
||||
social_discountrate = snakemake.config["costs"]["social_discountrate"]
|
||||
for o in opts:
|
||||
if "sdr" in o:
|
||||
social_discountrate = float(o.replace("sdr",""))/100
|
||||
social_discountrate = float(o.replace("sdr", "")) / 100
|
||||
|
||||
logger.info("Concat networks of investment period {} with social discount rate of {}%"
|
||||
.format(years, social_discountrate*100))
|
||||
logger.info(
|
||||
"Concat networks of investment period {} with social discount rate of {}%".format(
|
||||
years, social_discountrate * 100
|
||||
)
|
||||
)
|
||||
|
||||
# concat prenetworks of planning horizon to single network ------------
|
||||
n = concat_networks(years)
|
||||
@ -289,7 +314,7 @@ if __name__ == "__main__":
|
||||
n = adjust_stores(n)
|
||||
|
||||
# set carbon constraints
|
||||
opts = snakemake.wildcards.sector_opts.split('-')
|
||||
opts = snakemake.wildcards.sector_opts.split("-")
|
||||
n = set_carbon_constraints(n, opts)
|
||||
|
||||
# export network
|
||||
|
||||
@ -246,12 +246,24 @@ def build_carbon_budget(o, input_eurostat, input_eea, fn, emissions_scope, repor
|
||||
countries = snakemake.config["countries"]
|
||||
|
||||
e_1990 = co2_emissions_year(
|
||||
countries, input_eurostat, input_eea, opts, emissions_scope, report_year, year=1990
|
||||
countries,
|
||||
input_eurostat,
|
||||
input_eea,
|
||||
opts,
|
||||
emissions_scope,
|
||||
report_year,
|
||||
year=1990,
|
||||
)
|
||||
|
||||
# emissions at the beginning of the path (last year available 2018)
|
||||
e_0 = co2_emissions_year(
|
||||
countries, input_eurostat, input_eea, opts, emissions_scope, report_year, year=2018
|
||||
countries,
|
||||
input_eurostat,
|
||||
input_eea,
|
||||
opts,
|
||||
emissions_scope,
|
||||
report_year,
|
||||
year=2018,
|
||||
)
|
||||
|
||||
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
|
||||
@ -1130,7 +1142,9 @@ def add_storage_and_grids(n, costs):
|
||||
e_cyclic=True,
|
||||
carrier="H2 Store",
|
||||
capital_cost=h2_capital_cost,
|
||||
lifetime=costs.at["hydrogen storage tank type 1 including compressor", "lifetime"],
|
||||
lifetime=costs.at[
|
||||
"hydrogen storage tank type 1 including compressor", "lifetime"
|
||||
],
|
||||
)
|
||||
|
||||
if options["gas_network"] or options["H2_retrofit"]:
|
||||
@ -3287,7 +3301,7 @@ if __name__ == "__main__":
|
||||
|
||||
spatial = define_spatial(pop_layout.index, options)
|
||||
|
||||
if snakemake.config["foresight"] in ['myopic', 'perfect']:
|
||||
if snakemake.config["foresight"] in ["myopic", "perfect"]:
|
||||
add_lifetime_wind_solar(n, costs)
|
||||
|
||||
conventional = snakemake.config["existing_capacities"]["conventional_carriers"]
|
||||
@ -3369,7 +3383,12 @@ if __name__ == "__main__":
|
||||
emissions_scope = snakemake.config["energy"]["emissions"]
|
||||
report_year = snakemake.config["energy"]["eurostat_report_year"]
|
||||
build_carbon_budget(
|
||||
o, snakemake.input.eurostat, snakemake.input.co2, fn, emissions_scope, report_year
|
||||
o,
|
||||
snakemake.input.eurostat,
|
||||
snakemake.input.co2,
|
||||
fn,
|
||||
emissions_scope,
|
||||
report_year,
|
||||
)
|
||||
co2_cap = pd.read_csv(fn, index_col=0).squeeze()
|
||||
limit = co2_cap.loc[investment_year]
|
||||
@ -3402,7 +3421,7 @@ if __name__ == "__main__":
|
||||
if options["electricity_grid_connection"]:
|
||||
add_electricity_grid_connection(n, costs)
|
||||
|
||||
first_year_multi = (snakemake.config["foresight"] in ['myopic', 'perfect']) and (
|
||||
first_year_multi = (snakemake.config["foresight"] in ["myopic", "perfect"]) and (
|
||||
snakemake.config["scenario"]["planning_horizons"][0] == investment_year
|
||||
)
|
||||
|
||||
|
||||
@ -156,9 +156,9 @@ def prepare_network(n, solve_opts=None, config=None):
|
||||
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
|
||||
# TODO: retrieve color and nice name from config
|
||||
n.add("Carrier", "load", color="#dd2e23", nice_name="Load shedding")
|
||||
buses_i = n.buses.index # query("carrier == 'AC'").index
|
||||
#if not np.isscalar(load_shedding):
|
||||
# TODO: do not scale via sign attribute (use Eur/MWh instead of Eur/kWh)
|
||||
buses_i = n.buses.index # query("carrier == 'AC'").index
|
||||
# if not np.isscalar(load_shedding):
|
||||
# TODO: do not scale via sign attribute (use Eur/MWh instead of Eur/kWh)
|
||||
load_shedding = 1e2 # Eur/kWh
|
||||
|
||||
n.madd(
|
||||
@ -629,14 +629,15 @@ def solve_network(n, config, opts="", **kwargs):
|
||||
|
||||
return n
|
||||
|
||||
#%%
|
||||
|
||||
# %%
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"solve_sector_network_perfect",
|
||||
#configfiles="config.yaml",
|
||||
# configfiles="config.yaml",
|
||||
simpl="",
|
||||
opts="",
|
||||
clusters="37",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user