{make,plot}_summary: Add rules to flexibly create summaries and plot them
This commit is contained in:
Jonas Hörsch
parent
0070b1c87c
commit
5b201de7d4
55
Snakefile
55
Snakefile
@ -3,9 +3,9 @@ configfile: "config.yaml"
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COSTS="data/costs.csv"
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wildcard_constraints:
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lv="[0-9\.]+|inf",
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simpl="[a-zA-Z0-9]*",
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clusters="[0-9]+m?",
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lv="[0-9\.]+|inf|all",
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simpl="[a-zA-Z0-9]*|all",
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clusters="[0-9]+m?|all",
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sectors="[+a-zA-Z0-9]+",
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opts="[-+a-zA-Z0-9]*"
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@ -256,42 +256,23 @@ rule plot_network:
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ext="results/plots/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{attr}_ext.{ext}"
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script: "scripts/plot_network.py"
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# rule plot_costs:
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# input: 'results/summaries/costs2-summary.csv'
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# output:
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# expand('results/plots/costs_{cost}_{resarea}_{sectors}_{opt}',
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# **dict(chain(config['scenario'].items(), (('{param}')))
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# touch('results/plots/scenario_plots')
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# params:
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# tmpl="results/plots/costs_[cost]_[resarea]_[sectors]_[opt]"
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# exts=["pdf", "png"]
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# scripts: "scripts/plot_costs.py"
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def summary_networks(w):
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# It's mildly hacky to include the separate costs input as first entry
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return ([COSTS] +
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expand("results/networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc",
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network=w.network,
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**{k: config["scenario"][k] if getattr(w, k) == "all" else getattr(w, k)
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for k in ["simpl", "clusters", "lv", "opts"]}))
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# rule scenario_comparison:
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# input:
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# expand('results/plots/network_{cost}_{sectors}_{opts}_{attr}.pdf',
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# version=config['version'],
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# attr=['p_nom'],
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# **config['scenario'])
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# output:
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# html='results/plots/scenario_{param}.html'
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# params:
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# tmpl="network_[cost]_[resarea]_[sectors]_[opts]_[attr]",
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# plot_dir='results/plots'
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# script: "scripts/scenario_comparison.py"
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# rule extract_summaries:
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# input:
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# expand("results/networks/{cost}_{sectors}_{opts}.nc",
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# **config['scenario'])
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# output:
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# **{n: "results/summaries/{}-summary.csv".format(n)
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# for n in ['costs', 'costs2', 'e_curtailed', 'e_nom_opt', 'e', 'p_nom_opt']}
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# params:
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# scenario_tmpl="[cost]_[resarea]_[sectors]_[opts]",
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# scenarios=config['scenario']
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# script: "scripts/extract_summaries.py"
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rule make_summary:
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input: summary_networks
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output: directory("results/summaries/{network}_s{simpl}_{clusters}_lv{lv}_{opts}")
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script: "scripts/make_summary.py"
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rule plot_summary:
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input: directory("results/summaries/{network}_s{simpl}_{clusters}_lv{lv}_{opts}")
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output: "results/plots/summary_{summary}_{network}_s{simpl}_{clusters}_lv{lv}_{opts}.{ext}"
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script: "scripts/plot_summary.py"
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# Local Variables:
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# mode: python
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@ -205,6 +205,7 @@ plotting:
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'offwind' : "c"
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'offshore wind' : "c"
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"hydro" : "#3B5323"
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"hydro+PHS" : "#3B5323"
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"hydro reservoir" : "#3B5323"
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"ror" : "#78AB46"
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"run of river" : "#78AB46"
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@ -267,6 +268,9 @@ plotting:
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"transport" : "grey"
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"electricity" : "k"
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"transport fuel cell" : "#AAAAAA"
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# _helpers.load_network requirements
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"AC-AC" : "k"
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"AC line" : "k"
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nice_names:
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# OCGT: "Gas"
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# OCGT marginal: "Gas (marginal)"
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@ -1,30 +1,16 @@
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import os
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from six import iteritems
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import sys
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sys.path = ['/home/vres/lib/python3.5/site-packages'] + sys.path
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from itertools import product
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import pandas as pd
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import pypsa
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from vresutils.costdata import annuity
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from prepare_network import generate_periodic_profiles
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from add_electricity import load_costs
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import yaml
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from add_electricity import load_costs, update_transmission_costs
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idx = pd.IndexSlice
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opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
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#separator to find group name
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find_by = " "
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def assign_carriers(n):
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if "carrier" not in n.loads:
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@ -35,7 +21,7 @@ def assign_carriers(n):
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if "carrier" not in n.lines:
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n.lines["carrier"] = "AC"
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if n.stores.loc["EU gas Store","carrier"] == "":
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if "EU gas store" in n.stores.index and n.stores.loc["EU gas Store","carrier"] == "":
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n.stores.loc["EU gas Store","carrier"] = "gas Store"
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@ -68,17 +54,6 @@ def calculate_costs(n,label,costs):
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costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
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#add back in costs of links if there is a line volume limit
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if label[1] != "opt":
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costs.loc[("links-added","capital","transmission lines"),label] = ((costs_db.at['HVDC overhead', 'capital_cost']*n.links.length + costs_db.at['HVDC inverter pair', 'capital_cost'])*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
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costs.loc[("lines-added","capital","transmission lines"),label] = costs_db.at["HVAC overhead", "capital_cost"]*(n.lines.length*n.lines.s_nom_opt).sum()
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#add back in all hydro
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#costs.loc[("storage_units","capital","hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro","p_nom"].sum()
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#costs.loc[("storage_units","capital","PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS","p_nom"].sum()
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#costs.loc[("generators","capital","ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror","p_nom"].sum()
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return costs
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@ -203,6 +178,8 @@ def calculate_metrics(n,label,metrics):
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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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@ -284,83 +261,59 @@ def calculate_weighted_prices(n,label,weighted_prices):
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# BROKEN don't use
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#
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# def calculate_market_values(n, label, market_values):
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# # Warning: doesn't include storage units
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def calculate_market_values(n, label, market_values):
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# Warning: doesn't include storage units
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# n.buses["suffix"] = n.buses.index.str[2:]
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# suffix = ""
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# buses = n.buses.index[n.buses.suffix == suffix]
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n.buses["suffix"] = n.buses.index.str[2:]
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# ## First do market value of generators ##
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# generators = n.generators.index[n.buses.loc[n.generators.bus,"suffix"] == suffix]
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# techs = n.generators.loc[generators,"carrier"].value_counts().index
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# market_values = market_values.reindex(market_values.index | techs)
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suffix = ""
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# for tech in techs:
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# gens = generators[n.generators.loc[generators,"carrier"] == tech]
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# dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens,"bus"],axis=1).sum().reindex(columns=buses,fill_value=0.)
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# revenue = dispatch*n.buses_t.marginal_price[buses]
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# market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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buses = n.buses.index[n.buses.suffix == suffix]
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# ## Now do market value of links ##
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# for i in ["0","1"]:
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# all_links = n.links.index[n.buses.loc[n.links["bus"+i],"suffix"] == suffix]
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# techs = n.links.loc[all_links,"carrier"].value_counts().index
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# market_values = market_values.reindex(market_values.index | techs)
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# for tech in techs:
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# links = all_links[n.links.loc[all_links,"carrier"] == tech]
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# dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links,"bus"+i],axis=1).sum().reindex(columns=buses,fill_value=0.)
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# revenue = dispatch*n.buses_t.marginal_price[buses]
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# market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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# return market_values
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## First do market value of generators ##
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# OLD CODE must be adapted
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generators = n.generators.index[n.buses.loc[n.generators.bus,"suffix"] == suffix]
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techs = n.generators.loc[generators,"carrier"].value_counts().index
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market_values = market_values.reindex(market_values.index | techs)
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# def calculate_price_statistics(n, label, price_statistics):
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for tech in techs:
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gens = generators[n.generators.loc[generators,"carrier"] == tech]
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# price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
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# n.buses["suffix"] = n.buses.index.str[2:]
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# suffix = ""
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# buses = n.buses.index[n.buses.suffix == suffix]
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dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens,"bus"],axis=1).sum().reindex(columns=buses,fill_value=0.)
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revenue = dispatch*n.buses_t.marginal_price[buses]
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market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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## Now do market value of links ##
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for i in ["0","1"]:
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all_links = n.links.index[n.buses.loc[n.links["bus"+i],"suffix"] == suffix]
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techs = n.links.loc[all_links,"carrier"].value_counts().index
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market_values = market_values.reindex(market_values.index | techs)
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for tech in techs:
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links = all_links[n.links.loc[all_links,"carrier"] == tech]
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dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links,"bus"+i],axis=1).sum().reindex(columns=buses,fill_value=0.)
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revenue = dispatch*n.buses_t.marginal_price[buses]
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market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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return market_values
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def calculate_price_statistics(n, label, price_statistics):
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price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
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n.buses["suffix"] = n.buses.index.str[2:]
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suffix = ""
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buses = n.buses.index[n.buses.suffix == suffix]
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threshold = 0.1 #higher than phoney marginal_cost of wind/solar
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df = pd.DataFrame(data=0.,columns=buses,index=n.snapshots)
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df[n.buses_t.marginal_price[buses] < threshold] = 1.
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price_statistics.at["zero_hours", label] = df.sum().sum()/(df.shape[0]*df.shape[1])
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price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean()
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price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std()
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return price_statistics
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# threshold = 0.1 #higher than phoney marginal_cost of wind/solar
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# df = pd.DataFrame(data=0.,columns=buses,index=n.snapshots)
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# df[n.buses_t.marginal_price[buses] < threshold] = 1.
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# price_statistics.at["zero_hours", label] = df.sum().sum()/(df.shape[0]*df.shape[1])
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# price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean()
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# price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std()
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# return price_statistics
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outputs = ["costs",
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@ -370,69 +323,66 @@ outputs = ["costs",
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"supply_energy",
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"prices",
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"weighted_prices",
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"price_statistics",
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"market_values",
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# "price_statistics",
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# "market_values",
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"metrics",
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]
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def make_summaries(networks_dict):
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columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["cluster","lv","opt"])
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columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["simpl","clusters","lv","opts"])
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df = {}
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dfs = {}
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for output in outputs:
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df[output] = pd.DataFrame(columns=columns,dtype=float)
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dfs[output] = pd.DataFrame(columns=columns,dtype=float)
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for label, filename in iteritems(networks_dict):
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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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n = pypsa.Network(filename)
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assign_carriers(n)
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Nyears = n.snapshot_weightings.sum()/8760.
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costs = load_costs(Nyears, snakemake.input[0],
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snakemake.config['costs'], snakemake.config['electricity'])
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update_transmission_costs(n, costs)
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for output in outputs:
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df[output] = globals()["calculate_" + output](n, label, df[output])
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dfs[output] = globals()["calculate_" + output](n, label, dfs[output])
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return df
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return dfs
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def to_csv(df):
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for key in df:
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df[key].to_csv(snakemake.output[key])
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def to_csv(dfs):
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dir = snakemake.output[0]
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os.makedirs(dir, exist_ok=True)
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for key, df in iteritems(dfs):
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df.to_csv(os.path.join(dir, f"{key}.csv"))
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils import Dict
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import yaml
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snakemake = Dict()
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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snakemake.input = Dict()
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snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5'
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snakemake.output = Dict()
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name = "37-lv"
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clusters = [37]
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lvs = [1.0,1.25,2.0,3.0]
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opts = ["Co2L-3H-T-H"]
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for item in outputs:
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snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=name,item=item)
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def expand_from_wildcard(key):
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w = getattr(snakemake.wildcards, key)
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return snakemake.config["scenario"][key] if w == "all" else [w]
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networks_dict = {(simpl,clusters,lv,opts) : ('results/networks/elec_s{simpl}_{clusters}_lv{lv}_{opts}.nc'
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.format(simpl=simpl,
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clusters=clusters,
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opts=opts,
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lv=lv))
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for simpl in expand_from_wildcard("simpl")
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for clusters in expand_from_wildcard("clusters")
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for lv in expand_from_wildcard("lv")
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for opts in expand_from_wildcard("opts")}
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networks_dict = {(cluster,lv,opt) :
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'results/networks/elec_s_{cluster}_lv{lv}_{opt}.nc'\
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.format(cluster=cluster,
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opt=opt,
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lv=lv)\
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for cluster in clusters \
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for opt in opts \
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for lv in lvs}
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print(networks_dict)
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costs_db = load_costs(Nyears=1.,tech_costs="data/costs.csv",config=snakemake.config["costs"],elec_config=snakemake.config['electricity'])
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dfs = make_summaries(networks_dict)
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df = make_summaries(networks_dict)
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to_csv(df)
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to_csv(dfs)
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@ -1,56 +1,47 @@
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import os
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import pandas as pd
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#allow plotting without Xwindows
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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#consolidate and rename
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def rename_techs(label):
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if label[:8] == "central ":
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label = label[8:]
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if label[:6] == "urban ":
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label = label[6:]
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if label.startswith("central "):
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label = label[len("central "):]
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elif label.startswith("urban "):
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label = label[len("urban "):]
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if "retrofitting" in label:
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label = "building retrofitting"
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if "H2" in label:
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elif "H2" in label:
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label = "hydrogen storage"
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if "CHP" in label:
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elif "CHP" in label:
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||||
label = "CHP"
|
||||
if "water tank" in label:
|
||||
elif "water tank" in label:
|
||||
label = "water tanks"
|
||||
if label=="water tanks":
|
||||
elif label == "water tanks":
|
||||
label = "hot water storage"
|
||||
if "gas" in label and label != "gas boiler":
|
||||
elif "gas" in label and label != "gas boiler":
|
||||
label = "natural gas"
|
||||
if "solar thermal" in label:
|
||||
elif "solar thermal" in label:
|
||||
label = "solar thermal"
|
||||
if label == "solar":
|
||||
elif label == "solar":
|
||||
label = "solar PV"
|
||||
if label == "heat pump":
|
||||
elif label == "heat pump":
|
||||
label = "air heat pump"
|
||||
if label == "Sabatier":
|
||||
elif label == "Sabatier":
|
||||
label = "methanation"
|
||||
if label == "offwind":
|
||||
elif label == "offwind":
|
||||
label = "offshore wind"
|
||||
if label == "onwind":
|
||||
elif label == "onwind":
|
||||
label = "onshore wind"
|
||||
if label == "ror":
|
||||
elif label == "ror":
|
||||
label = "hydroelectricity"
|
||||
if label == "hydro":
|
||||
elif label == "hydro":
|
||||
label = "hydroelectricity"
|
||||
if label == "PHS":
|
||||
elif label == "PHS":
|
||||
label = "hydroelectricity"
|
||||
if label == "co2 Store":
|
||||
elif label == "co2 Store":
|
||||
label = "DAC"
|
||||
if "battery" in label:
|
||||
elif "battery" in label:
|
||||
label = "battery storage"
|
||||
|
||||
return label
|
||||
@ -58,11 +49,10 @@ def rename_techs(label):
|
||||
|
||||
preferred_order = pd.Index(["transmission lines","hydroelectricity","hydro reservoir","run of river","pumped hydro storage","onshore wind","offshore wind","solar PV","solar thermal","building retrofitting","ground heat pump","air heat pump","resistive heater","CHP","OCGT","gas boiler","gas","natural gas","methanation","hydrogen storage","battery storage","hot water storage"])
|
||||
|
||||
def plot_costs():
|
||||
|
||||
|
||||
cost_df = pd.read_csv(snakemake.input.costs,index_col=list(range(3)),header=[0,1,2])
|
||||
def plot_costs(infn, fn=None):
|
||||
|
||||
## For now ignore the simpl header
|
||||
cost_df = pd.read_csv(infn,index_col=list(range(3)),header=[1,2,3])
|
||||
|
||||
df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
|
||||
|
||||
@ -109,12 +99,13 @@ def plot_costs():
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
fig.savefig(snakemake.output.costs,transparent=True)
|
||||
if fn is not None:
|
||||
fig.savefig(fn, transparent=True)
|
||||
|
||||
|
||||
def plot_energy():
|
||||
def plot_energy(infn, fn=None):
|
||||
|
||||
energy_df = pd.read_csv(snakemake.input.energy,index_col=list(range(2)),header=[0,1,2])
|
||||
energy_df = pd.read_csv(infn, index_col=list(range(2)),header=[1,2,3])
|
||||
|
||||
df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
|
||||
|
||||
@ -161,25 +152,15 @@ def plot_energy():
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
fig.savefig(snakemake.output.energy,transparent=True)
|
||||
if fn is not None:
|
||||
fig.savefig(fn, transparent=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils import Dict
|
||||
import yaml
|
||||
snakemake = Dict()
|
||||
with open('config.yaml') as f:
|
||||
snakemake.config = yaml.load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
name = "37-lv"
|
||||
summary = snakemake.wildcards.summary
|
||||
try:
|
||||
func = globals()[f"plot_{summary}"]
|
||||
except KeyError:
|
||||
raise RuntimeError(f"plotting function for {summary} has not been defined")
|
||||
|
||||
for item in ["costs","energy"]:
|
||||
snakemake.input[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=name,item=item)
|
||||
snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/graphs/{item}.pdf'.format(name=name,item=item)
|
||||
|
||||
plot_costs()
|
||||
|
||||
plot_energy()
|
||||
func(os.path.join(snakemake.input[0], f"{summary}.csv"), snakemake.output[0])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user