From 98b3ef5e2003cc16c65b71baea6154bc6165e582 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20H=C3=B6rsch?= <jonas.hoersch@kit.edu>
Date: Fri, 26 Oct 2018 10:33:58 +0200
Subject: [PATCH] Add _helpers, used by plot_network

---
 scripts/_helpers.py | 106 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)
 create mode 100644 scripts/_helpers.py

diff --git a/scripts/_helpers.py b/scripts/_helpers.py
new file mode 100644
index 00000000..ccc1f38c
--- /dev/null
+++ b/scripts/_helpers.py
@@ -0,0 +1,106 @@
+import pandas as pd
+import numpy as np
+from six import iteritems, iterkeys, itervalues
+
+import pypsa
+
+from add_electricity import load_costs, update_transmission_costs
+
+def pdbcast(v, h):
+    return pd.DataFrame(v.values.reshape((-1, 1)) * h.values,
+                        index=v.index, columns=h.index)
+
+def load_network(fn, tech_costs, config, combine_hydro_ps=True):
+    opts = config['plotting']
+
+    n = pypsa.Network(fn)
+
+    n.loads["carrier"] = n.loads.bus.map(n.buses.carrier) + " load"
+    n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
+
+    n.links["carrier"] = (n.links.bus0.map(n.buses.carrier) + "-" + n.links.bus1.map(n.buses.carrier))
+    n.lines["carrier"] = "AC line"
+    n.transformers["carrier"] = "AC transformer"
+
+    n.lines['s_nom'] = n.lines['s_nom_min']
+    n.links['p_nom'] = n.links['p_nom_min']
+
+    if combine_hydro_ps:
+        n.storage_units.loc[n.storage_units.carrier.isin({'PHS', 'hydro'}), 'carrier'] = 'hydro+PHS'
+
+    # #if the carrier was not set on the heat storage units
+    # bus_carrier = n.storage_units.bus.map(n.buses.carrier)
+    # n.storage_units.loc[bus_carrier == "heat","carrier"] = "water tanks"
+
+    for name in opts['heat_links'] + opts['heat_generators']:
+        n.links.loc[n.links.index.to_series().str.endswith(name), "carrier"] = name
+
+    Nyears = n.snapshot_weightings.sum()/8760.
+    costs = load_costs(Nyears, tech_costs, config['costs'], config['electricity'])
+    update_transmission_costs(n, costs)
+
+    return n
+
+def aggregate_p_nom(n):
+    return pd.concat([
+        n.generators.groupby("carrier").p_nom_opt.sum(),
+        n.storage_units.groupby("carrier").p_nom_opt.sum(),
+        n.links.groupby("carrier").p_nom_opt.sum(),
+        n.loads_t.p.groupby(n.loads.carrier,axis=1).sum().mean()
+    ])
+
+def aggregate_p(n):
+    return pd.concat([
+        n.generators_t.p.sum().groupby(n.generators.carrier).sum(),
+        n.storage_units_t.p.sum().groupby(n.storage_units.carrier).sum(),
+        n.stores_t.p.sum().groupby(n.stores.carrier).sum(),
+        -n.loads_t.p.sum().groupby(n.loads.carrier).sum()
+    ])
+
+def aggregate_e_nom(n):
+    return pd.concat([
+        (n.storage_units["p_nom_opt"]*n.storage_units["max_hours"]).groupby(n.storage_units["carrier"]).sum(),
+        n.stores["e_nom_opt"].groupby(n.stores.carrier).sum()
+    ])
+
+def aggregate_p_curtailed(n):
+    return pd.concat([
+        ((n.generators_t.p_max_pu.sum().multiply(n.generators.p_nom_opt) - n.generators_t.p.sum())
+         .groupby(n.generators.carrier).sum()),
+        ((n.storage_units_t.inflow.sum() - n.storage_units_t.p.sum())
+         .groupby(n.storage_units.carrier).sum())
+    ])
+
+def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
+    components = dict(Link=("p_nom", "p0"),
+                      Generator=("p_nom", "p"),
+                      StorageUnit=("p_nom", "p"),
+                      Store=("e_nom", "p"),
+                      Line=("s_nom", None),
+                      Transformer=("s_nom", None))
+
+    costs = {}
+    for c, (p_nom, p_attr) in zip(
+        n.iterate_components(iterkeys(components), skip_empty=False),
+        itervalues(components)
+    ):
+        if not existing_only: p_nom += "_opt"
+        costs[(c.list_name, 'capital')] = (c.df[p_nom] * c.df.capital_cost).groupby(c.df.carrier).sum()
+        if p_attr is not None:
+            p = c.pnl[p_attr].sum()
+            if c.name == 'StorageUnit':
+                p = p.loc[p > 0]
+            costs[(c.list_name, 'marginal')] = (p*c.df.marginal_cost).groupby(c.df.carrier).sum()
+    costs = pd.concat(costs)
+
+    if flatten:
+        assert opts is not None
+        conv_techs = opts['conv_techs']
+
+        costs = costs.reset_index(level=0, drop=True)
+        costs = costs['capital'].add(
+            costs['marginal'].rename({t: t + ' marginal' for t in conv_techs}),
+            fill_value=0.
+        )
+
+    return costs