Improve transmission plots

Author: staadecker

switch_model/tools/graph/main.py

@@ -15,7 +15,7 @@
 import numpy as np
 import pandas as pd
 from PIL import Image
-from matplotlib import pyplot as plt
+from matplotlib import pyplot as plt, colors
 import seaborn as sns
 import matplotlib
 import plotnine
@@ -124,7 +124,6 @@ def __init__(self, graph_tools):
         self._tools = graph_tools
         self._wecc_states = None
         self._center_points = None
-        self._figure_size = (11, 12)
         self._shapely = None
         self._geopandas = None
 
@@ -216,7 +215,7 @@ def graph_pie_chart(self, df, max_size=2500):
         current_max_size = df.groupby("gen_load_zone")["value"].sum().max()
         df["value"] *= max_size / current_max_size
 
-        ax = self._tools.get_axes(size=self._figure_size)
+        ax = self._tools.get_axes()
         self._plot_states(ax)
         df = df.merge(center_points, on="gen_load_zone")
 
@@ -232,15 +231,41 @@ def graph_pie_chart(self, df, max_size=2500):
             ratios = (group_sum / total_size).values
             GraphMapTools._pie_plot(x, y, ratios, tech_color, ax=ax, size=total_size)
 
-    def graph_transmission(self, df):
+    def graph_points(self, df, ax=None):
+        """
+        Graphs a point in each load zone based on a dataframe with two columns
+        - gen_load_zone
+        - value
+        """
+        _, center_points = self._load_maps()
+
+        df = df.merge(center_points, on="gen_load_zone")
+        # Cast to GeoDataFrame
+        df = self._geopandas.GeoDataFrame(
+            df[["geometry", "value"]], geometry="geometry"
+        )
+
+        if ax is None:
+            ax = self._tools.get_axes()
+        self._plot_states(ax)
+        df.plot(
+            ax=ax,
+            column="value",
+            legend=True,
+            cmap="coolwarm",
+            markersize=30,
+            norm=colors.CenteredNorm(),
+        )
+
+    def graph_transmission(self, df, cutoff):
         """
         Graphs the data frame a dataframe onto a map.
         The dataframe should have 4 columns:
         - from: the load zone where we're starting from
         - to: the load zone where we're going to
         - value: the value to plot
         """
-        ax = self._tools.get_axes(size=self._figure_size)
+        ax = self._tools.get_axes()
         _, center_points = self._load_maps()
 
         # Merge duplicate rows if table was unidirectional
@@ -265,12 +290,21 @@ def make_line(r):
             return LineString([r["from_geometry"], r["to_geometry"]])
 
         df["geometry"] = df.apply(make_line, axis=1)
+        # Cast to GeoDataFrame
         df = self._geopandas.GeoDataFrame(
             df[["geometry", "value"]], geometry="geometry"
         )
 
         self._plot_states(ax)
-        df.plot(ax=ax, column="value", legend=True, cmap="Reds")
+        df.plot(
+            ax=ax,
+            column="value",
+            legend=True,
+            cmap="Greens",
+            zorder=-50,
+            norm=colors.LogNorm(vmin=cutoff, vmax=df.value.max()),
+        )
+        return ax
 
 
 class TransformTools:

switch_model/transmission/transport/dispatch.py

@@ -5,7 +5,7 @@
 Defines model components to describe transmission dispatch for the
 Switch model.
 """
-
+import pandas as pd
 from pyomo.environ import *
 
 import os
@@ -136,7 +136,7 @@ def post_solve(instance, outdir):
     title="Transmission limit duals per period",
     note="Note: Outliers and zero-valued duals are ignored from box plot.",
 )
-def graph(tools):
+def transmission_limits(tools):
     dispatch = tools.get_dataframe("transmission_dispatch")
     dispatch = tools.transform.timestamp(dispatch)
     dispatch["transmission_limit_dual"] = tools.pd.to_numeric(
@@ -156,3 +156,43 @@ def graph(tools):
             ylabel="Transmission limit duals ($/MW)",
             showfliers=False,
         )
+
+
+@graph(
+    "transmission_dispatch",
+    title="Dispatched electricity over transmission lines during last period (in TWh)",
+    note="Blue dots are net importers, red dots are net exports, greener lines indicate more use. Lines carrying <1TWh total not shown.",
+)
+def transmission_dispatch(tools):
+    dispatch = tools.get_dataframe("transmission_dispatch.csv")
+    dispatch = tools.transform.timestamp(dispatch).astype({"period": int})
+    # Keep only the last period
+    last_period = dispatch["period"].max()
+    dispatch = dispatch[dispatch["period"] == last_period]
+    dispatch = dispatch.rename(
+        {
+            "load_zone_from": "from",
+            "load_zone_to": "to",
+            "transmission_dispatch": "value",
+        },
+        axis=1,
+    )
+    dispatch["value"] *= (
+        dispatch["tp_duration"] * 1e-6
+    )  # Change from power value to energy value
+    dispatch = dispatch.groupby(["from", "to"], as_index=False)["value"].sum()
+    ax = tools.maps.graph_transmission(dispatch, cutoff=1)
+    exports = (
+        dispatch[["from", "value"]].rename({"from": "gen_load_zone"}, axis=1).copy()
+    )
+    imports = dispatch[["to", "value"]].rename({"to": "gen_load_zone"}, axis=1).copy()
+    imports["value"] *= -1
+    exports = pd.concat([imports, exports])
+    exports = exports.groupby("gen_load_zone", as_index=False).sum()
+    tools.maps.graph_points(exports, ax)
+
+
+@graph("test")
+def test(tools):
+    ax = tools.get_axes()
+    tools.maps._plot_states(ax)