Heat pump active power was determined regardless its maximum power. N…

.pre-commit-config.yaml

@@ -9,10 +9,6 @@ repos:
   rev: 23.3.0
   hooks:
     - id: black
-- repo: https://github.com/pycqa/flake8
-  rev: 6.0.0
-  hooks:
-    - id: flake8
 - repo: https://github.com/pycqa/isort
   rev: 5.12.0
   hooks:
@@ -22,6 +18,10 @@ repos:
   rev: v3.4.0
   hooks:
     - id: pyupgrade
+- repo: https://github.com/pycqa/flake8
+  rev: 6.0.0
+  hooks:
+    - id: flake8
 #- repo: https://github.com/pycqa/pylint
 #  rev: pylint-2.6.0
 #  hooks:

doc/whatsnew/v0-3-0.rst

@@ -28,3 +28,4 @@ Changes
 * Move function to assign feeder to Topology class and add methods to the Grid class to get information on the feeders `#360 <https://github.com/openego/eDisGo/pull/360>`_
 * Added a storage operation strategy where the storage is charged when PV feed-in is higher than electricity demand of the household and discharged when electricity demand exceeds PV generation `#386 <https://github.com/openego/eDisGo/pull/386>`_
 * Added an estimation of the voltage deviation over a cable when selecting a suitable cable to connect a new component `#411 <https://github.com/openego/eDisGo/pull/411>`_
+* Added clipping of heat pump electrical power at its maximum value #428 <https://github.com/openego/eDisGo/pull/428>

edisgo/flex_opt/heat_pump_operation.py

@@ -1,3 +1,4 @@
+import copy
 import logging
 
 import pandas as pd
@@ -31,11 +32,37 @@ def operating_strategy(
     if heat_pump_names is None:
         heat_pump_names = edisgo_obj.heat_pump.cop_df.columns
 
+    missing = set(heat_pump_names) - set(edisgo_obj.topology.loads_df.index)
+    if missing:
+        logger.warning(
+            f"The following heat pumps are are in the heat pump class but not yet "
+            f"integrated into the topology class. Therefore, their maximum capacity "
+            f"cannot be considered in the operating strategies. {missing=}"
+        )
+
     if strategy == "uncontrolled":
         ts = (
             edisgo_obj.heat_pump.heat_demand_df.loc[:, heat_pump_names]
             / edisgo_obj.heat_pump.cop_df.loc[:, heat_pump_names]
         )
+
+        ts_prev = copy.deepcopy(ts)
+
+        # clips heat pump load at maximum level
+        in_topology = list(set(heat_pump_names) - set(missing))
+
+        ts_clipped = ts[in_topology].clip(
+            upper=edisgo_obj.topology.loads_df.p_set[in_topology].values
+        )
+        ts[in_topology] = ts_clipped[in_topology]
+
+        clipped = ts.eq(ts_prev).all()[lambda x: ~x].index
+        logger.warning(
+            f"Heat pump power at {clipped} was "
+            f"clipped at its maximum capacity. The heat demand at this bus "
+            f"should be covered by additional heat sources."
+        )
+
         edisgo_obj.timeseries.add_component_time_series(
             "loads_active_power",
             ts,

edisgo/io/heat_pump_import.py

@@ -149,6 +149,7 @@ def _get_central_heat_pump_or_resistive_heaters(carrier):
             <= edisgo_object.config["grid_connection"]["upper_limit_voltage_level_4"],
         )
         df = pd.read_sql(query.statement, engine, index_col=None)
+        # Alternativ Code einfügen, falls Tabelle leer ist
         if not df.empty:
             # get geom of heat bus, weather_cell_id, district_heating_id and area_id
             srid_etrago_bus = db.get_srid_of_db_table(session, egon_etrago_bus.geom)

edisgo/network/timeseries.py

@@ -2120,7 +2120,6 @@ def add_component_time_series(self, df_name, ts_new):
             :attr:`~.network.timeseries.TimeSeries.generators_active_power`.
         ts_new : :pandas:`pandas.DataFrame<DataFrame>`
             Dataframe with new time series to add to existing time series dataframe.
-
         """
         # drop possibly already existing time series
         self.drop_component_time_series(df_name, ts_new.columns)

examples/edisgo_simple_example.ipynb

@@ -892,7 +892,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.10.16"
   },
   "toc": {
    "base_numbering": 1,

examples/electromobility_example.ipynb

@@ -72,7 +72,7 @@
    "outputs": [],
    "source": [
     "# interactive matplotlib\n",
-    "%matplotlib notebook"
+    "%matplotlib ipympl"
    ]
   },
   {
@@ -407,6 +407,28 @@
     "    * If the power rating of the charging point is <= 4.5 MVA, it is connected to the nearest grid connection point or cable. If a cable is selected, the line is cut at the point closest to the charging station and a new branch tee is added to which the charging station is connected."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5997b187-e1dd-4a55-a13b-f665f5fa5921",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simbev_example_data_path = os.path.join(\n",
+    "        os.path.expanduser(\"~\"), \".edisgo\", \"simbev_example_data\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feb0cc6b-45ce-46b1-ad7e-ff1095aad480",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simbev_example_data_path"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -774,7 +796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.16"
   },
   "toc": {
    "base_numbering": 1,

setup.py

@@ -39,6 +39,7 @@ def read(fname):
     "geoalchemy2 < 0.7.0",
     "geopandas >= 0.12.0",
     "geopy >= 2.0.0",
+    "ipympl",
     "jupyterlab",
     "jupyter_dash",
     "matplotlib >= 3.3.0",

tests/flex_opt/test_heat_pump_operation.py

@@ -1,6 +1,8 @@
 import pandas as pd
 import pytest
 
+from numpy.random import default_rng
+
 from edisgo import EDisGo
 from edisgo.flex_opt.heat_pump_operation import operating_strategy
 
@@ -9,23 +11,45 @@ class TestHeatPumpOperation:
     @classmethod
     def setup_class(self):
         self.timeindex = pd.date_range("1/1/2011 12:00", periods=2, freq="H")
+
+        self.edisgo = EDisGo(
+            ding0_grid=pytest.ding0_test_network_path, timeindex=self.timeindex
+        )
+
+        # insert heat pumps into topology
+        rng = default_rng(1)
+        number_of_heat_pumps = 2
+        self.name = []
+        for i in range(number_of_heat_pumps):
+            self.name.append(
+                self.edisgo.add_component(
+                    comp_type="load",
+                    bus=rng.choice(self.edisgo.topology.buses_df.index, size=1)[0],
+                    p_set=0.43,
+                    type="heat_pump",
+                )
+            )
+
+        # add one addtional hp that is not in the topology
+        self.name.append("hp" + str(len(self.name) + 1))
+
         self.cop = pd.DataFrame(
             data={
-                "hp1": [5.0, 6.0],
-                "hp2": [7.0, 8.0],
+                self.name[0]: [5.0, 6.0],
+                self.name[1]: [7.0, 8.0],
+                self.name[2]: [7.2, 6.7],
             },
             index=self.timeindex,
         )
         self.heat_demand = pd.DataFrame(
             data={
-                "hp1": [1.0, 2.0],
-                "hp2": [3.0, 4.0],
+                self.name[0]: [1.0, 4.0],
+                self.name[1]: [3.0, 4.0],
+                self.name[2]: [2.0, 3.0],
             },
             index=self.timeindex,
         )
-        self.edisgo = EDisGo(
-            ding0_grid=pytest.ding0_test_network_path, timeindex=self.timeindex
-        )
+
         self.edisgo.heat_pump.cop_df = self.cop
         self.edisgo.heat_pump.heat_demand_df = self.heat_demand
 
@@ -35,8 +59,9 @@ def test_operating_strategy(self):
 
         hp_ts = pd.DataFrame(
             data={
-                "hp1": [0.2, 1 / 3],
-                "hp2": [3 / 7, 0.5],
+                self.name[0]: [0.2, 0.43],
+                self.name[1]: [3 / 7, 0.43],
+                self.name[2]: [2 / 7.2, 3 / 6.7],
             },
             index=self.timeindex,
         )
@@ -46,8 +71,9 @@ def test_operating_strategy(self):
         )
         hp_ts = pd.DataFrame(
             data={
-                "hp1": [0.0, 0.0],
-                "hp2": [0.0, 0.0],
+                self.name[0]: [0.0, 0.0],
+                self.name[1]: [0.0, 0.0],
+                self.name[2]: [0.0, 0.0],
             },
             index=self.timeindex,
         )
@@ -58,13 +84,18 @@ def test_operating_strategy(self):
 
         # test with providing heat pump names
         timestep = self.timeindex[0]
-        self.edisgo.heat_pump.heat_demand_df.at[timestep, "hp1"] = 0.0
-        self.edisgo.heat_pump.heat_demand_df.at[timestep, "hp2"] = 0.0
+        self.edisgo.heat_pump.heat_demand_df.at[timestep, self.name[0]] = 0.0
+        self.edisgo.heat_pump.heat_demand_df.at[timestep, self.name[1]] = 0.0
 
-        operating_strategy(self.edisgo, heat_pump_names=["hp1"])
+        operating_strategy(self.edisgo, heat_pump_names=[self.name[0]])
 
-        assert self.edisgo.timeseries.loads_active_power.at[timestep, "hp1"] == 0.0
-        assert self.edisgo.timeseries.loads_active_power.at[timestep, "hp2"] == 3 / 7
+        assert (
+            self.edisgo.timeseries.loads_active_power.at[timestep, self.name[0]] == 0.0
+        )
+        assert (
+            self.edisgo.timeseries.loads_active_power.at[timestep, self.name[1]]
+            == 3 / 7
+        )
 
         # test error raising
         msg = "Heat pump operating strategy dummy is not a valid option."