Various scheduler improvements

flexmeasures/data/models/planning/battery.py

@@ -127,7 +127,7 @@ def schedule_battery(
             beliefs_before=belief_time,
             sensor=inflexible_sensor,
         )
-    if soc_targets is not None:
+    if soc_targets is not None and not soc_targets.empty:
         device_constraints[0]["equals"] = soc_targets.shift(
             -1, freq=resolution
         ).values * (timedelta(hours=1) / resolution) - soc_at_start * (
@@ -141,10 +141,16 @@ def schedule_battery(
     device_constraints[0]["max"] = (soc_max - soc_at_start) * (
         timedelta(hours=1) / resolution
     )
-    device_constraints[0]["derivative min"] = (
-        sensor.get_attribute("capacity_in_mw") * -1
-    )
-    device_constraints[0]["derivative max"] = sensor.get_attribute("capacity_in_mw")
+    if sensor.get_attribute("is_strictly_non_positive"):
+        device_constraints[0]["derivative min"] = 0
+    else:
+        device_constraints[0]["derivative min"] = (
+            sensor.get_attribute("capacity_in_mw") * -1
+        )
+    if sensor.get_attribute("is_strictly_non_negative"):
+        device_constraints[0]["derivative max"] = 0
+    else:
+        device_constraints[0]["derivative max"] = sensor.get_attribute("capacity_in_mw")
 
     # Apply round-trip efficiency evenly to charging and discharging
     device_constraints[0]["derivative down efficiency"] = roundtrip_efficiency**0.5

flexmeasures/data/models/planning/utils.py

@@ -9,7 +9,6 @@
 
 from flexmeasures.data.models.time_series import Sensor, TimedBelief
 from flexmeasures.data.models.planning.exceptions import (
-    UnknownForecastException,
     UnknownMarketException,
     UnknownPricesException,
 )
@@ -150,11 +149,17 @@ def get_power_values(
         one_deterministic_belief_per_event=True,
     )  # consumption is negative, production is positive
     df = simplify_index(bdf)
+    df = df.reindex(initialize_index(query_window[0], query_window[1], resolution))
     nan_values = df.isnull().values
     if nan_values.any() or df.empty:
-        raise UnknownForecastException(
-            f"Forecasts unknown for planning window. (sensor {sensor.id})"
+        current_app.logger.warning(
+            f"Assuming zero power values for (partially) unknown power values for planning window. (sensor {sensor.id})"
         )
+        df = df.fillna(0)
+    if sensor.get_attribute(
+        "consumption_is_positive", False
+    ):  # FlexMeasures default is to store consumption as negative power values
+        return df.values
     return -df.values
 
 

flexmeasures/utils/config_utils.py

@@ -8,6 +8,7 @@
 
 from flask import Flask
 from inflection import camelize
+import pandas as pd
 
 from flexmeasures.utils.config_defaults import (
     Config as DefaultConfig,
@@ -45,6 +46,7 @@
 
 def configure_logging():
     """Configure and register logging"""
+    pd.options.display.expand_frame_repr = False  # Don't wrap DataFrame representations
     loggingDictConfig(flexmeasures_logging_config)