diff --git a/assume/common/forecasts.py b/assume/common/forecasts.py
index d8a1d324b..f79cd7881 100644
--- a/assume/common/forecasts.py
+++ b/assume/common/forecasts.py
@@ -466,7 +466,9 @@ def __init__(
         *args,
         **kwargs,
     ):
-        super().__init__(index, powerplants_units, demand_units, market_configs, *args, **kwargs)
+        super().__init__(
+            index, powerplants_units, demand_units, market_configs, *args, **kwargs
+        )
 
         self.index = FastIndex(start=index[0], end=index[-1], freq=pd.infer_freq(index))
         self.sigma = sigma
diff --git a/assume/common/units_operator.py b/assume/common/units_operator.py
index bcef0e27e..b130f5d5a 100644
--- a/assume/common/units_operator.py
+++ b/assume/common/units_operator.py
@@ -8,7 +8,6 @@
 from itertools import groupby
 from operator import itemgetter
 
-import pandas as pd
 from mango import Role, create_acl, sender_addr
 from mango.messages.message import Performatives
 
@@ -294,7 +293,7 @@ def set_unit_dispatch(
 
     def get_actual_dispatch(
         self, product_type: str, last: datetime
-    ) -> tuple[pd.DataFrame, list[pd.DataFrame]]:
+    ) -> tuple[list[tuple[datetime, float, str, str]], list[dict]]:
         """
         Retrieves the actual dispatch and commits it in the unit.
         We calculate the series of the actual market results dataframe with accepted bids.
@@ -305,7 +304,7 @@ def get_actual_dispatch(
             last (datetime): the last date until which the dispatch was already sent
 
         Returns:
-            tuple[pd.DataFrame, list[pd.DataFrame]]: market_dispatch and unit_dispatch dataframes
+            tuple[list[tuple[datetime, float, str, str]], list[dict]]: market_dispatch and unit_dispatch dataframes
         """
         now = timestamp2datetime(self.context.current_timestamp)
         start = timestamp2datetime(last + 1)
diff --git a/assume/units/demand.py b/assume/units/demand.py
index 38f5ec828..0c5cbe9a5 100644
--- a/assume/units/demand.py
+++ b/assume/units/demand.py
@@ -3,8 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
 import numbers
-
-import pandas as pd
+from datetime import datetime
 
 from assume.common.base import SupportsMinMax
 from assume.common.fast_pandas import FastSeries
@@ -41,7 +40,7 @@ def __init__(
         min_power: float,
         forecaster: Forecaster,
         node: str = "node0",
-        price: float | pd.Series = 3000.0,
+        price: float | FastSeries = 3000.0,
         location: tuple[float, float] = (0.0, 0.0),
         **kwargs,
     ):
@@ -71,8 +70,8 @@ def __init__(
 
     def execute_current_dispatch(
         self,
-        start: pd.Timestamp,
-        end: pd.Timestamp,
+        start: datetime,
+        end: datetime,
     ):
         """
         Execute the current dispatch of the unit.
@@ -83,14 +82,14 @@ def execute_current_dispatch(
             end (pandas.Timestamp): The end time of the dispatch.
 
         Returns:
-            pd.Series: The volume of the unit within the gicen time range.
+            FastSeries: The volume of the unit within the gicen time range.
         """
 
         return self.volume[start:end]
 
     def calculate_min_max_power(
-        self, start: pd.Timestamp, end: pd.Timestamp, product_type="energy"
-    ) -> tuple[pd.Series, pd.Series]:
+        self, start: datetime, end: datetime, product_type="energy"
+    ) -> tuple[FastSeries, FastSeries]:
         """
         Calculates the minimum and maximum power output of the unit and returns the bid volume as both the minimum and maximum power output of the unit.
 
@@ -105,7 +104,7 @@ def calculate_min_max_power(
         bid_volume = (self.volume - self.outputs[product_type]).loc[start:end_excl]
         return bid_volume, bid_volume
 
-    def calculate_marginal_cost(self, start: pd.Timestamp, power: float) -> float:
+    def calculate_marginal_cost(self, start: datetime, power: float) -> float:
         """
         Calculate the marginal cost of the unit returns the marginal cost of the unit based on the provided time and power.
 
diff --git a/assume/units/powerplant.py b/assume/units/powerplant.py
index 24b2efe14..4088561b6 100644
--- a/assume/units/powerplant.py
+++ b/assume/units/powerplant.py
@@ -6,9 +6,8 @@
 from datetime import datetime, timedelta
 from functools import lru_cache
 
-import pandas as pd
-
 from assume.common.base import SupportsMinMax
+from assume.common.fast_pandas import FastSeries
 from assume.common.forecasts import Forecaster
 from assume.common.market_objects import MarketConfig, Orderbook
 from assume.common.utils import get_products_index
@@ -29,7 +28,7 @@ class PowerPlant(SupportsMinMax):
         max_power (float): The maximum power output capacity of the power plant in MW.
         min_power (float, optional): The minimum power output capacity of the power plant in MW. Defaults to 0.0 MW.
         efficiency (float, optional): The efficiency of the power plant in converting fuel to electricity. Defaults to 1.0.
-        additional_cost (Union[float, pd.Series], optional): Additional costs associated with power generation, in EUR/MWh. Defaults to 0.
+        additional_cost (Union[float, FastSeries], optional): Additional costs associated with power generation, in EUR/MWh. Defaults to 0.
         partial_load_eff (bool, optional): Does the efficiency vary at part loads? Defaults to False.
         fuel_type (str, optional): The type of fuel used by the power plant for power generation. Defaults to "others".
         emission_factor (float, optional): The emission factor associated with the power plant's fuel type (CO2 emissions per unit of energy produced). Defaults to 0.0.
@@ -59,7 +58,7 @@ def __init__(
         max_power: float,
         min_power: float = 0.0,
         efficiency: float = 1.0,
-        additional_cost: float | pd.Series = 0.0,
+        additional_cost: float | FastSeries = 0.0,
         partial_load_eff: bool = False,
         fuel_type: str = "others",
         emission_factor: float = 0.0,
@@ -127,8 +126,8 @@ def init_marginal_cost(self):
 
     def execute_current_dispatch(
         self,
-        start: pd.Timestamp,
-        end: pd.Timestamp,
+        start: datetime,
+        end: datetime,
     ):
         """
         Executes the current dispatch of the unit based on the provided timestamps.
@@ -141,7 +140,7 @@ def execute_current_dispatch(
             end (pandas.Timestamp): The end time of the dispatch.
 
         Returns:
-            pd.Series: The volume of the unit within the given time range.
+            FastSeries: The volume of the unit within the given time range.
         """
         start = max(start, self.index[0])
 
@@ -242,18 +241,18 @@ def calc_simple_marginal_cost(
     def calc_marginal_cost_with_partial_eff(
         self,
         power_output: float,
-        timestep: pd.Timestamp = None,
-    ) -> float | pd.Series:
+        timestep: datetime = None,
+    ) -> float | FastSeries:
         """
         Calculates the marginal cost of the unit based on power output and timestamp, considering partial efficiency.
         Returns the marginal cost of the unit.
 
         Args:
             power_output (float): The power output of the unit.
-            timestep (pd.Timestamp, optional): The timestamp of the unit. Defaults to None.
+            timestep (datetime, optional): The timestamp of the unit. Defaults to None.
 
         Returns:
-            float | pd.Series: The marginal cost of the unit.
+            float | FastSeries: The marginal cost of the unit.
         """
         fuel_price = self.forecaster.get_price(self.fuel_type).at[timestep]
 
@@ -294,7 +293,7 @@ def calc_marginal_cost_with_partial_eff(
 
         additional_cost = (
             self.additional_cost.at[timestep]
-            if isinstance(self.additional_cost, pd.Series)
+            if isinstance(self.additional_cost, FastSeries)
             else self.additional_cost
         )
 
@@ -307,8 +306,8 @@ def calc_marginal_cost_with_partial_eff(
         return marginal_cost
 
     def calculate_min_max_power(
-        self, start: pd.Timestamp, end: pd.Timestamp, product_type="energy"
-    ) -> tuple[pd.Series, pd.Series]:
+        self, start: datetime, end: datetime, product_type="energy"
+    ) -> tuple[FastSeries, FastSeries]:
         """
         Calculates the minimum and maximum power output of the unit and returns it.
 
diff --git a/assume/units/steel_plant.py b/assume/units/steel_plant.py
index eaa09c2d8..283a65ecd 100644
--- a/assume/units/steel_plant.py
+++ b/assume/units/steel_plant.py
@@ -37,7 +37,7 @@ class SteelPlant(DSMFlex, SupportsMinMax):
         bidding_strategies (dict): The bidding strategies of the unit.
         technology (str): The technology of the unit.
         node (str): The node of the unit.
-        index (pd.DatetimeIndex): The index for the data of the unit.
+        index (FastIndex): The index for the data of the unit.
         location (tuple[float, float]): The location of the unit.
         components (dict[str, dict]): The components of the unit such as Electrolyser, DRI Plant, DRI Storage, and Electric Arc Furnace.
         objective (str): The objective of the unit, e.g. minimize variable cost ("min_variable_cost").
@@ -552,7 +552,7 @@ def set_dispatch_plan(
             orderbook=orderbook,
         )
 
-    def calculate_marginal_cost(self, start: pd.Timestamp, power: float) -> float:
+    def calculate_marginal_cost(self, start: datetime, power: float) -> float:
         """
         Calculate the marginal cost of the unit based on the provided time and power.
 
diff --git a/assume/units/storage.py b/assume/units/storage.py
index 2531fc044..12529e652 100644
--- a/assume/units/storage.py
+++ b/assume/units/storage.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
 import logging
-from datetime import timedelta
+from datetime import datetime, timedelta
 from functools import lru_cache
 
 import numpy as np
@@ -160,7 +160,7 @@ def __init__(
         self.warm_start_cost = warm_start_cost * max_power_discharge
         self.cold_start_cost = cold_start_cost * max_power_discharge
 
-    def execute_current_dispatch(self, start: pd.Timestamp, end: pd.Timestamp):
+    def execute_current_dispatch(self, start: datetime, end: datetime):
         """
         Executes the current dispatch of the unit based on the provided timestamps.
 
@@ -286,7 +286,7 @@ def set_dispatch_plan(
     @lru_cache(maxsize=256)
     def calculate_marginal_cost(
         self,
-        start: pd.Timestamp,
+        start: datetime,
         power: float,
     ) -> float:
         """
@@ -359,8 +359,8 @@ def calculate_soc_max_charge(
         return power
 
     def calculate_min_max_charge(
-        self, start: pd.Timestamp, end: pd.Timestamp, product_type="energy"
-    ) -> tuple[pd.Series]:
+        self, start: datetime, end: datetime, product_type="energy"
+    ) -> tuple[FastSeries, FastSeries]:
         """
         Calculates the min and max charging power for the given time period.
         This is relative to the already sold output on other markets for the same period.
@@ -372,7 +372,7 @@ def calculate_min_max_charge(
             product_type (str): The product type of the storage unit.
 
         Returns:
-            tuple[pd.Series]: The minimum and maximum charge power levels of the storage unit in MW.
+            tuple[FastSeries, FastSeries]: The minimum and maximum charge power levels of the storage unit in MW.
         """
         end_excl = end - self.index.freq
 
@@ -408,8 +408,8 @@ def calculate_min_max_charge(
         return min_power_charge, max_power_charge
 
     def calculate_min_max_discharge(
-        self, start: pd.Timestamp, end: pd.Timestamp, product_type="energy"
-    ) -> tuple[pd.Series]:
+        self, start: datetime, end: datetime, product_type="energy"
+    ) -> tuple[FastSeries, FastSeries]:
         """
         Calculates the min and max discharging power for the given time period.
         This is relative to the already sold output on other markets for the same period.
@@ -421,7 +421,7 @@ def calculate_min_max_discharge(
             product_type (str): The product type of the storage unit.
 
         Returns:
-            tuple[pd.Series]: The minimum and maximum discharge power levels of the storage unit in MW.
+            tuple[FastSeries, FastSeries]: The minimum and maximum discharge power levels of the storage unit in MW.
         """
         end_excl = end - self.index.freq
 
diff --git a/tests/conftest.py b/tests/conftest.py
index 34dc37965..3e3b6e71c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -27,13 +27,13 @@ def __init__(self, forecaster, **kwargs):
         self.ramp_up = 400
 
     def calculate_min_max_power(
-        self, start: pd.Timestamp, end: pd.Timestamp, product_type="energy"
-    ) -> tuple[pd.Series, pd.Series]:
+        self, start: datetime, end: datetime, product_type="energy"
+    ) -> tuple[FastSeries, FastSeries]:
         min = FastSeries(value=100, index=self.index).loc[start:end]
         max = FastSeries(value=400, index=self.index).loc[start:end]
         return min, max
 
-    def calculate_marginal_cost(self, start: pd.Timestamp, power: float) -> float:
+    def calculate_marginal_cost(self, start: datetime, power: float) -> float:
         return 3