42 add datadriven mpc

.github/workflows/ci.yml

@@ -21,4 +21,4 @@ jobs:
       CREATE_PAGES_ON_FAILURE: true
       EXECUTE_TESTS: true
       EXECUTE_COVERAGE_TEST: true
-
+      EXTRA_REQUIREMENTS: '["ml"]'

agentlib_flexquant/data_structures/flex_kpis.py

@@ -134,6 +134,10 @@ class FlexibilityKPIs(pydantic.BaseModel):
         default=KPISeries(name="power_flex_offer", unit="kW", integration_method=LINEAR),
         description="Power flexibility",
     )
+    power_flex_offer_prepared: KPISeries = pydantic.Field(
+        default=KPISeries(name="power_flex_offer_prepared", unit="kW", integration_method=LINEAR),
+        description="Power flexibility Series prepared for integration",
+    )
     power_flex_offer_max: KPI = pydantic.Field(
         default=KPI(name="power_flex_offer_max", unit="kW"),
         description="Maximum power flexibility",
@@ -201,7 +205,7 @@ def calculate(
         enable_energy_costs_correction: bool,
         calculate_flex_cost: bool,
         integration_method: INTEGRATION_METHOD,
-        collocation_time_grid: list = None,
+        time_grid_info: dict = None,
     ):
         """Calculate the KPIs based on the power and electricity price input profiles.
 
@@ -217,7 +221,8 @@ def calculate(
             enable_energy_costs_correction: whether the energy costs should be corrected
             calculate_flex_cost: whether the cost of the flexibility should be calculated
             integration_method: method used for integration of KPISeries e.g. linear, constant
-            collocation_time_grid: Time grid of the mpc output with collocation discretization
+            time_grid_info: Dictionary with 'type' ('collocation', 'multiple_shooting', 'none')
+                and 'grid' (list of time points) keys
 
 
         """
@@ -229,10 +234,10 @@ def calculate(
             integration_method=integration_method,
         )
         self._calculate_power_flex_stats(
-            mpc_time_grid=mpc_time_grid, collocation_time_grid=collocation_time_grid
+            mpc_time_grid=mpc_time_grid, time_grid_info=time_grid_info
         )
         self._calculate_energy_flex(
-            mpc_time_grid=mpc_time_grid, collocation_time_grid=collocation_time_grid
+            mpc_time_grid=mpc_time_grid, time_grid_info=time_grid_info
         )
 
         # Costs KPIs
@@ -250,7 +255,7 @@ def calculate(
                 stored_energy_diff=stored_energy_diff,
                 integration_method=integration_method,
                 mpc_time_grid=mpc_time_grid,
-                collocation_time_grid=collocation_time_grid,
+                time_grid_info=time_grid_info,
             )
             self._calculate_costs_rel()
 
@@ -292,10 +297,6 @@ def _calculate_power_flex(
         # Set values to zero if the difference is small
         relative_difference = (power_flex / power_profile_base).abs()
         power_flex.loc[relative_difference < relative_error_acceptance] = 0
-        # Set the first value of power_flex to zero, since it comes from the measurement/simulator
-        # and is the same for baseline and shadow mpcs.
-        # For quantification of flexibility, only power difference is of interest.
-        power_flex.iloc[0] = 0
 
         # Set values
         self.power_flex_full.value = power_flex
@@ -308,28 +309,38 @@ def _calculate_power_flex(
         self.power_flex_offer.integration_method = integration_method
 
     def _calculate_power_flex_stats(
-        self, mpc_time_grid: np.array, collocation_time_grid: list = None
+        self, mpc_time_grid: np.array, time_grid_info: dict = None
     ):
-        """Calculate the characteristic values of the power flexibility for the offer."""
+        """Calculate the characteristic values of the power flexibility for the offer.
+
+        Args:
+            mpc_time_grid: the MPC time grid over the horizon
+            time_grid_info: Dictionary with 'type' and 'grid' keys for discretization info
+        """
         if self.power_flex_offer.value is None:
             raise ValueError("Power flexibility value is empty.")
 
         # Calculate characteristic values
         # max and min of power flex offer
-        power_flex_offer = self.power_flex_offer.value.iloc[:-1].drop(
-            collocation_time_grid, errors="ignore"
-        )
+
+        self.power_flex_offer_prepared = self.power_flex_offer.__deepcopy__()
+
+        # Only drop collocation points if using collocation method
+        if time_grid_info and time_grid_info.get("type") == "collocation":
+            self.power_flex_offer_prepared.value = self.power_flex_offer_prepared.value.drop(
+                time_grid_info["grid"], errors="ignore"
+            )
+        power_flex_offer = self.power_flex_offer_prepared.value.iloc[:-1]
+
         power_flex_offer_max = power_flex_offer.max()
         power_flex_offer_min = power_flex_offer.min()
+
         # Average of the power flex offer
         # Get the series for integration before calculating average
         power_flex_offer_integration = self._get_series_for_integration(
-            series=self.power_flex_offer, mpc_time_grid=mpc_time_grid
-        )
-        power_flex_offer_integration.value = power_flex_offer_integration.value.drop(
-            collocation_time_grid, errors="ignore"
+            series=self.power_flex_offer_prepared, mpc_time_grid=mpc_time_grid
         )
-        # Calculate the average and stores the original value
+
         power_flex_offer_avg = power_flex_offer_integration.avg()
 
         # Set values
@@ -338,7 +349,7 @@ def _calculate_power_flex_stats(
         self.power_flex_offer_avg.value = power_flex_offer_avg
 
     def _get_series_for_integration(
-        self, series: KPISeries, mpc_time_grid: np.ndarray
+        self, series: Union[pd.Series, KPISeries], mpc_time_grid: np.ndarray
     ) -> KPISeries:
         """Return the KPISeries value sampled on the MPC time grid when the integration
         method is constant.
@@ -357,20 +368,23 @@ def _get_series_for_integration(
         else:
             return series.__deepcopy__()
 
-    def _calculate_energy_flex(self, mpc_time_grid, collocation_time_grid: list = None):
+    def _calculate_energy_flex(self, mpc_time_grid, time_grid_info: dict = None):
         """Calculate the energy flexibility by integrating the power flexibility
-        of the offer window."""
+        of the offer window.
+
+        Args:
+            mpc_time_grid: the MPC time grid over the horizon
+            time_grid_info: Dictionary with 'type' and 'grid' keys for discretization info
+        """
         if self.power_flex_offer.value is None:
             raise ValueError("Power flexibility value of the offer is empty.")
 
         # Calculate flexibility
         # Get the series for integration before calculating average
         power_flex_offer_integration = self._get_series_for_integration(
-            series=self.power_flex_offer, mpc_time_grid=mpc_time_grid
-        )
-        power_flex_offer_integration.value = power_flex_offer_integration.value.drop(
-            collocation_time_grid, errors="ignore"
+            series=self.power_flex_offer_prepared, mpc_time_grid=mpc_time_grid
         )
+
         # Calculate the energy flex and stores the original value
         energy_flex = power_flex_offer_integration.integrate(time_unit="hours")
 
@@ -386,7 +400,7 @@ def _calculate_costs(
         stored_energy_diff: float,
         integration_method: INTEGRATION_METHOD,
         mpc_time_grid: np.ndarray,
-        collocation_time_grid: list = None,
+        time_grid_info: dict = None,
     ):
         """Calculate the costs of the flexibility event based on the electricity costs profile,
         the power flexibility profile and difference of stored energy.
@@ -399,7 +413,7 @@ def _calculate_costs(
             stored_energy_diff: the difference of the stored energy between baseline and shadow mpc
             integration_method: the integration method used to integrate KPISeries
             mpc_time_grid: the MPC time grid over the horizon
-            collocation_time_grid: Time grid of the mpc output with collocation discretization
+            time_grid_info: Dictionary with 'type' and 'grid' keys for discretization info
 
 
         """
@@ -435,9 +449,10 @@ def _calculate_costs(
         power_flex_full_integration = self._get_series_for_integration(
             series=self.power_flex_full, mpc_time_grid=mpc_time_grid
         )
-        power_flex_full_integration.value = power_flex_full_integration.value.drop(
-            collocation_time_grid, errors="ignore"
-        )
+        if time_grid_info and time_grid_info.get("type") == "collocation":
+            power_flex_full_integration.value = power_flex_full_integration.value.drop(
+                time_grid_info["grid"], errors="ignore"
+            )
 
         # Difference in costs between shadow and baseline mpc
         delta_cost = cost_profile_shadow - cost_profile_base
@@ -447,7 +462,6 @@ def _calculate_costs(
 
         # Calculate the costs and stores the original value
         costs = self.electricity_costs_series.integrate(time_unit="hours")
-
         # correct the costs
         corrected_costs = costs - stored_energy_diff * np.mean(electricity_price_signal)
 
@@ -640,15 +654,16 @@ def calculate(
         enable_energy_costs_correction: bool,
         calculate_flex_cost: bool,
         integration_method: INTEGRATION_METHOD,
-        collocation_time_grid: list = None,
+        time_grid_info: dict = None,
     ):
         """Calculate the KPIs for the positive and negative flexibility.
 
         Args:
             enable_energy_costs_correction: whether the energy costs should be corrected
             calculate_flex_cost: whether the cost of the flexibility should be calculated
             integration_method: method used for integration of KPISeries e.g. linear, constant
-            collocation_time_grid: Time grid of the mpc output with collocation discretization
+            time_grid_info: Dictionary with 'type' ('collocation', 'multiple_shooting', 'none')
+                and 'grid' (list of time points) keys
 
         """
         self.kpis_pos.calculate(
@@ -663,7 +678,7 @@ def calculate(
             enable_energy_costs_correction=enable_energy_costs_correction,
             calculate_flex_cost=calculate_flex_cost,
             integration_method=integration_method,
-            collocation_time_grid=collocation_time_grid,
+            time_grid_info=time_grid_info,
         )
         self.kpis_neg.calculate(
             power_profile_base=self.power_profile_base,
@@ -677,7 +692,7 @@ def calculate(
             enable_energy_costs_correction=enable_energy_costs_correction,
             calculate_flex_cost=calculate_flex_cost,
             integration_method=integration_method,
-            collocation_time_grid=collocation_time_grid,
+            time_grid_info=time_grid_info,
         )
         self.reset_time_grid()
         return self.kpis_pos, self.kpis_neg
@@ -701,5 +716,3 @@ def update_profile(self, name: str, value: pd.Series, mpc:bool) -> None:
         if value is not None: 
             value = self.unify_inputs(series=value, mpc= mpc)
         setattr(self, name, value)
-
-

agentlib_flexquant/data_structures/globals.py

@@ -15,7 +15,9 @@
 CONSTANT = 'constant'
 COLLOCATION = 'collocation'
 INTEGRATION_METHOD = Literal[LINEAR, CONSTANT]
+
 FlexibilityDirections = Literal["positive", "negative"]
+
 POWER_ALIAS_BASE = "_P_el_base"
 POWER_ALIAS_NEG = "_P_el_neg"
 POWER_ALIAS_POS = "_P_el_pos"
@@ -25,7 +27,7 @@
 full_trajectory_suffix: str = "_full"
 base_vars_to_communicate_suffix: str = "_base"
 shadow_suffix: str = "_shadow"
-COLLOCATION_TIME_GRID = 'collocation_time_grid'
+TIME_GRID_INFO = 'time_grid_info'
 PROVISION_VAR_NAME = "in_provision"
 ACCEPTED_POWER_VAR_NAME = "_P_external"
 RELATIVE_EVENT_START_TIME_VAR_NAME = "rel_start"
@@ -43,11 +45,9 @@
 
 def return_baseline_cost_function(power_variable: str, comfort_variable: str) -> str:
     """Return baseline cost function
-
     Args:
         power_variable: name of the power variable
         comfort_variable: name of the comfort variable
-
     Returns:
         Cost function in the baseline mpc, obj_std is to be evaluated according to
         user definition