2.0.0.a4

idaes/apps/grid_integration/bidder.py

@@ -256,6 +256,7 @@ def __init__(
         n_scenario,
         solver,
         forecaster,
+        real_time_underbid_penalty,
     ):
 
         """
@@ -284,6 +285,7 @@ def __init__(
         self.n_scenario = n_scenario
         self.solver = solver
         self.forecaster = forecaster
+        self.real_time_underbid_penalty = real_time_underbid_penalty
 
         self._check_inputs()
 
@@ -337,6 +339,10 @@ def formulate_DA_bidding_problem(self):
         model = self._set_up_bidding_problem(self.day_ahead_horizon)
         self._add_DA_bidding_constraints(model)
 
+        # do not relax the DA offering UB
+        for i in model.SCENARIOS:
+            model.fs[i].real_time_underbid_power.fix(0)
+
         return model
 
     def formulate_RT_bidding_problem(self):
@@ -352,6 +358,10 @@ def formulate_RT_bidding_problem(self):
         model = self._set_up_bidding_problem(self.real_time_horizon)
         self._add_RT_bidding_constraints(model)
 
+        # relax the DA offering UB
+        for i in model.SCENARIOS:
+            model.fs[i].real_time_underbid_power.unfix()
+
         return model
 
     def _save_power_outputs(self, model):
@@ -396,6 +406,9 @@ def _add_bidding_params(self, model):
             model.fs[i].real_time_energy_price = pyo.Param(
                 time_index, initialize=0, mutable=True
             )
+            model.fs[i].real_time_underbid_penalty = pyo.Param(
+                initialize=self.real_time_underbid_penalty, mutable=True
+            )
 
         return
 
@@ -411,17 +424,24 @@ def _add_bidding_vars(self, model):
             None
         """
 
-        def day_ahead_power_ub_rule(fs, t):
-            return fs.power_output_ref[t] >= fs.day_ahead_power[t]
+        def relaxed_day_ahead_power_ub_rule(fs, t):
+            return (
+                fs.power_output_ref[t] + fs.real_time_underbid_power[t]
+                >= fs.day_ahead_power[t]
+            )
 
         for i in model.SCENARIOS:
             time_index = model.fs[i].power_output_ref.index_set()
             model.fs[i].day_ahead_power = pyo.Var(
                 time_index, initialize=0, within=pyo.NonNegativeReals
             )
 
+            model.fs[i].real_time_underbid_power = pyo.Var(
+                time_index, initialize=0, within=pyo.NonNegativeReals
+            )
+
             model.fs[i].day_ahead_power_ub = pyo.Constraint(
-                time_index, rule=day_ahead_power_ub_rule
+                time_index, rule=relaxed_day_ahead_power_ub_rule
             )
 
         return
@@ -459,6 +479,8 @@ def _add_bidding_objective(self, model):
                     + model.fs[k].real_time_energy_price[t]
                     * (model.fs[k].power_output_ref[t] - model.fs[k].day_ahead_power[t])
                     - weight * cost[t]
+                    - model.fs[k].real_time_underbid_penalty
+                    * model.fs[k].real_time_underbid_power[t]
                 )
 
         return
@@ -654,8 +676,12 @@ def _pass_realized_day_ahead_dispatches(self, realized_day_ahead_dispatches, hou
                     dispatch = realized_day_ahead_dispatches[t + hour]
                 except IndexError as ex:
                     self.real_time_model.fs[s].day_ahead_power[t].unfix()
+                    # unrelax the DA offering UB
+                    self.real_time_model.fs[s].real_time_underbid_power[t].fix(0)
                 else:
                     self.real_time_model.fs[s].day_ahead_power[t].fix(dispatch)
+                    # relax the DA offering UB
+                    self.real_time_model.fs[s].real_time_underbid_power[t].unfix()
 
     def update_day_ahead_model(self, **kwargs):
 
@@ -808,6 +834,7 @@ def __init__(
         n_scenario,
         solver,
         forecaster,
+        real_time_underbid_penalty=10000,
         fixed_to_schedule=False,
     ):
         """
@@ -839,6 +866,7 @@ def __init__(
             n_scenario,
             solver,
             forecaster,
+            real_time_underbid_penalty,
         )
         self.fixed_to_schedule = fixed_to_schedule
 
@@ -922,6 +950,7 @@ def _assemble_bids(self, model, power_var_name, energy_price_param_name, hour):
         """
 
         bids = {}
+        is_thermal = self.bidding_model_object.model_data.generator_type == "thermal"
 
         power_output_var = getattr(model.fs[0], power_var_name)
         time_index = power_output_var.index_set()
@@ -939,29 +968,34 @@ def _assemble_bids(self, model, power_var_name, energy_price_param_name, hour):
 
             if self.fixed_to_schedule:
                 bids[t][self.generator]["p_min"] = bids[t][self.generator]["p_max"]
-                bids[t][self.generator]["min_up_time"] = 0
-                bids[t][self.generator]["min_down_time"] = 0
                 bids[t][self.generator]["fixed_commitment"] = (
                     1 if bids[t][self.generator]["p_min"] > 0 else 0
                 )
-                bids[t][self.generator]["startup_fuel"] = [
-                    (bids[t][self.generator]["min_down_time"], 0)
-                ]
-                bids[t][self.generator]["startup_cost"] = [
-                    (bids[t][self.generator]["min_down_time"], 0)
-                ]
 
-            bids[t][self.generator]["p_cost"] = [
-                (bids[t][self.generator]["p_min"], 0),
-                (bids[t][self.generator]["p_max"], 0),
-            ]
+                if is_thermal:
+                    bids[t][self.generator]["min_up_time"] = 0
+                    bids[t][self.generator]["min_down_time"] = 0
+
+                    bids[t][self.generator]["startup_fuel"] = [
+                        (bids[t][self.generator]["min_down_time"], 0)
+                    ]
+                    bids[t][self.generator]["startup_cost"] = [
+                        (bids[t][self.generator]["min_down_time"], 0)
+                    ]
+
+            if is_thermal:
 
-            bids[t][self.generator]["startup_capacity"] = bids[t][self.generator][
-                "p_min"
-            ]
-            bids[t][self.generator]["shutdown_capacity"] = bids[t][self.generator][
-                "p_min"
-            ]
+                bids[t][self.generator]["p_cost"] = [
+                    (bids[t][self.generator]["p_min"], 0),
+                    (bids[t][self.generator]["p_max"], 0),
+                ]
+
+                bids[t][self.generator]["startup_capacity"] = bids[t][self.generator][
+                    "p_min"
+                ]
+                bids[t][self.generator]["shutdown_capacity"] = bids[t][self.generator][
+                    "p_min"
+                ]
 
         return bids
 
@@ -1023,6 +1057,7 @@ def __init__(
         n_scenario,
         solver,
         forecaster,
+        real_time_underbid_penalty=10000,
     ):
 
         """
@@ -1052,6 +1087,7 @@ def __init__(
             n_scenario,
             solver,
             forecaster,
+            real_time_underbid_penalty,
         )
 
     def _add_DA_bidding_constraints(self, model):
@@ -1234,7 +1270,6 @@ def _assemble_bids(self, model, power_var_name, energy_price_param_name, hour):
                 full_bids[t][gen]["p_cost"] = bids[t_idx][gen]
                 full_bids[t][gen]["p_min"] = min([p[0] for p in bids[t_idx][gen]])
                 full_bids[t][gen]["p_max"] = max([p[0] for p in bids[t_idx][gen]])
-
                 full_bids[t][gen]["startup_capacity"] = full_bids[t][gen]["p_min"]
                 full_bids[t][gen]["shutdown_capacity"] = full_bids[t][gen]["p_min"]
 

idaes/apps/grid_integration/coordinator.py

@@ -35,10 +35,10 @@ def __init__(self, bidder, tracker, projection_tracker):
         Arguments:
             bidder: an initialized bidder object
 
-            tracker: an initialized bidder object
+            tracker: an initialized tracker object
 
-            projection_tracker: an initialized bidder object, this object is
-                                mimicking the behaviro of the projection SCED in
+            projection_tracker: an initialized tracker object, this object is
+                                mimicking the behaviror of the projection SCED in
                                 Prescient and to projecting the system states
                                 and updating bidder model.
 
@@ -285,7 +285,7 @@ def assemble_project_tracking_signal(self, options, simulator, hour):
 
         """
         This function assembles the signals for the tracking model to estimate the
-        state of the bidding model at the begining of next RUC.
+        state of the bidding model at the beginning of next RUC.
 
         Arguments:
             options: Prescient options from prescient.simulator.config.
@@ -398,18 +398,32 @@ def _update_static_params(self, gen_dict):
             None
         """
 
+        is_thermal = (
+            self.bidder.bidding_model_object.model_data.generator_type == "thermal"
+        )
+        is_renewable = (
+            self.bidder.bidding_model_object.model_data.generator_type == "renewable"
+        )
+
         for param, value in self.bidder.bidding_model_object.model_data:
             if param == "gen_name" or value is None:
                 continue
             elif param == "p_cost":
-                curve_value = convert_marginal_costs_to_actual_costs(value)
-                gen_dict[param] = {
-                    "data_type": "cost_curve",
-                    "cost_curve_type": "piecewise",
-                    "values": curve_value,
-                }
+                if is_thermal:
+                    curve_value = convert_marginal_costs_to_actual_costs(value)
+                    gen_dict[param] = {
+                        "data_type": "cost_curve",
+                        "cost_curve_type": "piecewise",
+                        "values": curve_value,
+                    }
+                elif is_renewable:
+                    gen_dict[param] = value
+
                 if "p_fuel" in gen_dict:
                     gen_dict.pop("p_fuel")
+            elif param == "initial_status" or param == "initial_p_output":
+                if param not in gen_dict:
+                    gen_dict[param] = value
             else:
                 gen_dict[param] = value
 

idaes/apps/grid_integration/examples/thermal_generator.py

@@ -16,7 +16,7 @@
 from idaes.apps.grid_integration import Tracker
 from idaes.apps.grid_integration import Bidder
 from idaes.apps.grid_integration import PlaceHolderForecaster
-from idaes.apps.grid_integration.model_data import GeneratorModelData
+from idaes.apps.grid_integration.model_data import ThermalGeneratorModelData
 
 from pyomo.common.dependencies import attempt_import
 
@@ -138,10 +138,9 @@ def assemble_model_data(self, generator_name, gen_params):
                 model_data["Power Segments"][l]
             ] = model_data["Marginal Costs"][l]
 
-        self._model_data = GeneratorModelData(
+        self._model_data = ThermalGeneratorModelData(
             gen_name=generator_name,
             bus=model_data["Bus Name"],
-            generator_type="thermal",
             p_min=model_data["PMin MW"],
             p_max=model_data["PMax MW"],
             min_down_time=model_data["Min Down Time Hr"],
@@ -150,6 +149,9 @@ def assemble_model_data(self, generator_name, gen_params):
             ramp_down_60min=model_data["RD"],
             shutdown_capacity=model_data["SD"],
             startup_capacity=model_data["SU"],
+            initial_status=-1,
+            initial_p_output=0,
+            fixed_commitment=None,
             production_cost_bid_pairs=[
                 (
                     model_data["PMin MW"],
@@ -163,7 +165,6 @@ def assemble_model_data(self, generator_name, gen_params):
             startup_cost_pairs=[
                 (model_data["Min Down Time Hr"], model_data["SU Cost"])
             ],
-            fixed_commitment=None,
         )
 
         return model_data

idaes/apps/grid_integration/forecaster.py

@@ -336,10 +336,7 @@ def fetch_day_ahead_stats_from_prescient(self, uc_date, uc_hour, day_ahead_resul
             None
         """
 
-        forecasts_arr = np.random.normal(
-            loc=corresponding_means, scale=corresponding_stds, size=(n_samples, horizon)
-        )
-        forecasts_arr[forecasts_arr < 0] = 0
+        return
 
 
 class Backcaster(AbstractPrescientPriceForecaster):