Merge pull request #99 from tum-ens/new_version

v0.1.5

.gitignore

@@ -1,6 +1,10 @@
 .idea
 
 # Folders
+00_docs/*
+01_examples/*
+02_config/*
+03_input_data/*
 04_scenarios/*
 05_results/*
 

hamlet/executor/markets/lem/lem.py

@@ -254,18 +254,18 @@ def __method_none(self, bids, offers, pricing_method):
         """Clears the market with no clearing method, i.e. only the retailer acts as trading partner"""
 
         # Merge bids and offers on the energy_cumsum column
-        bids_offers = bids.join(offers, on=C_ENERGY_CUMSUM, how='outer')
+        bids_offers = pl.concat([bids, offers], how='diagonal')
 
         # Sort the bids and offers by the energy_cumsum
         bids_offers = bids_offers.sort(C_ENERGY_CUMSUM, descending=False)
 
-        # Remove all columns that end on _right
-        double_cols = [col for col in bids_offers.columns if col.endswith('_right')]
-        for col in double_cols:
-            orig_col = col.rsplit('_', 1)[0]
-            bids_offers = bids_offers.with_columns(pl.coalesce([orig_col, col]).alias(orig_col))
-        bids_offers = bids_offers.drop(double_cols)
-
+        # Fill the NaN values with the retailer
+        bids_offers = bids_offers.with_columns([
+            pl.when(pl.col(c.TC_ID_AGENT_IN).is_not_null()).then(pl.col(c.TC_ID_AGENT_IN))
+            .otherwise(pl.lit('retailer')).alias(c.TC_ID_AGENT_IN),
+            pl.when(pl.col(c.TC_ID_AGENT_OUT).is_not_null()).then(pl.col(c.TC_ID_AGENT_OUT))
+            .otherwise(pl.lit('retailer')).alias(c.TC_ID_AGENT_OUT),
+        ])
 
         # Create bids and offers table where retailer is the only trading partner
         # Note: This currently works only for one retailer named 'retailer' in the future it needs to first obtain the
@@ -277,6 +277,10 @@ def __method_none(self, bids, offers, pricing_method):
         bids = bids.fill_null(strategy='backward')
         offers = offers.fill_null(strategy='backward')
 
+        # Filter rows that have the same agent id in the in and out column (cannot trade with themselves)
+        bids = bids.filter(pl.col(c.TC_ID_AGENT_IN) != pl.col(c.TC_ID_AGENT_OUT))
+        offers = offers.filter(pl.col(c.TC_ID_AGENT_IN) != pl.col(c.TC_ID_AGENT_OUT))
+
         # Clear bids and offers
         bids_cleared = bids.filter(pl.col(c.TC_PRICE_PU_IN) >= pl.col(c.TC_PRICE_PU_OUT))
         offers_cleared = offers.filter(pl.col(c.TC_PRICE_PU_IN) >= pl.col(c.TC_PRICE_PU_OUT))
@@ -288,7 +292,6 @@ def __method_none(self, bids, offers, pricing_method):
         # Create new dataframe with the cleared bids and offers
         trades_cleared = pl.concat([bids_cleared, offers_cleared], how='vertical')
 
-
         # Calculate the price and energy of the trades
         trades_cleared = trades_cleared.with_columns(
             (trades_cleared.select([c.TC_ENERGY_IN, c.TC_ENERGY_OUT]).min(axis=1).alias(c.TC_ENERGY)),
@@ -321,6 +324,7 @@ def __create_bids_offers(self, include_retailer=True):
             retailer = retailer.with_columns(
                 [
                     pl.col(c.TC_TIMESTAMP).alias(c.TC_TIMESTEP),
+                    pl.lit(self.bids_offers.select(pl.first(c.TC_TIMESTAMP))).alias(c.TC_TIMESTAMP),
                     pl.lit(None).alias(c.TC_ENERGY_TYPE),
                     # TODO: This can be removed once the energy type is added to the retailer table
                 ]
@@ -332,6 +336,8 @@ def __create_bids_offers(self, include_retailer=True):
 
             retailer = retailer.with_columns(
                 [
+                    pl.col(c.TC_TIMESTAMP).cast(pl.Datetime(time_unit='ns', time_zone='UTC'), strict=False),
+                    pl.col(c.TC_TIMESTEP).cast(pl.Datetime(time_unit='ns', time_zone='UTC'), strict=False),
                     pl.col(c.TC_REGION).cast(pl.Categorical, strict=False),
                     pl.col(c.TC_MARKET).cast(pl.Categorical, strict=False),
                     pl.col(c.TC_NAME).cast(pl.Categorical, strict=False),
@@ -691,6 +697,10 @@ def __apply_grid_levies(self, transactions):
         # Concat the grids and levies
         transactions = pl.concat([grid, levies], how='align')
 
+        # Set the timestamp column to the current timestamp
+        transactions = transactions.with_columns(pl.lit(self.tasks[c.TC_TIMESTAMP]).alias(c.TC_TIMESTAMP)
+                                                 .cast(pl.Datetime(time_unit='ns', time_zone='UTC')))
+
         return transactions
 
     def __method_pda(self, bids, offers, pricing_method):

hamlet/executor/utilities/controller/mpc/lincomps.py

@@ -941,6 +941,31 @@ def __init__(self, name, **kwargs):
         self.b2g = self.info['sizing']['b2g']
         self.g2b = self.info['sizing']['g2b']
 
+    def define_constraints(self, model: Model) -> Model:
+        model = super().define_constraints(model)
+
+        # Add constraint that the battery can only charge from the grid if the b2g flag is set to true
+        model = self._constraint_b2g(model)
+
+        return model
+
+    def _constraint_b2g(self, model: Model) -> Model:
+        """Adds the constraint that the battery can only charge from the grid if the b2g flag is set to true."""
+
+        # Define the variables
+        markets = self.info['markets']
+
+        # Define the constraint if b2g is disabled
+        if not self.b2g:
+            for market, energy in markets.items():
+                if energy == c.ET_ELECTRICITY:  # Only electricity markets are considered
+                    equation_disable_b2g = (model.variables[f'{self.name}_{self.comp_type}_mode'] -
+                                            model.variables[f'{market}_mode'] <= 0)
+                    model.add_constraints(equation_disable_b2g, name=f'{self.name}_b2g_{market}',
+                                          coords=[self.timesteps])
+
+        return model
+
 
 class Psh(SimpleStorage):
 

hamlet/executor/utilities/controller/mpc/mpc.py

@@ -95,7 +95,7 @@ def __init__(self, **kwargs):
             self.timesteps = pd.Index(range(len(self.timesteps)), name='timesteps')
             # self.timesteps = self.n_steps  # Use this line if the timesteps are not needed and the index is sufficient
             # Reduce the socs to the current timestamp
-            self.socs = self.socs.filter(self.socs[c.TC_TIMESTAMP] == self.timestamp)
+            self.socs = self.socs.filter(self.socs[c.TC_TIMESTAMP] == self.timestamp + self.dt)
 
             # Get the market types
             # TODO: Still needs to be done and then adjusted in the market objects (right now the names are simply
@@ -125,19 +125,32 @@ def __init__(self, **kwargs):
                 c.P_HEAT_STORAGE: lincomps.HeatStorage,
             }
 
-            # Create the plant objects
-            self.plant_objects = {}
-            self.create_plants()
-
             # Create the market objects
             self.market_objects = {}
             self.create_markets()
 
+            # Create the plant objects
+            self.plant_objects = {}
+            self.create_plants()
+
             # Define the model
             self.define_variables()
             self.define_constraints()
             self.define_objective()
 
+        def create_markets(self):
+            """"""
+
+            # Define variables from the market results and a balancing variable for each energy type
+            for market in self.markets:
+                # Create market object
+                self.market_objects[f'{market}'] = lincomps.Market(name=market,
+                                                                   forecasts=self.forecasts,
+                                                                   timesteps=self.timesteps,
+                                                                   delta=self.dt)
+
+            return self.market_objects
+
         def create_plants(self):
             for plant_name, plant_data in self.plants.items():
 
@@ -163,23 +176,11 @@ def create_plants(self):
                                                              **plant_data,
                                                              socs=socs,
                                                              delta=self.dt,
-                                                             timesteps=self.timesteps)
+                                                             timesteps=self.timesteps,
+                                                             markets=self.markets)
 
             return self.plant_objects
 
-        def create_markets(self):
-            """"""
-
-            # Define variables from the market results and a balancing variable for each energy type
-            for market in self.markets:
-                # Create market object
-                self.market_objects[f'{market}'] = lincomps.Market(name=market,
-                                                                   forecasts=self.forecasts,
-                                                                   timesteps=self.timesteps,
-                                                                   delta=self.dt)
-
-            return self.market_objects
-
         def define_variables(self):
             # Define variables for each plant
             for plant_name, plant in self.plant_objects.items():

hamlet/executor/utilities/controller/rtc/lincomps.py

@@ -33,13 +33,13 @@ def define_variables(self, model, **kwargs):
     def define_constraints(model):
         return model
 
-    def define_electricity_variable(self, model, comp_type, lower, upper, integer=True) -> Model:
+    def define_electricity_variable(self, model, comp_type, lower, upper, integer=False) -> Model:
         # Define the power variable
         model.add_variables(name=f'{self.name}_{comp_type}_{c.ET_ELECTRICITY}', lower=lower, upper=upper, integer=integer)
 
         return model
 
-    def define_heat_variable(self, model, comp_type, lower, upper, load_target=None, integer=True) -> Model:
+    def define_heat_variable(self, model, comp_type, lower, upper, load_target=None, integer=False) -> Model:
         # Define the power variable
         if load_target is None:
             name = f'{self.name}_{comp_type}_{c.ET_HEAT}'
@@ -49,7 +49,7 @@ def define_heat_variable(self, model, comp_type, lower, upper, load_target=None,
 
         return model
 
-    def define_cool_variable(self, model, comp_type, lower, upper, load_target=None, integer=True) -> Model:
+    def define_cool_variable(self, model, comp_type, lower, upper, load_target=None, integer=False) -> Model:
         # Define the power variable
         if load_target is None:
             name = f'{self.name}_{comp_type}_{c.ET_COOLING}'
@@ -59,7 +59,7 @@ def define_cool_variable(self, model, comp_type, lower, upper, load_target=None,
 
         return model
 
-    def define_h2_variable(self, model, comp_type, lower, upper, integer=True) -> Model:
+    def define_h2_variable(self, model, comp_type, lower, upper, integer=False) -> Model:
         # Define the power variable
         model.add_variables(name=f'{self.name}_{comp_type}_{c.ET_H2}', lower=lower, upper=upper, integer=integer)
 
@@ -398,7 +398,8 @@ def __init__(self, name, **kwargs):
 
         # Kwargs variables
         self.dt = kwargs['delta'].total_seconds()  # time delta in seconds
-        self.soc = kwargs['socs'][f'{self.name}'][0] - self.energy  # state of charge at timestep (energy)
+        self.soc = max(0, kwargs['socs'][f'{self.name}'][0] - self.energy)  # state of charge at timestep (energy);
+        # must be greater than 0, overconsumption is assumed to be compensated elsewhere
 
         # Calculate the energy needed to charge to full
         self.energy_to_full = self.capacity - self.soc  # energy needed to charge to full

hamlet/executor/utilities/controller/rtc/rtc.py

@@ -181,7 +181,7 @@ def create_plants(self):
 
                 # Retrieve the soc data for the plant (if applicable)
                 cols = [col for col in self.socs.columns if col.startswith(plant_name)]
-                socs = self.socs.select(cols)
+                socs = self.socs.filter(pl.col(c.TC_TIMESTAMP) == self.timestamp).select(cols)
 
                 # Get the plant class
                 plant_class = self.available_plants.get(plant_type)