Feature/com building (#9)

* change renewable directory * incorporate com building load * add com load electrification * format code
boyuan276 · Jun 28, 2024 · fabb2d2 · fabb2d2
1 parent 07c2d09
commit fabb2d2
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diff --git a/examples/ex_mopf_w_esr_vre.ipynb b/examples/ex_mopf_w_esr_vre.ipynb
@@ -78,14 +78,8 @@
     "results_dir = os.path.join(os.path.dirname(data_dir), 'results')\n",
     "print('Results directory: {}'.format(results_dir))\n",
     "\n",
-    "solar_data_dir = os.path.join(data_dir, 'solar')\n",
-    "print('Solar data directory: {}'.format(solar_data_dir))\n",
-    "\n",
-    "onshore_wind_data_dir = os.path.join(data_dir, 'onshore_wind')\n",
-    "print('Onshore wind data directory: {}'.format(onshore_wind_data_dir))\n",
-    "\n",
-    "offshore_wind_data_dir = os.path.join(data_dir, 'offshore_wind')\n",
-    "print('Offshore wind data directory: {}'.format(offshore_wind_data_dir))"
+    "renewable_data_dir = os.path.join(data_dir, 'renewable')\n",
+    "print('Renewable data directory: {}'.format(renewable_data_dir))\n"
    ]
   },
   {
@@ -199,6 +193,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "solar_data_dir = os.path.join(data_dir, 'solar')\n",
+    "print('Solar data directory: {}'.format(solar_data_dir))\n",
+    "\n",
+    "onshore_wind_data_dir = os.path.join(data_dir, 'onshore_wind')\n",
+    "print('Onshore wind data directory: {}'.format(onshore_wind_data_dir))\n",
+    "\n",
+    "offshore_wind_data_dir = os.path.join(data_dir, 'offshore_wind')\n",
+    "print('Offshore wind data directory: {}'.format(offshore_wind_data_dir))\n",
+    "\n",
     "vre_prop, genmax_profile_vre = read_vre_data(\n",
     "    solar_data_dir, onshore_wind_data_dir, offshore_wind_data_dir)"
    ]

diff --git a/examples/ex_mopf_w_esr_vre_elec.ipynb b/examples/ex_mopf_w_esr_vre_elec.ipynb
diff --git a/nygrid/allocate.py b/nygrid/allocate.py
@@ -64,7 +64,7 @@ def nearest_neighbor_lat_lon(left_gdf: gpd.GeoDataFrame,
                              leaf_size: int = 20) -> Union[Dict, Tuple]:
     """
     For each point in left_gdf, find closest point in right GeoDataFrame and return them.
-    
+
     NOTICE: Assumes that the input Points are in WGS84 projection (lat/lon).
 
     Parameters
@@ -110,7 +110,7 @@ def nearest_neighbor_lat_lon(left_gdf: gpd.GeoDataFrame,
     # Ensure that the index corresponds the one in left_gdf
     closest_points = closest_points.reset_index(drop=True)
 
-    # Add distance if requested 
+    # Add distance if requested
     if return_dist:
         # Convert to meters from radians
         earth_radius = 6371000  # meters
@@ -125,7 +125,7 @@ def nearest_neighbor_meters(left_gdf: gpd.GeoDataFrame,
                             leaf_size: int = 20) -> Union[Dict, Tuple]:
     """
     For each point in left_gdf, find the closest point in right GeoDataFrame and return them.
-    
+
     NOTICE: Assumes that the input Points are in WGS84 projection (meters).
 
     Parameters
@@ -151,24 +151,27 @@ def nearest_neighbor_meters(left_gdf: gpd.GeoDataFrame,
     # Ensure that index in right gdf is formed of sequential numbers
     right = right_gdf.copy().reset_index(drop=True)
 
-    # Parse coordinates from points and insert them into a numpy array as METERS   
-    left_meters = np.array(left_gdf[left_geom_col].apply(lambda geom: (geom.x, geom.y)).to_list())
-    right_meters = np.array(right[right_geom_col].apply(lambda geom: (geom.x, geom.y)).to_list())
+    # Parse coordinates from points and insert them into a numpy array as METERS
+    left_meters = np.array(left_gdf[left_geom_col].apply(
+        lambda geom: (geom.x, geom.y)).to_list())
+    right_meters = np.array(right[right_geom_col].apply(
+        lambda geom: (geom.x, geom.y)).to_list())
 
     # Find the nearest points
     # -----------------------
     # closest ==> index in right_gdf that corresponds to the closest point
     # dist ==> distance between the nearest neighbors (in meters)
 
-    closest, dist = get_nearest(src_points=left_meters, candidates=right_meters, leaf_size=leaf_size)
+    closest, dist = get_nearest(
+        src_points=left_meters, candidates=right_meters, leaf_size=leaf_size)
 
     # Return points from right GeoDataFrame that are closest to points in left GeoDataFrame
     closest_points = right.loc[closest]
 
     # Ensure that the index corresponds the one in left_gdf
     closest_points = closest_points.reset_index(drop=True)
 
-    # Add distance if requested 
+    # Add distance if requested
     if return_dist:
         # Convert to meters from radians
         # earth_radius = 6371000  # meters

diff --git a/nygrid/nygrid.py b/nygrid/nygrid.py
@@ -694,7 +694,8 @@ def set_gen_mw_sch(self, gen_mw_sch: pd.DataFrame) -> None:
         """
 
         # Slice the generation profile to the simulation period
-        gen_mw_sch = gen_mw_sch[self.start_datetime:self.end_datetime].to_numpy()
+        gen_mw_sch = gen_mw_sch[self.start_datetime:
+                                self.end_datetime].to_numpy()
 
         # Generator schedule in p.u.
         if gen_mw_sch is not None and gen_mw_sch.size > 0:
@@ -724,7 +725,8 @@ def set_gen_max_sch(self, gen_max_sch: pd.DataFrame) -> None:
         """
 
         # Slice the generator profile to the simulation period
-        gen_max_sch = gen_max_sch[self.start_datetime:self.end_datetime].to_numpy()
+        gen_max_sch = gen_max_sch[self.start_datetime:
+                                  self.end_datetime].to_numpy()
 
         # Generator upper operating limit in p.u.
         if gen_max_sch is not None and gen_max_sch.size > 0:
@@ -749,7 +751,8 @@ def set_vre_max_sch(self, vre_max_sch: pd.DataFrame) -> None:
         """
 
         # Slice the generator profile to the simulation period
-        vre_max_sch = vre_max_sch[self.start_datetime:self.end_datetime].to_numpy()
+        vre_max_sch = vre_max_sch[self.start_datetime:
+                                  self.end_datetime].to_numpy()
 
         # Generator upper operating limit in p.u.
         if vre_max_sch is not None and vre_max_sch.size > 0:
@@ -774,7 +777,8 @@ def set_gen_min_sch(self, gen_min_sch: pd.DataFrame) -> None:
         """
 
         # Slice the generator profile to the simulation period
-        gen_min_sch = gen_min_sch[self.start_datetime:self.end_datetime].to_numpy()
+        gen_min_sch = gen_min_sch[self.start_datetime:
+                                  self.end_datetime].to_numpy()
 
         # Generator lower operating limit in p.u.
         if gen_min_sch is not None and gen_min_sch.size > 0:
@@ -804,7 +808,8 @@ def set_gen_ramp_sch(self, gen_ramp_sch: pd.DataFrame,
         # Convert 30min ramp rate to hourly ramp rate
         if interval == '30min':
             gen_ramp_sch = gen_ramp_sch * 2
-            gen_ramp_sch = gen_ramp_sch[self.start_datetime:self.end_datetime].to_numpy()
+            gen_ramp_sch = gen_ramp_sch[self.start_datetime:
+                                        self.end_datetime].to_numpy()
 
             # Convert default value 0 (Unlimited) to 1e6
             gen_ramp_sch[gen_ramp_sch == 0] = 1e6
@@ -841,8 +846,10 @@ def set_gen_cost_sch(self, gen_cost0_sch: pd.DataFrame,
         """
 
         # Slice the generator profile to the simulation period
-        gen_cost0_sch = gen_cost0_sch[self.start_datetime:self.end_datetime].to_numpy()
-        gen_cost1_sch = gen_cost1_sch[self.start_datetime:self.end_datetime].to_numpy()
+        gen_cost0_sch = gen_cost0_sch[self.start_datetime:
+                                      self.end_datetime].to_numpy()
+        gen_cost1_sch = gen_cost1_sch[self.start_datetime:
+                                      self.end_datetime].to_numpy()
 
         # Linear cost intercept coefficients in p.u.
         if gen_cost0_sch is not None and gen_cost0_sch.size > 0:

diff --git a/nygrid/optimizer.py b/nygrid/optimizer.py
@@ -179,7 +179,8 @@ def gen_cost_ene_expr(model):
         # ESR energy cost
         def esr_cost_ene_expr(model):
             return sum(self.nygrid.esrcost_crg[t, esr] * model.esrPCrg[t, esr]
-                       + self.nygrid.esrcost_dis[t, esr] * model.esrPDis[t, esr]
+                       + self.nygrid.esrcost_dis[t,
+                                                 esr] * model.esrPDis[t, esr]
                        for esr in self.esrs for t in self.times)
 
         def over_gen_penalty_expr(model):
@@ -439,7 +440,8 @@ def esr_soc_update_rule(model, t, esr):
                 if self.nygrid.esr_init is not None:
                     return model.esrSOC[t, esr] == self.nygrid.esr_init[esr] \
                         + model.esrPCrg[t, esr] * self.nygrid.esr_crg_eff[t, esr] \
-                        - model.esrPDis[t, esr] / self.nygrid.esr_dis_eff[t, esr]
+                        - model.esrPDis[t, esr] / \
+                        self.nygrid.esr_dis_eff[t, esr]
                 else:
                     return pyo.Constraint.Skip
             else:

diff --git a/nygrid/plots.py b/nygrid/plots.py
@@ -4,9 +4,9 @@
 from typing import List, Tuple, Optional
 
 
-def plot_gen(thermal_pg: pd.Series, 
-             gen_hist: pd.Series, 
-             gen_max: pd.Series, 
+def plot_gen(thermal_pg: pd.Series,
+             gen_hist: pd.Series,
+             gen_max: pd.Series,
              gen_min: pd.Series,
              ax: plt.Axes,
              title: Optional[str] = None) -> plt.Axes:
@@ -23,4 +23,4 @@ def plot_gen(thermal_pg: pd.Series,
     if title:
         ax.set_title(title)
 
-    return ax
+    return ax
diff --git a/nygrid/postprocessing.py b/nygrid/postprocessing.py
@@ -3,4 +3,4 @@
 
 Created: 2023-12-26, by Bo Yuan (Cornell University)
 Last modified: 2023-12-26, by Bo Yuan (Cornell University)
-"""
+"""
diff --git a/nygrid/preprocessing.py b/nygrid/preprocessing.py
@@ -9,19 +9,35 @@
 import pandas as pd
 from typing import List, Tuple, Optional
 
-def agg_demand_county2bus(demand_inc_county: pd.DataFrame, 
-                          county2bus: pd.DataFrame) -> pd.DataFrame:
+
+def agg_demand_county2bus(demand_inc_county: pd.DataFrame,
+                          county2bus: pd.DataFrame
+                          ) -> pd.DataFrame:
     """
-    County-level consumption to bus-level consumption
+    County-level consumption to bus-level consumption.
+
+    Parameters
+    ----------
+    demand_inc_county : pd.DataFrame
+        County-level consumption
+    county2bus : pd.DataFrame
+        County to bus mapping
+
+    Returns
+    -------
+    demand_inc_bus : pd.DataFrame
+        Bus-level consumption
     """
+
     demand_inc_county_erie = demand_inc_county['Erie']
     demand_inc_county_westchester = demand_inc_county['Westchester']
-    demand_inc_county_rest = demand_inc_county.drop(columns=['Erie', 'Westchester'])
+    demand_inc_county_rest = demand_inc_county.drop(
+        columns=['Erie', 'Westchester'])
 
     county2bus_erie = county2bus[county2bus['NAME'] == 'Erie']
     county2bus_westchester = county2bus[county2bus['NAME'] == 'Westchester']
-    county2bus_rest = county2bus[(county2bus['NAME'] != 'Erie') & 
-                                (county2bus['NAME'] != 'Westchester')]
+    county2bus_rest = county2bus[(county2bus['NAME'] != 'Erie') &
+                                 (county2bus['NAME'] != 'Westchester')]
 
     demand_inc_bus_erie = demand_inc_county_erie.to_frame()
     demand_inc_bus_erie['55'] = demand_inc_bus_erie['Erie'] * 0.5
@@ -33,35 +49,41 @@ def agg_demand_county2bus(demand_inc_county: pd.DataFrame,
     demand_inc_bus_westchester = demand_inc_county_westchester.to_frame()
     demand_inc_bus_westchester['74'] = demand_inc_bus_westchester['Westchester'] * 0.5
     demand_inc_bus_westchester['78'] = demand_inc_bus_westchester['Westchester'] * 0.5
-    demand_inc_bus_westchester = demand_inc_bus_westchester.drop(columns=['Westchester'])
-    demand_inc_bus_westchester.columns = demand_inc_bus_westchester.columns.astype(int)
+    demand_inc_bus_westchester = demand_inc_bus_westchester.drop(columns=[
+                                                                 'Westchester'])
+    demand_inc_bus_westchester.columns = demand_inc_bus_westchester.columns.astype(
+        int)
 
-    county_bus_alloc_rest = county2bus_rest.set_index('NAME').to_dict()['busIdx']
-    demand_inc_bus_rest = demand_inc_county_rest.T.groupby(county_bus_alloc_rest).sum().T
+    county_bus_alloc_rest = county2bus_rest.set_index('NAME').to_dict()[
+        'busIdx']
+    demand_inc_bus_rest = demand_inc_county_rest.T.groupby(
+        county_bus_alloc_rest).sum().T
 
     demand_inc_bus = demand_inc_bus_rest.add(demand_inc_bus_erie, fill_value=0)
-    demand_inc_bus = demand_inc_bus.add(demand_inc_bus_westchester, fill_value=0)
+    demand_inc_bus = demand_inc_bus.add(
+        demand_inc_bus_westchester, fill_value=0)
 
     return demand_inc_bus
 
 
-def get_res_load_change_county(county_id: str, 
-                               upgrade_id: int, 
-                               res_bldg_type_list: List[str], 
-                               resstock_bldg_proc_dir: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+def get_building_load_change_county(county_id: str,
+                                    upgrade_id: int,
+                                    bldg_type_list: List[str],
+                                    bldg_proc_dir: str
+                                    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """
 
-    Read building timeseries data aggregated by county and building type
+    Read building timeseries data aggregated by county and building type.
 
     Parameters
     ----------
     county_id : str
         County ID
     upgrade_id : int
         Upgrade ID
-    res_bldg_type_list : list
+    bldg_type_list : list
         List of building types
-    resstock_bldg_proc_dir : str
+    bldg_proc_dir : str
         Directory for processed building data
 
     Returns
@@ -77,19 +99,22 @@ def get_res_load_change_county(county_id: str,
     # Read building timeseries data aggregated by county and building type
     first_df = True
 
-    for bldg_type in res_bldg_type_list:
+    for bldg_type in bldg_type_list:
 
-        filename = os.path.join(resstock_bldg_proc_dir, 
+        filename = os.path.join(bldg_proc_dir,
                                 f"{county_id.lower()}_{bldg_type.replace(' ', '_').lower()}.parquet")
-        
+
         if os.path.isfile(filename):
             # Future
-            df_county_bldg_type_future = pd.read_parquet(filename, engine='pyarrow')
-            col_total_cons = [col for col in df_county_bldg_type_future.columns if col.endswith('.energy_consumption') and 'total' in col]
+            df_county_bldg_type_future = pd.read_parquet(
+                filename, engine='pyarrow')
+            col_total_cons = [col for col in df_county_bldg_type_future.columns if col.endswith(
+                '.energy_consumption') and 'total' in col]
             df_county_bldg_type_future = df_county_bldg_type_future[col_total_cons]
 
             # Baseline
-            df_county_bldg_type_base = pd.read_parquet(filename.replace(f'upgrade={upgrade_id}', 'upgrade=0'), engine='pyarrow')
+            df_county_bldg_type_base = pd.read_parquet(filename.replace(
+                f'upgrade={upgrade_id}', 'upgrade=0'), engine='pyarrow')
             df_county_bldg_type_base = df_county_bldg_type_base[col_total_cons]
 
             # Savings = Baseline - Future
@@ -105,15 +130,16 @@ def get_res_load_change_county(county_id: str,
                 df_county_base = df_county_base + df_county_bldg_type_base
                 df_county_future = df_county_future + df_county_bldg_type_future
                 df_county_saving = df_county_saving + df_county_bldg_type_saving
-            
+
         else:
-            print(f'Residential load data is not available for county {county_id} {bldg_type}. Skipping...')
+            print(
+                f'Building load data is not available for county {county_id} {bldg_type}. Skipping...')
             continue
 
     # Rename columns
     col_rename = {col: col.split('.')[1] for col in df_county_base.columns}
     df_county_base = df_county_base.rename(columns=col_rename)
     df_county_future = df_county_future.rename(columns=col_rename)
-    df_county_saving = df_county_saving.rename(columns=col_rename) 
+    df_county_saving = df_county_saving.rename(columns=col_rename)
 
-    return df_county_base, df_county_future, df_county_saving
+    return df_county_base, df_county_future, df_county_saving