Merge pull request #81 from staadecker/wind_to_solar_ratio

Wind to solar ratio

Author: staadecker

.gitignore

@@ -15,6 +15,7 @@
 *.vcf
 *.xml
 *.pickle
+examples/**/temp/**
 examples/**/outputs/*.csv
 examples/**/outputs/*.txt
 examples/**/graphs/*.png

database/2021-07-07_add_wind_solar_ratio_column.sql

@@ -0,0 +1,12 @@
+/*
+####################
+Add wind_to_solar_ratio column
+
+Date applied: 2021-07-07
+Description:
+Adds a column called wind_to_solar_ratio to the database which is used by
+switch_model.policies.wind_to_solar_ratio
+#################
+*/
+
+ALTER TABLE switch.scenario ADD COLUMN wind_to_solar_ratio real;

switch_model/policies/wind_to_solar_ratio.py

@@ -0,0 +1,95 @@
+"""
+This module gives us the possibility to enforce a wind to solar capacity ratio.
+
+It takes in wind_to_solar_ratio.csv that has the following format
+
+PERIOD,wind_to_solar_ratio,wind_to_solar_ratio_const_gt
+2020,.,.
+2030,0.5,0
+2040,1,0
+2050,1.5,1
+
+Here when wind_to_solar_ratio is specified (i.e. not '.') a constraint is activated that enforces that
+
+Online wind capacity >=/<= Online solar capacity * wind_to_solar_ratio
+
+for the entire period.
+
+When wind_to_solar_ratio_const_gt is true (1) the constraint is a >= constraint.
+When wind_to_solar_ratio_const_gt is False (0) the constraint is a <= constraint.
+"""
+import os
+
+import pandas as pd
+from pyomo.environ import *
+
+from switch_model.reporting import write_table
+
+_WIND_ENERGY_TYPE = "Wind"
+_SOLAR_ENERGY_TYPE = "Solar"
+
+
+def define_components(mod):
+    mod.WindCapacity = Expression(
+        mod.PERIODS,
+        rule=lambda m, p: sum(
+            m.GenCapacity[g, p] for g in m.VARIABLE_GENS if m.gen_energy_source[g] == _WIND_ENERGY_TYPE
+        )
+    )
+
+    mod.SolarCapacity = Expression(
+        mod.PERIODS,
+        rule=lambda m, p: sum(
+            m.GenCapacity[g, p] for g in m.VARIABLE_GENS if m.gen_energy_source[g] == _SOLAR_ENERGY_TYPE
+        )
+    )
+
+    mod.wind_to_solar_ratio = Param(
+        mod.PERIODS,
+        default=0,  # 0 means the constraint is inactive
+        within=NonNegativeReals
+    )
+
+    mod.wind_to_solar_ratio_const_gt = Param(
+        mod.PERIODS,
+        default=True,
+        within=Boolean
+    )
+
+    # We use a scaling factor to improve the numerical properties
+    # of the model.
+    # Learn more by reading the documentation on Numerical Issues.
+    # 1e-3 was picked since this value is normally on the order of GW instead of MW
+    scaling_factor = 1e-3
+
+    def wind_to_solar_ratio_const_rule(m, p):
+        if m.wind_to_solar_ratio[p] == 0:  # 0 means Constraint is inactive
+            return Constraint.Skip
+
+        lhs = m.WindCapacity[p] * scaling_factor
+        rhs = m.SolarCapacity[p] * m.wind_to_solar_ratio[p] * scaling_factor
+        if m.wind_to_solar_ratio_const_gt[p]:
+            return lhs >= rhs
+        else:
+            return lhs <= rhs
+
+    mod.wind_to_solar_ratio_const = Constraint(mod.PERIODS, rule=wind_to_solar_ratio_const_rule)
+
+
+def load_inputs(mod, switch_data, inputs_dir):
+    switch_data.load_aug(
+        filename=os.path.join(inputs_dir, 'wind_to_solar_ratio.csv'),
+        auto_select=True,
+        param=(mod.wind_to_solar_ratio, mod.wind_to_solar_ratio_const_gt),
+        optional=True  # We want to allow including this module even if the file isn't there
+    )
+
+
+def post_solve(m, outdir):
+    df = pd.DataFrame({
+                          "WindCapacity (GW)": value(m.WindCapacity[p]) / 1000,
+                          "SolarCapacity (GW)": value(m.SolarCapacity[p]) / 1000,
+                          "ComputedRatio": value(m.WindCapacity[p] / m.SolarCapacity[p]) if value(m.SolarCapacity[p]) != 0 else ".",
+                          "ExpectedRatio": value(m.wind_to_solar_ratio[p]) if m.wind_to_solar_ratio[p] != 0 else "."
+                      } for p in m.PERIODS)
+    write_table(m, output_file=os.path.join(outdir, "wind_to_solar_ratio.csv"), df=df, index=False)

switch_model/tools/templates/config.yaml

@@ -40,6 +40,7 @@ get_inputs:
   #  enable_planning_reserves:
   #  generation_plant_technologies_scenario_id:
   #  variable_o_m_cost_scenario_id:
+  #  wind_to_solar_ratio:
 # add_storage was used by Martin when studying LDES
 # you likely don't need to use these parameters
 # they won't impact your runs

switch_model/utilities/__init__.py

@@ -582,14 +582,21 @@ def load_aug(switch_data, optional=False, auto_select=False,
         switch_data.load(**kwds)
         return
 
+    # Use our custom DataManager to allow 'inf' in csvs.
+    if extension == ".csv":
+        kwds['using'] = "switch_csv"
+
     # copy the optional_params to avoid side-effects when the list is altered below
     optional_params=list(optional_params)
     # Parse header and first row
     with open(path) as infile:
         headers_line = infile.readline()
         second_line = infile.readline()
-    file_is_empty = (headers_line == '')
-    file_has_no_data_rows = (second_line == '')
+
+    # Skip if the file is empty.
+    if optional and headers_line == '':
+        return
+
     suffix = path.split('.')[-1]
     if suffix in {'tab', 'tsv'}:
         separator = '\t'
@@ -599,9 +606,7 @@ def load_aug(switch_data, optional=False, auto_select=False,
         raise InputError(f'Unrecognized file type for input file {path}')
     # TODO: parse this more formally, e.g. using csv module
     headers = headers_line.strip().split(separator)
-    # Skip if the file is empty.
-    if optional and file_is_empty:
-        return
+
     # Try to get a list of parameters. If param was given as a
     # singleton or a tuple, make it into a list that can be edited.
     params = []
@@ -681,14 +686,11 @@ def load_aug(switch_data, optional=False, auto_select=False,
             del kwds['select'][i]
             del kwds['param'][p_i]
 
-    if optional and file_has_no_data_rows:
-        # Skip the file.  Note that we are only doing this after having
+    if optional and second_line == '':
+        # Skip the file if it has no data.  Note that we are only doing this after having
         # validated the file's column headings.
         return
 
-    # Use our custom DataManager to allow 'inf' in csvs.
-    if kwds["filename"][-4:] == ".csv":
-        kwds['using'] = "switch_csv"
     # All done with cleaning optional bits. Pass the updated arguments
     # into the DataPortal.load() function.
     switch_data.load(**kwds)

switch_model/wecc/get_inputs/get_inputs.py

@@ -69,6 +69,8 @@ def write_csv(data: Iterable[List], fname, headers: List[str], log=True):
     "switch_model.policies.rps_unbundled",
     "switch_model.policies.min_per_tech",  # Always include since it provides useful outputs even when unused
     # "switch_model.reporting.basic_exports_wecc",
+    # Always include since by default it does nothing except output useful data
+    "switch_model.policies.wind_to_solar_ratio",
 ]
 
 
@@ -103,7 +105,8 @@ def query_db(full_config, skip_cf):
         "ca_policies_scenario_id",
         "enable_planning_reserves",
         "generation_plant_technologies_scenario_id",
-        "variable_o_m_cost_scenario_id"
+        "variable_o_m_cost_scenario_id",
+        "wind_to_solar_ratio"
     ]
 
     db_cursor.execute(
@@ -140,6 +143,7 @@ def query_db(full_config, skip_cf):
     enable_planning_reserves = s_details[18]
     generation_plant_technologies_scenario_id =s_details[19]
     variable_o_m_cost_scenario_id = s_details[20]
+    wind_to_solar_ratio = s_details[21]
 
     print(f"Scenario: {scenario_id}: {name}.")
 
@@ -856,6 +860,7 @@ def query_db(full_config, skip_cf):
         )
 
     ca_policies(db_cursor, ca_policies_scenario_id, study_timeframe_id)
+    write_wind_to_solar_ratio(wind_to_solar_ratio)
     if enable_planning_reserves:
         planning_reserves(db_cursor, time_sample_id, hydro_simple_scenario_id)
     create_modules_txt()
@@ -868,6 +873,32 @@ def query_db(full_config, skip_cf):
     shutil.copy(os.path.join(graph_config, "graph_tech_types.csv"), "graph_tech_types.csv")
 
 
+def write_wind_to_solar_ratio(wind_to_solar_ratio):
+    # TODO ideally we'd have a table where we can specify the wind_to_solar_ratios per period.
+    #   At the moment only the wind_to_solar_ratio is specified and which doesn't allow different values per period
+    if wind_to_solar_ratio is None:
+        return
+
+    print("wind_to_solar_ratio.csv...")
+    df = pd.read_csv("periods.csv")[["INVESTMENT_PERIOD"]]
+    df["wind_to_solar_ratio"] = wind_to_solar_ratio
+
+    # wind_to_solar_ratio.csv requires a column called wind_to_solar_ratio_const_gt that is True (1) or False (0)
+    # This column specifies whether the constraint is a greater than constraint or a less than constraint.
+    # In our case we want it to be a greater than constraint if we're trying to force wind-to-solar ratio above its default
+    # and we want it to be a less than constraint if we're trying to force the ratio below its default.
+    # Here the default is the ratio if we didn't have the constraint.
+    cutoff_ratio = 0.28
+    warnings.warn(
+        "To determine the sign of the wind-to-solar ratio constraint we have "
+        f"assumed that without the constraint, the wind-to-solar ratio is {cutoff_ratio}. "
+        f"This value was accurate for Martin's LDES runs however it may not be accurate for you. "
+        f"You should update this value in get_inputs or manually specify whether you want a greater than "
+        f"or a less than constraint."
+    )
+    df["wind_to_solar_ratio_const_gt"] = 1 if wind_to_solar_ratio > cutoff_ratio else 0
+
+    df.to_csv("wind_to_solar_ratio.csv", index=False)
 
 def ca_policies(db_cursor, ca_policies_scenario_id, study_timeframe_id):
     if ca_policies_scenario_id is None: