Merge pull request #456 from NREL/pp/minor_updates

Minor updates

reV/SAM/SAM.py

@@ -950,4 +950,7 @@ def _add_sys_capacity(sam_inputs):
         if cap is not None:
             cap = max(cap)
 
+    if cap is None:
+        cap = sam_inputs.get("nameplate")
+
     sam_inputs["system_capacity"] = cap

reV/SAM/generation.py

@@ -1786,6 +1786,10 @@ def _set_nameplate_to_match_resource_potential(self, resource):
                 "{}".format(self.sam_sys_inputs["nameplate"])
             )
             logger.info(msg)
+            # required for downstream LCOE calcs
+            self.sam_sys_inputs["system_capacity"] = (
+                self.sam_sys_inputs["nameplate"]
+            )
             return
 
         val = set(resource["potential_MW"].unique())
@@ -1801,6 +1805,8 @@ def _set_nameplate_to_match_resource_potential(self, resource):
 
         logger.debug("Setting the nameplate to {}".format(val))
         self.sam_sys_inputs["nameplate"] = val
+        # required for downstream LCOE calcs
+        self.sam_sys_inputs["system_capacity"] = val
 
     def _set_resource_potential_to_match_gross_output(self):
         """Set the resource potential input to match the gross generation.
@@ -1861,7 +1867,9 @@ def _set_costs(self):
             logger.debug(
                 "Setting the capital_cost to ${:,.2f}".format(capital_cost)
             )
-            self.sam_sys_inputs["capital_cost"] = capital_cost
+            reg_mult = self.sam_sys_inputs.get("capital_cost_multiplier", 1)
+            self.sam_sys_inputs["base_capital_cost"] = capital_cost
+            self.sam_sys_inputs["capital_cost"] = capital_cost * reg_mult
 
         dc_per_well = self.sam_sys_inputs.pop("drill_cost_per_well", None)
         num_wells = self.sam_sys_inputs.pop(
@@ -1884,19 +1892,35 @@ def _set_costs(self):
                         drill_cost, num_wells, dc_per_well
                     )
                 )
-                self.sam_sys_inputs["capital_cost"] = capital_cost + drill_cost
+                reg_mult = self.sam_sys_inputs.get(
+                    "capital_cost_multiplier", 1
+                )
+                base_cc = capital_cost / reg_mult
+                new_base_cc = base_cc + drill_cost
+                self.sam_sys_inputs["base_capital_cost"] = new_base_cc
+                self.sam_sys_inputs["capital_cost"] = new_base_cc * reg_mult
 
         foc_per_kw = self.sam_sys_inputs.pop(
             "fixed_operating_cost_per_kw", None
         )
         if foc_per_kw is not None:
-            fixed_operating_cost = foc_per_kw * plant_size_kw
+            foc = foc_per_kw * plant_size_kw
             logger.debug(
-                "Setting the fixed_operating_cost to ${:,.2f}".format(
-                    capital_cost
-                )
+                "Setting the fixed_operating_cost to ${:,.2f}".format(foc)
+            )
+            self.sam_sys_inputs["base_fixed_operating_cost"] = foc
+            self.sam_sys_inputs["fixed_operating_cost"] = foc
+
+        voc_per_kw = self.sam_sys_inputs.pop(
+            "variable_operating_cost_per_kw", None
+        )
+        if voc_per_kw is not None:
+            voc = voc_per_kw * plant_size_kw
+            logger.debug(
+                "Setting the variable_operating_cost to ${:,.2f}".format(voc)
             )
-            self.sam_sys_inputs["fixed_operating_cost"] = fixed_operating_cost
+            self.sam_sys_inputs["base_variable_operating_cost"] = voc
+            self.sam_sys_inputs["variable_operating_cost"] = voc
 
     def _create_pysam_wfile(self, resource, meta):
         """Create PySAM weather input file.

reV/bespoke/bespoke.py

@@ -1263,10 +1263,10 @@ def run_wind_plant_ts(self):
         self._outputs.update(means)
 
         self._meta[SupplyCurveField.MEAN_RES] = self.res_df["windspeed"].mean()
-        self._meta[SupplyCurveField.MEAN_CF_DC] = None
-        self._meta[SupplyCurveField.MEAN_CF_AC] = None
-        self._meta[SupplyCurveField.MEAN_LCOE] = None
-        self._meta[SupplyCurveField.SC_POINT_ANNUAL_ENERGY_MW] = None
+        self._meta[SupplyCurveField.MEAN_CF_DC] = np.nan
+        self._meta[SupplyCurveField.MEAN_CF_AC] = np.nan
+        self._meta[SupplyCurveField.MEAN_LCOE] = np.nan
+        self._meta[SupplyCurveField.SC_POINT_ANNUAL_ENERGY_MW] = np.nan
         # copy dataset outputs to meta data for supply curve table summary
         if "cf_mean-means" in self.outputs:
             self._meta.loc[:, SupplyCurveField.MEAN_CF_AC] = self.outputs[
@@ -1373,7 +1373,7 @@ def run_plant_optimization(self):
         # convert SAM system capacity in kW to reV supply curve cap in MW
         capacity_ac_mw = system_capacity_kw / 1e3
         self._meta[SupplyCurveField.CAPACITY_AC_MW] = capacity_ac_mw
-        self._meta[SupplyCurveField.CAPACITY_DC_MW] = None
+        self._meta[SupplyCurveField.CAPACITY_DC_MW] = np.nan
 
         # add required ReEDS multipliers to meta
         baseline_cost = self.plant_optimizer.capital_cost_per_kw(
@@ -1618,7 +1618,7 @@ def __init__(self, excl_fpath, res_fpath, tm_dset, objective_function,
             multiple sites can be specified to evaluate ``reV`` at
             multiple specific locations. A string pointing to a project
             points CSV file may also be specified. Typically, the CSV
-            contains two columns:
+            contains the following columns:
 
                 - ``gid``: Integer specifying the supply curve GID of
                   each site.
@@ -1635,7 +1635,7 @@ def __init__(self, excl_fpath, res_fpath, tm_dset, objective_function,
             site-specific capital cost value for each location). Columns
             that do not correspond to a config key may also be included,
             but they will be ignored. The CSV file input can also have
-            these extra columns:
+            these extra, optional columns:
 
                 - ``capital_cost_multiplier``
                 - ``fixed_operating_cost_multiplier``

reV/econ/econ.py

@@ -76,25 +76,36 @@ def __init__(self, project_points, sam_files, cf_file, site_data=None,
             (or slice) representing the GIDs of multiple sites can be
             specified to evaluate reV at multiple specific locations.
             A string pointing to a project points CSV file may also be
-            specified. Typically, the CSV contains two columns:
+            specified. Typically, the CSV contains the following
+            columns:
 
-                - ``gid``: Integer specifying the GID of each site.
+                - ``gid``: Integer specifying the generation GID of each
+                  site.
                 - ``config``: Key in the `sam_files` input dictionary
                   (see below) corresponding to the SAM configuration to
                   use for each particular site. This value can also be
                   ``None`` (or left out completely) if you specify only
                   a single SAM configuration file as the `sam_files`
                   input.
-
-            The CSV file may also contain site-specific inputs by
+                - ``capital_cost_multiplier``: This is an *optional*
+                  multiplier input that, if included, will be used to
+                  regionally scale the ``capital_cost`` input in the SAM
+                  config. If you include this column in your CSV, you
+                  *do not* need to specify ``capital_cost``, unless you
+                  would like that value to vary regionally and
+                  independently of the multiplier (i.e. the multiplier
+                  will still be applied on top of the ``capital_cost``
+                  input).
+
+            The CSV file may also contain other site-specific inputs by
             including a column named after a config keyword (e.g. a
-            column called ``capital_cost`` may be included to specify a
-            site-specific capital cost value for each location). Columns
-            that do not correspond to a config key may also be included,
-            but they will be ignored. A DataFrame following the same
-            guidelines as the CSV input (or a dictionary that can be
-            used to initialize such a DataFrame) may be used for this
-            input as well.
+            column called ``wind_turbine_rotor_diameter`` may be
+            included to specify a site-specific turbine diameter for
+            each location). Columns that do not correspond to a config
+            key may also be included, but they will be ignored. A
+            DataFrame following the same guidelines as the CSV input
+            (or a dictionary that can be used to initialize such a
+            DataFrame) may be used for this input as well.
         sam_files : dict | str
             A dictionary mapping SAM input configuration ID(s) to SAM
             configuration(s). Keys are the SAM config ID(s) which

reV/generation/generation.py

@@ -171,7 +171,7 @@ def __init__(
             multiple sites can be specified to evaluate reV at multiple
             specific locations. A string pointing to a project points
             CSV file may also be specified. Typically, the CSV contains
-            two columns:
+            the following columns:
 
                 - ``gid``: Integer specifying the generation GID of each
                   site.
@@ -181,16 +181,25 @@ def __init__(
                   ``None`` (or left out completely) if you specify only
                   a single SAM configuration file as the `sam_files`
                   input.
-
-            The CSV file may also contain site-specific inputs by
+                - ``capital_cost_multiplier``: This is an *optional*
+                  multiplier input that, if included, will be used to
+                  regionally scale the ``capital_cost`` input in the SAM
+                  config. If you include this column in your CSV, you
+                  *do not* need to specify ``capital_cost``, unless you
+                  would like that value to vary regionally and
+                  independently of the multiplier (i.e. the multiplier
+                  will still be applied on top of the ``capital_cost``
+                  input).
+
+            The CSV file may also contain other site-specific inputs by
             including a column named after a config keyword (e.g. a
-            column called ``capital_cost`` may be included to specify a
-            site-specific capital cost value for each location). Columns
-            that do not correspond to a config key may also be included,
-            but they will be ignored. A DataFrame following the same
-            guidelines as the CSV input (or a dictionary that can be
-            used to initialize such a DataFrame) may be used for this
-            input as well.
+            column called ``wind_turbine_rotor_diameter`` may be
+            included to specify a site-specific turbine diameter for
+            each location). Columns that do not correspond to a config
+            key may also be included, but they will be ignored. A
+            DataFrame following the same guidelines as the CSV input
+            (or a dictionary that can be used to initialize such a
+            DataFrame) may be used for this input as well.
 
             .. Note:: By default, the generation GID of each site is
               assumed to match the resource GID to be evaluated for that

reV/supply_curve/points.py

@@ -2537,6 +2537,10 @@ def summarize(
             if cap_cost_scale is not None:
                 summary = point.economies_of_scale(cap_cost_scale, summary)
 
+        for arg, val in summary.items():
+            if val is None:
+                summary[arg] = np.nan
+
         return summary
 
 

reV/supply_curve/supply_curve.py

@@ -1116,8 +1116,7 @@ def add_sum_cols(table, sum_cols):
 
         return table
 
-    # pylint: disable=C901
-    def _full_sort(
+    def _full_sort(  # noqa: C901
         self,
         trans_table,
         trans_costs=None,
@@ -1289,7 +1288,7 @@ def _adjust_output_columns(self, columns, consider_friction):
 
         for col in _REQUIRED_OUTPUT_COLS:
             if col not in self._trans_table:
-                self._trans_table[col] = None
+                self._trans_table[col] = np.nan
             if col not in columns:
                 columns.append(col)
 

reV/version.py

@@ -2,4 +2,4 @@
 reV Version number
 """
 
-__version__ = "0.9.0"
+__version__ = "0.9.1"

tests/test_gen_geothermal.py

@@ -103,6 +103,14 @@ def test_gen_geothermal(depth, sample_resource_data):
         "lcoe_fcr": 12.52,
         "nameplate": 200_000,
         "resource_temp": 150,
+        "capital_cost": 172485199.53989035,
+        "fixed_operating_cost": 4885201.7298879623,
+        "variable_operating_cost": 0,
+        "fixed_charge_rate": 0.098000000000000004,
+        "base_capital_cost": 172485199.53989035,
+        "base_fixed_operating_cost": 4885201.7298879623,
+        "base_variable_operating_cost": 0,
+        "system_capacity": 200_000,
     }
     for dset in output_request:
         truth = truth_vals[dset]
@@ -156,6 +164,14 @@ def test_gen_geothermal_temp_too_low(sample_resource_data):
         "lcoe_fcr": 0,
         "nameplate": 0,
         "resource_temp": 60,
+        "capital_cost": 0,
+        "fixed_operating_cost": 0,
+        "variable_operating_cost": 0,
+        "fixed_charge_rate": 0,
+        "base_capital_cost": 0,
+        "base_fixed_operating_cost": 0,
+        "base_variable_operating_cost": 0,
+        "system_capacity": 0,
     }
     for dset in output_request:
         truth = truth_vals[dset]
@@ -207,7 +223,13 @@ def test_per_kw_cost_inputs(sample_resource_data):
     truth_vals = {
         "capital_cost": 383_086_656,
         "fixed_operating_cost": 25539104,
+        "variable_operating_cost": 0,
         "lcoe_fcr": 72.5092,
+        "fixed_charge_rate": 0.098000000000000004,
+        "base_capital_cost": 383_086_656,
+        "base_fixed_operating_cost": 25539104,
+        "base_variable_operating_cost": 0,
+        "system_capacity": 383_086_656 / 3_000,
     }
     for dset in output_request:
         truth = truth_vals[dset]
@@ -257,7 +279,12 @@ def test_drill_cost_inputs(sample_resource_data):
     truth_vals = {
         "capital_cost": 466_134_733,
         "fixed_operating_cost": 25539104,
+        "variable_operating_cost": 0,
         "lcoe_fcr": 81.8643,
+        "fixed_charge_rate": 0.098000000000000004,
+        "base_capital_cost": 466_134_733,
+        "base_fixed_operating_cost": 25539104,
+        "base_variable_operating_cost": 0,
     }
     for dset in output_request:
         truth = truth_vals[dset]
@@ -315,6 +342,14 @@ def test_gen_with_nameplate_input(sample_resource_data):
         "lcoe_fcr": 62.613,
         "nameplate": 40_000,
         "resource_temp": 150,
+        "capital_cost": 172485199.53989035,
+        "fixed_operating_cost": 4885201.7298879623,
+        "variable_operating_cost": 0,
+        "fixed_charge_rate": 0.098000000000000004,
+        "base_capital_cost": 172485199.53989035,
+        "base_fixed_operating_cost": 4885201.7298879623,
+        "base_variable_operating_cost": 0,
+        "system_capacity": 40_000,
     }
     for dset in output_request:
         truth = truth_vals[dset]