Merge pull request #447 from NREL/pp/report_scaled_capital_cost

Report scaled capital cost in output supply curve

reV/supply_curve/points.py

@@ -1585,6 +1585,9 @@ def mean_lcoe(self):
                 if all(k in self.mean_h5_dsets_data for k in required):
                     aep = (self.mean_h5_dsets_data['system_capacity']
                            * self.mean_cf * 8760)
+                    # Note the AEP computation uses the SAM config
+                    # `system_capacity`, so no need to scale `capital_cost`
+                    # or `fixed_operating_cost` by anything
                     mean_lcoe = lcoe_fcr(
                         self.mean_h5_dsets_data['fixed_charge_rate'],
                         self.mean_h5_dsets_data['capital_cost'],
@@ -1796,6 +1799,94 @@ def capacity_ac(self):
 
         return self.area * self.power_density_ac
 
+    @property
+    def sc_point_capital_cost(self):
+        """Get the capital cost for the entire SC point.
+
+        This method scales the capital cost based on the included-area
+        capacity. The calculation requires 'capital_cost' and
+        'system_capacity' in the generation file and passed through as
+        `h5_dsets`, otherwise it returns `None`.
+
+        Returns
+        -------
+        sc_point_capital_cost : float | None
+            Total supply curve point capital cost ($).
+        """
+        if self.mean_h5_dsets_data is None:
+            return None
+
+        required = ('capital_cost', 'system_capacity')
+        if not all(k in self.mean_h5_dsets_data for k in required):
+            return None
+
+        cap_cost_per_mw = (self.mean_h5_dsets_data['capital_cost']
+                           / self.mean_h5_dsets_data['system_capacity'])
+        return cap_cost_per_mw * self.capacity
+
+    @property
+    def sc_point_fixed_operating_cost(self):
+        """Get the fixed operating cost for the entire SC point.
+
+        This method scales the fixed operating cost based on the
+        included-area capacity. The calculation requires
+        'fixed_operating_cost' and 'system_capacity' in the generation
+        file and passed through as `h5_dsets`, otherwise it returns
+        `None`.
+
+        Returns
+        -------
+        sc_point_fixed_operating_cost : float | None
+            Total supply curve point fixed operating cost ($).
+        """
+        if self.mean_h5_dsets_data is None:
+            return None
+
+        required = ('fixed_operating_cost', 'system_capacity')
+        if not all(k in self.mean_h5_dsets_data for k in required):
+            return None
+
+        fixed_cost_per_mw = (self.mean_h5_dsets_data['fixed_operating_cost']
+                             / self.mean_h5_dsets_data['system_capacity'])
+        return fixed_cost_per_mw * self.capacity
+
+    @property
+    def sc_point_annual_energy(self):
+        """Get the total annual energy (MWh) for the entire SC point.
+
+        This value is computed using the capacity of the supply curve
+        point as well as the mean capacity factor. If the mean capacity
+        factor is `None`, this value will also be `None`.
+
+        Returns
+        -------
+        sc_point_annual_energy : float | None
+            Total annual energy (MWh) for the entire SC point.
+        """
+        if self.mean_cf is None:
+            return None
+
+        return self.mean_cf * self.capacity * 8760
+
+    @property
+    def sc_point_annual_energy_ac(self):
+        """Get the total AC annual energy (MWh) for the entire SC point.
+
+        This value is computed using the AC capacity of the supply curve
+        point as well as the mean capacity factor. If either the mean
+        capacity factor or the AC capacity value is `None`, this value
+        will also be `None`.
+
+        Returns
+        -------
+        sc_point_annual_energy_ac : float | None
+            Total AC annual energy (MWh) for the entire SC point.
+        """
+        if self.mean_cf is None or self.capacity_ac is None:
+            return None
+
+        return self.mean_cf * self.capacity_ac * 8760
+
     @property
     def h5_dsets_data(self):
         """Get any additional/supplemental h5 dataset data to summarize.
@@ -1928,11 +2019,13 @@ def point_summary(self, args=None):
                 'timezone': self.timezone,
                 }
 
-        if self.capacity_ac is not None:
-            ARGS['capacity_ac'] = self.capacity_ac
-
-        if self.offshore is not None:
-            ARGS['offshore'] = self.offshore
+        extra_atts = ['capacity_ac', 'offshore', 'sc_point_capital_cost',
+                      'sc_point_fixed_operating_cost',
+                      'sc_point_annual_energy', 'sc_point_annual_energy_ac']
+        for attr in extra_atts:
+            value = getattr(self, attr)
+            if value is not None:
+                ARGS[attr] = value
 
         if self._friction_layer is not None:
             ARGS['mean_friction'] = self.mean_friction
@@ -1980,6 +2073,11 @@ def economies_of_scale(cap_cost_scale, summary):
         summary['raw_lcoe'] = eos.raw_lcoe
         summary['mean_lcoe'] = eos.scaled_lcoe
         summary['capital_cost_scalar'] = eos.capital_cost_scalar
+        summary['scaled_capital_cost'] = eos.scaled_capital_cost
+        if "sc_point_capital_cost" in summary:
+            scaled_costs = (summary["sc_point_capital_cost"]
+                            * eos.capital_cost_scalar)
+            summary['scaled_sc_point_capital_cost'] = scaled_costs
 
         return summary
 

reV/version.py

@@ -2,4 +2,4 @@
 reV Version number
 """
 
-__version__ = "0.8.6"
+__version__ = "0.8.7"

tests/test_econ_of_scale.py

@@ -162,6 +162,8 @@ def test_econ_of_scale_baseline():
         sc_df = pd.read_csv(out_fp_sc + ".csv")
         assert np.allclose(base_df['mean_lcoe'], sc_df['mean_lcoe'])
         assert (sc_df['capital_cost_scalar'] == 1).all()
+        assert np.allclose(sc_df['mean_capital_cost'],
+                           sc_df['scaled_capital_cost'])
 
 
 def test_sc_agg_econ_scale():
@@ -225,6 +227,8 @@ def test_sc_agg_econ_scale():
                             / aep + data['variable_operating_cost'])
 
         assert np.allclose(scalars, sc_df['capital_cost_scalar'])
+        assert np.allclose(scalars * sc_df['mean_capital_cost'],
+                           sc_df['scaled_capital_cost'])
 
         assert np.allclose(true_scaled_lcoe, sc_df['mean_lcoe'])
         assert np.allclose(true_raw_lcoe, sc_df['raw_lcoe'])

tests/test_supply_curve_sc_aggregation.py

@@ -110,6 +110,7 @@ def test_agg_summary():
 
         summary = summary.fillna('None')
         s_baseline = s_baseline.fillna('None')
+        summary = summary[list(s_baseline.columns)]
 
         assert_frame_equal(summary, s_baseline, check_dtype=False, rtol=0.0001)
 
@@ -205,6 +206,7 @@ def test_pre_extract_inclusions(pre_extract):
 
         summary = summary.fillna('None')
         s_baseline = s_baseline.fillna('None')
+        summary = summary[list(s_baseline.columns)]
 
         assert_frame_equal(summary, s_baseline, check_dtype=False, rtol=0.0001)
 
@@ -356,7 +358,9 @@ def test_data_layer_methods():
             assert slope_min <= slope_mean <= slope_max
 
 
-def test_recalc_lcoe():
+@pytest.mark.parametrize("cap_cost_scale",
+                         ['1', '2 * np.multiply(1000, capacity) ** -0.3'])
+def test_recalc_lcoe(cap_cost_scale):
     """Test supply curve aggregation with the re-calculation of lcoe using the
     multi-year mean capacity factor"""
 
@@ -401,28 +405,40 @@ def test_recalc_lcoe():
             res.create_dataset('lcoe_fcr-means',
                                res['meta'].shape, data=lcoe_arr)
 
-        h5_dsets = ('capital_cost', 'fixed_operating_cost',
+        h5_dsets = ['capital_cost', 'fixed_operating_cost',
                     'fixed_charge_rate', 'variable_operating_cost',
-                    'system_capacity')
+                    'system_capacity']
 
         base = SupplyCurveAggregation(EXCL, TM_DSET, excl_dict=EXCL_DICT,
                                       res_class_dset=None, res_class_bins=None,
                                       data_layers=DATA_LAYERS,
                                       h5_dsets=h5_dsets,
                                       gids=list(np.arange(10)),
-                                      recalc_lcoe=False)
+                                      recalc_lcoe=False,
+                                      cap_cost_scale=cap_cost_scale)
         summary_base = base.summarize(gen_temp, max_workers=1)
 
         sca = SupplyCurveAggregation(EXCL, TM_DSET, excl_dict=EXCL_DICT,
                                      res_class_dset=None, res_class_bins=None,
                                      data_layers=DATA_LAYERS,
                                      h5_dsets=h5_dsets,
                                      gids=list(np.arange(10)),
-                                     recalc_lcoe=True)
+                                     recalc_lcoe=True,
+                                     cap_cost_scale=cap_cost_scale)
         summary = sca.summarize(gen_temp, max_workers=1)
 
     assert not np.allclose(summary_base['mean_lcoe'], summary['mean_lcoe'])
 
+    if cap_cost_scale == '1':
+        cc_dset = 'sc_point_capital_cost'
+    else:
+        cc_dset = 'scaled_sc_point_capital_cost'
+    lcoe = lcoe_fcr(summary['mean_fixed_charge_rate'], summary[cc_dset],
+                    summary['sc_point_fixed_operating_cost'],
+                    summary['sc_point_annual_energy'],
+                    summary['mean_variable_operating_cost'])
+    assert np.allclose(lcoe, summary['mean_lcoe'])
+
 
 @pytest.mark.parametrize('tm_dset', ("techmap_ri", "techmap_ri_new"))
 @pytest.mark.parametrize('pre_extract', (True, False))