Surface power plant costs figure reference, tooltip var interpolation; various minor TODO cleanup

Author: softwareengineerprogrammer

src/geophires_x/Economics.py

@@ -1841,11 +1841,14 @@ def __init__(self, model: Model):
         )
 
         # TODO switch order to align with theoretical basis, which lists indirect costs first
-        contingency_and_indirect_costs_tooltip = (
-            f'plus {self.contingency_percentage.quantity().to(convertible_unit("%")).magnitude:g}% contingency '
+        contingency_and_indirect_costs_tooltip_stem = (
+            f'{self.contingency_percentage.quantity().to(convertible_unit("%")).magnitude:g}% contingency '
             f'plus {self.indirect_capital_cost_percentage.quantity().to(convertible_unit("%")).magnitude}% '
             f'indirect costs'
         )
+        contingency_and_indirect_costs_tooltip = (
+            f'plus {contingency_and_indirect_costs_tooltip_stem}'
+        )
 
         self.Cexpl = self.OutputParameterDict[self.Cexpl.Name] = OutputParameter(
             Name="Exploration cost",
@@ -1888,11 +1891,11 @@ def __init__(self, model: Model):
             UnitType=Units.CURRENCYFREQUENCY,
             PreferredUnits=CurrencyFrequencyUnit.MDOLLARSPERYEAR,
             CurrentUnits=CurrencyFrequencyUnit.MDOLLARSPERYEAR,
+            # TODO parameterize relevant constants in tooltip text
             ToolTipText='The built-in correlation for the wellfield O&M costs is similar as the surface plant O&M '
                         'costs: it assumes that it consists of 1% of the total wellfield plus field gathering system '
                         'costs (for annual non-labor costs) and 25% of the labor costs (the other 75% of the labor '
                         'costs are assigned to the surface plant O&M costs).'
-            # TODO parameterize relevant constants
         )
 
         self.redrilling_annual_cost = self.OutputParameterDict[self.redrilling_annual_cost.Name] = OutputParameter(
@@ -1906,23 +1909,25 @@ def __init__(self, model: Model):
                         f'The total is then divided over {model.surfaceplant.plant_lifetime.Name} years to calculate '
                         f'Redrilling costs per year.'
         )
+        # noinspection SpellCheckingInspection
         self.Cplant = self.OutputParameterDict[self.Cplant.Name] = OutputParameter(
             Name="Surface Plant cost",
             display_name='Surface power plant costs',
             UnitType=Units.CURRENCY,
             PreferredUnits=CurrencyUnit.MDOLLARS,
             CurrentUnits=CurrencyUnit.MDOLLARS,
+            # TODO incorporate direct references to relevant parameters for adjusting correlation in tooltip text
+            # TODO interpolate relevant constants (that are currently hardcoded) in tooltip text
             ToolTipText='The built-in power plant cost correlations are based on the original correlations developed '
                         'by Beckers (2016), indexed to 2017 using the IHS Markit North American Power Capital Costs '
                         'Index (NAPCCI) excluding nuclear plants (IHS 2018). The ORC power plant cost data have been '
                         'updated with data from the 2016 GETEM tool (DOE 2016) and the geothermal binary power plants '
                         # 'study by Verkís (2014). '
                         'study by Verkis (2014). '  # unicode accented i may cause unexpected problems in consumers...
-                        # TODO incorporate reference to figure (commented out for now)
-                        # 'Figure 4 shows the power plant capital cost expressed in $ kWe−1 as a function of plant
-                        # size and initial production temperature for subcritical ORC and double-flash power plants. '
-                        # TODO use tooltip var
-                        'The correlations in GEOPHIRES include 12% for indirect costs and 15% contingency. '
+                        'Figure 4 in the Theoretical Basis shows the power plant capital cost expressed in $ kWe−1 '
+                        'as a function of plant size and initial production temperature for subcritical ORC and '
+                        'double-flash power plants. '
+                        f'The default correlations in GEOPHIRES include {contingency_and_indirect_costs_tooltip_stem}. '
                         'For the same plant size and production temperature, double-flash power plants are considered '
                         'about 25% more expensive than single-flash power plants (Zeyghami 2010), and supercritical '
                         'ORC plants are roughly 10% more than subcritical ORC plants (Astolfi et al. 2014). A wide '
@@ -1943,16 +1948,15 @@ def __init__(self, model: Model):
                         'However, users are encouraged to provide their own cost figures for '
                         'their specific application. Beckers and Young (2017) collected several cost figures to '
                         'estimate the surface equipment cost for geothermal district-heating systems.'
-            # TODO incorporate direct references to relevant parameters for adjusting correlation
         )
         self.Coamplant = self.OutputParameterDict[self.Coamplant.Name] = OutputParameter(
             Name="O&M Surface Plant costs",
             display_name='Power plant maintenance costs',
             UnitType=Units.CURRENCYFREQUENCY,
             PreferredUnits=CurrencyFrequencyUnit.MDOLLARSPERYEAR,
             CurrentUnits=CurrencyFrequencyUnit.MDOLLARSPERYEAR,
-            # TODO parameterize relevant constants
-            # TODO update index year and/or make indexing parameterizable
+            # TODO parameterize relevant constants in tooltip text
+            # TODO update index year and/or make indexing parameterizable in tooltip text
             ToolTipText='GEOPHIRES estimates the annual surface plant O&M costs as the sum of 1.5% of the total plant '
                         'capital cost (for annual non-labor costs), and 75% of the annual labor costs. The other 25% '
                         'of the labor costs are assigned to the wellfield O&M cost. The labor costs are calculated '
@@ -1969,7 +1973,7 @@ def __init__(self, model: Model):
             UnitType=Units.CURRENCY,
             PreferredUnits=CurrencyUnit.MDOLLARS,
             CurrentUnits=CurrencyUnit.MDOLLARS,
-            # TODO interpolate constant values in tooltip text instead of hardcoding
+            # TODO interpolate constant values in tooltip text instead of hardcoding in tooltip text
             ToolTipText='The built-in cost correlation for estimating the field gathering system cost includes '
                         'the cost for surface piping from each well to the plant and pumps for production and '
                         'injection wells. The length of the surface piping is assumed 750 m per well at a cost of '

src/geophires_x_schema_generator/geophires-result.json

@@ -410,7 +410,7 @@
         "Stimulation costs (for redrilling)": {},
         "Surface power plant costs": {
           "type": "number",
-          "description": "Surface Plant cost. The built-in power plant cost correlations are based on the original correlations developed by Beckers (2016), indexed to 2017 using the IHS Markit North American Power Capital Costs Index (NAPCCI) excluding nuclear plants (IHS 2018). The ORC power plant cost data have been updated with data from the 2016 GETEM tool (DOE 2016) and the geothermal binary power plants study by Verkis (2014). The correlations in GEOPHIRES include 12% for indirect costs and 15% contingency. For the same plant size and production temperature, double-flash power plants are considered about 25% more expensive than single-flash power plants (Zeyghami 2010), and supercritical ORC plants are roughly 10% more than subcritical ORC plants (Astolfi et al. 2014). A wide range in power plant specific cost values is reported in academic and popular literature. The GEOPHIRES built-in surface plant cost correlations represent typical values. However, the user is recommended to provide their own power plant cost data if available for their case study. The ORC plant specific cost decreases only moderately at higher temperatures. The reasons are that when increasing the temperature, the ORC plant design also changes: (1) a different organic fluid is selected, (2) piping, pump, heat exchangers, and other equipment are designed to handle the higher temperature (and potentially also pressure), requiring thicker walls, potentially different materials, etc., and (3) additional components may be implemented, such as a heat recuperator, making the design and operation more complex. Unlike flash power plants, ORC plants are a small, niche market, typically case specific, and rely on relatively young technology, which has not been subject yet to decades of technological advancement. The cost for direct-use heat applications is highly dependent on the type of application. A generic cost of $250 kWth\u22121 is assumed plus 15% contingency plus 12% indirect costs. However, users are encouraged to provide their own cost figures for their specific application. Beckers and Young (2017) collected several cost figures to estimate the surface equipment cost for geothermal district-heating systems.",
+          "description": "Surface Plant cost. The built-in power plant cost correlations are based on the original correlations developed by Beckers (2016), indexed to 2017 using the IHS Markit North American Power Capital Costs Index (NAPCCI) excluding nuclear plants (IHS 2018). The ORC power plant cost data have been updated with data from the 2016 GETEM tool (DOE 2016) and the geothermal binary power plants study by Verkis (2014). Figure 4 in the Theoretical Basis shows the power plant capital cost expressed in $ kWe\u22121 as a function of plant size and initial production temperature for subcritical ORC and double-flash power plants. The default correlations in GEOPHIRES include 15% contingency plus 12% indirect costs. For the same plant size and production temperature, double-flash power plants are considered about 25% more expensive than single-flash power plants (Zeyghami 2010), and supercritical ORC plants are roughly 10% more than subcritical ORC plants (Astolfi et al. 2014). A wide range in power plant specific cost values is reported in academic and popular literature. The GEOPHIRES built-in surface plant cost correlations represent typical values. However, the user is recommended to provide their own power plant cost data if available for their case study. The ORC plant specific cost decreases only moderately at higher temperatures. The reasons are that when increasing the temperature, the ORC plant design also changes: (1) a different organic fluid is selected, (2) piping, pump, heat exchangers, and other equipment are designed to handle the higher temperature (and potentially also pressure), requiring thicker walls, potentially different materials, etc., and (3) additional components may be implemented, such as a heat recuperator, making the design and operation more complex. Unlike flash power plants, ORC plants are a small, niche market, typically case specific, and rely on relatively young technology, which has not been subject yet to decades of technological advancement. The cost for direct-use heat applications is highly dependent on the type of application. A generic cost of $250 kWth\u22121 is assumed plus 15% contingency plus 12% indirect costs. However, users are encouraged to provide their own cost figures for their specific application. Beckers and Young (2017) collected several cost figures to estimate the surface equipment cost for geothermal district-heating systems.",
           "units": "MUSD"
         },
         "of which Absorption Chiller Cost": {},