Add regional cost multipliers

The multipliers are applied to measure and baseline costs by state and building type (residential/commercial), based on data collected from the RSMeans 2021 City Price Index. https://www.google.com/url?q=https://www.rsmeans.com/media/wysiwyg/quarterly_updates/2021-CCI-LocationFactors-V2.pdf?srsltid%3DAfmBOop3jyUcK1-aFa0Vn-MhKx9FCphrlf92quKpA1y-l4W_iIf1hw7q&sa=D&source=editors&ust=1740606379681878&usg=AOvVaw36C0xgCna5378cn3r1yR46

Author: jtlangevin

scout/ecm_prep.py

@@ -150,6 +150,7 @@ class UsefulInputFiles(object):
         hp_convert_rates (tuple): Fuel switching conversion rates.
         fug_emissions_dat (tuple): Refrigerant and supply chain methane leakage
             data to asses fugitive emissions sources.
+        local_cost_adj (tuple): State-level cost adjustment indices from RSMeans 2021.
     """
 
     def __init__(self, opts):
@@ -211,6 +212,7 @@ def __init__(self, opts):
         self.health_data = fp.CONVERT_DATA / "epa_costs.csv"
         self.hp_convert_rates = fp.CONVERT_DATA / "hp_convert_rates.json"
         self.fug_emissions_dat = fp.CONVERT_DATA / "fugitive_emissions_convert.json"
+        self.local_cost_adj = fp.CONVERT_DATA / "loc_cost_adj.csv"
 
     def set_decarb_grid_vars(self, opts: argparse.NameSpace):  # noqa: F821
         """Assign instance variables related to grid decarbonization which are dependent on the
@@ -545,6 +547,21 @@ def __init__(self, base_dir, handyfiles, opts):
         except ValueError as e:
             raise ValueError(
                 f"Error reading in '{handyfiles.cpi_data}': {str(e)}") from None
+        # If states are used, read in state-level cost adjustment data
+        if self.regions == "State":
+            # Read in adjustment factors
+            reg_cost_adj_array = pd.read_csv(handyfiles.local_cost_adj)
+            # Store factors for each state and building type (res/com) in a dict for
+            # efficient access subsequently
+            self.reg_cost_adj = {}
+            for row in reg_cost_adj_array.index:
+                # Dict is organized by state and building type (res/com) levels
+                self.reg_cost_adj[reg_cost_adj_array.loc[row, "state"]] = {
+                    bldg: reg_cost_adj_array.loc[row, bldg] for
+                    bldg in ["residential", "commercial"]
+                }
+        else:
+            self.reg_cost_adj = None
         # Read in commercial equipment capacity factors
         self.cap_facts = Utils.load_json(handyfiles.cap_facts)
         # Read in national-level site-source, emissions, and costs data
@@ -5286,6 +5303,12 @@ def fill_mkts(self, msegs, msegs_cpl, convert_data, tsv_data_init, opts,
                         "Invalid performance or cost units for ECM '" +
                         self.name + "'")
 
+                # Adjust baseline and measure costs to current region, if applicable
+                if self.handyvars.reg_cost_adj is not None:
+                    cost_meas, cost_base = [
+                        {yr: x[yr] * self.handyvars.reg_cost_adj[mskeys[1]][bldg_sect]
+                         for yr in self.handyvars.aeo_years} for x in [cost_meas, cost_base]]
+
                 # Set stock turnover info. and consumer choice info. to
                 # appropriate building type
                 if bldg_sect == "residential":

scout/supporting_data/convert_data/loc_cost_adj.csv

@@ -0,0 +1,52 @@
+state,city,residential,commercial
+AL,Huntsville,0.83,0.85
+AK,Anchorage,1.17,1.16
+AZ,Phoenix,0.84,0.87
+AR,Little Rock,0.83,0.82
+CA,Los Angeles,1.15,1.12
+CO,Denver,0.91,0.92
+CT,Bridgeport,1.1,1.08
+DE,Wilmington,1.01,1.04
+DC,"Washington, D.C.",0.92,0.96
+FL,Jacksonville,0.81,0.84
+GA,Atlanta,0.9,0.89
+HI,Honolulu,1.22,1.19
+ID,Boise,0.89,0.91
+IL,Chicago,1.25,1.2
+IN,Indianapolis,0.92,0.92
+IA,Des Moines,0.92,0.94
+KS,Wichita,0.81,0.86
+KY,Louisville,0.89,0.88
+LA,New Orleans,0.85,0.85
+ME,Portland,0.94,0.94
+MD,Baltimore,0.93,0.94
+MA,Boston,1.18,1.14
+MI,Detroit,1,1
+MN,Minneapolis,1.09,1.07
+MS,Jackson,0.84,0.85
+MO,Kansas City,0.99,0.99
+MT,Billings,0.89,0.91
+NE,Omaha,0.9,0.92
+NV,Las Vegas,1.03,1.05
+NH,Manchester,0.99,0.97
+NJ,Newark,1.2,1.17
+NM,Albuquerque,0.86,0.87
+NY,New York,1.36,1.32
+NC,Charlotte,0.99,0.87
+ND,Fargo,0.87,0.91
+OH,Columbus,0.91,0.92
+OK,Oklahoma City,0.84,0.85
+OR,Portland,1.02,1.03
+PA,Philadelphia,1.17,1.16
+RI,Providence,1.09,1.06
+SC,Charleston,0.98,0.85
+SD,Sioux Falls,0.92,0.92
+TN,Nashville,0.87,0.89
+TX,Houston,0.84,0.87
+UT,Salt Lake City,0.85,0.91
+VT,Burlington,1,0.95
+VA,Newport News,0.97,0.87
+WA,Seattle,1.06,1.07
+WV,Charleston,0.94,0.94
+WI,Milwaukee,1.07,1.04
+WY,Cheyenne,0.86,0.89

tests/ecm_prep_test.py

@@ -907,6 +907,8 @@ def setUpClass(cls):
         handyfiles_state = ecm_prep.UsefulInputFiles(cls.opts_state)
         handyvars_state = ecm_prep.UsefulVars(
             base_dir, handyfiles_state, cls.opts_state)
+        # Suppress regional cost adjustment factors for states
+        handyvars_state.reg_cost_adj = None
         # Fuel switching with exogenous rates
         cls.opts_hp_rates, opts_hp_rates_dict = [
             copy.deepcopy(x) for x in [cls.opts_emm, opts_emm_dict]]