Merge pull request #195 from iiasa/costs/fom-vintage

Create option in `tools.costs` to keep fixed O&M costs same across vintages

doc/api/tools-costs.rst

@@ -118,6 +118,7 @@ Those settings include the following; click each for the full description, allow
    :attr:`~.Config.scenario_version`,
    :attr:`~.Config.base_year`,
    :attr:`~.Config.convergence_year`,
+   :attr:`~.Config.use_vintages`, 
    :attr:`~.Config.fom_rate`, and
    :attr:`~.Config.format`.
 

doc/whatsnew.rst

@@ -8,15 +8,15 @@ Next release
 - Update :doc:`/material/index` (:pull:`201`).
 - Add :doc:`/project/edits` project code and documentation (:pull:`204`).
 - Reduce log verbosity of :func:`.apply_spec` (:pull:`202`).
+- Made fixes and updates to :doc:`/api/tools-costs` (:pull:`186`, :pull:`187`, :pull:`190`, :pull:`195`).
 
-Changes to :doc:`/api/tools-costs`
-----------------------------------
   - Fix jumps in cost projections for technologies with first technology year that's after than the first model year (:pull:`186`).
   - Change the use of base_year to mean the year to start modeling cost changes (:pull:`186`).
   - Update cost assumptions for certain CCS technologies (:pull:`186`).
   - Change the default fixed O&M reduction rate to 0 (:pull:`186`).
   - Modify to use 2023 release of IEA WEO data and to use 2022 historic data for the base year (:pull:`187`).
   - Change the default final year to 2110 (:pull:`190`).
+  - Add :attr:`~.costs.Config.use_vintages` to control whether vintages are used in computing fixed O&M costs (:pull:`195`).  
 
 v2024.4.22
 ==========

message_ix_models/tools/costs/config.py

@@ -55,6 +55,14 @@ class Config:
     #: Model variant for which to project costs.
     module: Literal["energy", "materials"] = "energy"
 
+    #: Use vintages.
+    #:
+    #: If True, for each vintage, the fixed O&M costs will be calculated as a
+    #: ratio of the investment costs, that decreases by the rate of :attr:`fom_rate`.
+    #: If False, the fix_cost is the ratio of the investment cost for each year_act.
+    #: In this case, the fix_cost is the same for all vintages of the same year_act.
+    use_vintages: bool = False
+
     #: .. todo:: Document the meaning of this setting.
     pre_last_year_rate: float = 0.01
 

message_ix_models/tools/costs/demo.py

@@ -17,6 +17,7 @@
 # - format="message",
 # - method="gdp",
 # - module="energy",
+# - use_vintages=False,
 # - node="R12",
 # - ref_region —automatically determined from node
 # - scenario="all",

message_ix_models/tools/costs/projections.py

@@ -362,6 +362,15 @@ def create_message_outputs(
 
     y_base = config.base_year
 
+    dims = [
+        "scenario_version",
+        "scenario",
+        "message_technology",
+        "first_technology_year",
+        "region",
+        "year",
+    ]
+
     df_prod = pd.DataFrame(
         product(
             df_projections.scenario_version.unique(),
@@ -371,14 +380,7 @@ def create_message_outputs(
             df_projections.region.unique(),
             config.seq_years,
         ),
-        columns=[
-            "scenario_version",
-            "scenario",
-            "message_technology",
-            "first_technology_year",
-            "region",
-            "year",
-        ],
+        columns=dims,
     )
 
     val_2020 = (
@@ -395,26 +397,9 @@ def create_message_outputs(
 
     df_merge = (
         (
-            df_prod.merge(
-                val_2020,
-                on=["scenario_version", "scenario", "message_technology", "region"],
-            )
-            .merge(
-                val_2100,
-                on=["scenario_version", "scenario", "message_technology", "region"],
-            )
-            .merge(
-                df_projections,
-                on=[
-                    "scenario_version",
-                    "scenario",
-                    "message_technology",
-                    "first_technology_year",
-                    "region",
-                    "year",
-                ],
-                how="left",
-            )
+            df_prod.merge(val_2020, on=dims[:-1])
+            .merge(val_2100, on=dims[:-1])
+            .merge(df_projections, on=dims, how="left")
         )
         .assign(
             inv_cost=lambda x: np.where(x.year <= y_base, x.inv_cost_2020, x.inv_cost),
@@ -477,45 +462,38 @@ def create_message_outputs(
     )
 
     dtypes.update(year_act=int)
+
+    to_merge = pd.DataFrame(
+        {"year_act" if config.use_vintages else "year_vtg": config.seq_years}
+    ).assign(key=1)
+
+    def _compute_value(df: pd.DataFrame) -> pd.Series:
+        if not config.use_vintages:
+            return df.fix_cost
+
+        rate = 1.0 + config.fom_rate
+
+        return np.where(
+            df.year_vtg <= y_base,
+            np.where(
+                df.year_act <= y_base,
+                df.fix_cost,
+                # NB if fom_rate was 0, the latter terms collapse to 1.0 ** (…) = 1.0
+                df.fix_cost * rate ** (df.year_act - y_base),
+            ),
+            df.fix_cost * rate ** (df.year_act - df.year_vtg),
+        )
+
     fom = (
         df_merge.copy()
         .drop(columns=["inv_cost"])
+        .rename(columns={"year_vtg": "year_vtg" if config.use_vintages else "year_act"})
         .assign(key=1)
-        .merge(
-            pd.DataFrame(data={"year_act": config.seq_years}).assign(key=1),
-            on="key",
-        )
+        .merge(to_merge, on="key")
         .drop(columns=["key"])
         .query("year_act >= year_vtg")
-        .assign(
-            val=lambda x: np.where(
-                x.year_vtg <= y_base,
-                np.where(
-                    x.year_act <= y_base,
-                    x.fix_cost,
-                    np.where(
-                        config.fom_rate == 0,
-                        x.fix_cost,
-                        x.fix_cost
-                        * (1 + float(config.fom_rate)) ** (x.year_act - y_base),
-                    ),
-                ),
-                np.where(
-                    config.fom_rate == 0,
-                    x.fix_cost,
-                    x.fix_cost
-                    * (1 + float(config.fom_rate)) ** (x.year_act - x.year_vtg),
-                ),
-            )
-        )
-        .assign(unit="USD/kWa")
-        .rename(
-            columns={
-                "val": "value",
-                "message_technology": "technology",
-                "region": "node_loc",
-            }
-        )
+        .assign(value=_compute_value, unit="USD/kWa")
+        .rename(columns={"message_technology": "technology", "region": "node_loc"})
         .reindex(
             [
                 "scenario_version",