feat(demand): CPUC SERVM 2026 demand profile with selectable weather year (CPUC stack 2/5)

docs/source/configtables/electricity.csv

@@ -33,12 +33,13 @@ gaslimit,MWh thermal,float,"Cap on annual gas-fired primary energy from gas carr
 ,,,
 demand:,,,
 -- bus_allocation,--,"One of {``population``, ``breakthrough``}","How zone-level demand is distributed to individual buses. ``population`` (default) weights buses by 2020 Decennial Census county populations (split evenly across each county's substations, then each substation's buses). ``breakthrough`` uses the legacy nominal-demand column (``Pd``) from the 2016-vintage Breakthrough Energy grid model."
--- profile,--,"One of {``efs``, ``eia``, ``eer``}","Datasource for electrical load data. ``EFS`` pulls future state level electrical demand data. ``EIA`` pulls historical balancing level electrical demand data. ``EER`` pulls future state-level profiles from the EER dataset; when selected, ``planning_horizons`` must be one of 2021, 2025, 2030, 2035, 2040, 2045, or 2050 and ``renewable_weather_years`` must contain exactly one year from 2007-2013 or 2016-2023."
+-- profile,--,"One of {``efs``, ``eia``, ``eer``, ``servm``}","Datasource for electrical load data. ``EFS`` pulls future state level electrical demand data. ``EIA`` pulls historical balancing level electrical demand data. ``EER`` pulls future state-level profiles from the EER dataset; when selected, ``planning_horizons`` must be one of 2021, 2025, 2030, 2035, 2040, 2045, or 2050 and ``renewable_weather_years`` must contain exactly one year from 2007-2013 or 2016-2023. ``SERVM`` pulls CPUC SERVM hourly load for the six California load regions (California models only); when selected, ``planning_horizons`` must be one of 2026, 2028, 2030, 2032, 2035, 2037, 2040, 2042, or 2045."
 -- scenario:,,,
 -- -- efs_case,--,"One of {``reference``, ``medium``, ``high``}",(UNDER DEVELOPMENT) Extracts EFS data according to level of adoption
 -- -- efs_speed,--,"One of {``slow``, ``moderate``, ``fast``}",(UNDER DEVELOPMENT) Extracts EFS data according to speed of electrification
 -- -- eer_file,--,"One of {``demand_EER2025_100by2050.h5``, ``demand_EER2025_Baseline_AEO2023.h5``, ``demand_EER2025_IRAlow.h5``}",Selects the EER demand dataset file to download and use when ``profile`` is ``eer``.
 -- -- aeo,--,One of the AEO scenarios `here <https://www.eia.gov/outlooks/aeo/data/browser/>`_,(UNDER DEVELOPMENT) Scales future demand according to the AEO scenario
+-- -- servm_weather_years,--,List with exactly one year from 2000-2024,"Weather year drawn from the stacked SERVM record when ``profile`` is ``servm``. Multiple entries are reserved for stochastic scenarios and currently raise ``NotImplementedError``. Keep equal to the top-level ``renewable_weather_years`` so load and renewable profiles share a weather year; a mismatch logs a warning."
 ,,,
 demand_response:,,,Settings to activate and configure demand response
 -- shift,per_unit,"float {0 <=, >= 1} or 'inf'",Allowable load to be shifted per snapshot. Set to 0 to turn off demand response. Set to 'inf' to not enforce capacity limits.

workflow/repo_data/config/config.default.yaml

@@ -163,13 +163,20 @@ electricity:
 
   # ---------------- Demand ----------------
   demand:
-    profile: efs                           # demand time series source; efs (EIA-EFS) | eia (historical actuals) | eer (EER scenarios)
+    profile: efs                           # demand time series source; efs (EIA-EFS) | eia (historical actuals) | eer (EER scenarios) | servm (CPUC SERVM, California only)
     bus_allocation: population             # per-bus demand weight; population (2020 census counties) | breakthrough (legacy BE Pd)
-    scenario:                              # EFS/EER scenario knobs (ignored if profile=eia)
+    scenario:                              # EFS/EER/SERVM scenario knobs (ignored if profile=eia)
       efs_case:  reference                 # reference | medium | high
       efs_speed: moderate                  # slow | moderate | rapid
       eer_file: demand_EER2025_100by2050.h5 # EER h5 (profile=eer): demand_EER2025_100by2050 | demand_EER2025_Baseline_AEO2023 | demand_EER2025_IRAlow
       aeo:       reference                 # AEO (EIA Annual Energy Outlook) scaling case; reference | high | low
+      # ---- profile: servm (CPUC SERVM 2026, California regions) ----
+      # Weather year drawn from the stacked 2000-2024 SERVM record. Single-entry
+      # list = deterministic; multiple entries are reserved for stochastic
+      # scenarios (phase 3) and currently raise NotImplementedError.
+      # RECOMMENDED: keep equal to top-level `renewable_weather_years` so load
+      # and renewable profiles share a weather year (mismatch logs a warning).
+      servm_weather_years: [2019]
 
   demand_response:                         # price-responsive shiftable load; 0 = disabled
     shift: 0                               # fraction of hourly load that can shift in time

workflow/rules/build_electricity.smk

@@ -315,6 +315,15 @@ def eer_demand_file():
     return filename
 
 
+SERVM_LOAD_FILE = "cpuc/servm/HourlyLoad_CA_Regions_V2025E_2224_Mon_{year}.csv"
+
+
+def servm_demand_files():
+    """One CPUC SERVM hourly-load file per planning horizon."""
+    horizons = sorted(set(config["scenario"]["planning_horizons"]))
+    return [DATA + SERVM_LOAD_FILE.format(year=year) for year in horizons]
+
+
 def demand_raw_data(wildcards):
     # get profile to use
     end_use = wildcards.end_use
@@ -346,6 +355,8 @@ def demand_raw_data(wildcards):
         return DATA + f"nrel_efs/EFSLoadProfile_{efs_case}_{efs_speed}.csv"
     elif profile == "eer":
         return DATA + f"eer/{eer_demand_file()}"
+    elif profile == "servm":
+        return servm_demand_files()
     elif profile == "ferc":
         return [
             DATA + "pudl/out_ferc714__hourly_estimated_state_demand.parquet",
@@ -381,13 +392,23 @@ def demand_raw_data(wildcards):
 
 
 def demand_disaggregate_data(wildcards):
-    """CLIU county-level industrial loads are the only disaggregation input.
+    """Extra per-profile input needed to spread zonal demand over buses.
 
-    All other end uses disaggregate by population and need no extra file.
+    CLIU county-level industrial loads serve the industry end use; the SERVM
+    profile needs its precomputed (region, bus) allocation weights. Everything
+    else disaggregates by population and needs no extra file.
     """
-    if wildcards.end_use != "industry":
-        return []
-    return DATA + "industry_load/2014_update_20170910-0116.csv"
+    if wildcards.end_use == "industry":
+        return DATA + "industry_load/2014_update_20170910-0116.csv"
+    if (
+        wildcards.end_use == "power"
+        and config["electricity"]["demand"]["profile"] == "servm"
+    ):
+        return (
+            DEMAND
+            + f"{wildcards.interconnect}/servm_load_weights_s{wildcards.simpl}.csv"
+        )
+    return []
 
 
 def demand_scaling_data(wildcards):
@@ -410,6 +431,8 @@ def demand_scaling_data(wildcards):
         return []
     elif profile == "eer":
         return []
+    elif profile == "servm":
+        return []
     else:
         return ""
 
@@ -423,14 +446,20 @@ rule build_electrical_demand:
         profile_year=pd.to_datetime(config["snapshots"]["start"]).year,
         planning_horizons=config["scenario"]["planning_horizons"],
         renewable_weather_years=config["renewable_weather_years"],
+        servm_weather_years=config["electricity"]["demand"]["scenario"].get(
+            "servm_weather_years", []
+        ),
         snapshots=config["snapshots"],
         pudl_path=config_provider("pudl_path"),
     input:
         network=NETWORKS + "{interconnect}/elec_s{simpl}.nc",
         demand_files=demand_raw_data,
+        dissagregate_files=demand_disaggregate_data,
         demand_scaling_file=demand_scaling_data,
     output:
         elec_demand=DEMAND + "{interconnect}/{end_use}_electricity_s{simpl}.csv",
+        zonal_components=DEMAND
+        + "{interconnect}/{end_use}_zonal_components_s{simpl}.parquet",
     log:
         LOGS + "{interconnect}/{end_use}_build_demand_s{simpl}.log",
     benchmark:

workflow/rules/retrieve.smk

@@ -103,6 +103,40 @@ rule retrieve_eer_demand_data:
         "../scripts/retrieve_eer_data.py"
 
 
+CPUC_SERVM_URL = "https://files.cpuc.ca.gov/energy/modeling/2026_servm_updates/"
+
+
+rule retrieve_cpuc_servm_load:
+    wildcard_constraints:
+        servm_year="2026|2028|2030|2032|2035|2037|2040|2042|2045",
+    params:
+        url=lambda wildcards: CPUC_SERVM_URL
+        + f"HourlyLoad_CA_Regions_V2025E_2224_Mon_{wildcards.servm_year}.csv",
+    output:
+        DATA + "cpuc/servm/HourlyLoad_CA_Regions_V2025E_2224_Mon_{servm_year}.csv",
+    resources:
+        mem_mb=5000,
+    log:
+        "logs/retrieve/retrieve_cpuc_servm_load_{servm_year}.log",
+    retries: 2
+    script:
+        "../scripts/retrieve_cpuc_data.py"
+
+
+rule retrieve_cpuc_baseline_generators:
+    params:
+        url=CPUC_SERVM_URL + "BaselineGeneratorList_CAISO.xlsx",
+    output:
+        DATA + "cpuc/BaselineGeneratorList_CAISO.xlsx",
+    resources:
+        mem_mb=5000,
+    log:
+        "logs/retrieve/retrieve_cpuc_baseline_generators.log",
+    retries: 2
+    script:
+        "../scripts/retrieve_cpuc_data.py"
+
+
 sector_datafiles = [
     # heating sector
     "population/DECENNIALDHC2020.P1-Data.csv",