streamline code for year-dependent technologies (turbines/panels)

config/config.default.yaml

@@ -164,14 +164,11 @@ atlite:
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#renewable
 renewable:
-  year: 2020
   onwind:
     cutout: europe-2013-era5
     resource:
       method: wind
-      turbine:
-        2020: Vestas_V112_3MW
-        2030: NREL_ReferenceTurbine_2020ATB_5.5MW
+      turbine: Vestas_V112_3MW
       add_cutout_windspeed: true
     capacity_per_sqkm: 3
     # correction_factor: 0.93
@@ -190,9 +187,7 @@ renewable:
     cutout: europe-2013-era5
     resource:
       method: wind
-      turbine:
-        2020: NREL_ReferenceTurbine_5MW_offshore.yaml
-        2030: NREL_ReferenceTurbine_2020ATB_15MW_offshore
+      turbine: NREL_ReferenceTurbine_5MW_offshore
       add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
@@ -208,10 +203,7 @@ renewable:
     cutout: europe-2013-era5
     resource:
       method: wind
-      turbine:
-        2020: Vestas_V164_7MW_offshore
-        2025: NREL_ReferenceTurbine_2020ATB_15MW_offshore
-        2030: NREL_ReferenceTurbine_2020ATB_18MW_offshore
+      turbine: Vestas_V164_7MW_offshore
       add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
@@ -227,8 +219,7 @@ renewable:
     cutout: europe-2013-sarah
     resource:
       method: pv
-      panel:
-        2020: CSi
+      panel: CSi
       orientation:
         slope: 35.
         azimuth: 180.

doc/configtables/offwind-ac.csv

@@ -2,7 +2,7 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
--- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_.  Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."

doc/configtables/offwind-dc.csv

@@ -2,7 +2,7 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
--- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 correction_factor,--,float,"Correction factor for capacity factor time series."
 excluder_resolution,m,float,"Resolution on which to perform geographical elibility analysis."

doc/configtables/onwind.csv

@@ -2,7 +2,7 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
 resource,,,
 -- method,--,"Must be 'wind'","A superordinate technology type."
--- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
+-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
 capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
 corine,,,
 -- grid_codes,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for wind turbine placement."

doc/configtables/solar.csv

@@ -2,7 +2,7 @@
 cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2.","Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work."
 resource,,,
 -- method,--,"Must be 'pv'","A superordinate technology type."
--- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`__","Specifies the solar panel technology and its characteristic attributes."
+-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the solar panel technology and its characteristic attributes."
 -- orientation,,,
 -- -- slope,°,"Realistically any angle in [0., 90.]","Specifies the tilt angle (or slope) of the solar panel. A slope of zero corresponds to the face of the panel aiming directly overhead. A positive tilt angle steers the panel towards the equator."
 -- -- azimuth,°,"Any angle in [0., 360.]","Specifies the `azimuth <https://en.wikipedia.org/wiki/Azimuth>`_ orientation of the solar panel. South corresponds to 180.°."

doc/release_notes.rst

@@ -71,6 +71,12 @@ Upcoming Release
   Energiewende (2021)
   <https://static.agora-energiewende.de/fileadmin/Projekte/2021/2021_02_EU_CEAP/A-EW_254_Mobilising-circular-economy_study_WEB.pdf>`_.
 
+* Added option to specify turbine and solar panel models for specific years as a
+  dictionary (e.g. ``renewable: onwind: resource: turbine:``). The years will be
+  interpreted as years from when the the corresponding turbine model substitutes
+  the previous model for new installations. This will only have an effect on
+  workflows with foresight "myopic" and still needs to be added foresight option
+  "perfect".
 
 PyPSA-Eur 0.9.0 (5th January 2024)
 ==================================

rules/build_electricity.smk

@@ -261,7 +261,6 @@ rule build_renewable_profiles:
     params:
         snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
         renewable=config["renewable"],
-        foresight=config["foresight"],
     input:
         **opt,
         base_network=RESOURCES + "networks/base.nc",

rules/solve_myopic.smk

@@ -85,10 +85,13 @@ rule add_brownfield:
         H2_retrofit=config["sector"]["H2_retrofit"],
         H2_retrofit_capacity_per_CH4=config["sector"]["H2_retrofit_capacity_per_CH4"],
         threshold_capacity=config["existing_capacities"]["threshold_capacity"],
+        snapshots={k: config["snapshots"][k] for k in ["start", "end", "inclusive"]},
+        carriers=config["electricity"]["renewable_carriers"],
     input:
         **{
             f"profile_{tech}": RESOURCES + f"profile_{tech}.nc"
             for tech in config["electricity"]["renewable_carriers"]
+            if tech != "hydro"
         },
         simplify_busmap=RESOURCES + "busmap_elec_s{simpl}.csv",
         cluster_busmap=RESOURCES + "busmap_elec_s{simpl}_{clusters}.csv",

scripts/add_brownfield.py

@@ -145,78 +145,53 @@ def disable_grid_expansion_if_LV_limit_hit(n):
         n.global_constraints.drop("lv_limit", inplace=True)
 
 
-def adjust_renewable_profiles(n, input_profiles, config, year):
+def adjust_renewable_profiles(n, input_profiles, params, year):
     """
-    Adjusts renewable profiles according to the renewable technology specified.
-
-    If the planning horizon is not available, the closest year is used
-    instead.
+    Adjusts renewable profiles according to the renewable technology specified,
+    using the latest year below or equal to the selected year.
     """
 
+    # spatial clustering
     cluster_busmap = pd.read_csv(snakemake.input.cluster_busmap, index_col=0).squeeze()
     simplify_busmap = pd.read_csv(
         snakemake.input.simplify_busmap, index_col=0
     ).squeeze()
     clustermaps = simplify_busmap.map(cluster_busmap)
     clustermaps.index = clustermaps.index.astype(str)
-    dr = pd.date_range(**config["snapshots"], freq="H")
+
+    # temporal clustering
+    dr = pd.date_range(**params["snapshots"], freq="h")
     snapshotmaps = (
         pd.Series(dr, index=dr).where(lambda x: x.isin(n.snapshots), pd.NA).ffill()
     )
 
-    for carrier in config["electricity"]["renewable_carriers"]:
-        if carrier == "hydro":
-            continue
-
-    clustermaps.index = clustermaps.index.astype(str)
-    dr = pd.date_range(**config["snapshots"], freq="H")
-    snapshotmaps = (
-        pd.Series(dr, index=dr).where(lambda x: x.isin(n.snapshots), pd.NA).ffill()
-    )
-    for carrier in config["electricity"]["renewable_carriers"]:
+    for carrier in params["carriers"]:
         if carrier == "hydro":
             continue
         with xr.open_dataset(getattr(input_profiles, "profile_" + carrier)) as ds:
             if ds.indexes["bus"].empty or "year" not in ds.indexes:
                 continue
-            if year in ds.indexes["year"]:
-                p_max_pu = (
-                    ds["year_profiles"]
-                    .sel(year=year)
-                    .transpose("time", "bus")
-                    .to_pandas()
-                )
-            else:
-                available_previous_years = [
-                    available_year
-                    for available_year in ds.indexes["year"]
-                    if available_year < year
-                ]
-                available_following_years = [
-                    available_year
-                    for available_year in ds.indexes["year"]
-                    if available_year > year
-                ]
-                if available_previous_years:
-                    closest_year = max(available_previous_years)
-                if available_following_years:
-                    closest_year = min(available_following_years)
-                logging.warning(
-                    f"Planning horizon {year} not in {carrier} profiles. Using closest year {closest_year} instead."
+
+            closest_year = max(
+                (y for y in ds.year.values if y <= year), default=min(ds.year.values)
             )
+
             p_max_pu = (
-                    ds["year_profiles"]
+                ds["profile"]
                 .sel(year=closest_year)
                 .transpose("time", "bus")
                 .to_pandas()
             )
+
             # spatial clustering
-            weight = ds["weight"].to_pandas()
+            weight = ds["weight"].sel(year=closest_year).to_pandas()
             weight = weight.groupby(clustermaps).transform(normed_or_uniform)
             p_max_pu = (p_max_pu * weight).T.groupby(clustermaps).sum().T
             p_max_pu.columns = p_max_pu.columns + f" {carrier}"
+
             # temporal_clustering
             p_max_pu = p_max_pu.groupby(snapshotmaps).mean()
+
             # replace renewable time series
             n.generators_t.p_max_pu.loc[:, p_max_pu.columns] = p_max_pu
 
@@ -245,7 +220,7 @@ if __name__ == "__main__":
 
     n = pypsa.Network(snakemake.input.network)
 
-    adjust_renewable_profiles(n, snakemake.input, snakemake.config, year)
+    adjust_renewable_profiles(n, snakemake.input, snakemake.params, year)
 
     add_build_year_to_new_assets(n, year)
 

scripts/add_electricity.py

@@ -374,6 +374,10 @@ def attach_wind_and_solar(
             if ds.indexes["bus"].empty:
                 continue
 
+            # if-statement for compatibility with old profiles
+            if "year" in ds.indexes:
+                ds = ds.sel(year=ds.year.min(), drop=True)
+
             supcar = car.split("-", 2)[0]
             if supcar == "offwind":
                 underwater_fraction = ds["underwater_fraction"].to_pandas()

scripts/build_renewable_profiles.py

@@ -200,24 +200,20 @@ if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
 
-        snakemake = mock_snakemake("build_renewable_profiles", technology="onwind")
+        snakemake = mock_snakemake("build_renewable_profiles", technology="offwind-dc")
     configure_logging(snakemake)
 
     nprocesses = int(snakemake.threads)
     noprogress = snakemake.config["run"].get("disable_progressbar", True)
     noprogress = noprogress or not snakemake.config["atlite"]["show_progress"]
-    year = snakemake.params.renewable["year"]
-    foresight = snakemake.params.foresight
     params = snakemake.params.renewable[snakemake.wildcards.technology]
     resource = params["resource"]  # pv panel params / wind turbine params
 
-    year_dependent_techs = {
-        k: resource.get(k)
-        for k in ["panel", "turbine"]
-        if isinstance(resource.get(k), dict)
-    }
-    for key, techs in year_dependent_techs.items():
-        resource[key] = resource[key][year]
+    tech = next(t for t in ["panel", "turbine"] if t in resource)
+    models = resource[tech]
+    if not isinstance(models, dict):
+        models = {0: models}
+    resource[tech] = models[next(iter(models))]
 
     correction_factor = params.get("correction_factor", 1.0)
     capacity_per_sqkm = params["capacity_per_sqkm"]
@@ -334,10 +330,18 @@ if __name__ == "__main__":
     duration = time.time() - start
     logger.info(f"Completed average capacity factor calculation ({duration:2.2f}s)")
 
-    logger.info("Calculate weighted capacity factor time series...")
+    profiles = []
+    capacities = []
+    for year, model in models.items():
+
+        logger.info(
+            f"Calculate weighted capacity factor time series for model {model}..."
+        )
         start = time.time()
 
-    profile, capacities = func(
+        resource[tech] = model
+
+        profile, capacity = func(
             matrix=availability.stack(spatial=["y", "x"]),
             layout=layout,
             index=buses,
@@ -346,34 +350,21 @@ if __name__ == "__main__":
             **resource,
         )
 
-    if year_dependent_techs and foresight != "overnight":
-        for key, techs in year_dependent_techs.items():
-            year_profiles = list()
-            tech_profiles = dict()
-            tech_profiles[resource[key]] = profile
-            for year, tech in techs.items():
-                resource[key] = tech
-                if tech not in tech_profiles:
-                    tech_profiles[tech] = func(
-                        matrix=availability.stack(spatial=["y", "x"]),
-                        layout=layout,
-                        index=buses,
-                        per_unit=True,
-                        return_capacity=False,
-                        **resource,
-                    )
-                year_profile = tech_profiles[tech]
-                year_profile = year_profile.expand_dims({"year": [year]}).rename(
-                    "year_profiles"
-                )
-                year_profiles.append(year_profile)
-        year_profiles = xr.merge(year_profiles)
+        dim = {"year": [year]}
+        profile = profile.expand_dims(dim)
+        capacity = capacity.expand_dims(dim)
+
+        profiles.append(profile.rename("profile"))
+        capacities.append(capacity.rename("weight"))
 
         duration = time.time() - start
         logger.info(
-        f"Completed weighted capacity factor time series calculation ({duration:2.2f}s)"
+            f"Completed weighted capacity factor time series calculation for model {model} ({duration:2.2f}s)"
         )
 
+    profiles = xr.merge(profiles)
+    capacities = xr.merge(capacities)
+
     logger.info("Calculating maximal capacity per bus")
     p_nom_max = capacity_per_sqkm * availability @ area
 
@@ -399,17 +390,14 @@ if __name__ == "__main__":
 
     ds = xr.merge(
         [
-            (correction_factor * profile).rename("profile"),
-            capacities.rename("weight"),
+            correction_factor * profiles,
+            capacities,
             p_nom_max.rename("p_nom_max"),
             potential.rename("potential"),
             average_distance.rename("average_distance"),
         ]
     )
 
-    if year_dependent_techs:
-        ds = xr.merge([ds, year_profiles * correction_factor])
-
     if snakemake.wildcards.technology.startswith("offwind"):
         logger.info("Calculate underwater fraction of connections.")
         offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).unary_union
@@ -425,7 +413,7 @@ if __name__ == "__main__":
     # select only buses with some capacity and minimal capacity factor
     ds = ds.sel(
         bus=(
-            (ds["profile"].mean("time") > params.get("min_p_max_pu", 0.0))
+            (ds["profile"].mean("time").max("year") > params.get("min_p_max_pu", 0.0))
             & (ds["p_nom_max"] > params.get("min_p_nom_max", 0.0))
         )
     )
@@ -433,9 +421,6 @@ if __name__ == "__main__":
     if "clip_p_max_pu" in params:
         min_p_max_pu = params["clip_p_max_pu"]
         ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0)
-        ds["year_profiles"] = ds["year_profiles"].where(
-            ds["year_profiles"] >= min_p_max_pu, 0
-        )
 
     ds.to_netcdf(snakemake.output.profile)
 

scripts/prepare_sector_network.py

@@ -421,6 +421,11 @@ def update_wind_solar_costs(n, costs):
         tech = "offwind-" + connection
         profile = snakemake.input["profile_offwind_" + connection]
         with xr.open_dataset(profile) as ds:
+
+            # if-statement for compatibility with old profiles
+            if "year" in ds.indexes:
+                ds = ds.sel(year=ds.year.min(), drop=True)
+
             underwater_fraction = ds["underwater_fraction"].to_pandas()
             connection_cost = (
                 snakemake.params.length_factor