Update Model Energg Demand and Fix Problem of Submodels

.github/workflows/ci-linux.yaml

@@ -56,7 +56,7 @@ jobs:
       id: cache
       with:
         path: ${{ matrix.prefix }}
-        key: ${{ matrix.label }}-conda-${{ hashFiles('pypsa-earth/envs/environment.yaml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+        key: ${{ matrix.label }}-conda-${{ hashFiles('pypsa-distribution/envs/environment.yaml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
 
     - name: Update environment due to outdated or unavailable cache
       if: steps.cache.outputs.cache-hit != 'true'

Snakefile

@@ -104,6 +104,7 @@ rule ramp_build_demand_profile:
 rule build_demand:
     params:
         tier=config["tier"],
+        snapshots=config["snapshots"],
         build_demand_model=config["build_demand_type"],
     input:
         **{
@@ -225,7 +226,7 @@ rule build_renewable_profiles:
             "data/copernicus/PROBAV_LC100_global_v3.0.1_2019-nrt_Discrete-Classification-map_EPSG-4326.tif"
         ),
         gebco=pypsaearth("data/gebco/GEBCO_2021_TID.nc"),
-        country_shapes=pypsaearth("resources/shapes/country_shapes.geojson"),
+        country_shapes="resources/shapes/microgrid_shapes.geojson",
         offshore_shapes=pypsaearth("resources/shapes/offshore_shapes.geojson"),
         hydro_capacities="pypsa-earth/data/hydro_capacities.csv",
         eia_hydro_generation="pypsa-earth/data/eia_hydro_annual_generation.csv",

config.distribution.yaml

@@ -39,8 +39,8 @@ countries: ["NG"]
 year: "2019"  # Year setting allows the choice of which data to download (es. Worldpop_data)
 
 snapshots:
-  start: "2013-01-01"
-  end: "2014-01-01"
+  start: "2013-03-01"
+  end: "2013-03-07"
   inclusive: "left"  # end is not inclusive
 
 ramp:
@@ -58,9 +58,9 @@ house_area_limit:
 build_demand_type:
   type: 0
 # type allows to select the mode by which the microgrid demand profile is generated.
-# 1 = a predetermined hourly profile is used
-# 2 = an average hourly profile is calculated by exploiting the ramp tool
-# 3 = an average hourly profile and its standard deviation is calculated using the ramp tool,
+# 0 = a predetermined hourly profile is used
+# 1 = an average hourly profile is calculated by exploiting the ramp tool
+# 2 = an average hourly profile and its standard deviation is calculated using the ramp tool,
 #     and both quantities are used to calculate demand.
 
 # definition of the Coordinate Reference Systems
@@ -79,7 +79,7 @@ electricity:
     H2: 168
 
   extendable_carriers:
-    Generator: [solar, onwind]
+    Generator: [solar, onwind, diesel]
     StorageUnit: ["lithium", "lead acid"]
   conventional_carriers: [diesel]
 

data/costs.csv

@@ -117,7 +117,9 @@ battery inverter,2030,lifetime,20,years,budischak2013;
 battery inverter,2030,efficiency,0.9,per unit charge/discharge,budischak2013; Lund and Kempton (2008) http://dx.doi.org/10.1016/j.enpol.2008.06.007
 battery inverter,2030,FOM,3,%/year,budischak2013;
 lithium,2030,investment,192,USD/kWh,budischak2013;
-lead acid,2030,lifetime,15,years,budischak2013;
+lithium,2030,lifetime,15,years,b;
+lead acid,2030,lifetime,5,years,b;
+lead acid,2030,investment,150,USD/kWh,bu;
 decentral air-sourced heat pump,2030,investment,1050,EUR/kWth,HP; Palzer thesis
 decentral air-sourced heat pump,2030,lifetime,20,years,HP; Palzer thesis
 decentral air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis;
@@ -181,7 +183,7 @@ decentral solar thermal,2030,lifetime,20,years,HP;
 central solar thermal,2030,FOM,1.4,%/year,HP;
 central solar thermal,2030,investment,140000,EUR/1000m2,HP;
 central solar thermal,2030,lifetime,20,years,HP;
-HVAC overhead,2030,investment,400,EUR/MW/km,Hagspiel;
+HVAC overhead,2030,investment,40,EUR/MW/km,Hagspiel;
 HVAC overhead,2030,lifetime,40,years,Hagspiel;
 HVAC overhead,2030,FOM,2,%/year,Hagspiel;
 HVDC overhead,2030,investment,400,EUR/MW/km,Hagspiel;
@@ -193,4 +195,7 @@ HVDC submarine,2030,FOM,2,%/year,Hagspiel;
 HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel;
 HVDC inverter pair,2030,lifetime,40,years,Hagspiel;
 HVDC inverter pair,2030,FOM,2,%/year,Hagspiel;
-diesel,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348;
+diesel,2030,investment,400,EUR/kWel,DIW;
+diesel,2030,efficiency,0.30,per unit,DIW ;
+diesel,2030,VOM,0.5,EUR/MWhel,DIW;
+diesel,2030,fuel,100,USD/MWhth,DIW;

envs/environment.yaml

@@ -0,0 +1,91 @@
+# SPDX-FileCopyrightText:  PyPSA-Earth and PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+name: pypsa-earth
+channels:
+- conda-forge
+- bioconda
+- gurobi
+dependencies:
+- python>=3.8
+- pip
+- mamba   # esp for windows build
+
+- pypsa>=0.24, <0.25
+# - atlite>=0.2.4  # until https://github.com/PyPSA/atlite/issues/244 is not merged
+- dask
+- powerplantmatching
+- earth-osm>=2.1
+- atlite
+
+  # Dependencies of the workflow itself
+- xlrd
+- openpyxl
+- seaborn
+- snakemake-minimal<8
+- memory_profiler
+- ruamel.yaml<=0.17.26
+- pytables
+- lxml
+- numpy
+- pandas
+- geopandas>=0.11.0, <=0.14.3
+- fiona<1.10.0
+- xarray>=2023.11.0, <2023.12.0
+- netcdf4
+- networkx
+- scipy
+- pydoe2
+- shapely!=2.0.4
+- pre-commit
+- pyomo
+- matplotlib<=3.5.2
+- reverse-geocode
+- country_converter
+- pyogrio
+- numba
+- py7zr
+
+  # Keep in conda environment when calling ipython
+- ipython
+  # Jupyter notebook requirement
+- ipykernel
+- jupyterlab
+
+  # GIS dependencies:
+- cartopy
+- descartes
+- rasterio!=1.2.10, <=1.3.11
+- rioxarray
+
+ # Plotting
+- geoviews
+- hvplot
+- graphviz
+- contextily
+- graphviz
+
+  # PyPSA-Eur-Sec Dependencies
+- geopy
+- tqdm
+- pytz
+- country_converter
+
+  # Cloud download
+# - googledrivedownloader  # Commented until https://github.com/ndrplz/google-drive-downloader/pull/28 is merged: PR installed using pip
+
+# Default solver for tests (required for CI)
+- glpk
+- ipopt
+- gurobi
+
+- pip:
+  - earth-osm>=2.2 # until conda release it out
+  - powerplantmatching>=0.5.19 # until conda release it out
+  - rampdemand
+  - git+https://github.com/davide-f/google-drive-downloader@master  # google drive with fix for virus scan
+  - git+https://github.com/FRESNA/vresutils@master  # until new pip release > 0.3.1 (strictly)
+  - tsam>=1.1.0
+  - chaospy  # lastest version only available on pip
+  - fake_useragent

resources/powerplants.csv

@@ -1,2 +1,2 @@
 ,Name,Fueltype,Technology,Set,Country,Capacity,Efficiency,Duration,Volume_Mm3,DamHeight_m,StorageCapacity_MWh,DateIn,DateRetrofit,DateOut,lat,lon,EIC,projectID,bus
-1,New_Diesel_Generator,Diesel,,PP,SL,14.0,,0.0,0.0,0.0,0.0,1986,1986.0,2031,-21.1667,27.5167,{nan},{'GPD': {'WRI1023018'}},bus_9
+1,New_Diesel_Generator,Diesel,,PP,SL,0.0,,0.0,0.0,0.0,0.0,1986,1986.0,2031,-21.1667,27.5167,{nan},{'GPD': {'WRI1023018'}},bus_9

scripts/add_electricity.py

@@ -87,13 +87,20 @@ def _add_missing_carriers_from_costs(n, costs, carriers):
 
 def load_costs(tech_costs, config, elec_config, Nyears=1):
     """
-    set all asset costs and other parameters
+    Set all asset costs and other parameters.
     """
+    idx = pd.IndexSlice
     costs = pd.read_csv(tech_costs, index_col=list(range(3))).sort_index()
 
-    # correct units to MW and EUR
-    costs.loc[costs.unit.str.contains("/kW"), "value"] *= 1e3
-    costs.loc[costs.unit.str.contains("USD"), "value"] *= config["USD2013_to_EUR2013"]
+    costs.loc[
+        costs.unit.str.contains("/kWel"), "value"
+    ] *= 1e3  # Convert EUR/kW to EUR/MW
+    costs.loc[
+        costs.unit.str.contains("/kWh"), "value"
+    ] *= 1e3  # Convert EUR/kWh to EUR/MWh
+    costs.loc[costs.unit.str.contains("USD"), "value"] *= config[
+        "USD2013_to_EUR2013"
+    ]  # Convert USD to EUR
 
     costs = (
         costs.loc[idx[:, config["year"], :], "value"]
@@ -126,6 +133,7 @@ def load_costs(tech_costs, config, elec_config, Nyears=1):
 
     costs.at["OCGT", "fuel"] = costs.at["gas", "fuel"]
     costs.at["CCGT", "fuel"] = costs.at["gas", "fuel"]
+    costs.at["diesel", "fuel"] = costs.at["diesel", "fuel"]
 
     costs["marginal_cost"] = costs["VOM"] + costs["fuel"] / costs["efficiency"]
 
@@ -149,17 +157,13 @@ def costs_for_storage(store, link1, link2=None, max_hours=1.0):
 
     max_hours = elec_config["max_hours"]
     costs.loc["battery"] = costs_for_storage(
-        costs.loc[
-            "lithium"
-        ],  # line 119 in file costs.csv' which was battery storage was modified into lithium (same values left)
+        costs.loc["lithium"],
         costs.loc["battery inverter"],
         max_hours=max_hours["battery"],
     )
-    max_hours = elec_config["max_hours"]
+
     costs.loc["battery"] = costs_for_storage(
-        costs.loc[
-            "lead acid"
-        ],  # line 120 in file 'costs.csv' which was battery storage was modified into lithium (same values left)
+        costs.loc["lead acid"],
         costs.loc["battery inverter"],
         max_hours=max_hours["battery"],
     )
@@ -260,33 +264,28 @@ def attach_conventional_generators(
     conventional_config,
     conventional_inputs,
 ):
-    # Create a set of all conventional and extendable carriers
     carriers = set(conventional_carriers) | set(extendable_carriers["Generator"])
 
-    # Add any missing carriers from the costs data to the "carriers" variable
     _add_missing_carriers_from_costs(n, costs, carriers)
 
-    # Filter the ppl dataframe to only include the relevant carriers
     ppl = (
         ppl.query("carrier in @carriers")
         .join(costs, on="carrier", rsuffix="_r")
         .rename(index=lambda s: "C" + str(s))
     )
     ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency)
 
-    # Get the index of the buses in the power network
     buses_i = n.buses.index
 
-    # Add conventional generators to each bus in the power network (one for microgrid)
-
     n.madd(
         "Generator",
         ppl.index,
         carrier=ppl.carrier,
         bus=ppl.bus,
         p_nom_min=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
         p_nom=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
-        p_nom_extendable=ppl.carrier.isin(extendable_carriers["Generator"]),
+        p_nom_extendable=ppl.carrier.isin(extendable_carriers["Generator"])
+        | (ppl.carrier == "diesel"),
         efficiency=ppl.efficiency,
         marginal_cost=ppl.marginal_cost,
         capital_cost=ppl.capital_cost,
@@ -295,22 +294,22 @@ def attach_conventional_generators(
     )
 
     for carrier in conventional_config:
-        # Generators with technology affected
+        # Generatori con tecnologia influenzata
         idx = n.generators.query("carrier == @carrier").index
 
         for attr in list(set(conventional_config[carrier]) & set(n.generators)):
             values = conventional_config[carrier][attr]
 
             if f"conventional_{carrier}_{attr}" in conventional_inputs:
-                # Values affecting generators of technology k country-specific
-                # First map generator buses to countries; then map countries to p_max_pu
+                # Valori che influenzano i generatori di tecnologia k specifici per paese
+                # Prima mappa i bus dei generatori ai paesi; poi mappa i paesi ai p_max_pu
                 values = pd.read_csv(values, index_col=0).iloc[:, 0]
                 bus_values = n.buses.country.map(values)
                 n.generators[attr].update(
                     n.generators.loc[idx].bus.map(bus_values).dropna()
                 )
             else:
-                # Single value affecting all generators of technology k indiscriminately of country
+                # Singolo valore che influenza tutti i generatori di tecnologia k indipendentemente dal paese
                 n.generators.loc[idx, attr] = values
 
 
@@ -357,6 +356,12 @@ def attach_load(n, load_file, tech_modelling):
     n.madd("Load", demand_df.columns, bus=demand_df.columns, p_set=demand_df)
 
 
+def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=False):
+    n.lines["capital_cost"] = (
+        n.lines["length"] * length_factor * costs.at["HVAC overhead", "capital_cost"]
+    )
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers_dist import mock_snakemake
@@ -419,4 +424,6 @@ def attach_load(n, load_file, tech_modelling):
         snakemake.config["tech_modelling"]["load_carriers"],
     )
 
+    update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=False)
+
     n.export_to_netcdf(snakemake.output[0])