From d5f4ce3b42898956b0abb0b61a891505042b97d2 Mon Sep 17 00:00:00 2001 From: irr-github Date: Wed, 17 Dec 2025 14:42:49 +0100 Subject: [PATCH 1/9] fix subsidy module --- workflow/scripts/plot_time_series.py | 7 +- workflow/scripts/prepare_network.py | 89 ---------------- workflow/scripts/solve_network.py | 147 ++++++++++++++++++++++++--- 3 files changed, 135 insertions(+), 108 deletions(-) diff --git a/workflow/scripts/plot_time_series.py b/workflow/scripts/plot_time_series.py index e833fd60..07e9ad60 100644 --- a/workflow/scripts/plot_time_series.py +++ b/workflow/scripts/plot_time_series.py @@ -267,10 +267,9 @@ def plot_residual_load_duration_curve( ) .groupby(level=1) .sum() - .loc[vre_techs] - .sum() ) - + tech_filter = [t for t in vre_techs if t in vre_supply.index] + vre_supply = vre_supply.loc[tech_filter].sum() residual = (load - vre_supply).sort_values(ascending=False) / PLOT_CAP_UNITS residual.reset_index(drop=True).plot(ax=ax, lw=3) ax.set_ylabel(f"Residual Load [{PLOT_CAP_LABEL}]") @@ -529,7 +528,7 @@ def plot_vre_timemap( # co2_pathway="SSP2-PkBudg1000-CHA-pypsaelh2", heating_demand="positive", # configfiles=["resources/tmp/remind_coupled_cg.yaml"], - planning_horizons="2050", + planning_horizons="2025", winter_day1="12-10 21:00", # mm-dd HH:MM winter_day2="12-17 12:00", # mm-dd HH:MM spring_day1="03-31 21:00", # mm-dd HH:MM diff --git a/workflow/scripts/prepare_network.py b/workflow/scripts/prepare_network.py index 48aad651..d8538acc 100644 --- a/workflow/scripts/prepare_network.py +++ b/workflow/scripts/prepare_network.py @@ -246,86 +246,6 @@ def add_co2_capture_support( ) -def add_fuel_subsidies(n: pypsa.Network, subsidy_config: dict): - """Apply fuel subsidies to generators as a post-processing step. - - Subsidies are applied to generators based on their carrier and location. - The subsidy values (in EUR/MWh fuel) are divided by efficiency to convert - to electricity basis (EUR/MWhel). Links are not modified as they get their - fuel from generators. - - Args: - n (pypsa.Network): The network object to modify. - subsidy_config (dict): Subsidy configuration dictionary with keys like - "coal" or "gas", each containing a dict mapping provinces to subsidy values. - """ - if not subsidy_config: - return - - carriers = subsidy_config.keys() - - for carrier in carriers: - subs_dict = subsidy_config.get(carrier, {}) - if not subs_dict: - continue - - # Convert subsidy dict to Series indexed by province - subs = pd.Series(subs_dict, dtype=float) - - # Check that subsidies are non-positive (negative = subsidy, positive would be a reward) - if (subs > 0).any(): - raise ValueError( - f"Positive subsidy values found for carrier '{carrier}': " - f"{subs[subs > 0].to_dict()}. Only zero or negative values are allowed " - f"(negative reduces marginal cost, positive would increase it)." - ) - - # Check if location column exists - if "location" not in n.generators.columns: - logger.warning( - f"Location column not found in generators. " - f"Cannot apply subsidies for carrier '{carrier}'." - ) - continue - - # Query generators with matching carrier and location in subsidy provinces - mask = n.generators.query( - f"carrier == @carrier and location in @subs.index" - ).index - - if mask.empty: - logger.warning( - f"No generators found with carrier '{carrier}' and locations " - f"in {list(subs.index)}. Skipping subsidy application." - ) - continue - - # Merge subsidies with generators by location - gen_locs = n.generators.loc[mask, "location"] - subs_to_apply = gen_locs.map(subs).fillna(0.0) - - # Check if all provinces were found - missing_provs = set(subs.index) - set(gen_locs.unique()) - if missing_provs: - logger.warning( - f"Subsidies specified for provinces {missing_provs} but no " - f"generators found with carrier '{carrier}' in these provinces." - ) - - # Apply subsidies: divide by efficiency to convert from fuel to electricity basis - # Handle cases where efficiency might be NaN or missing - efficiencies = n.generators.loc[mask, "efficiency"].fillna(1.0) - subs_electricity = subs_to_apply / efficiencies - - # Subtract subsidy from marginal cost (negative subsidy = cost reduction) - n.generators.loc[mask, "marginal_cost"] += subs_electricity - - logger.info( - f"Applied subsidies for carrier '{carrier}' to {len(mask)} generators " - f"in provinces {sorted(gen_locs.unique())}" - ) - - def add_conventional_generators( network: pypsa.Network, nodes: pd.Index, @@ -786,7 +706,6 @@ def add_wind_and_solar( Raises: ValueError: If unsupported technologies are specified or if paths not specified """ - unsupported = set(techs).difference({"solar", "onwind", "offwind"}) if unsupported: raise ValueError(f"Carrier(s) {unsupported} not wind or solar pv") @@ -1698,14 +1617,6 @@ def prepare_network( assign_locations(network) - # Apply fuel subsidies as post-processing step - subsidy_config = config.get("subsidies", {}) - if subsidy_config and subsidy_config.get("enabled", True): - # Remove 'enabled' key before passing to add_fuel_subsidies - subsidy_config_clean = {k: v for k, v in subsidy_config.items() if k != "enabled"} - if subsidy_config_clean: - add_fuel_subsidies(network, subsidy_config_clean) - return network diff --git a/workflow/scripts/solve_network.py b/workflow/scripts/solve_network.py index b4962935..c87399a5 100644 --- a/workflow/scripts/solve_network.py +++ b/workflow/scripts/solve_network.py @@ -14,6 +14,7 @@ import pypsa import xarray as xr import os +import re from _helpers import ConfigManager, configure_logging, mock_snakemake, setup_gurobi_tunnel_and_env from _pypsa_helpers import filter_carriers, mock_solve, store_duals_to_network from constants import YEAR_HRS @@ -23,6 +24,112 @@ logger = logging.getLogger(__name__) +# TODO move to prepare_network after refactor of workflow +def add_fuel_subsidies(n: pypsa.Network, subsidy_config: dict, planning_year: int = None): + """Apply fuel subsidies to generators as a post-processing step. + + Subsidies are applied to generators based on their carrier and location. + The subsidy values (in EUR/MWh fuel) are divided by efficiency to convert + to electricity basis (EUR/MWhel). Links are not modified as they get their + fuel from generators. + + Args: + n (pypsa.Network): The network object to modify. + subsidy_config (dict): Subsidy configuration dictionary with keys like + "coal" or "gas", each containing either: + - dict mapping provinces to subsidy values (year-independent) + - dict mapping years to dicts of provinces to subsidy values (year-dependent) + planning_year (int, optional): Planning year for year-dependent subsidies. + Required if subsidies are year-dependent. + """ + if not subsidy_config: + return + + carriers = subsidy_config.keys() + + for carrier in carriers: + carrier_config = subsidy_config.get(carrier, {}) + carrier_config = {str(k): v for k, v in carrier_config.items()} + if not carrier_config: + continue + + # Determine if subsidies are year-dependent + # Check if all keys are years (int or str matching 4-digit year pattern) + all_keys_are_years = all( + isinstance(k, int) or (isinstance(k, str) and re.match(r"^\d{4}$", k)) + for k in carrier_config.keys() + ) + + if all_keys_are_years: + # Extract subsidies for the specific planning year + year_key = str(planning_year) if str(planning_year) in carrier_config else planning_year + if year_key not in carrier_config: + logger.warning( + f"No subsidies configured for carrier '{carrier}' in year {planning_year}. " + f"Available years: {list(carrier_config.keys())}. Skipping subsidy application." + ) + continue + subs_dict = carrier_config[year_key] + else: + # Year-independent: use the config directly + subs_dict = carrier_config + + # Convert subsidy dict to Series indexed by province + subs = pd.Series(subs_dict, dtype=float) + + # Check that subsidies are non-positive (negative = subsidy, positive would be a reward) + if (subs > 0).any(): + raise ValueError( + f"Positive subsidy values found for carrier '{carrier}': " + f"{subs[subs > 0].to_dict()}. Only zero or negative values are allowed " + f"(negative reduces marginal cost, positive would increase it)." + ) + + # Check if location column exists + if "location" not in n.generators.columns: + logger.warning( + f"Location column not found in generators. " + f"Cannot apply subsidies for carrier '{carrier}'." + ) + continue + + # Query generators with matching carrier and location in subsidy provinces + mask = n.generators.query( + "carrier == @carrier and location in @subs.index" + ).index + + if mask.empty: + logger.warning( + f"No generators found with carrier '{carrier}' and locations " + f"in {list(subs.index)}. Skipping subsidy application." + ) + continue + + # Merge subsidies with generators by location with fallback to province + gen_locs = n.generators.loc[mask, "location"] + subs_to_apply = gen_locs.map(subs).fillna(0.0) + + # Check if all provinces were found + missing_provs = set(subs.index) - set(gen_locs.unique()) + if missing_provs: + logger.warning( + f"Subsidies specified for provinces | nodes {missing_provs} but no " + f"generators found with carrier '{carrier}' in these nodes | provinces." + ) + + # Apply subsidies: divide by efficiency to convert from fuel to electricity basis + # Handle cases where efficiency might be NaN or missing + efficiencies = n.generators.loc[mask, "efficiency"].fillna(1.0) + subs_electricity = subs_to_apply / efficiencies + + # Subtract subsidy from marginal cost (negative subsidy = cost reduction) + n.generators.loc[mask, "marginal_cost"] += subs_electricity + logger.info( + f"Applied subsidies for carrier '{carrier}' to {len(mask)} generators " + f"in provinces {sorted(gen_locs.unique())}" + ) + + def calc_nuclear_expansion_limit( n: pypsa.Network, config: dict, @@ -31,7 +138,7 @@ def calc_nuclear_expansion_limit( ) -> None: """ Calculate and apply the nuclear expansion limit from configuration. - + Args: n (pypsa.Network): the network object config (dict): full configuration dictionary (mutated in place) @@ -41,12 +148,12 @@ def calc_nuclear_expansion_limit( nuclear_cfg = config.setdefault("nuclear_reactors", {}) if not nuclear_cfg.get("enable_growth_limit"): return - + annual_addition = nuclear_cfg.get("max_annual_capacity_addition") if not annual_addition: logger.warning("Nuclear growth limit enabled but max_annual_capacity_addition missing") return - + base_year = nuclear_cfg.get("base_year", 2020) n_years = planning_year - base_year if n_years <= 0: @@ -56,7 +163,7 @@ def calc_nuclear_expansion_limit( base_year, ) return - + base_capacity = nuclear_cfg.get("base_capacity") if base_capacity is None: base_path = network_path.replace(f"ntwk_{planning_year}.nc", f"ntwk_{base_year}.nc") @@ -65,7 +172,7 @@ def calc_nuclear_expansion_limit( base_capacity = n_base.generators[n_base.generators.carrier == "nuclear"]["p_nom"].sum() else: base_capacity = n.generators[n.generators.carrier == "nuclear"]["p_nom"].sum() - + max_capacity = base_capacity + annual_addition * n_years logger.info( f"Adding nuclear expansion limit for {planning_year}: {max_capacity:.0f} MW " @@ -75,11 +182,11 @@ def calc_nuclear_expansion_limit( nuclear_gens_ext = n.generators[ (n.generators.carrier == "nuclear") & (n.generators.p_nom_extendable == True) ].index - + if len(nuclear_gens_ext) == 0: logger.warning("No extendable nuclear generators found") return - + n.generators.loc[nuclear_gens_ext, "p_nom_max"] = max_capacity nuclear_cfg["expansion_limit"] = max_capacity logger.info( @@ -325,21 +432,21 @@ def prepare_network( def add_nuclear_expansion_constraints(n: pypsa.Network): """ Add nuclear expansion limit constraint if configured. - + Args: n (pypsa.Network): the network object """ limit = n.config.get("nuclear_reactors", {}).get("expansion_limit") if limit is None: return - + nuclear_gens_ext = n.generators[ (n.generators.carrier == "nuclear") & (n.generators.p_nom_extendable == True) ].index - + if len(nuclear_gens_ext) == 0: return - + # Add global constraint: sum of all nuclear p_nom <= limit lhs = n.model["Generator-p_nom"].loc[nuclear_gens_ext].sum() n.model.add_constraints(lhs <= limit, name="nuclear_expansion_limit") @@ -825,7 +932,7 @@ def extra_functionality(n: pypsa.Network, _) -> None: add_battery_constraints(n) add_transmission_constraints(n) add_nuclear_expansion_constraints(n) - + if config["heat_coupling"]: add_water_tank_charger_constraints(n, config) add_chp_constraints(n) @@ -905,13 +1012,13 @@ def solve_network( if "snakemake" not in globals(): snakemake = mock_snakemake( "solve_networks", - co2_pathway="SSP2-PkBudg1000-pseudo-coupled", - planning_horizons="2030", + co2_pathway="exp175default", + planning_horizons="2025", topology="current+FCG", # heating_demand="positive", # configfiles="resources/tmp/remind_coupled_cg.yaml", heating_demand="positive", - configfiles="resources/tmp/pseudo-coupled.yaml", + # configfiles="resources/tmp/pseudo-coupled.yaml", ) configure_logging(snakemake) config = snakemake.config @@ -938,6 +1045,16 @@ def solve_network( n, solve_opts, snakemake.config, snakemake.wildcards.planning_horizons, co2_pathway ) + # Apply fuel subsidies + subsidy_config = snakemake.config.get("subsidies", {"enabled": False}) + if subsidy_config and subsidy_config.get("enabled", True): + # Remove 'enabled' key before passing to add_fuel_subsidies + subsidy_config_clean = {k: v for k, v in subsidy_config.items() if k != "enabled"} + if subsidy_config_clean: + add_fuel_subsidies( + n, subsidy_config_clean, planning_year=snakemake.wildcards.planning_horizons + ) + line_exp_limits = snakemake.config["lines"].get( "expansion", {"transmission_limit": "copt", "base_year": 2020} ) From 5b98d4d54c817a2fa7ec4484b364d0d4f7b6c192 Mon Sep 17 00:00:00 2001 From: irr-github Date: Wed, 17 Dec 2025 17:17:06 +0100 Subject: [PATCH 2/9] update docu add example --- docs/configuration.md | 39 +++++++++++++++++++++++++++------------ examples/historical.yml | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 61 insertions(+), 12 deletions(-) create mode 100644 examples/historical.yml diff --git a/docs/configuration.md b/docs/configuration.md index 8ad3e03a..238dc79a 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -542,18 +542,33 @@ nodes: - **`splits`**: Custom groupings of admin level 2 regions within provinces ## Fuel Subsidies - -```yaml -subsidies: - enabled: false - gas: - Guangdong: -10 - Jiangsu: -10 - Zhejiang: -10 - Beijing: -11 - Tianjin: -11 - Shanghai: -11 -``` +Fuel subsidies can be speficied for all years or per year + + === "All years" + ```yaml + subsidies: + enabled: false + gas: + Guangdong: -10 + Jiangsu: -10 + Zhejiang: -10 + Beijing: -11 + Tianjin: -11 + Shanghai: -11 + ``` + === "Year by year" + ```yaml + subsidies: + enabled: false + gas: + 2020: + Guangdong: -10 + Jiangsu: -10 + Zhejiang: -10 + Beijing: -11 + Tianjin: -11 + Shanghai: -11 + ``` Provincial fuel subsidies configuration: - **`enabled`**: Enable/disable fuel subsidy system diff --git a/examples/historical.yml b/examples/historical.yml new file mode 100644 index 00000000..cd8c51d0 --- /dev/null +++ b/examples/historical.yml @@ -0,0 +1,34 @@ +# A Configuration to reproduce historical mix for 2020 and 2025 +# run with `snakemake --configfile=examples/historical.yml` + +run: + name: "reproduce_historical_load" +foresight: "overnight" + +scenario: + co2_pathway: ["exp175default"] # co2_scenarios that will be used + topology: "current+FCG" # "current" or "FCG" or "current+FCG" or "current+Neighbor" + planning_horizons: + - 2020 + - 2025 + +subsidies: + enabled: True # Set to false to disable fuel subsidies + # Year-dependent format: subsidies.fuel_type -> year -> province -> value (EUR/MWh) + # Only negative values allowed (subsidies reduce marginal cost) + # Gas favoured over coal in urban areas due to PM2.5 concerns + gas: + 2020: + Guangdong: -10.16 + Jiangsu: -10.16 + Zhejiang: -10.15 + Beijing: -12.5 + Tianjin: -12.5 + Shanghai: -12.5 + Xinjiang: -10 + # location dependent fuel prices (cheaper in Shaanxi and Inner Mongolia for example) + coal: + 2020: + Xinjiang: -4 + InnerMongolia: -4.5 + Hebei: -4 From c72e97f366402769bd9b9ae2acd34602510cfa31 Mon Sep 17 00:00:00 2001 From: irr-github Date: Wed, 17 Dec 2025 17:19:03 +0100 Subject: [PATCH 3/9] update change log --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f3d63177..c316f138 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -27,6 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Global Energy Monitor data integration - Technology configuration system - Network plotting with customizable styles +- Policies (subsidies) and differentiated fuel costs ### Changed - Improved documentation structure with tutorials and reference guides @@ -67,7 +68,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Version History Notes -PyPSA-China (PIK) is adapted from the Zhou et al. version, which was originally developed by Hailiang Liu et al. This changelog tracks changes from version 1.0.0 onwards in the PIK implementation. +PyPSA-China (PIK) is based on the paper by Zhou et al, which extends a version original developed by Hailiang Liu et al. This changelog tracks changes from version 1.0.0 onwards in the PIK implementation. For detailed information about specific changes, see the [commit history](https://github.com/pik-piam/PyPSA-China-PIK/commits/main) on GitHub. From 0a5f4a3636c20820f3e9aa0f18d460e2f0ec7ee1 Mon Sep 17 00:00:00 2001 From: irr-github Date: Fri, 19 Dec 2025 16:05:31 +0100 Subject: [PATCH 4/9] appease linter, fix merge issues --- workflow/__init__.py | 1 + workflow/scripts/__init__.py | 1 + workflow/scripts/_plot_utilities.py | 35 ++++++++++--------- workflow/scripts/_pypsa_helpers.py | 2 +- workflow/scripts/add_sectors.py | 4 +-- workflow/scripts/build_population.py | 4 +-- .../scripts/determine_availability_matrix.py | 1 - workflow/scripts/functions.py | 24 ++++++------- workflow/scripts/plot_network.py | 2 +- workflow/scripts/prepare_network.py | 2 +- workflow/scripts/readers.py | 14 ++++---- .../ev_refshare_extrapolator.py | 4 +-- .../extrapolate_regional_references.py | 4 +-- workflow/scripts/solve_network.py | 7 ++-- 14 files changed, 55 insertions(+), 50 deletions(-) diff --git a/workflow/__init__.py b/workflow/__init__.py index 31c1b72a..525ef52c 100644 --- a/workflow/__init__.py +++ b/workflow/__init__.py @@ -1,2 +1,3 @@ +"""Track version""" # pypsa-China PIK editions __version__ = "1.3.2" diff --git a/workflow/scripts/__init__.py b/workflow/scripts/__init__.py index 4add34d6..bc89d37a 100644 --- a/workflow/scripts/__init__.py +++ b/workflow/scripts/__init__.py @@ -1 +1,2 @@ +"""init file for scope""" # for make the docs diff --git a/workflow/scripts/_plot_utilities.py b/workflow/scripts/_plot_utilities.py index 9b5291b2..fda5e610 100644 --- a/workflow/scripts/_plot_utilities.py +++ b/workflow/scripts/_plot_utilities.py @@ -14,25 +14,25 @@ def validate_hex_colors(tech_colors: dict[str, str], fill_color = "#999999") -> dict[str, str]: """Validate and standardize hex color codes in technology color mappings. - + Ensures all color codes in the technology colors dictionary are valid hexadecimal color codes. Invalid or malformed colors are replaced with a default gray color. - + Args: tech_colors (Dict[str, str]): Dictionary mapping technology names to color codes. Expected format is {'tech_name': '#RRGGBB'} or {'tech_name': '#RGB'}. fill_color (str, optional): Default color to use for invalid entries. Defaults to '#999999'. - + Returns: dict[str,str] with validated hex color codes. All valid colors are converted to lowercase, while invalid colors are replaced with '#999999' (gray). - + Example: >>> colors = {'solar': '#FFD700', 'wind': 'invalid', 'coal': '#8B4513'} >>> validated = validate_hex_colors(colors) >>> print(validated) {'solar': '#ffd700', 'wind': '#999999', 'coal': '#8b4513'} - + Note: Accepts both 3-digit (#RGB) and 6-digit (#RRGGBB) hex color formats. All valid colors are standardized to lowercase. @@ -463,16 +463,16 @@ def annotate_heatmap( def setup_plot_export_hook(plot_accessor_class, export_dir="plot_exports", verbose=True): """Setup a monkey patch to auto-export data to CSV whenever pandas plots are created. - + Args: plot_accessor_class: The PlotAccessor class to patch (e.g., pandas.plotting.PlotAccessor). export_dir (str, optional): Directory where CSV exports will be saved. Defaults to "plot_exports". verbose (bool, optional): Whether to print export messages. Defaults to True. - + Returns: callable: Function to remove the patch and restore original behavior. - + Example: >>> from pandas.plotting import PlotAccessor >>> remove_hook = setup_plot_export_hook(PlotAccessor) @@ -481,15 +481,16 @@ def setup_plot_export_hook(plot_accessor_class, export_dir="plot_exports", verbo """ import os import time + import pandas as pd - + # Create export directory os.makedirs(export_dir, exist_ok=True) - + # Store original __call__ if not already stored if not hasattr(plot_accessor_class, '_original_call'): plot_accessor_class._original_call = plot_accessor_class.__call__ - + def patched_plot_call(self, *args, **kwargs): """Patched __call__ method for PlotAccessor to export data before plotting.""" # Create timestamped filename @@ -498,22 +499,22 @@ def patched_plot_call(self, *args, **kwargs): else: ts = time.strftime("%Y%m%d_%H%M%S") fname = os.path.join(export_dir, f"plot_export_{ts}.csv") - + # Export the data if isinstance(self._parent, pd.Series): self._parent.to_frame().to_csv(fname, index=True) else: self._parent.to_csv(fname, index=True) - + if verbose: print(f"[pandas-plot-hook] Exported plotted data to {fname}") - + # Call the original __call__ method return self._original_call(*args, **kwargs) - + # Apply the patch plot_accessor_class.__call__ = patched_plot_call - + # Return function to remove the patch def remove_hook(): """Remove the plot export hook and restore original behavior.""" @@ -522,7 +523,7 @@ def remove_hook(): delattr(plot_accessor_class, '_original_call') if verbose: print("[pandas-plot-hook] Hook removed, original behavior restored.") - + return remove_hook diff --git a/workflow/scripts/_pypsa_helpers.py b/workflow/scripts/_pypsa_helpers.py index e6e02b11..e0a30993 100644 --- a/workflow/scripts/_pypsa_helpers.py +++ b/workflow/scripts/_pypsa_helpers.py @@ -8,8 +8,8 @@ import pandas as pd import pypsa import pytz - from constants import PROV_NAMES + # get root logger logger = logging.getLogger() diff --git a/workflow/scripts/add_sectors.py b/workflow/scripts/add_sectors.py index 6fc2abb3..ab6bd825 100644 --- a/workflow/scripts/add_sectors.py +++ b/workflow/scripts/add_sectors.py @@ -11,7 +11,7 @@ def add_carrier_if_missing(n: pypsa.Network, carrier_name: str): """Add a carrier to the network if it doesn't already exist. - + Args: n (pypsa.Network): PyPSA network to modify. carrier_name (str): Name of the carrier to add. @@ -113,7 +113,7 @@ def attach_simple_ev( transport_cfg = ev_cfg.get("transport", {}) logger.info("Transport configuration: %s", transport_cfg) - + passenger_cfg = transport_cfg.get("passenger_bev", {}) if passenger_cfg.get("enable", False): charging = pd.read_csv( diff --git a/workflow/scripts/build_population.py b/workflow/scripts/build_population.py index 3cab3c27..6ace0700 100644 --- a/workflow/scripts/build_population.py +++ b/workflow/scripts/build_population.py @@ -14,7 +14,7 @@ def load_pop_csv(csv_path: os.PathLike) -> pd.DataFrame: """Load the national bureau of statistics of China population. - + Supports both formats: - Yearbook format (2.5 pop at year end by Region) - Historical data format with comment lines @@ -24,7 +24,7 @@ def load_pop_csv(csv_path: os.PathLike) -> pd.DataFrame: Returns: pd.DataFrame: The population for constants.POP_YEAR by province - + Raises: ValueError: If the province names do not match expected names """ diff --git a/workflow/scripts/determine_availability_matrix.py b/workflow/scripts/determine_availability_matrix.py index 8339f29d..1d8f2fe2 100644 --- a/workflow/scripts/determine_availability_matrix.py +++ b/workflow/scripts/determine_availability_matrix.py @@ -27,7 +27,6 @@ import numpy as np from _helpers import configure_logging, mock_snakemake from constants import OFFSHORE_WIND_NODES, PROV_NAMES -from pandas import concat from readers_geospatial import read_offshore_province_shapes, read_province_shapes logger = logging.getLogger(__name__) diff --git a/workflow/scripts/functions.py b/workflow/scripts/functions.py index 2473f8e5..f6ae7408 100644 --- a/workflow/scripts/functions.py +++ b/workflow/scripts/functions.py @@ -19,28 +19,28 @@ # polynomial centroid for plotting def get_poly_center(poly: Polygon): """Get the geographic centroid of a polygon geometry. - + Extracts the centroid coordinates from a polygon object, typically used for plotting and spatial analysis in geographic applications. - + Args: poly (Polygon): A (shapely) polygon geometry object with a centroid attribute that has x and y coordinate arrays. - + Returns: tuple: A tuple containing (x, y) coordinates of the polygon centroid as floating point numbers. - + Example: >>> from shapely.geometry import Polygon >>> polygon = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> center = get_poly_center(polygon) >>> print(center) (0.5, 0.5) - + Note: - This function assumes the polygon object has a centroid attribute - with xy arrays containing coordinate data. + This function assumes the polygon object has a centroid attribute + with xy arrays containing coordinate data. """ return (poly.centroid.xy[0][0], poly.centroid.xy[1][0]) @@ -69,18 +69,18 @@ def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame: def haversine(p1, p2) -> float: """Calculate the great circle distance between two points on Earth. - + Uses the Haversine formula to compute the shortest distance over the Earth's surface between two points specified in decimal degrees latitude and longitude. This is useful for calculating distances between geographic locations. - + Args: p1 (shapely.Point): location 1 in decimal deg p2 (shapely.Point): location 2 in decimal deg Returns: float: Great circle distance between the two points in kilometers. - + Example: >>> from shapely.geometry import Point >>> beijing = Point(116.4074, 39.9042) # longitude, latitude @@ -88,7 +88,7 @@ def haversine(p1, p2) -> float: >>> distance = haversine(beijing, shanghai) >>> print(f"Distance: {distance:.1f} km") Distance: 1067.1 km - + Note: The function assumes the Earth is a perfect sphere with radius 6371 km. """ @@ -112,7 +112,7 @@ def area_from_lon_lat_poly(geometry: Polygon): Args: geometry (Polygon): Polygon geometry in lon-lat coordinates. - + Returns: float: Area of the polygon in m^2. """ diff --git a/workflow/scripts/plot_network.py b/workflow/scripts/plot_network.py index 4bc9c495..6cb1d6b9 100644 --- a/workflow/scripts/plot_network.py +++ b/workflow/scripts/plot_network.py @@ -732,4 +732,4 @@ def calc_plot_width(row, carrier="AC"): save_path=snakemake.output.cost_map, ) - logger.info("Network successfully plotted") \ No newline at end of file + logger.info("Network successfully plotted") diff --git a/workflow/scripts/prepare_network.py b/workflow/scripts/prepare_network.py index d8538acc..7543e282 100644 --- a/workflow/scripts/prepare_network.py +++ b/workflow/scripts/prepare_network.py @@ -1616,7 +1616,7 @@ def prepare_network( add_voltage_links(network, config) assign_locations(network) - + return network diff --git a/workflow/scripts/readers.py b/workflow/scripts/readers.py index b018ba58..5d5df80c 100644 --- a/workflow/scripts/readers.py +++ b/workflow/scripts/readers.py @@ -12,7 +12,7 @@ def aggregate_sectoral_loads(yearly_proj: pd.DataFrame, config: dict) -> pd.DataFrame: """Aggregate REMIND load sectors according to the model configuration. - + Sectors that are NOT enabled for independent modeling will be aggregated into the main electricity load. For example, if EV sector is not enabled as an independent sector (enabled: false), its load will be added to the @@ -75,11 +75,11 @@ def read_yearly_load_projections( config: dict = None, ) -> pd.DataFrame: """Read and process yearly load projections from CSV files. - + Supports both simple load data and REMIND sector-coupled data with electric vehicle integration. Automatically detects data format and applies appropriate processing. - + Args: file_path (os.PathLike): Path to the yearly projections CSV file. Defaults to "resources/data/load/Province_Load_2020_2060.csv". @@ -88,21 +88,21 @@ def read_yearly_load_projections( config (dict, optional): Configuration dictionary for sector processing. Required when processing REMIND data with sector columns. Should contain 'sectors' and 'sector_mapping' keys. - + Returns: pd.DataFrame: Processed load projections data with: - Province names as index (for simple data) or columns - Year columns as integers - Data converted by the conversion factor - + Raises: ValueError: If required columns are missing or configuration is invalid FileNotFoundError: If the input file does not exist - + Examples: >>> # Simple load data >>> data = read_yearly_load_projections("simple_load.csv") - + >>> # REMIND data with electric vehicles >>> config = { ... "sectors": {"electric_vehicles": True}, diff --git a/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py b/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py index 2ae3b4cc..9894e87b 100644 --- a/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py +++ b/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py @@ -16,7 +16,7 @@ class GompertzModel: """Simplified Gompertz model for vehicle ownership prediction. - + Args: saturation_level: Maximum vehicle ownership per 1000 people (default: 500) alpha: Fixed Gompertz parameter (default: -5.58) @@ -189,7 +189,7 @@ def extrapolate_reference(years: list, input_files: dict, output_dir: str, confi sectors.electric_vehicles.gompertz configuration: - 'saturation_level': Maximum vehicles per 1000 people (default: 500) - 'alpha': Fixed Gompertz parameter (default: -5.58) - + Outputs: Saves two CSV files to output_dir: - ev_passenger_shares.csv: Provincial shares of passenger EV demand diff --git a/workflow/scripts/remind_coupling/extrapolate_regional_references.py b/workflow/scripts/remind_coupling/extrapolate_regional_references.py index 1d73d2d9..95116bdd 100644 --- a/workflow/scripts/remind_coupling/extrapolate_regional_references.py +++ b/workflow/scripts/remind_coupling/extrapolate_regional_references.py @@ -21,7 +21,7 @@ class SectorReferenceGenerator: """General framework for generating sectoral disaggregation shares. - + Coordinates the extrapolation of provincial share/ratio data for different sectors (e.g., EV, heat). These shares are used to spatially disaggregate REMIND national outputs to provincial level. @@ -53,7 +53,7 @@ def _load_sector_modules(self): def extrapolate_references(self, years: list[int], input_files: dict[str, str], output_dir: str): """Extrapolate provincial disaggregation shares for all available sectors. - + Generates reference share files that indicate what fraction of national-level sectoral demand/activity belongs to each province. For example, EV passenger shares show the provincial distribution of passenger EV demand. diff --git a/workflow/scripts/solve_network.py b/workflow/scripts/solve_network.py index a4b2dc07..74347935 100644 --- a/workflow/scripts/solve_network.py +++ b/workflow/scripts/solve_network.py @@ -421,10 +421,13 @@ def add_nuclear_expansion_constraints(n: pypsa.Network): Args: n (pypsa.Network): the network object """ - limit = n.config.get("nuclear_reactors", {}).get("expansion_limit") - if limit is None: + config = getattr(n, "config", {}) + max_capacity = config.get("nuclear_max_capacity") if isinstance(config, dict) else None + + if max_capacity is None: return + nuclear_gens_ext = n.generators[ (n.generators.carrier == "nuclear") & (n.generators.p_nom_extendable == True) ].index From 82b2feb67c6481361b474e8f2d825d68d99fe5df Mon Sep 17 00:00:00 2001 From: irr-github Date: Fri, 19 Dec 2025 16:06:41 +0100 Subject: [PATCH 5/9] strip --- workflow/notebooks/update_cost_data.ipynb | 1935 +-------------------- 1 file changed, 35 insertions(+), 1900 deletions(-) diff --git a/workflow/notebooks/update_cost_data.ipynb b/workflow/notebooks/update_cost_data.ipynb index b38def82..a162dadf 100644 --- a/workflow/notebooks/update_cost_data.ipynb +++ b/workflow/notebooks/update_cost_data.ipynb @@ -2,29 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['../../resources/data/costs/default/costs_2020.csv',\n", - " '../../resources/data/costs/default/costs_2025.csv',\n", - " '../../resources/data/costs/default/costs_2030.csv',\n", - " '../../resources/data/costs/default/costs_2035.csv',\n", - " '../../resources/data/costs/default/costs_2040.csv',\n", - " '../../resources/data/costs/default/costs_2045.csv',\n", - " '../../resources/data/costs/default/costs_2050.csv',\n", - " '../../resources/data/costs/default/costs_2055.csv',\n", - " '../../resources/data/costs/default/costs_2060.csv']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", @@ -39,7 +20,7 @@ }, { "cell_type": "markdown", - "id": "c705e2c4", + "id": "1", "metadata": {}, "source": [ "### compare the cost change" @@ -48,1710 +29,9 @@ { "cell_type": "code", "execution_count": null, - "id": "d4bb4d45", + "id": "2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "🔍 Comparing: costs_2020.csv\n", - "====================================================================================================\n", - "\n", - "🟢 Added rows:\n", - " technology \\\n", - "0 Alkaline electrolyzer large size \n", - "1 Alkaline electrolyzer large size \n", - "2 Alkaline electrolyzer large size \n", - "3 Alkaline electrolyzer large size \n", - "4 Alkaline electrolyzer large size \n", - "5 Alkaline electrolyzer medium size \n", - "6 Alkaline electrolyzer medium size \n", - "7 Alkaline electrolyzer medium size \n", - "8 Alkaline electrolyzer medium size \n", - "9 Alkaline electrolyzer medium size \n", - "10 Alkaline electrolyzer small size \n", - "11 Alkaline electrolyzer small size \n", - "12 Alkaline electrolyzer small size \n", - "13 Alkaline electrolyzer small size \n", - "14 Alkaline electrolyzer small size \n", - "16 Ammonia cracker \n", - "19 BEV Bus city \n", - "20 BEV Bus city \n", - "21 BEV Bus city \n", - "22 BEV Bus city \n", - "23 BEV Bus city \n", - "24 BEV Bus city \n", - "25 BEV Coach \n", - "26 BEV Coach \n", - "27 BEV Coach \n", - "28 BEV Coach \n", - "29 BEV Coach \n", - "30 BEV Coach \n", - "31 BEV Truck Semi-Trailer max 50 tons \n", - "32 BEV Truck Semi-Trailer max 50 tons \n", - "33 BEV Truck Semi-Trailer max 50 tons \n", - "34 BEV Truck Semi-Trailer max 50 tons \n", - "35 BEV Truck Semi-Trailer max 50 tons \n", - "36 BEV Truck Semi-Trailer max 50 tons \n", - "37 BEV Truck Solo max 26 tons \n", - "38 BEV Truck Solo max 26 tons \n", - "39 BEV Truck Solo max 26 tons \n", - "40 BEV Truck Solo max 26 tons \n", - "41 BEV Truck Solo max 26 tons \n", - "42 BEV Truck Solo max 26 tons \n", - "43 BEV Truck Trailer max 56 tons \n", - "44 BEV Truck Trailer max 56 tons \n", - "45 BEV Truck Trailer max 56 tons \n", - "46 BEV Truck Trailer max 56 tons \n", - "47 BEV Truck Trailer max 56 tons \n", - "48 BEV Truck Trailer max 56 tons \n", - "49 Battery electric (passenger cars) \n", - "50 Battery electric (passenger cars) \n", - "51 Battery electric (passenger cars) \n", - "52 Battery electric (passenger cars) \n", - "53 Battery electric (trucks) \n", - "54 Battery electric (trucks) \n", - "55 Battery electric (trucks) \n", - "85 CH4 (g) pipeline \n", - "89 CH4 (g) submarine pipeline \n", - "100 CH4 liquefaction \n", - "103 CH4 liquefaction \n", - "105 CO2 liquefaction \n", - "106 CO2 liquefaction \n", - "107 CO2 liquefaction \n", - "118 Charging infrastructure fast (purely) battery electric vehicles passenger cars \n", - "119 Charging infrastructure fast (purely) battery electric vehicles passenger cars \n", - "120 Charging infrastructure fast (purely) battery electric vehicles passenger cars \n", - "121 Charging infrastructure fuel cell vehicles passenger cars \n", - "122 Charging infrastructure fuel cell vehicles passenger cars \n", - "123 Charging infrastructure fuel cell vehicles passenger cars \n", - "124 Charging infrastructure fuel cell vehicles trucks \n", - "125 Charging infrastructure fuel cell vehicles trucks \n", - "126 Charging infrastructure fuel cell vehicles trucks \n", - "127 Charging infrastructure slow (purely) battery electric vehicles passenger cars \n", - "128 Charging infrastructure slow (purely) battery electric vehicles passenger cars \n", - "129 Charging infrastructure slow (purely) battery electric vehicles passenger cars \n", - "130 Compressed-Air-Adiabatic-bicharger \n", - "131 Compressed-Air-Adiabatic-bicharger \n", - "132 Compressed-Air-Adiabatic-bicharger \n", - "133 Compressed-Air-Adiabatic-bicharger \n", - "134 Compressed-Air-Adiabatic-store \n", - "135 Compressed-Air-Adiabatic-store \n", - "136 Compressed-Air-Adiabatic-store \n", - "137 Concrete-charger \n", - "138 Concrete-charger \n", - "139 Concrete-charger \n", - "140 Concrete-charger \n", - "141 Concrete-discharger \n", - "142 Concrete-discharger \n", - "143 Concrete-discharger \n", - "144 Concrete-discharger \n", - "145 Concrete-store \n", - "146 Concrete-store \n", - "147 Concrete-store \n", - "148 Container feeder, ammonia \n", - "149 Container feeder, ammonia \n", - "150 Container feeder, ammonia \n", - "151 Container feeder, diesel \n", - "152 Container feeder, diesel \n", - "153 Container feeder, diesel \n", - "154 Container feeder, methanol \n", - "155 Container feeder, methanol \n", - "156 Container feeder, methanol \n", - "157 Container, ammonia \n", - "158 Container, ammonia \n", - "159 Container, ammonia \n", - "160 Container, diesel \n", - "161 Container, diesel \n", - "162 Container, diesel \n", - "163 Container, methanol \n", - "164 Container, methanol \n", - "165 Container, methanol \n", - "166 Diesel Bus city \n", - "167 Diesel Bus city \n", - "168 Diesel Bus city \n", - "169 Diesel Bus city \n", - "170 Diesel Bus city \n", - "171 Diesel Bus city \n", - "172 Diesel Coach \n", - "173 Diesel Coach \n", - "174 Diesel Coach \n", - "175 Diesel Coach \n", - "176 Diesel Coach \n", - "177 Diesel Coach \n", - "178 Diesel Truck Semi-Trailer max 50 tons \n", - "179 Diesel Truck Semi-Trailer max 50 tons \n", - "180 Diesel Truck Semi-Trailer max 50 tons \n", - "181 Diesel Truck Semi-Trailer max 50 tons \n", - "182 Diesel Truck Semi-Trailer max 50 tons \n", - "183 Diesel Truck Semi-Trailer max 50 tons \n", - "184 Diesel Truck Solo max 26 tons \n", - "185 Diesel Truck Solo max 26 tons \n", - "186 Diesel Truck Solo max 26 tons \n", - "187 Diesel Truck Solo max 26 tons \n", - "188 Diesel Truck Solo max 26 tons \n", - "189 Diesel Truck Solo max 26 tons \n", - "190 Diesel Truck Trailer max 56 tons \n", - "191 Diesel Truck Trailer max 56 tons \n", - "192 Diesel Truck Trailer max 56 tons \n", - "193 Diesel Truck Trailer max 56 tons \n", - "194 Diesel Truck Trailer max 56 tons \n", - "195 Diesel Truck Trailer max 56 tons \n", - "196 FCV Bus city \n", - "197 FCV Bus city \n", - "198 FCV Bus city \n", - "199 FCV Bus city \n", - "200 FCV Bus city \n", - "201 FCV Bus city \n", - "202 FCV Coach \n", - "203 FCV Coach \n", - "204 FCV Coach \n", - "205 FCV Coach \n", - "206 FCV Coach \n", - "207 FCV Coach \n", - "208 FCV Truck Semi-Trailer max 50 tons \n", - "209 FCV Truck Semi-Trailer max 50 tons \n", - "210 FCV Truck Semi-Trailer max 50 tons \n", - "211 FCV Truck Semi-Trailer max 50 tons \n", - "212 FCV Truck Semi-Trailer max 50 tons \n", - "213 FCV Truck Semi-Trailer max 50 tons \n", - "214 FCV Truck Solo max 26 tons \n", - "215 FCV Truck Solo max 26 tons \n", - "216 FCV Truck Solo max 26 tons \n", - "217 FCV Truck Solo max 26 tons \n", - "218 FCV Truck Solo max 26 tons \n", - "219 FCV Truck Solo max 26 tons \n", - "220 FCV Truck Trailer max 56 tons \n", - "221 FCV Truck Trailer max 56 tons \n", - "222 FCV Truck Trailer max 56 tons \n", - "223 FCV Truck Trailer max 56 tons \n", - "224 FCV Truck Trailer max 56 tons \n", - "225 FCV Truck Trailer max 56 tons \n", - "233 Fischer-Tropsch \n", - "235 Fischer-Tropsch \n", - "236 Fischer-Tropsch \n", - "237 Fischer-Tropsch \n", - "249 Gravity-Brick-bicharger \n", - "250 Gravity-Brick-bicharger \n", - "251 Gravity-Brick-bicharger \n", - "252 Gravity-Brick-bicharger \n", - "253 Gravity-Brick-store \n", - "254 Gravity-Brick-store \n", - "255 Gravity-Water-Aboveground-bicharger \n", - "256 Gravity-Water-Aboveground-bicharger \n", - "257 Gravity-Water-Aboveground-bicharger \n", - "258 Gravity-Water-Aboveground-bicharger \n", - "259 Gravity-Water-Aboveground-store \n", - "260 Gravity-Water-Aboveground-store \n", - "261 Gravity-Water-Underground-bicharger \n", - "262 Gravity-Water-Underground-bicharger \n", - "263 Gravity-Water-Underground-bicharger \n", - "264 Gravity-Water-Underground-bicharger \n", - "265 Gravity-Water-Underground-store \n", - "266 Gravity-Water-Underground-store \n", - "271 H2 (g) pipeline \n", - "275 H2 (g) pipeline repurposed \n", - "279 H2 (g) submarine pipeline \n", - "282 H2 (g) submarine pipeline repurposed \n", - "283 H2 (g) submarine pipeline repurposed \n", - "284 H2 (g) submarine pipeline repurposed \n", - "285 H2 (g) submarine pipeline repurposed \n", - "297 H2 liquefaction \n", - "298 H2 liquefaction \n", - "304 H2 production biomass gasification \n", - "305 H2 production biomass gasification \n", - "306 H2 production biomass gasification \n", - "307 H2 production biomass gasification \n", - "308 H2 production biomass gasification \n", - "309 H2 production biomass gasification \n", - "310 H2 production biomass gasification CC \n", - "311 H2 production biomass gasification CC \n", - "312 H2 production biomass gasification CC \n", - "313 H2 production biomass gasification CC \n", - "314 H2 production biomass gasification CC \n", - "315 H2 production biomass gasification CC \n", - "316 H2 production coal gasification \n", - "317 H2 production coal gasification \n", - "318 H2 production coal gasification \n", - "319 H2 production coal gasification \n", - "320 H2 production coal gasification \n", - "321 H2 production coal gasification \n", - "322 H2 production coal gasification CC \n", - "323 H2 production coal gasification CC \n", - "324 H2 production coal gasification CC \n", - "325 H2 production coal gasification CC \n", - "326 H2 production coal gasification CC \n", - "327 H2 production coal gasification CC \n", - "328 H2 production heavy oil partial oxidation \n", - "329 H2 production heavy oil partial oxidation \n", - "330 H2 production heavy oil partial oxidation \n", - "331 H2 production heavy oil partial oxidation \n", - "332 H2 production heavy oil partial oxidation \n", - "333 H2 production heavy oil partial oxidation \n", - "334 H2 production natural gas steam reforming \n", - "335 H2 production natural gas steam reforming \n", - "336 H2 production natural gas steam reforming \n", - "337 H2 production natural gas steam reforming \n", - "338 H2 production natural gas steam reforming \n", - "339 H2 production natural gas steam reforming \n", - "340 H2 production natural gas steam reforming CC \n", - "341 H2 production natural gas steam reforming CC \n", - "342 H2 production natural gas steam reforming CC \n", - "343 H2 production natural gas steam reforming CC \n", - "344 H2 production natural gas steam reforming CC \n", - "345 H2 production natural gas steam reforming CC \n", - "346 H2 production solid biomass steam reforming \n", - "347 H2 production solid biomass steam reforming \n", - "348 H2 production solid biomass steam reforming \n", - "349 H2 production solid biomass steam reforming \n", - "350 H2 production solid biomass steam reforming \n", - "351 H2 production solid biomass steam reforming \n", - "364 HVDC underground \n", - "365 HVDC underground \n", - "366 HVDC underground \n", - "369 Haber-Bosch \n", - "370 Haber-Bosch \n", - "371 Haber-Bosch \n", - "374 Haber-Bosch \n", - "375 HighT-Molten-Salt-charger \n", - "376 HighT-Molten-Salt-charger \n", - "377 HighT-Molten-Salt-charger \n", - "378 HighT-Molten-Salt-charger \n", - "379 HighT-Molten-Salt-discharger \n", - "380 HighT-Molten-Salt-discharger \n", - "381 HighT-Molten-Salt-discharger \n", - "382 HighT-Molten-Salt-discharger \n", - "383 HighT-Molten-Salt-store \n", - "384 HighT-Molten-Salt-store \n", - "385 HighT-Molten-Salt-store \n", - "386 Hydrogen fuel cell (passenger cars) \n", - "387 Hydrogen fuel cell (passenger cars) \n", - "388 Hydrogen fuel cell (passenger cars) \n", - "389 Hydrogen fuel cell (passenger cars) \n", - "390 Hydrogen fuel cell (trucks) \n", - "391 Hydrogen fuel cell (trucks) \n", - "392 Hydrogen fuel cell (trucks) \n", - "393 Hydrogen fuel cell (trucks) \n", - "394 Hydrogen-charger \n", - "395 Hydrogen-charger \n", - "396 Hydrogen-charger \n", - "397 Hydrogen-charger \n", - "398 Hydrogen-discharger \n", - "399 Hydrogen-discharger \n", - "400 Hydrogen-discharger \n", - "401 Hydrogen-discharger \n", - "402 Hydrogen-store \n", - "403 Hydrogen-store \n", - "404 Hydrogen-store \n", - "417 LOHC hydrogenation \n", - "418 LOHC hydrogenation \n", - "421 LOHC hydrogenation \n", - "432 Lead-Acid-bicharger \n", - "433 Lead-Acid-bicharger \n", - "434 Lead-Acid-bicharger \n", - "435 Lead-Acid-bicharger \n", - "436 Lead-Acid-store \n", - "437 Lead-Acid-store \n", - "438 Lead-Acid-store \n", - "439 Liquid fuels ICE (passenger cars) \n", - "440 Liquid fuels ICE (passenger cars) \n", - "441 Liquid fuels ICE (passenger cars) \n", - "442 Liquid fuels ICE (passenger cars) \n", - "443 Liquid fuels ICE (trucks) \n", - "444 Liquid fuels ICE (trucks) \n", - "445 Liquid fuels ICE (trucks) \n", - "446 Liquid fuels ICE (trucks) \n", - "447 Liquid-Air-charger \n", - "448 Liquid-Air-charger \n", - "449 Liquid-Air-charger \n", - "450 Liquid-Air-charger \n", - "451 Liquid-Air-discharger \n", - "452 Liquid-Air-discharger \n", - "453 Liquid-Air-discharger \n", - "454 Liquid-Air-discharger \n", - "455 Liquid-Air-store \n", - "456 Liquid-Air-store \n", - "457 Liquid-Air-store \n", - "458 Lithium-Ion-LFP-bicharger \n", - "459 Lithium-Ion-LFP-bicharger \n", - "460 Lithium-Ion-LFP-bicharger \n", - "461 Lithium-Ion-LFP-bicharger \n", - "462 Lithium-Ion-LFP-store \n", - "463 Lithium-Ion-LFP-store \n", - "464 Lithium-Ion-LFP-store \n", - "465 Lithium-Ion-NMC-bicharger \n", - "466 Lithium-Ion-NMC-bicharger \n", - "467 Lithium-Ion-NMC-bicharger \n", - "468 Lithium-Ion-NMC-bicharger \n", - "469 Lithium-Ion-NMC-store \n", - "470 Lithium-Ion-NMC-store \n", - "471 Lithium-Ion-NMC-store \n", - "472 LowT-Molten-Salt-charger \n", - "473 LowT-Molten-Salt-charger \n", - "474 LowT-Molten-Salt-charger \n", - "475 LowT-Molten-Salt-charger \n", - "476 LowT-Molten-Salt-discharger \n", - "477 LowT-Molten-Salt-discharger \n", - "478 LowT-Molten-Salt-discharger \n", - "479 LowT-Molten-Salt-discharger \n", - "480 LowT-Molten-Salt-store \n", - "481 LowT-Molten-Salt-store \n", - "482 LowT-Molten-Salt-store \n", - "490 Methanol steam reforming \n", - "498 Ni-Zn-bicharger \n", - "499 Ni-Zn-bicharger \n", - "500 Ni-Zn-bicharger \n", - "501 Ni-Zn-bicharger \n", - "502 Ni-Zn-store \n", - "503 Ni-Zn-store \n", - "504 Ni-Zn-store \n", - "510 PEM electrolyzer small size \n", - "511 PEM electrolyzer small size \n", - "512 PEM electrolyzer small size \n", - "513 PEM electrolyzer small size \n", - "518 Pumped-Heat-charger \n", - "519 Pumped-Heat-charger \n", - "520 Pumped-Heat-charger \n", - "521 Pumped-Heat-charger \n", - "522 Pumped-Heat-discharger \n", - "523 Pumped-Heat-discharger \n", - "524 Pumped-Heat-discharger \n", - "525 Pumped-Heat-discharger \n", - "526 Pumped-Heat-store \n", - "527 Pumped-Heat-store \n", - "528 Pumped-Heat-store \n", - "529 Pumped-Storage-Hydro-bicharger \n", - "530 Pumped-Storage-Hydro-bicharger \n", - "531 Pumped-Storage-Hydro-bicharger \n", - "532 Pumped-Storage-Hydro-bicharger \n", - "533 Pumped-Storage-Hydro-store \n", - "534 Pumped-Storage-Hydro-store \n", - "535 Pumped-Storage-Hydro-store \n", - "545 SOEC \n", - "546 SOEC \n", - "547 SOEC \n", - "548 SOEC \n", - "549 Sand-charger \n", - "550 Sand-charger \n", - "551 Sand-charger \n", - "552 Sand-charger \n", - "553 Sand-discharger \n", - "554 Sand-discharger \n", - "555 Sand-discharger \n", - "556 Sand-discharger \n", - "557 Sand-store \n", - "558 Sand-store \n", - "559 Sand-store \n", - "563 Steam methane reforming \n", - "564 Tank&bulk, diesel \n", - "565 Tank&bulk, diesel \n", - "566 Tank&bulk, diesel \n", - "567 Tank&bulk, methanol \n", - "568 Tank&bulk, methanol \n", - "569 Tank&bulk, methanol \n", - "570 Tankbulk, ammonia \n", - "571 Tankbulk, ammonia \n", - "572 Tankbulk, ammonia \n", - "573 Vanadium-Redox-Flow-bicharger \n", - "574 Vanadium-Redox-Flow-bicharger \n", - "575 Vanadium-Redox-Flow-bicharger \n", - "576 Vanadium-Redox-Flow-bicharger \n", - "577 Vanadium-Redox-Flow-store \n", - "578 Vanadium-Redox-Flow-store \n", - "579 Vanadium-Redox-Flow-store \n", - "580 Zn-Air-bicharger \n", - "581 Zn-Air-bicharger \n", - "582 Zn-Air-bicharger \n", - "583 Zn-Air-bicharger \n", - "584 Zn-Air-store \n", - "585 Zn-Air-store \n", - "586 Zn-Air-store \n", - "587 Zn-Br-Flow-bicharger \n", - "588 Zn-Br-Flow-bicharger \n", - "589 Zn-Br-Flow-bicharger \n", - "590 Zn-Br-Flow-bicharger \n", - "591 Zn-Br-Flow-store \n", - "592 Zn-Br-Flow-store \n", - "593 Zn-Br-Flow-store \n", - "594 Zn-Br-Nonflow-bicharger \n", - "595 Zn-Br-Nonflow-bicharger \n", - "596 Zn-Br-Nonflow-bicharger \n", - "597 Zn-Br-Nonflow-bicharger \n", - "598 Zn-Br-Nonflow-store \n", - "599 Zn-Br-Nonflow-store \n", - "600 Zn-Br-Nonflow-store \n", - "602 air separation unit \n", - "605 allam \n", - "606 allam \n", - "607 allam \n", - "608 allam \n", - "609 ammonia carbon capture retrofit \n", - "610 ammonia carbon capture retrofit \n", - "611 ammonia carbon capture retrofit \n", - "612 ammonia carbon capture retrofit \n", - "613 ammonia carbon capture retrofit \n", - "620 biochar pyrolysis \n", - "621 biochar pyrolysis \n", - "622 biochar pyrolysis \n", - "623 biochar pyrolysis \n", - "624 biochar pyrolysis \n", - "625 biochar pyrolysis \n", - "626 biochar pyrolysis \n", - "627 biodiesel crops \n", - "628 bioethanol crops \n", - "629 bioethanol crops \n", - "643 biogas manure \n", - "645 biogas plus hydrogen \n", - "692 biomass-to-methanol \n", - "693 biomass-to-methanol \n", - "694 biomass-to-methanol \n", - "695 biomass-to-methanol \n", - "696 biomass-to-methanol \n", - "697 biomass-to-methanol \n", - "698 biomass-to-methanol \n", - "699 biomass-to-methanol \n", - "700 biomass-to-methanol \n", - "701 biomass-to-methanol \n", - "702 biomass-to-methanol \n", - "703 blast furnace-basic oxygen furnace \n", - "704 blast furnace-basic oxygen furnace \n", - "705 blast furnace-basic oxygen furnace \n", - "706 blast furnace-basic oxygen furnace \n", - "707 blast furnace-basic oxygen furnace \n", - "708 blast furnace-basic oxygen furnace \n", - "709 blast furnace-basic oxygen furnace \n", - "719 cement carbon capture retrofit \n", - "720 cement carbon capture retrofit \n", - "721 cement carbon capture retrofit \n", - "722 cement carbon capture retrofit \n", - "723 cement carbon capture retrofit \n", - "724 cement carbon capture retrofit \n", - "725 cement dry clinker \n", - "726 cement dry clinker \n", - "727 cement dry clinker \n", - "728 cement dry clinker \n", - "729 cement dry clinker \n", - "730 cement dry clinker \n", - "731 cement dry clinker \n", - "732 cement finishing \n", - "733 cement finishing \n", - "734 cement finishing \n", - "735 cement finishing \n", - "736 cement finishing \n", - "737 cement finishing \n", - "738 cement finishing \n", - "757 central excess-heat-sourced heat pump \n", - "758 central excess-heat-sourced heat pump \n", - "759 central excess-heat-sourced heat pump \n", - "760 central excess-heat-sourced heat pump \n", - "761 central excess-heat-sourced heat pump \n", - "781 central geothermal heat source \n", - "782 central geothermal heat source \n", - "783 central geothermal heat source \n", - "784 central geothermal heat source \n", - "828 central water pit charger \n", - "829 central water pit discharger \n", - "830 central water pit storage \n", - "831 central water pit storage \n", - "832 central water pit storage \n", - "833 central water pit storage \n", - "834 central water pit storage \n", - "835 central water pit storage \n", - "836 central water pit storage \n", - "837 central water tank charger \n", - "838 central water tank discharger \n", - "840 central water tank storage \n", - "843 central water tank storage \n", - "844 central water tank storage \n", - "904 decentral water tank charger \n", - "905 decentral water tank discharger \n", - "907 decentral water tank storage \n", - "909 decentral water tank storage \n", - "912 decentral water tank storage \n", - "913 decentral water tank storage \n", - "947 dry bulk carrier Capesize \n", - "948 dry bulk carrier Capesize \n", - "949 dry bulk carrier Capesize \n", - "950 dry bulk carrier Capesize \n", - "951 electric arc furnace \n", - "952 electric arc furnace \n", - "953 electric arc furnace \n", - "954 electric arc furnace \n", - "955 electric arc furnace \n", - "956 electric arc furnace \n", - "957 electric arc furnace with hbi and scrap \n", - "958 electric arc furnace with hbi and scrap \n", - "959 electric arc furnace with hbi and scrap \n", - "960 electric arc furnace with hbi and scrap \n", - "961 electric arc furnace with hbi and scrap \n", - "962 electric arc furnace with hbi and scrap \n", - "963 electric arc furnace with hbi and scrap \n", - "969 electric steam cracker \n", - "970 electric steam cracker \n", - "971 electric steam cracker \n", - "972 electric steam cracker \n", - "973 electric steam cracker \n", - "974 electric steam cracker \n", - "975 electric steam cracker \n", - "995 electrolysis small \n", - "996 electrolysis small \n", - "997 electrolysis small \n", - "998 electrolysis small \n", - "999 electrolysis small \n", - "1000 ethanol carbon capture retrofit \n", - "1001 ethanol carbon capture retrofit \n", - "1002 ethanol carbon capture retrofit \n", - "1003 ethanol carbon capture retrofit \n", - "1004 ethanol carbon capture retrofit \n", - "1005 ethanol from starch crop \n", - "1006 ethanol from starch crop \n", - "1007 ethanol from starch crop \n", - "1008 ethanol from starch crop \n", - "1009 ethanol from starch crop \n", - "1010 ethanol from sugar crops \n", - "1011 ethanol from sugar crops \n", - "1012 ethanol from sugar crops \n", - "1013 ethanol from sugar crops \n", - "1014 ethanol from sugar crops \n", - "1020 fuelwood \n", - "1035 geothermal \n", - "1036 geothermal \n", - "1054 hydrogen direct iron reduction furnace \n", - "1055 hydrogen direct iron reduction furnace \n", - "1056 hydrogen direct iron reduction furnace \n", - "1057 hydrogen direct iron reduction furnace \n", - "1058 hydrogen direct iron reduction furnace \n", - "1059 hydrogen direct iron reduction furnace \n", - "1060 hydrogen direct iron reduction furnace \n", - "1086 iron ore DRI-ready \n", - "1087 iron-air battery \n", - "1088 iron-air battery \n", - "1089 iron-air battery \n", - "1090 iron-air battery charge \n", - "1091 iron-air battery discharge \n", - "1100 methanation \n", - "1102 methanation \n", - "1108 methanol \n", - "1109 methanol-to-kerosene \n", - "1110 methanol-to-kerosene \n", - "1111 methanol-to-kerosene \n", - "1112 methanol-to-kerosene \n", - "1113 methanol-to-kerosene \n", - "1114 methanol-to-kerosene \n", - "1115 methanol-to-olefins/aromatics \n", - "1116 methanol-to-olefins/aromatics \n", - "1117 methanol-to-olefins/aromatics \n", - "1118 methanol-to-olefins/aromatics \n", - "1119 methanol-to-olefins/aromatics \n", - "1120 methanol-to-olefins/aromatics \n", - "1121 methanol-to-olefins/aromatics \n", - "1125 methanolisation \n", - "1126 methanolisation \n", - "1127 methanolisation \n", - "1128 methanolisation \n", - "1136 natural gas direct iron reduction furnace \n", - "1137 natural gas direct iron reduction furnace \n", - "1138 natural gas direct iron reduction furnace \n", - "1139 natural gas direct iron reduction furnace \n", - "1140 natural gas direct iron reduction furnace \n", - "1141 natural gas direct iron reduction furnace \n", - "1158 offwind-float \n", - "1159 offwind-float \n", - "1160 offwind-float \n", - "1161 offwind-float-connection-submarine \n", - "1162 offwind-float-connection-underground \n", - "1163 offwind-float-station \n", - "1175 organic rankine cycle \n", - "1176 organic rankine cycle \n", - "1177 organic rankine cycle \n", - "1178 organic rankine cycle \n", - "1186 seawater RO desalination \n", - "1191 shipping fuel methanol \n", - "1192 shipping fuel methanol \n", - "1210 solar-utility single-axis tracking \n", - "1211 solar-utility single-axis tracking \n", - "1212 solar-utility single-axis tracking \n", - "1229 steel carbon capture retrofit \n", - "1230 steel carbon capture retrofit \n", - "1231 steel carbon capture retrofit \n", - "1232 steel carbon capture retrofit \n", - "1233 steel carbon capture retrofit \n", - "1234 steel carbon capture retrofit \n", - "\n", - " year parameter value_new \n", - "0 2020 FOM 6.400000e+00 \n", - "1 2020 VOM 6.142000e-01 \n", - "2 2020 electricity-input 1.500000e+00 \n", - "3 2020 investment 7.119042e+02 \n", - "4 2020 lifetime 4.000000e+01 \n", - "5 2020 FOM 1.810000e+01 \n", - "6 2020 VOM 2.389000e-01 \n", - "7 2020 electricity-input 1.633000e+00 \n", - "8 2020 investment 5.660884e+02 \n", - "9 2020 lifetime 2.000000e+01 \n", - "10 2020 FOM 1.810000e+01 \n", - "11 2020 VOM 1.091900e+00 \n", - "12 2020 electricity-input 1.620000e+00 \n", - "13 2020 investment 9.848823e+02 \n", - "14 2020 lifetime 2.000000e+01 \n", - "16 2020 ammonia-input 1.460000e+00 \n", - "19 2020 FOM 1.000000e-04 \n", - "20 2020 Motor size 3.000000e+02 \n", - "21 2020 VOM 9.520000e-02 \n", - "22 2020 efficiency 9.700000e-01 \n", - "23 2020 investment 4.093736e+05 \n", - "24 2020 lifetime 1.200000e+01 \n", - "25 2020 FOM 1.000000e-04 \n", - "26 2020 Motor size 2.500000e+02 \n", - "27 2020 VOM 9.520000e-02 \n", - "28 2020 efficiency 9.613000e-01 \n", - "29 2020 investment 4.899134e+05 \n", - "30 2020 lifetime 1.200000e+01 \n", - "31 2020 FOM 2.000000e-04 \n", - "32 2020 Motor size 4.000000e+02 \n", - "33 2020 VOM 9.520000e-02 \n", - "34 2020 efficiency 1.536200e+00 \n", - "35 2020 investment 3.251912e+05 \n", - "36 2020 lifetime 1.050000e+01 \n", - "37 2020 FOM 2.000000e-04 \n", - "38 2020 Motor size 3.300000e+02 \n", - "39 2020 VOM 9.520000e-02 \n", - "40 2020 efficiency 9.603000e-01 \n", - "41 2020 investment 3.386232e+05 \n", - "42 2020 lifetime 1.380000e+01 \n", - "43 2020 FOM 2.000000e-04 \n", - "44 2020 Motor size 4.900000e+02 \n", - "45 2020 VOM 9.520000e-02 \n", - "46 2020 efficiency 1.698800e+00 \n", - "47 2020 investment 3.606942e+05 \n", - "48 2020 lifetime 1.380000e+01 \n", - "49 2020 FOM 9.000000e-01 \n", - "50 2020 efficiency 6.800000e-01 \n", - "51 2020 investment 3.300000e+04 \n", - "52 2020 lifetime 1.500000e+01 \n", - "53 2020 FOM 1.400000e+01 \n", - "54 2020 investment 2.040670e+05 \n", - "55 2020 lifetime 1.500000e+01 \n", - "85 2020 electricity-input 1.000000e-02 \n", - "89 2020 electricity-input 1.000000e-02 \n", - "100 2020 electricity-input 3.600000e-02 \n", - "103 2020 methane-input 1.000000e+00 \n", - "105 2020 carbondioxide-input 1.000000e+00 \n", - "106 2020 electricity-input 1.230000e-01 \n", - "107 2020 heat-input 6.700000e-03 \n", - "118 2020 FOM 1.600000e+00 \n", - "119 2020 investment 6.291020e+05 \n", - "120 2020 lifetime 3.000000e+01 \n", - "121 2020 FOM 2.200000e+00 \n", - "122 2020 investment 2.243051e+06 \n", - "123 2020 lifetime 3.000000e+01 \n", - "124 2020 FOM 2.200000e+00 \n", - "125 2020 investment 2.243051e+06 \n", - "126 2020 lifetime 3.000000e+01 \n", - "127 2020 FOM 1.800000e+00 \n", - "128 2020 investment 1.283000e+03 \n", - "129 2020 lifetime 3.000000e+01 \n", - "130 2020 FOM 9.265000e-01 \n", - "131 2020 efficiency 7.211000e-01 \n", - "132 2020 investment 9.461809e+05 \n", - "133 2020 lifetime 6.000000e+01 \n", - "134 2020 FOM 4.300000e-01 \n", - "135 2020 investment 5.448789e+03 \n", - "136 2020 lifetime 6.000000e+01 \n", - "137 2020 FOM 1.075000e+00 \n", - "138 2020 efficiency 9.900000e-01 \n", - "139 2020 investment 1.880184e+05 \n", - "140 2020 lifetime 3.500000e+01 \n", - "141 2020 FOM 2.688000e-01 \n", - "142 2020 efficiency 4.343000e-01 \n", - "143 2020 investment 7.520736e+05 \n", - "144 2020 lifetime 3.500000e+01 \n", - "145 2020 FOM 3.231000e-01 \n", - "146 2020 investment 2.943258e+04 \n", - "147 2020 lifetime 3.500000e+01 \n", - "148 2020 efficiency 7.754000e-01 \n", - "149 2020 investment 4.195945e+07 \n", - "150 2020 lifetime 3.000000e+01 \n", - "151 2020 efficiency 7.718000e-01 \n", - "152 2020 investment 3.496621e+07 \n", - "153 2020 lifetime 3.000000e+01 \n", - "154 2020 efficiency 7.711000e-01 \n", - "155 2020 investment 3.846283e+07 \n", - "156 2020 lifetime 3.000000e+01 \n", - "157 2020 efficiency 1.709400e+00 \n", - "158 2020 investment 1.435835e+08 \n", - "159 2020 lifetime 3.000000e+01 \n", - "160 2020 efficiency 1.639900e+00 \n", - "161 2020 investment 1.196529e+08 \n", - "162 2020 lifetime 3.000000e+01 \n", - "163 2020 efficiency 1.700100e+00 \n", - "164 2020 investment 1.316182e+08 \n", - "165 2020 lifetime 3.000000e+01 \n", - "166 2020 FOM 4.000000e-04 \n", - "167 2020 Motor size 2.500000e+02 \n", - "168 2020 VOM 1.068000e-01 \n", - "169 2020 efficiency 2.435600e+00 \n", - "170 2020 investment 1.507563e+05 \n", - "171 2020 lifetime 1.200000e+01 \n", - "172 2020 FOM 3.000000e-04 \n", - "173 2020 Motor size 3.500000e+02 \n", - "174 2020 VOM 1.068000e-01 \n", - "175 2020 efficiency 2.546600e+00 \n", - "176 2020 investment 2.312961e+05 \n", - "177 2020 lifetime 1.200000e+01 \n", - "178 2020 FOM 5.000000e-04 \n", - "179 2020 Motor size 3.800000e+02 \n", - "180 2020 VOM 1.068000e-01 \n", - "181 2020 efficiency 3.552300e+00 \n", - "182 2020 investment 1.420121e+05 \n", - "183 2020 lifetime 1.050000e+01 \n", - "184 2020 FOM 4.000000e-04 \n", - "185 2020 Motor size 2.200000e+02 \n", - "186 2020 VOM 1.068000e-01 \n", - "187 2020 efficiency 2.496100e+00 \n", - "188 2020 investment 1.554441e+05 \n", - "189 2020 lifetime 1.380000e+01 \n", - "190 2020 FOM 4.000000e-04 \n", - "191 2020 Motor size 3.823529e+02 \n", - "192 2020 VOM 1.068000e-01 \n", - "193 2020 efficiency 3.625000e+00 \n", - "194 2020 investment 1.775151e+05 \n", - "195 2020 lifetime 1.380000e+01 \n", - "196 2020 FOM 1.000000e-04 \n", - "197 2020 Motor size 3.640000e+02 \n", - "198 2020 VOM 9.790000e-02 \n", - "199 2020 efficiency 1.780500e+00 \n", - "200 2020 investment 3.826753e+05 \n", - "201 2020 lifetime 1.200000e+01 \n", - "202 2020 FOM 1.000000e-04 \n", - "203 2020 Motor size 3.640000e+02 \n", - "204 2020 VOM 9.520000e-02 \n", - "205 2020 efficiency 1.774700e+00 \n", - "206 2020 investment 8.899426e+05 \n", - "207 2020 lifetime 1.200000e+01 \n", - "208 2020 FOM 2.000000e-04 \n", - "209 2020 Motor size 4.750000e+02 \n", - "210 2020 VOM 9.520000e-02 \n", - "211 2020 efficiency 2.753700e+00 \n", - "212 2020 investment 3.486795e+05 \n", - "213 2020 lifetime 1.050000e+01 \n", - "214 2020 FOM 2.000000e-04 \n", - "215 2020 Motor size 3.500000e+02 \n", - "216 2020 VOM 9.520000e-02 \n", - "217 2020 efficiency 1.862900e+00 \n", - "218 2020 investment 2.936811e+05 \n", - "219 2020 lifetime 1.380000e+01 \n", - "220 2020 FOM 2.000000e-04 \n", - "221 2020 Motor size 3.500000e+02 \n", - "222 2020 VOM 9.520000e-02 \n", - "223 2020 efficiency 3.072700e+00 \n", - "224 2020 investment 3.157521e+05 \n", - "225 2020 lifetime 1.380000e+01 \n", - "233 2020 carbondioxide-input 3.600000e-01 \n", - "235 2020 efficiency-heat 2.500000e-01 \n", - "236 2020 electricity-input 8.000000e-03 \n", - "237 2020 hydrogen-input 1.531000e+00 \n", - "249 2020 FOM 1.500000e+00 \n", - "250 2020 efficiency 9.274000e-01 \n", - "251 2020 investment 4.155705e+05 \n", - "252 2020 lifetime 4.170000e+01 \n", - "253 2020 investment 1.873258e+05 \n", - "254 2020 lifetime 4.170000e+01 \n", - "255 2020 FOM 1.500000e+00 \n", - "256 2020 efficiency 9.014000e-01 \n", - "257 2020 investment 3.656307e+05 \n", - "258 2020 lifetime 6.000000e+01 \n", - "259 2020 investment 1.447135e+05 \n", - "260 2020 lifetime 6.000000e+01 \n", - "261 2020 FOM 1.500000e+00 \n", - "262 2020 efficiency 9.014000e-01 \n", - "263 2020 investment 9.051590e+05 \n", - "264 2020 lifetime 6.000000e+01 \n", - "265 2020 investment 1.138875e+05 \n", - "266 2020 lifetime 6.000000e+01 \n", - "271 2020 electricity-input 2.100000e-02 \n", - "275 2020 electricity-input 2.100000e-02 \n", - "279 2020 electricity-input 2.100000e-02 \n", - "282 2020 FOM 3.000000e+00 \n", - "283 2020 electricity-input 2.100000e-02 \n", - "284 2020 investment 1.601562e+02 \n", - "285 2020 lifetime 3.000000e+01 \n", - "297 2020 electricity-input 2.030000e-01 \n", - "298 2020 hydrogen-input 1.017000e+00 \n", - "304 2020 FOM 5.000000e+00 \n", - "305 2020 VOM 5.118000e-01 \n", - "306 2020 electricity-input 9.700000e-02 \n", - "307 2020 investment 1.467940e+03 \n", - "308 2020 lifetime 2.000000e+01 \n", - "309 2020 wood-input 1.804000e+00 \n", - "310 2020 FOM 5.000000e+00 \n", - "311 2020 VOM 5.232000e-01 \n", - "312 2020 electricity-input 1.430000e-01 \n", - "313 2020 investment 1.489096e+03 \n", - "314 2020 lifetime 2.000000e+01 \n", - "315 2020 wood-input 1.804000e+00 \n", - "316 2020 FOM 5.900000e+00 \n", - "317 2020 VOM 6.677000e-01 \n", - "318 2020 coal-input 1.770000e+00 \n", - "319 2020 electricity-input 7.000000e-02 \n", - "320 2020 investment 5.260516e+02 \n", - "321 2020 lifetime 2.000000e+01 \n", - "322 2020 FOM 7.900000e+00 \n", - "323 2020 VOM 2.275000e-01 \n", - "324 2020 coal-input 1.770000e+00 \n", - "325 2020 electricity-input 1.110000e-01 \n", - "326 2020 investment 5.919076e+02 \n", - "327 2020 lifetime 2.000000e+01 \n", - "328 2020 FOM 5.000000e+00 \n", - "329 2020 VOM 1.592000e-01 \n", - "330 2020 electricity-input 6.300000e-02 \n", - "331 2020 investment 4.911331e+02 \n", - "332 2020 lifetime 2.500000e+01 \n", - "333 2020 oil-input 1.300000e+00 \n", - "334 2020 FOM 4.900000e+00 \n", - "335 2020 VOM 3.298000e-01 \n", - "336 2020 electricity-input 2.000000e-02 \n", - "337 2020 gas-input 1.320000e+00 \n", - "338 2020 investment 2.288467e+02 \n", - "339 2020 lifetime 2.000000e+01 \n", - "340 2020 FOM 5.200000e+00 \n", - "341 2020 VOM 6.028000e-01 \n", - "342 2020 electricity-input 5.000000e-02 \n", - "343 2020 gas-input 1.520000e+00 \n", - "344 2020 investment 3.102851e+02 \n", - "345 2020 lifetime 2.000000e+01 \n", - "346 2020 FOM 4.000000e+00 \n", - "347 2020 VOM 7.393000e-01 \n", - "348 2020 electricity-input 4.400000e-02 \n", - "349 2020 investment 5.906564e+02 \n", - "350 2020 lifetime 2.000000e+01 \n", - "351 2020 wood-input 1.360000e+00 \n", - "364 2020 FOM 3.500000e-01 \n", - "365 2020 investment 1.008293e+03 \n", - "366 2020 lifetime 4.000000e+01 \n", - "369 2020 efficiency-heat 1.460000e-01 \n", - "370 2020 electricity-input 2.473000e-01 \n", - "371 2020 hydrogen-input 1.148400e+00 \n", - "374 2020 nitrogen-input 1.597000e-01 \n", - "375 2020 FOM 1.075000e+00 \n", - "376 2020 efficiency 9.900000e-01 \n", - "377 2020 investment 1.878995e+05 \n", - "378 2020 lifetime 3.500000e+01 \n", - "379 2020 FOM 2.688000e-01 \n", - "380 2020 efficiency 4.444000e-01 \n", - "381 2020 investment 7.515980e+05 \n", - "382 2020 lifetime 3.500000e+01 \n", - "383 2020 FOM 3.308000e-01 \n", - "384 2020 investment 1.125600e+05 \n", - "385 2020 lifetime 3.500000e+01 \n", - "386 2020 FOM 1.100000e+00 \n", - "387 2020 efficiency 4.800000e-01 \n", - "388 2020 investment 5.500000e+04 \n", - "389 2020 lifetime 1.500000e+01 \n", - "390 2020 FOM 1.010000e+01 \n", - "391 2020 efficiency 5.600000e-01 \n", - "392 2020 investment 1.515740e+05 \n", - "393 2020 lifetime 1.500000e+01 \n", - "394 2020 FOM 4.600000e-01 \n", - "395 2020 efficiency 6.963000e-01 \n", - "396 2020 investment 1.304350e+06 \n", - "397 2020 lifetime 3.000000e+01 \n", - "398 2020 FOM 4.801000e-01 \n", - "399 2020 efficiency 4.869000e-01 \n", - "400 2020 investment 1.265835e+06 \n", - "401 2020 lifetime 3.000000e+01 \n", - "402 2020 FOM 4.300000e-01 \n", - "403 2020 investment 4.779953e+03 \n", - "404 2020 lifetime 3.000000e+01 \n", - "417 2020 electricity-input 4.000000e-03 \n", - "418 2020 hydrogen-input 1.867000e+00 \n", - "421 2020 lohc-input 9.440000e-01 \n", - "432 2020 FOM 2.406400e+00 \n", - "433 2020 efficiency 8.832000e-01 \n", - "434 2020 investment 1.497312e+05 \n", - "435 2020 lifetime 1.200000e+01 \n", - "436 2020 FOM 2.386000e-01 \n", - "437 2020 investment 3.652899e+05 \n", - "438 2020 lifetime 1.200000e+01 \n", - "439 2020 FOM 1.600000e+00 \n", - "440 2020 efficiency 2.150000e-01 \n", - "441 2020 investment 2.356100e+04 \n", - "442 2020 lifetime 1.500000e+01 \n", - "443 2020 FOM 1.800000e+01 \n", - "444 2020 efficiency 3.730000e-01 \n", - "445 2020 investment 9.977200e+04 \n", - "446 2020 lifetime 1.500000e+01 \n", - "447 2020 FOM 3.660000e-01 \n", - "448 2020 efficiency 9.900000e-01 \n", - "449 2020 investment 5.036637e+05 \n", - "450 2020 lifetime 3.500000e+01 \n", - "451 2020 FOM 5.212000e-01 \n", - "452 2020 efficiency 5.500000e-01 \n", - "453 2020 investment 3.536362e+05 \n", - "454 2020 lifetime 3.500000e+01 \n", - "455 2020 FOM 3.280000e-01 \n", - "456 2020 investment 1.867491e+05 \n", - "457 2020 lifetime 3.500000e+01 \n", - "458 2020 FOM 2.070100e+00 \n", - "459 2020 efficiency 9.193000e-01 \n", - "460 2020 investment 9.558419e+04 \n", - "461 2020 lifetime 1.600000e+01 \n", - "462 2020 FOM 4.470000e-02 \n", - "463 2020 investment 3.256908e+05 \n", - "464 2020 lifetime 1.600000e+01 \n", - "465 2020 FOM 2.070100e+00 \n", - "466 2020 efficiency 9.193000e-01 \n", - "467 2020 investment 9.558419e+04 \n", - "468 2020 lifetime 1.300000e+01 \n", - "469 2020 FOM 3.790000e-02 \n", - "470 2020 investment 3.721120e+05 \n", - "471 2020 lifetime 1.300000e+01 \n", - "472 2020 FOM 1.075000e+00 \n", - "473 2020 efficiency 9.900000e-01 \n", - "474 2020 investment 1.493745e+05 \n", - "475 2020 lifetime 3.500000e+01 \n", - "476 2020 FOM 2.688000e-01 \n", - "477 2020 efficiency 5.394000e-01 \n", - "478 2020 investment 5.974981e+05 \n", - "479 2020 lifetime 3.500000e+01 \n", - "480 2020 FOM 3.308000e-01 \n", - "481 2020 investment 6.942183e+04 \n", - "482 2020 lifetime 3.500000e+01 \n", - "490 2020 methanol-input 1.201000e+00 \n", - "498 2020 FOM 2.070100e+00 \n", - "499 2020 efficiency 9.000000e-01 \n", - "500 2020 investment 9.558419e+04 \n", - "501 2020 lifetime 1.500000e+01 \n", - "502 2020 FOM 2.238000e-01 \n", - "503 2020 investment 3.448284e+05 \n", - "504 2020 lifetime 1.500000e+01 \n", - "510 2020 FOM 3.000000e+00 \n", - "511 2020 electricity-input 1.430000e+00 \n", - "512 2020 investment 1.364891e+03 \n", - "513 2020 lifetime 6.000000e+00 \n", - "518 2020 FOM 3.660000e-01 \n", - "519 2020 efficiency 9.900000e-01 \n", - "520 2020 investment 8.071893e+05 \n", - "521 2020 lifetime 3.300000e+01 \n", - "522 2020 FOM 5.212000e-01 \n", - "523 2020 efficiency 6.300000e-01 \n", - "524 2020 investment 5.667499e+05 \n", - "525 2020 lifetime 3.300000e+01 \n", - "526 2020 FOM 6.150000e-02 \n", - "527 2020 investment 3.129383e+04 \n", - "528 2020 lifetime 3.300000e+01 \n", - "529 2020 FOM 9.951000e-01 \n", - "530 2020 efficiency 8.944000e-01 \n", - "531 2020 investment 1.397128e+06 \n", - "532 2020 lifetime 6.000000e+01 \n", - "533 2020 FOM 4.300000e-01 \n", - "534 2020 investment 5.707406e+04 \n", - "535 2020 lifetime 6.000000e+01 \n", - "545 2020 FOM 4.000000e+00 \n", - "546 2020 electricity-input 1.220000e+00 \n", - "547 2020 investment 2.359238e+03 \n", - "548 2020 lifetime 3.000000e+01 \n", - "549 2020 FOM 1.075000e+00 \n", - "550 2020 efficiency 9.900000e-01 \n", - "551 2020 investment 1.526246e+05 \n", - "552 2020 lifetime 3.500000e+01 \n", - "553 2020 FOM 2.688000e-01 \n", - "554 2020 efficiency 5.300000e-01 \n", - "555 2020 investment 6.104983e+05 \n", - "556 2020 lifetime 3.500000e+01 \n", - "557 2020 FOM 3.308000e-01 \n", - "558 2020 investment 8.014744e+03 \n", - "559 2020 lifetime 3.500000e+01 \n", - "563 2020 methane-input 1.483000e+00 \n", - "564 2020 efficiency 4.620000e-01 \n", - "565 2020 investment 3.512931e+07 \n", - "566 2020 lifetime 2.500000e+01 \n", - "567 2020 efficiency 4.695000e-01 \n", - "568 2020 investment 3.864224e+07 \n", - "569 2020 lifetime 2.500000e+01 \n", - "570 2020 efficiency 4.710000e-01 \n", - "571 2020 investment 4.215517e+07 \n", - "572 2020 lifetime 2.500000e+01 \n", - "573 2020 FOM 2.402800e+00 \n", - "574 2020 efficiency 8.062000e-01 \n", - "575 2020 investment 1.499502e+05 \n", - "576 2020 lifetime 1.200000e+01 \n", - "577 2020 FOM 2.335000e-01 \n", - "578 2020 investment 3.176142e+05 \n", - "579 2020 lifetime 1.200000e+01 \n", - "580 2020 FOM 2.439500e+00 \n", - "581 2020 efficiency 7.937000e-01 \n", - "582 2020 investment 1.290231e+05 \n", - "583 2020 lifetime 2.500000e+01 \n", - "584 2020 FOM 1.893000e-01 \n", - "585 2020 investment 1.948990e+05 \n", - "586 2020 lifetime 2.500000e+01 \n", - "587 2020 FOM 2.475000e+00 \n", - "588 2020 efficiency 8.307000e-01 \n", - "589 2020 investment 1.342974e+05 \n", - "590 2020 lifetime 1.000000e+01 \n", - "591 2020 FOM 2.849000e-01 \n", - "592 2020 investment 4.766239e+05 \n", - "593 2020 lifetime 1.000000e+01 \n", - "594 2020 FOM 2.439500e+00 \n", - "595 2020 efficiency 8.888000e-01 \n", - "596 2020 investment 1.290231e+05 \n", - "597 2020 lifetime 1.500000e+01 \n", - "598 2020 FOM 2.481000e-01 \n", - "599 2020 investment 2.768736e+05 \n", - "600 2020 lifetime 1.500000e+01 \n", - "602 2020 electricity-input 2.500000e-01 \n", - "605 2020 VOM 2.000000e+00 \n", - "606 2020 efficiency 6.000000e-01 \n", - "607 2020 investment 1.500000e+03 \n", - "608 2020 lifetime 3.000000e+01 \n", - "609 2020 FOM 5.000000e+00 \n", - "610 2020 capture_rate 9.900000e-01 \n", - "611 2020 electricity-input 1.000000e-01 \n", - "612 2020 investment 9.297530e+05 \n", - "613 2020 lifetime 2.000000e+01 \n", - "620 2020 FOM 3.461500e+00 \n", - "621 2020 VOM 8.234970e+02 \n", - "622 2020 efficiency-biochar 4.040000e-01 \n", - "623 2020 efficiency-heat 4.848000e-01 \n", - "624 2020 investment 1.672728e+05 \n", - "625 2020 lifetime 2.500000e+01 \n", - "626 2020 yield-biochar 5.820000e-02 \n", - "627 2020 fuel 9.620770e+01 \n", - "628 2020 CO2 intensity 1.289000e-01 \n", - "629 2020 fuel 6.215190e+01 \n", - "643 2020 fuel 1.975750e+01 \n", - "645 2020 VOM 4.593900e+00 \n", - "692 2020 C in fuel 3.926000e-01 \n", - "693 2020 C stored 6.074000e-01 \n", - "694 2020 CO2 stored 2.227000e-01 \n", - "695 2020 FOM 1.111100e+00 \n", - "696 2020 VOM 2.169790e+01 \n", - "697 2020 capture rate 9.000000e-01 \n", - "698 2020 efficiency 5.800000e-01 \n", - "699 2020 efficiency-electricity 2.000000e-02 \n", - "700 2020 efficiency-heat 2.200000e-01 \n", - "701 2020 investment 5.591392e+03 \n", - "702 2020 lifetime 2.000000e+01 \n", - "703 2020 FOM 1.418000e+01 \n", - "704 2020 coal-input 5.340000e+00 \n", - "705 2020 economic_lifetime 2.000000e+01 \n", - "706 2020 investment 7.637406e+06 \n", - "707 2020 lifetime 4.000000e+01 \n", - "708 2020 ore-input 1.539000e+00 \n", - "709 2020 scrap-input 5.100000e-02 \n", - "719 2020 FOM 7.000000e+00 \n", - "720 2020 capture_rate 9.000000e-01 \n", - "721 2020 electricity-input 1.600000e-01 \n", - "722 2020 gas-input 7.600000e-01 \n", - "723 2020 investment 2.587727e+06 \n", - "724 2020 lifetime 2.000000e+01 \n", - "725 2020 FOM 4.000000e+00 \n", - "726 2020 VOM 5.291100e+00 \n", - "727 2020 electricity-input 6.940000e-02 \n", - "728 2020 gas-input 2.000000e-04 \n", - "729 2020 heat-input 9.444000e-01 \n", - "730 2020 investment 1.158753e+06 \n", - "731 2020 lifetime 3.000000e+01 \n", - "732 2020 FOM 3.000000e+01 \n", - "733 2020 VOM 3.174700e+00 \n", - "734 2020 clinker-input 6.560000e-01 \n", - "735 2020 electricity-input 1.736000e-01 \n", - "736 2020 investment 9.270021e+04 \n", - "737 2020 lifetime 2.500000e+01 \n", - "738 2020 slag-input 1.940000e-01 \n", - "757 2020 FOM 3.003000e-01 \n", - "758 2020 VOM 1.788400e+00 \n", - "759 2020 efficiency 5.100000e+00 \n", - "760 2020 investment 7.047435e+02 \n", - "761 2020 lifetime 2.500000e+01 \n", - "781 2020 FOM 8.406000e-01 \n", - "782 2020 VOM 4.854700e+00 \n", - "783 2020 investment 3.008729e+03 \n", - "784 2020 lifetime 2.500000e+01 \n", - "828 2020 efficiency 1.000000e+00 \n", - "829 2020 efficiency 1.000000e+00 \n", - "830 2020 Bottom storage temperature 3.500000e+01 \n", - "831 2020 FOM 2.354000e-01 \n", - "832 2020 Top storage temperature 9.000000e+01 \n", - "833 2020 energy to power ratio 1.500000e+02 \n", - "834 2020 investment 1.062200e+00 \n", - "835 2020 lifetime 2.000000e+01 \n", - "836 2020 standing losses 7.800000e-03 \n", - "837 2020 efficiency 1.000000e+00 \n", - "838 2020 efficiency 1.000000e+00 \n", - "840 2020 energy to power ratio 6.034480e+01 \n", - "843 2020 standing losses 7.700000e-03 \n", - "844 2020 temperature difference 5.500000e+01 \n", - "904 2020 efficiency 1.000000e+00 \n", - "905 2020 efficiency 1.000000e+00 \n", - "907 2020 VOM 7.168000e-01 \n", - "909 2020 energy to power ratio 1.500000e-01 \n", - "912 2020 standing losses 2.100000e+00 \n", - "913 2020 temperature difference 3.000000e+01 \n", - "947 2020 FOM 4.000000e+00 \n", - "948 2020 capacity 1.800000e+05 \n", - "949 2020 investment 4.000000e+07 \n", - "950 2020 lifetime 2.500000e+01 \n", - "951 2020 FOM 3.000000e+01 \n", - "952 2020 economic_lifetime 2.000000e+01 \n", - "953 2020 electricity-input 6.395000e-01 \n", - "954 2020 hbi-input 1.000000e+00 \n", - "955 2020 investment 1.839600e+06 \n", - "956 2020 lifetime 4.000000e+01 \n", - "957 2020 FOM 3.000000e+01 \n", - "958 2020 economic_lifetime 2.000000e+01 \n", - "959 2020 electricity-input 6.395000e-01 \n", - "960 2020 hbi-input 3.700000e-01 \n", - "961 2020 investment 1.839600e+06 \n", - "962 2020 lifetime 4.000000e+01 \n", - "963 2020 scrap-input 7.100000e-01 \n", - "969 2020 FOM 3.000000e+00 \n", - "970 2020 VOM 1.904799e+02 \n", - "971 2020 carbondioxide-output 5.500000e-01 \n", - "972 2020 electricity-input 2.700000e+00 \n", - "973 2020 investment 1.112403e+07 \n", - "974 2020 lifetime 3.000000e+01 \n", - "975 2020 naphtha-input 1.480000e+01 \n", - "995 2020 FOM 4.000000e+00 \n", - "996 2020 efficiency 5.773000e-01 \n", - "997 2020 efficiency-heat 2.762000e-01 \n", - "998 2020 investment 1.900000e+03 \n", - "999 2020 lifetime 2.500000e+01 \n", - "1000 2020 FOM 7.000000e+00 \n", - "1001 2020 capture_rate 9.400000e-01 \n", - "1002 2020 electricity-input 1.200000e-01 \n", - "1003 2020 investment 9.285597e+05 \n", - "1004 2020 lifetime 2.000000e+01 \n", - "1005 2020 FOM 1.380000e+01 \n", - "1006 2020 VOM 2.634970e+01 \n", - "1007 2020 efficiency 5.800000e-01 \n", - "1008 2020 investment 7.165113e+05 \n", - "1009 2020 lifetime 2.000000e+01 \n", - "1010 2020 FOM 1.643000e+01 \n", - "1011 2020 VOM 2.317510e+01 \n", - "1012 2020 efficiency 4.500000e-01 \n", - "1013 2020 investment 5.302643e+05 \n", - "1014 2020 lifetime 2.000000e+01 \n", - "1020 2020 fuel 1.599970e+01 \n", - "1035 2020 district heat surcharge 2.500000e+01 \n", - "1036 2020 district heat-input 8.000000e-01 \n", - "1054 2020 FOM 1.130000e+01 \n", - "1055 2020 economic_lifetime 2.000000e+01 \n", - "1056 2020 electricity-input 1.030000e+00 \n", - "1057 2020 hydrogen-input 2.100000e+00 \n", - "1058 2020 investment 4.277858e+06 \n", - "1059 2020 lifetime 4.000000e+01 \n", - "1060 2020 ore-input 1.590000e+00 \n", - "1086 2020 commodity 9.773000e+01 \n", - "1087 2020 FOM 1.021900e+00 \n", - "1088 2020 investment 2.513420e+01 \n", - "1089 2020 lifetime 1.750000e+01 \n", - "1090 2020 efficiency 7.000000e-01 \n", - "1091 2020 efficiency 5.900000e-01 \n", - "1100 2020 carbondioxide-input 1.980000e-01 \n", - "1102 2020 hydrogen-input 1.282000e+00 \n", - "1108 2020 CO2 intensity 2.482000e-01 \n", - "1109 2020 FOM 4.500000e+00 \n", - "1110 2020 VOM 1.350000e+00 \n", - "1111 2020 hydrogen-input 2.790000e-02 \n", - "1112 2020 investment 3.070000e+05 \n", - "1113 2020 lifetime 3.000000e+01 \n", - "1114 2020 methanol-input 1.076400e+00 \n", - "1115 2020 FOM 3.000000e+00 \n", - "1116 2020 VOM 3.174660e+01 \n", - "1117 2020 carbondioxide-output 6.107000e-01 \n", - "1118 2020 electricity-input 1.388900e+00 \n", - "1119 2020 investment 2.781006e+06 \n", - "1120 2020 lifetime 3.000000e+01 \n", - "1121 2020 methanol-input 1.803000e+01 \n", - "1125 2020 carbondioxide-input 2.480000e-01 \n", - "1126 2020 electricity-input 2.710000e-01 \n", - "1127 2020 heat-output 1.000000e-01 \n", - "1128 2020 hydrogen-input 1.138000e+00 \n", - "1136 2020 FOM 1.130000e+01 \n", - "1137 2020 economic_lifetime 2.000000e+01 \n", - "1138 2020 gas-input 2.780000e+00 \n", - "1139 2020 investment 4.277858e+06 \n", - "1140 2020 lifetime 4.000000e+01 \n", - "1141 2020 ore-input 1.590000e+00 \n", - "1158 2020 FOM 1.150000e+00 \n", - "1159 2020 investment 2.350000e+03 \n", - "1160 2020 lifetime 2.000000e+01 \n", - "1161 2020 investment 2.118560e+03 \n", - "1162 2020 investment 1.039478e+03 \n", - "1163 2020 investment 4.157911e+02 \n", - "1175 2020 FOM 2.000000e+00 \n", - "1176 2020 electricity-input 1.200000e-01 \n", - "1177 2020 investment 1.376000e+03 \n", - "1178 2020 lifetime 3.000000e+01 \n", - "1186 2020 electricity-input 3.000000e-03 \n", - "1191 2020 CO2 intensity 2.482000e-01 \n", - "1192 2020 fuel 7.200000e+01 \n", - "1210 2020 FOM 1.860500e+00 \n", - "1211 2020 investment 6.503522e+02 \n", - "1212 2020 lifetime 3.500000e+01 \n", - "1229 2020 FOM 5.000000e+00 \n", - "1230 2020 capture_rate 9.000000e-01 \n", - "1231 2020 electricity-input 1.600000e-01 \n", - "1232 2020 gas-input 7.600000e-01 \n", - "1233 2020 investment 3.561436e+06 \n", - "1234 2020 lifetime 2.000000e+01 \n", - "\n", - "🔴 Removed rows:\n", - " technology year parameter value_old\n", - "751 central coal boiler 2020 FOM 2.50\n", - "752 central coal boiler 2020 VOM 1.10\n", - "753 central coal boiler 2020 efficiency 0.90\n", - "754 central coal boiler 2020 hist_efficiency 0.78\n", - "755 central coal boiler 2020 investment 315.00\n", - "756 central coal boiler 2020 lifetime 25.00\n", - "873 decentral coal boiler 2020 FOM 3.49\n", - "874 decentral coal boiler 2020 discount rate 0.04\n", - "875 decentral coal boiler 2020 efficiency 0.90\n", - "876 decentral coal boiler 2020 hist_efficiency 0.78\n", - "877 decentral coal boiler 2020 investment 182.00\n", - "878 decentral coal boiler 2020 lifetime 20.00\n", - "1037 geothermal 2020 efficiency 0.24\n", - "1038 geothermal 2020 investment 3392.00\n", - "1123 methanolisation 2020 VOM 6.27\n", - "1179 retrofit 2020 VOM 69.00\n", - "1180 retrofit 2020 investment 180.00\n", - "1181 retrofit 2020 lifetime 40.00\n", - "\n", - "🟡 Changed values (|old - new| > 0.01):\n", - " technology year \\\n", - "17 Ammonia cracker 2020 \n", - "60 BioSNG 2020 \n", - "63 BioSNG 2020 \n", - "69 BtL 2020 \n", - "72 BtL 2020 \n", - "75 CCGT 2020 \n", - "79 CCGT 2020 \n", - "82 CH4 (g) fill compressor station 2020 \n", - "86 CH4 (g) pipeline 2020 \n", - "90 CH4 (g) submarine pipeline 2020 \n", - "94 CH4 (l) transport ship 2020 \n", - "97 CH4 evaporation 2020 \n", - "101 CH4 liquefaction 2020 \n", - "108 CO2 liquefaction 2020 \n", - "111 CO2 pipeline 2020 \n", - "114 CO2 storage tank 2020 \n", - "117 CO2 submarine pipeline 2020 \n", - "228 FT fuel transport ship 2020 \n", - "231 Fischer-Tropsch 2020 \n", - "238 Fischer-Tropsch 2020 \n", - "244 General liquid hydrocarbon storage (crude) 2020 \n", - "247 General liquid hydrocarbon storage (product) 2020 \n", - "268 H2 (g) fill compressor station 2020 \n", - "272 H2 (g) pipeline 2020 \n", - "276 H2 (g) pipeline repurposed 2020 \n", - "280 H2 (g) submarine pipeline 2020 \n", - "287 H2 (l) storage tank 2020 \n", - "291 H2 (l) transport ship 2020 \n", - "294 H2 evaporation 2020 \n", - "299 H2 liquefaction 2020 \n", - "302 H2 pipeline 2020 \n", - "353 HVAC overhead 2020 \n", - "356 HVDC inverter pair 2020 \n", - "359 HVDC overhead 2020 \n", - "362 HVDC submarine 2020 \n", - "372 Haber-Bosch 2020 \n", - "406 LNG storage tank 2020 \n", - "408 LOHC chemical 2020 \n", - "411 LOHC dehydrogenation 2020 \n", - "414 LOHC dehydrogenation (small scale) 2020 \n", - "419 LOHC hydrogenation 2020 \n", - "423 LOHC loaded DBT storage 2020 \n", - "427 LOHC transport ship 2020 \n", - "430 LOHC unloaded DBT storage 2020 \n", - "485 MeOH transport ship 2020 \n", - "488 Methanol steam reforming 2020 \n", - "492 NH3 (l) storage tank incl. liquefaction 2020 \n", - "496 NH3 (l) transport ship 2020 \n", - "506 OCGT 2020 \n", - "508 OCGT 2020 \n", - "516 PHS 2020 \n", - "538 SMR 2020 \n", - "543 SMR CC 2020 \n", - "561 Steam methane reforming 2020 \n", - "603 air separation unit 2020 \n", - "616 battery inverter 2020 \n", - "618 battery storage 2020 \n", - "630 biogas 2020 \n", - "631 biogas 2020 \n", - "634 biogas 2020 \n", - "635 biogas 2020 \n", - "637 biogas CC 2020 \n", - "638 biogas CC 2020 \n", - "641 biogas CC 2020 \n", - "646 biogas plus hydrogen 2020 \n", - "648 biogas upgrading 2020 \n", - "649 biogas upgrading 2020 \n", - "650 biogas upgrading 2020 \n", - "651 biogas upgrading 2020 \n", - "654 biomass 2020 \n", - "655 biomass 2020 \n", - "658 biomass CHP 2020 \n", - "663 biomass CHP 2020 \n", - "675 biomass EOP 2020 \n", - "680 biomass EOP 2020 \n", - "683 biomass HOP 2020 \n", - "685 biomass HOP 2020 \n", - "689 biomass boiler 2020 \n", - "740 central air-sourced heat pump 2020 \n", - "741 central air-sourced heat pump 2020 \n", - "742 central air-sourced heat pump 2020 \n", - "745 central coal CHP 2020 \n", - "748 central coal CHP 2020 \n", - "749 central coal CHP 2020 \n", - "763 central gas CHP 2020 \n", - "767 central gas CHP 2020 \n", - "771 central gas CHP CC 2020 \n", - "773 central gas CHP CC 2020 \n", - "774 central gas CHP CC 2020 \n", - "777 central gas boiler 2020 \n", - "779 central gas boiler 2020 \n", - "786 central ground-sourced heat pump 2020 \n", - "788 central ground-sourced heat pump 2020 \n", - "793 central hydrogen CHP 2020 \n", - "796 central resistive heater 2020 \n", - "798 central resistive heater 2020 \n", - "801 central solar thermal 2020 \n", - "804 central solid biomass CHP 2020 \n", - "809 central solid biomass CHP 2020 \n", - "813 central solid biomass CHP CC 2020 \n", - "818 central solid biomass CHP CC 2020 \n", - "821 central solid biomass CHP powerboost CC 2020 \n", - "826 central solid biomass CHP powerboost CC 2020 \n", - "839 central water tank storage 2020 \n", - "841 central water tank storage 2020 \n", - "842 central water tank storage 2020 \n", - "846 clean water tank storage 2020 \n", - "849 coal 2020 \n", - "850 coal 2020 \n", - "851 coal 2020 \n", - "852 coal 2020 \n", - "853 coal 2020 \n", - "856 csp-tower 2020 \n", - "859 csp-tower TES 2020 \n", - "862 csp-tower power block 2020 \n", - "866 decentral CHP 2020 \n", - "871 decentral air-sourced heat pump 2020 \n", - "882 decentral gas boiler 2020 \n", - "884 decentral gas boiler connection 2020 \n", - "889 decentral ground-sourced heat pump 2020 \n", - "893 decentral oil boiler 2020 \n", - "898 decentral resistive heater 2020 \n", - "902 decentral solar thermal 2020 \n", - "910 decentral water tank storage 2020 \n", - "911 decentral water tank storage 2020 \n", - "914 digestible biomass 2020 \n", - "918 digestible biomass to hydrogen 2020 \n", - "922 direct air capture 2020 \n", - "923 direct air capture 2020 \n", - "930 direct firing gas 2020 \n", - "935 direct firing gas CC 2020 \n", - "940 direct firing solid fuels 2020 \n", - "945 direct firing solid fuels CC 2020 \n", - "965 electric boiler steam 2020 \n", - "967 electric boiler steam 2020 \n", - "977 electricity distribution grid 2020 \n", - "980 electricity grid connection 2020 \n", - "984 electrobiofuels 2020 \n", - "989 electrobiofuels 2020 \n", - "990 electrolysis 2020 \n", - "991 electrolysis 2020 \n", - "992 electrolysis 2020 \n", - "993 electrolysis 2020 \n", - "1018 fuel cell 2020 \n", - "1022 gas 2020 \n", - "1026 gas boiler steam 2020 \n", - "1028 gas storage 2020 \n", - "1029 gas storage 2020 \n", - "1031 gas storage charger 2020 \n", - "1032 gas storage discharger 2020 \n", - "1033 geothermal 2020 \n", - "1034 geothermal 2020 \n", - "1039 geothermal 2020 \n", - "1042 helmeth 2020 \n", - "1046 home battery inverter 2020 \n", - "1048 home battery storage 2020 \n", - "1052 hydro 2020 \n", - "1063 hydrogen storage compressor 2020 \n", - "1066 hydrogen storage tank type 1 2020 \n", - "1070 hydrogen storage tank type 1 including compressor 2020 \n", - "1074 hydrogen storage underground 2020 \n", - "1077 industrial heat pump high temperature 2020 \n", - "1079 industrial heat pump high temperature 2020 \n", - "1082 industrial heat pump medium temperature 2020 \n", - "1084 industrial heat pump medium temperature 2020 \n", - "1093 lignite 2020 \n", - "1094 lignite 2020 \n", - "1096 lignite 2020 \n", - "1097 lignite 2020 \n", - "1103 methanation 2020 \n", - "1106 methane storage tank incl. compressor 2020 \n", - "1129 methanolisation 2020 \n", - "1134 micro CHP 2020 \n", - "1142 nuclear 2020 \n", - "1143 nuclear 2020 \n", - "1145 nuclear 2020 \n", - "1146 nuclear 2020 \n", - "1150 offwind 2020 \n", - "1152 offwind-ac-connection-submarine 2020 \n", - "1153 offwind-ac-connection-underground 2020 \n", - "1154 offwind-ac-station 2020 \n", - "1155 offwind-dc-connection-submarine 2020 \n", - "1156 offwind-dc-connection-underground 2020 \n", - "1157 offwind-dc-station 2020 \n", - "1166 oil 2020 \n", - "1168 oil 2020 \n", - "1169 oil 2020 \n", - "1172 onwind 2020 \n", - "1173 onwind 2020 \n", - "1184 ror 2020 \n", - "1189 seawater desalination 2020 \n", - "1195 solar 2020 \n", - "1199 solar-rooftop 2020 \n", - "1202 solar-rooftop commercial 2020 \n", - "1205 solar-rooftop residential 2020 \n", - "1208 solar-utility 2020 \n", - "1214 solid biomass 2020 \n", - "1216 solid biomass boiler steam 2020 \n", - "1218 solid biomass boiler steam 2020 \n", - "1221 solid biomass boiler steam CC 2020 \n", - "1223 solid biomass boiler steam CC 2020 \n", - "1228 solid biomass to hydrogen 2020 \n", - "1235 uranium 2020 \n", - "1237 waste CHP 2020 \n", - "1242 waste CHP 2020 \n", - "1245 waste CHP CC 2020 \n", - "1250 waste CHP CC 2020 \n", - "1252 water tank charger 2020 \n", - "1253 water tank discharger 2020 \n", - "\n", - " parameter value_old value_new delta \n", - "17 investment 1.062108e+06 1.123945e+06 6.183764e+04 \n", - "60 VOM 2.700000e+00 2.871200e+00 1.712000e-01 \n", - "63 investment 2.500000e+03 2.658500e+03 1.585000e+02 \n", - "69 VOM 1.060000e+00 1.129900e+00 6.990000e-02 \n", - "72 investment 3.500000e+03 3.638172e+03 1.381722e+02 \n", - "75 VOM 4.400000e+00 4.656200e+00 2.562000e-01 \n", - "79 investment 8.800000e+02 9.312350e+02 5.123500e+01 \n", - "82 investment 1.498950e+03 1.654960e+03 1.560100e+02 \n", - "86 investment 7.900000e+01 8.722000e+01 8.220000e+00 \n", - "90 investment 1.148900e+02 1.193173e+02 4.427300e+00 \n", - "94 investment 1.510000e+08 1.597915e+08 8.791466e+06 \n", - "97 investment 8.760000e+01 9.111010e+01 3.510100e+00 \n", - "101 investment 2.321300e+02 2.414430e+02 9.313000e+00 \n", - "108 investment 1.603000e+01 1.672260e+01 6.926000e-01 \n", - "111 investment 2.000000e+03 2.116443e+03 1.164433e+02 \n", - "114 investment 2.528170e+03 2.584346e+03 5.617620e+01 \n", - "117 investment 4.000000e+03 4.232886e+03 2.328865e+02 \n", - "228 investment 3.170058e+07 3.500000e+07 3.299422e+06 \n", - "231 VOM 5.300000e+00 5.636000e+00 3.360000e-01 \n", - "238 investment 7.574010e+05 8.191085e+05 6.170748e+04 \n", - "244 investment 1.358300e+02 1.378999e+02 2.069900e+00 \n", - "247 investment 1.697900e+02 1.723748e+02 2.584800e+00 \n", - "268 investment 4.478000e+03 4.738716e+03 2.607164e+02 \n", - "272 investment 2.264700e+02 3.036845e+02 7.721450e+01 \n", - "276 investment 1.058800e+02 1.294682e+02 2.358820e+01 \n", - "280 investment 3.293700e+02 4.561165e+02 1.267465e+02 \n", - "287 investment 7.500800e+02 7.937456e+02 4.366560e+01 \n", - "291 investment 3.612236e+08 3.937370e+08 3.251344e+07 \n", - "294 investment 1.436400e+02 1.468405e+02 3.200500e+00 \n", - "299 investment 8.705600e+02 8.899426e+02 1.938260e+01 \n", - "302 investment 2.670000e+02 2.825452e+02 1.554520e+01 \n", - "353 investment 1.829700e+02 4.421414e+02 2.591714e+02 \n", - "356 investment 1.623648e+05 1.658030e+05 3.438220e+03 \n", - "359 investment 1.829700e+02 4.421414e+02 2.591714e+02 \n", - "362 investment 4.711600e+02 1.008293e+03 5.371334e+02 \n", - "372 investment 1.586290e+03 1.785071e+03 1.987813e+02 \n", - "406 investment 6.115900e+02 6.666340e+02 5.504400e+01 \n", - "408 investment 2.264330e+03 2.500000e+03 2.356700e+02 \n", - "411 investment 5.072803e+04 5.368150e+04 2.953469e+03 \n", - "414 investment 7.599082e+05 8.390000e+05 7.909185e+04 \n", - "419 investment 5.125954e+04 5.424396e+04 2.984418e+03 \n", - "423 investment 1.492700e+02 1.515383e+02 2.268300e+00 \n", - "427 investment 3.170058e+07 3.500000e+07 3.299422e+06 \n", - "430 investment 1.322600e+02 1.342745e+02 2.014500e+00 \n", - "485 investment 3.170058e+07 3.500000e+07 3.299422e+06 \n", - "488 investment 1.631843e+04 1.801687e+04 1.698436e+03 \n", - "492 investment 1.619300e+02 1.668201e+02 4.890100e+00 \n", - "496 investment 7.446194e+07 8.116420e+07 6.702259e+06 \n", - "506 VOM 4.500000e+00 4.762000e+00 2.620000e-01 \n", - "508 investment 4.539600e+02 4.803903e+02 2.643030e+01 \n", - "516 investment 2.208160e+03 2.274818e+03 6.665770e+01 \n", - "538 investment 4.934704e+05 5.222010e+05 2.873065e+04 \n", - "543 investment 5.724257e+05 6.057532e+05 3.332756e+04 \n", - "561 investment 4.700855e+05 4.974546e+05 2.736914e+04 \n", - "603 investment 8.916791e+05 1.003392e+06 1.117131e+05 \n", - "616 investment 2.700000e+02 2.871180e+02 1.711800e+01 \n", - "618 investment 2.320000e+02 2.467088e+02 1.470880e+01 \n", - "630 CO2 stored 9.000000e-02 1.447000e-01 5.470000e-02 \n", - "631 FOM 1.138000e+01 7.776900e+00 3.603100e+00 \n", - "634 fuel 5.900000e+01 6.243510e+01 3.435100e+00 \n", - "635 investment 1.710690e+03 1.032458e+03 6.782323e+02 \n", - "637 CO2 stored 9.000000e-02 1.447000e-01 5.470000e-02 \n", - "638 FOM 1.138000e+01 7.776900e+00 3.603100e+00 \n", - "641 investment 1.710690e+03 1.032458e+03 6.782323e+02 \n", - "646 investment 9.072000e+02 9.647165e+02 5.751650e+01 \n", - "648 FOM 2.510000e+00 1.703970e+01 1.452970e+01 \n", - "649 VOM 3.690000e+00 4.161300e+00 4.713000e-01 \n", - "650 investment 4.230000e+02 1.929697e+02 2.300303e+02 \n", - "651 lifetime 1.500000e+01 2.000000e+01 5.000000e+00 \n", - "654 fuel 7.000000e+00 7.407600e+00 4.076000e-01 \n", - "655 investment 2.209000e+03 2.337612e+03 1.286116e+02 \n", - "658 VOM 2.110000e+00 2.229100e+00 1.191000e-01 \n", - "663 investment 3.381270e+03 3.578135e+03 1.968649e+02 \n", - "675 VOM 2.110000e+00 2.229100e+00 1.191000e-01 \n", - "680 investment 3.381270e+03 3.578135e+03 1.968649e+02 \n", - "683 VOM 2.110000e+00 2.236100e+00 1.261000e-01 \n", - "685 investment 8.754200e+02 9.263933e+02 5.097330e+01 \n", - "689 investment 6.826700e+02 7.224205e+02 3.975050e+01 \n", - "740 VOM 2.190000e+00 2.317500e+00 1.275000e-01 \n", - "741 efficiency 3.400000e+00 3.100000e+00 3.000000e-01 \n", - "742 investment 9.513900e+02 1.006777e+03 5.538650e+01 \n", - "745 VOM 2.900000e+00 3.068800e+00 1.688000e-01 \n", - "748 efficiency 4.383117e-01 4.850000e-01 4.668831e-02 \n", - "749 investment 1.900000e+03 2.010621e+03 1.106211e+02 \n", - "763 VOM 4.400000e+00 4.656200e+00 2.562000e-01 \n", - "767 investment 5.900000e+02 6.243508e+02 3.435080e+01 \n", - "771 VOM 4.400000e+00 4.656200e+00 2.562000e-01 \n", - "773 efficiency 5.900000e-01 4.000000e-01 1.900000e-01 \n", - "774 investment 8.800000e+02 6.243508e+02 2.556492e+02 \n", - "777 VOM 1.100000e+00 1.164000e+00 6.400000e-02 \n", - "779 investment 6.000000e+01 6.349330e+01 3.493300e+00 \n", - "786 VOM 9.800000e-01 1.039200e+00 5.920000e-02 \n", - "788 investment 5.640000e+02 5.968370e+02 3.283700e+01 \n", - "793 investment 1.300000e+03 1.375688e+03 7.568810e+01 \n", - "796 VOM 9.000000e-01 9.524000e-01 5.240000e-02 \n", - "798 investment 7.000000e+01 7.407550e+01 4.075500e+00 \n", - "801 investment 1.400000e+05 1.481510e+05 8.151028e+03 \n", - "804 VOM 4.600000e+00 4.869400e+00 2.694000e-01 \n", - "809 investment 3.534650e+03 3.740439e+03 2.057887e+02 \n", - "813 VOM 4.600000e+00 4.869400e+00 2.694000e-01 \n", - "818 investment 5.449800e+03 5.767099e+03 3.172987e+02 \n", - "821 VOM 4.600000e+00 4.869400e+00 2.694000e-01 \n", - "826 investment 3.534650e+03 3.740439e+03 2.057887e+02 \n", - "839 FOM 5.200000e-01 1.000000e+00 4.800000e-01 \n", - "841 investment 5.800000e-01 3.036100e+00 2.456100e+00 \n", - "842 lifetime 2.000000e+01 4.000000e+01 2.000000e+01 \n", - "846 investment 6.763000e+01 6.912860e+01 1.498600e+00 \n", - "849 FOM 1.600000e+00 1.310000e+00 2.900000e-01 \n", - "850 VOM 3.500000e+00 3.261200e+00 2.388000e-01 \n", - "851 efficiency 4.391892e-01 3.560000e-01 8.318919e-02 \n", - "852 fuel 8.150000e+00 9.554200e+00 1.404200e+00 \n", - "853 investment 3.845510e+03 3.827163e+03 1.834710e+01 \n", - "856 investment 1.448800e+02 1.599600e+02 1.508000e+01 \n", - "859 investment 1.941000e+01 2.143000e+01 2.020000e+00 \n", - "862 investment 1.014930e+03 1.120570e+03 1.056400e+02 \n", - "866 investment 1.400000e+03 1.481510e+03 8.151030e+01 \n", - "871 investment 9.400000e+02 9.947283e+02 5.472830e+01 \n", - "882 investment 3.120800e+02 3.302494e+02 1.816940e+01 \n", - "884 investment 1.950500e+02 2.064059e+02 1.135590e+01 \n", - "889 investment 1.500000e+03 1.587332e+03 8.733240e+01 \n", - "893 investment 1.560100e+02 1.650975e+02 9.087500e+00 \n", - "898 investment 1.000000e+02 1.058222e+02 5.822200e+00 \n", - "902 investment 2.700000e+05 2.857198e+05 1.571984e+04 \n", - "910 investment 1.838000e+01 4.198622e+02 4.014822e+02 \n", - "911 lifetime 2.000000e+01 3.000000e+01 1.000000e+01 \n", - "914 fuel 1.500000e+01 1.706110e+01 2.061100e+00 \n", - "918 investment 4.000000e+03 4.237119e+03 2.371194e+02 \n", - "922 electricity-input 3.500000e-01 4.000000e-01 5.000000e-02 \n", - "923 heat-input 2.500000e+00 1.600000e+00 9.000000e-01 \n", - "930 investment 1.500000e+01 1.510500e+01 1.050000e-01 \n", - "935 investment 1.500000e+01 1.510500e+01 1.050000e-01 \n", - "940 investment 2.200000e+02 2.215400e+02 1.540000e+00 \n", - "945 investment 2.200000e+02 2.215400e+02 1.540000e+00 \n", - "965 VOM 8.600000e-01 8.711000e-01 1.110000e-02 \n", - "967 investment 8.000000e+01 8.056000e+01 5.600000e-01 \n", - "977 investment 5.000000e+02 5.291108e+02 2.911080e+01 \n", - "980 investment 1.400000e+02 1.481510e+02 8.151000e+00 \n", - "984 VOM 4.660000e+00 5.153000e+00 4.930000e-01 \n", - "989 investment 5.178441e+05 5.598873e+05 4.204316e+04 \n", - "990 FOM 2.000000e+00 4.000000e+00 2.000000e+00 \n", - "991 efficiency 6.600000e-01 5.773000e-01 8.270000e-02 \n", - "992 efficiency-heat 1.800000e-01 2.762000e-01 9.620000e-02 \n", - "993 investment 6.500000e+02 2.000000e+03 1.350000e+03 \n", - "1018 investment 1.300000e+03 1.375688e+03 7.568810e+01 \n", - "1022 fuel 2.010000e+01 2.456800e+01 4.468000e+00 \n", - "1026 investment 5.455000e+01 5.492730e+01 3.773000e-01 \n", - "1028 FOM 3.590000e+00 5.368000e-01 3.053200e+00 \n", - "1029 investment 3.000000e-02 2.366000e-01 2.066000e-01 \n", - "1031 investment 1.434000e+01 1.524790e+01 9.079000e-01 \n", - "1032 investment 4.780000e+00 5.082600e+00 3.026000e-01 \n", - "1033 CO2 intensity 3.000000e-02 1.200000e-01 9.000000e-02 \n", - "1034 FOM 2.360000e+00 2.000000e+00 3.600000e-01 \n", - "1039 lifetime 4.000000e+01 3.000000e+01 1.000000e+01 \n", - "1042 investment 2.000000e+03 2.116443e+03 1.164433e+02 \n", - "1046 investment 3.770000e+02 4.009018e+02 2.390180e+01 \n", - "1048 investment 3.235300e+02 3.440435e+02 2.051350e+01 \n", - "1052 investment 2.208160e+03 2.274818e+03 6.665770e+01 \n", - "1063 investment 7.942000e+01 8.769000e+01 8.270000e+00 \n", - "1066 investment 1.223000e+01 1.350000e+01 1.270000e+00 \n", - "1070 investment 5.700000e+01 6.061380e+01 3.613800e+00 \n", - "1074 investment 3.000000e+00 3.190200e+00 1.902000e-01 \n", - "1077 VOM 3.260000e+00 3.282800e+00 2.280000e-02 \n", - "1079 investment 1.045440e+03 1.052758e+03 7.318100e+00 \n", - "1082 VOM 3.260000e+00 3.282800e+00 2.280000e-02 \n", - "1084 investment 8.712000e+02 8.772984e+02 6.098400e+00 \n", - "1093 FOM 1.600000e+00 1.310000e+00 2.900000e-01 \n", - "1094 VOM 3.500000e+00 3.261200e+00 2.388000e-01 \n", - "1096 fuel 2.900000e+00 3.298500e+00 3.985000e-01 \n", - "1097 investment 3.845510e+03 3.827163e+03 1.834710e+01 \n", - "1103 investment 7.189500e+02 7.775294e+02 5.857940e+01 \n", - "1106 investment 8.629200e+03 8.961507e+03 3.323075e+02 \n", - "1129 investment 7.574010e+05 8.191085e+05 6.170748e+04 \n", - "1134 investment 1.004531e+04 1.063017e+04 5.848581e+02 \n", - "1142 FOM 1.400000e+00 1.270000e+00 1.300000e-01 \n", - "1143 VOM 3.500000e+00 3.546400e+00 4.640000e-02 \n", - "1145 fuel 2.600000e+00 3.412200e+00 8.122000e-01 \n", - "1146 investment 2.340000e+03 8.594135e+03 6.254135e+03 \n", - "1150 investment 1.380000e+03 1.992611e+03 6.126105e+02 \n", - "1152 investment 2.685000e+03 2.841325e+03 1.563251e+02 \n", - "1153 investment 1.342000e+03 1.420133e+03 7.813340e+01 \n", - "1154 investment 2.500000e+02 2.645554e+02 1.455540e+01 \n", - "1155 investment 2.000000e+03 2.116443e+03 1.164433e+02 \n", - "1156 investment 1.000000e+03 1.058222e+03 5.822160e+01 \n", - "1157 investment 4.000000e+02 4.232887e+02 2.328870e+01 \n", - "1166 VOM 6.000000e+00 6.349300e+00 3.493000e-01 \n", - "1168 fuel 5.000000e+01 5.291110e+01 2.911100e+00 \n", - "1169 investment 3.430000e+02 3.629700e+02 1.997000e+01 \n", - "1172 VOM 1.500000e+00 1.587300e+00 8.730000e-02 \n", - "1173 investment 6.720000e+02 1.183912e+03 5.119119e+02 \n", - "1184 investment 3.312240e+03 3.412227e+03 9.998660e+01 \n", - "1189 investment 4.021978e+04 4.256144e+04 2.341661e+03 \n", - "1195 investment 7.334700e+02 8.098118e+02 7.634180e+01 \n", - "1199 investment 9.574700e+02 1.057124e+03 9.965370e+01 \n", - "1202 investment 7.900800e+02 8.723118e+02 8.223180e+01 \n", - "1205 investment 1.124860e+03 1.241936e+03 1.170755e+02 \n", - "1208 investment 3.240000e+02 5.625000e+02 2.385000e+02 \n", - "1214 fuel 1.200000e+01 1.364890e+01 1.648900e+00 \n", - "1216 VOM 2.780000e+00 2.798500e+00 1.850000e-02 \n", - "1218 investment 6.181800e+02 6.225091e+02 4.329100e+00 \n", - "1221 VOM 2.780000e+00 2.798500e+00 1.850000e-02 \n", - "1223 investment 6.181800e+02 6.225091e+02 4.329100e+00 \n", - "1228 investment 4.000000e+03 4.237119e+03 2.371194e+02 \n", - "1235 fuel 2.600000e+00 3.412200e+00 8.122000e-01 \n", - "1237 VOM 2.728000e+01 2.886480e+01 1.584800e+00 \n", - "1242 investment 8.577700e+03 9.077107e+03 4.994074e+02 \n", - "1245 VOM 2.728000e+01 2.886480e+01 1.584800e+00 \n", - "1250 investment 8.577700e+03 9.077107e+03 4.994074e+02 \n", - "1252 efficiency 8.400000e-01 9.000000e-01 6.000000e-02 \n", - "1253 efficiency 8.400000e-01 9.000000e-01 6.000000e-02 \n", - "\n", - "📁 Saved diff CSV files to folder: diff_2020/\n", - " - added_2020.csv\n", - " - removed_2020.csv\n", - " - changed_2020.csv\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", @@ -1825,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "b9feafdb", + "id": "3", "metadata": {}, "source": [ "### change the specific technology cost" @@ -1833,53 +113,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "6e5ec243", + "execution_count": null, + "id": "4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing costs_2020.csv...\n", - " ✅ Modified nuclear investment: [2340.0] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2020.csv\n", - "\n", - "Processing costs_2025.csv...\n", - " ✅ Modified nuclear investment: [2340.0] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2025.csv\n", - "\n", - "Processing costs_2030.csv...\n", - " ✅ Modified nuclear investment: [2340.0] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2030.csv\n", - "\n", - "Processing costs_2035.csv...\n", - " ✅ Modified nuclear investment: [2234.613] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2035.csv\n", - "\n", - "Processing costs_2040.csv...\n", - " ✅ Modified nuclear investment: [2129.226] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2040.csv\n", - "\n", - "Processing costs_2045.csv...\n", - " ✅ Modified nuclear investment: [2077.4352] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2045.csv\n", - "\n", - "Processing costs_2050.csv...\n", - " ✅ Modified nuclear investment: [2025.6444] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2050.csv\n", - "\n", - "Processing costs_2055.csv...\n", - " ✅ Modified nuclear investment: [1973.8542] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2055.csv\n", - "\n", - "Processing costs_2060.csv...\n", - " ✅ Modified nuclear investment: [1922.064] → 2700\n", - " 💾 Saved updated file to ../../resources/data/costs/default/costs_2060.csv\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import pandas as pd\n", @@ -1909,7 +146,7 @@ }, { "cell_type": "markdown", - "id": "e1de68a6", + "id": "5", "metadata": {}, "source": [ "### change the specific technology cost" @@ -1918,46 +155,9 @@ { "cell_type": "code", "execution_count": null, - "id": "cb8b779a", + "id": "6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "📌 Available references for solar\n", - "\n", - "[6] eur/kw | PyPSA-China_default\n", - "[7] cny/kw | LBNL\n", - "[8] cny/kw | PyPSA-China_new\n", - "\n", - "📈 Interpolated values:\n", - "2020 432.0\n", - "2025 366.0\n", - "2030 300.0\n", - "2035 276.0\n", - "2040 252.0\n", - "2045 246.0\n", - "2050 240.0\n", - "2055 228.0\n", - "2060 216.0\n", - "dtype: float64\n", - "✔ updated only year=2020: costs_2020.csv\n", - "✔ updated only year=2025: costs_2025.csv\n", - "✔ updated only year=2030: costs_2030.csv\n", - "✔ updated only year=2035: costs_2035.csv\n", - "✔ updated only year=2040: costs_2040.csv\n", - "✔ updated only year=2045: costs_2045.csv\n", - "✔ updated only year=2050: costs_2050.csv\n", - "✔ updated only year=2055: costs_2055.csv\n", - "✔ updated only year=2060: costs_2060.csv\n", - "\n", - "🎉 Completed. Each file now contains ONLY its year.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", @@ -2047,7 +247,7 @@ }, { "cell_type": "markdown", - "id": "7779451d", + "id": "7", "metadata": {}, "source": [ "### change the literature source" @@ -2056,52 +256,9 @@ { "cell_type": "code", "execution_count": null, - "id": "f2b950ec", + "id": "8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "📌 Available references for coal (investment):\n", - "\n", - "[53] Zhu et al.\n", - " link: Zhu Z, Zhang D, Zhang X, Zhang X. Integrated modeling for the transition pathway of China��s power system. Energy Environ Sci 2025:10.1039.D5EE00355E. https://doi.org/10.1039/D5EE00363E.\n", - "\n", - "[54] Switch-China\n", - " link: https://github.com/switch-model/switch-china-open-model/\n", - "\n", - "[55] PyPSA-China_default\n", - " link: nan\n", - "\n", - "[56] PyPSA-China_new\n", - " link: https://www.cctd.com.cn/show-19-203452-1.html \"China Power Industry Annual Development Report 2020\" in Chinese\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "🔗 Selected link to write into default CSVs:\n", - "https://www.cctd.com.cn/show-19-203452-1.html \"China Power Industry Annual Development Report 2020\" in Chinese\n", - "✔ updated SOURCE for year=2020: costs_2020.csv\n", - "✔ updated SOURCE for year=2025: costs_2025.csv\n", - "✔ updated SOURCE for year=2030: costs_2030.csv\n", - "✔ updated SOURCE for year=2035: costs_2035.csv\n", - "✔ updated SOURCE for year=2040: costs_2040.csv\n", - "✔ updated SOURCE for year=2045: costs_2045.csv\n", - "✔ updated SOURCE for year=2050: costs_2050.csv\n", - "✔ updated SOURCE for year=2055: costs_2055.csv\n", - "✔ updated SOURCE for year=2060: costs_2060.csv\n", - "\n", - "🎉 Completed: link has been written to SOURCE column.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", @@ -2158,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "5eed815e", + "id": "9", "metadata": {}, "source": [ "### draw the cost change trend" @@ -2167,20 +324,9 @@ { "cell_type": "code", "execution_count": null, - "id": "05808b40", + "id": "10", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import math\n", "import os\n", @@ -2233,7 +379,7 @@ }, { "cell_type": "markdown", - "id": "69fcc3c3", + "id": "11", "metadata": {}, "source": [ "### draw the cost comparision in the default list" @@ -2242,20 +388,9 @@ { "cell_type": "code", "execution_count": null, - "id": "2ccac105", + "id": "12", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9tHnzZrVr104RERF68MEHJWWGjwcOHFBoaKj69u0rSZo0aZKCg4MVERGRax9zCjbz0mbJkiW6evWqfv31V5lMphuOSMotHDMMQ+Hh4Xrsscestp84ccLy9xuFodcGsMUdkBbaGlMDBw5UixYt9NZbbxX4GD/99JPVa5PJpPDwcIWHh+f6Hjc3N7377rt69913c21ToUIFLV68uMD9AgAAAAAUHpPJJPdcptu1q1NZVXzdFB2XnOM6UyZJAb5ualcn5zWmbmTmzJlKTExUr169rLavXLky1/dUq1ZN3bt3V6NGjRQcHGxZauZaLi4uWrZsmUaPHm0ZsDFz5kzVrl1bw4YNU6dOnVS/fn2rdXnKlSungQMHqmHDhrr99ttzDaVCQkJyDGRsxWQy5TrlrnVga/l7+Otc4rkc15kyySR/D3+1DmydbY2pgurXr58aN26sChUqZJuVdb1ffvlFDRo0ULly5XT16lVFRUWpevXqWrFihUaNGmUZiGMYhmrUqCFPT0+rzz4xMVGvv/66NmzYoM6dO8swDH3yySd6+OGHb+ka4uPj5e/vLycnJy1fvlzJycm5tm3Tpo2WL1+uVq1aac2aNYqNjZWUGcS98cYbeuihh+Tu7q7Dhw9nGy11ozC0evXqioyMVLt27fT1119bDfbJklvIeqsKLZjaunXrDZ+qAAAAAADAzTg6mDS5V7BGLd4jk2QVb2TFUJN7Bec7lJKk+fPnW5Z4ud71AyIuXLhg+fusWbM0a9asGx67efPmOY6mefbZZ/Xss8/m+J4ZM2ZoxowZ2bafOHFCZrNZ8fHx2rNnzw3PW5I4OjjqpRYvadxP42SSySqcMv1dvRdbvFhooZSUGZaEhISoXbt2N2177NgxjRw5UiaTSRkZGXr88cfVvHlzjR8/XlOnTv2nryaTevbsqZUrV2rkyJGW7R4eHvr66681ZswYjRo1Sk5OTrrnnnuyjVLKr/79+6tHjx5q0aKF2rRpowoVso8EzPLUU09p4MCBaty4sTp06KBq1apJypzWt3//fsuC6H5+flq9erXVe28Uhr7yyit68sknVaVKFd15551KSEjIdu7cQtZbZTIMI6cQOlf333+/1WvDMHT27Fnt2rVLr7zyiiZPnlxonStJ4uPj5evrq7i4uGyPfyxN0tLS9P3336t79+6l+mkeyB/qbn+ouX2i7vaHmtsn6m6fykrdk5OTFRUVpZo1a950YMOa/Wc1ZfVBnY37Z/RIFV83Te4VrG4Nq9zgnWVDVjDl4+NTop7KlxfrT67XjB0zFJMYY9kW4BGgF1u8qNDqoYV6rvT0dN11113atGmTKlWqVKjHtoXSWPec7uv8ZCj5HjHl6+tr9drBwUF169bV1KlTLWs4AQAAAABwK7o1rKKw4ADtiLqkcwnJ8vN2U4uaFQo0UgrFK7R6qDoFddKec3t0PvG8KntUVohfSKGOlJKkyMhI9e3bV0OHDi0ToZS9yncw9emnnxZFPwAAAAAAsOLoYFKrWhVt3Q0UgKODo5oHNC/SczRu3Nhqce+yZN++fRo8eLDVttq1a+e4oHtpV6A1pi5fvqwVK1bo2LFj+te//qUKFSpoz5498vf312233VbYfQQAAAAAALAbjRo1KlGL3helfAdTe/fuVefOnVWuXDmdOHFCw4YNU4UKFbRq1SqdPHlSn332WVH0EwAAAAAAAGVMvlfSGjdunIYMGaKjR49aLVZ37733avPmzYXaOQAAAAAAAJRd+Q6mdu7cqREjRmTbfttttyk6OrpQOgUAAAAAgMwZUtTP0r4Vmf9rzrjlQ06dOlUNGzZUo0aN1KxZM0VFRd30PZ06dZIknThxQl988cUN2544cULNmjXLU19+/vlntWzZUvXq1VP9+vX16quvSpIef/xxfffdd9naf/vtt5o9e3aejg2UFvmeyufm5qb4+Phs2w8fPqzKlSsXSqcAAAAAAHbu4LfSmhel+DP/bPMJlLq9IQX3LtAhIyIi9NNPPykyMlJOTk46ffq0PD09b/q+H3/8UdI/wVS/fv0KdP5rnTlzRgMHDtTXX3+tJk2aKD09/aYPG+vdu2DXbQtGRoYSd+1W+vnzcqpcWR7NmsrkWLhP5UPZkO8RU3369NHUqVOVlpYmSTKZTDp16pReeuklPfDAA4XeQQAAAACAnTn4rfTFY9ahlCTFn83cfvDbAh02Ojpa5cuXl5NT5hiNqlWr6vvvv9fLL78sSZo4caK6du0qSfrqq6/0/PPPS5IqVaokSXr55Ze1fv16NW7cWJ9++qnS0tL0zDPPqFGjRrrrrru0cuVKSVJaWpoGDx6s+vXr6+GHH5ZhGNn6Mm/ePD3xxBNq0qSJJMnJyUnDhg2z7F+/fr3CwsJUt25dbdq0SZK0YMECjR8/XlLmqKoxY8bo7rvvVp06dSxtjh07pnbt2ikkJER33323Dh06VKDP6lbEr12rPzqH6tTgwTozfrxODR6sPzqHKn7t2gIf08nJSY0bN1bDhg310EMPKTExMde2P/30k8qVK6fGjRurfv36mjJliiTpypUrevjhh3XnnXcqODhY9957r9X7/vOf/8jV1VWXL1/O9dj79+9X586dVadOHdWvX1+jR49WWlqawsPDNXfu3Gztd+3apX/9618Fu2g7ke9g6q233tL58+fl5+enpKQkdejQQbVr15a3t7emTZtWFH0EAAAAAJQlhiGlXs35T3K89MMLkrKHOZZta17MbJfT+3MIgbKEhYXpyJEjql+/vsaMGaOdO3eqbdu22rJli6TMpWsSEhKUkZGhLVu2qE2bNlbvnzZtmkJDQxUZGakhQ4Zo/vz5iouL02+//abffvtN99xzjyTp0KFDmjBhgg4ePKiYmBjL8a918OBBNW7cONe+Xr58WevWrdP777+vqVOn5tpm27Ztmj9/vqVNlSpVtH79eu3Zs0ezZs3SxIkTcz1HUYhfu1Z/jRmr9OuW+kmPidFfY8YWOJwqV66cIiMjtX//frm4uOiDDz64YfusOu3atUuLFy/W7t279X//93+qW7eu9u7dq4MHD2rmzJlW7/niiy/UrFkzrVq1KsdjJiYmqk+fPnrppZd09OhRHTx4UG3atFFKSkqu/WjWrJnefPPN/F+wHcn3VD4fHx9t2bJFGzdu1J49e2Q2mxUSEqLQ0NCi6B8AAAAAoKxJS5TeuL2AbzYyR1LNCMp598QzkkvO0/O8vb3166+/6scff7SMSPriiy905swZXblyRWazWS1atNDevXv1yy+/6MUXX7xhTzZu3Khx48bJwSFzzEf58uUVFxenunXrql69epKkJk2a6MSJE2rXrp31VRiGTCZTrsfu1auXJKlp06Y6ceJEjm2ypvZd2yYlJUVPPfWU9u7dKwcHhxuGJgVhGIaMpKSc92VkKOa1aTmHg4YhmaSYadPl2apVtml9Jnf3G34e12rXrp327t2rSZMmqVq1aho+fLgkaeDAgRo4cKDVg9o8PT3VtGlTHTt2TNHR0apTp45lX6NGjSx/v3Dhgo4fP64FCxZo2rRpGjJkSLbzLl26VO3bt1dYWFhmn00mDRgwwLI/MjJS7du31+nTpzV9+nQ98sgj+umnnzR37lytWLFC4eHhOn36tI4cOWLVJj4+Xn379tXly5dlGIamT59uGbl3vQULFuj7779XbGysoqKiNGrUKMvIvk8//VTvv/++kpOT1bdvX02dOlXTpk2Tn5+fhg0bpv79+8vb21vz58/XrFmz5OzsrGeeeSZPn3lRyncwleWee+6xpMEAAAAAAJQGTk5OCgsLU1hYmCpVqqRvv/1WISEh+vDDD9W0aVO1aNFC69atU0JCgvz9/Qt0DldXV8vfHR0dlZGRoYiICI0ePVpS5jS+4OBg/frrr7muG5V1jKz357XNnDlzVLNmTS1ZskQxMTG6++67C3QNuTGSknQ4pGkB35w5cupI8xbZdtXds1smD4+bHiI9PV0//PCDunXrprCwMA0ZMkTDhw/XlStXtH37di1cuFA///yzpf3Fixe1bds2vfLKK6pdu7a6dOmi5cuXKywsTE888YSCgjIDzpUrV+r+++9XmzZt9Pvvv+vixYuqWLGi1blvNsotKipKGzdu1MmTJ9W1a1c98sgjeWrj7u6ub775Rt7e3jpx4oTuv//+XIMpKXM64c6dO5WWlqa6devqmWee0R9//KHvv/9eW7dulclkUp8+fbR161a1bdtWn3zyiYYNG6ZTp07J2dlZkrRlyxZNmjTppp93cchTMLVs2bIcP9Cc/Pnnnzp16lS2IY8AAAAAAEiSnD0yRzbl5GSEtOTBmx9jwAqpeuucj52Lw4cPy8nJSbVq1ZJhGDpw4IAaNGigunXraubMmXrvvffUvHlzPffcc+rcuXO293t7eyshIcHyOjQ0VB9++KFat24tBwcHxcbG5nru1q1bKzIy0vK6evXqat26tfr06aPGjRsrIyND8+bNu+URLPHx8apdu7ZMJpMWLVp0S8cqSS5fvmwJhdq1a6ehQ4fKxcVFJpNJx44d0+bNm3XffffJ8e+RWOvXr1eTJk3k4OCgCRMmqEGDBpKk48ePa82aNVq9erVCQkJ04MAB+fn5afny5Zo5c6Yl1Fm1apWefPJJqz7cbJRb9+7dLd+v3NapyqmNYRh64YUXtGXLFjk6Ouro0aM3/Cw6d+5sWbQ/MDBQMTEx2rBhg7Zu3aqmTTNDwytXrujYsWN64IEHNHLkSP3555+qVq2akpOTdfHiRe3du1d33XXXDc9TXPIUTL3//vsKDw/XkCFD1Lt3b9WvX99qf1xcnH755RctXrxY69ev18cff1wknQUAAAAAlAEmU67T7VTrnsyn78WfVc7rTJky99e6R3LI31Perly5oqefftrypPmmTZvq6aef1qFDh/TXX3+pTZs2qlixopydnXMcbHHnnXcqLS1NjRs31pgxYzR8+HD9/vvvatSokZycnDR58mSFhITkqS+33XabFi5cqOHDhys+Pl6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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -2351,7 +486,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1", + "id": "13", "metadata": {}, "outputs": [], "source": [ @@ -2364,7 +499,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2", + "id": "14", "metadata": {}, "outputs": [], "source": [ @@ -2377,7 +512,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "15", "metadata": {}, "outputs": [], "source": [ @@ -2392,7 +527,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4", + "id": "16", "metadata": {}, "outputs": [], "source": [ @@ -2439,7 +574,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5", + "id": "17", "metadata": {}, "outputs": [], "source": [ @@ -2502,7 +637,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6", + "id": "18", "metadata": {}, "outputs": [], "source": [ @@ -2529,7 +664,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "19", "metadata": {}, "outputs": [], "source": [ @@ -2539,7 +674,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8", + "id": "20", "metadata": {}, "outputs": [], "source": [ @@ -2562,7 +697,7 @@ }, { "cell_type": "markdown", - "id": "9", + "id": "21", "metadata": {}, "source": [ "# FIX CC CHP" @@ -2571,7 +706,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10", + "id": "22", "metadata": {}, "outputs": [], "source": [ @@ -2585,7 +720,7 @@ { "cell_type": "code", "execution_count": null, - "id": "11", + "id": "23", "metadata": {}, "outputs": [], "source": [ @@ -2620,7 +755,7 @@ { "cell_type": "code", "execution_count": null, - "id": "12", + "id": "24", "metadata": {}, "outputs": [], "source": [ @@ -2650,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "25", "metadata": {}, "outputs": [], "source": [ @@ -2669,7 +804,7 @@ { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "26", "metadata": {}, "outputs": [], "source": [ From dc327034816a05cd6fcb8d1fd292c48a6651af48 Mon Sep 17 00:00:00 2001 From: beijingzyl <1772066848@qq.com> Date: Fri, 19 Dec 2025 16:54:44 +0100 Subject: [PATCH 6/9] add cbc --- workflow/envs/environment.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/workflow/envs/environment.yaml b/workflow/envs/environment.yaml index 7a7b4dcf..8b4d8b2a 100644 --- a/workflow/envs/environment.yaml +++ b/workflow/envs/environment.yaml @@ -13,6 +13,7 @@ dependencies: - pypsa=0.35.1 - atlite>=0.4.0 - dask + - coincbc - snakemake-storage-plugin-http # - pyasyncore # - pyasynchat From 285cb18a395b821693ca10b2a9059b3c5be02420 Mon Sep 17 00:00:00 2001 From: beijingzyl <1772066848@qq.com> Date: Fri, 19 Dec 2025 17:15:01 +0100 Subject: [PATCH 7/9] fix precommit --- docs/configuration.md | 19 +++++++++---------- examples/historical.yml | 4 ++-- workflow/scripts/_plot_utilities.py | 15 ++++++++------- workflow/scripts/add_sectors.py | 12 +++++------- workflow/scripts/build_population.py | 2 +- .../scripts/determine_availability_matrix.py | 3 +-- workflow/scripts/functions.py | 2 +- workflow/scripts/prepare_network.py | 4 +--- workflow/scripts/readers.py | 13 ++++++------- .../ev_refshare_extrapolator.py | 2 +- .../extrapolate_regional_references.py | 8 +++++--- workflow/scripts/solve_network.py | 5 +---- 12 files changed, 41 insertions(+), 48 deletions(-) diff --git a/docs/configuration.md b/docs/configuration.md index 238dc79a..1acba65b 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -2,7 +2,7 @@ This is documentation for the PyPSA-China configuration (`config/default_config.yaml` & `config/technology_config.yaml`). The configuration file controls various aspects of the PyPSA-China energy system modeling workflow. -## Table of Contents +## Table of Contents - [Run Configuration](#run-configuration) - [File Paths](#file-paths) @@ -45,7 +45,7 @@ This is documentation for the PyPSA-China configuration (`config/default_config. 2. **Customization**: Do not edit `default_config.yaml`. Overwrite the variables you need in `my_config.yaml`. See [running section](../running). Then run `snakemake --configfile config/my_config.yaml` -3. **Technology Configuration**: Additional technology parameters are defined in separate files in `config/technology_config.yaml` +3. **Technology Configuration**: Additional technology parameters are defined in separate files in `config/technology_config.yaml` 4. **Solver Selection**: Remember to select a solver that is installed. @@ -69,7 +69,7 @@ foresight: "overnight" paths: results_dir: "results/" costs_dir: "resources/data/costs/default" - yearly_regional_load: + yearly_regional_load: ac: "resources/data/load/Provincial_Load_2020_2060_MWh.csv" ac_to_mwh: 1 ``` @@ -96,7 +96,7 @@ Paths to named transmission line topology files for different scenarios. Predefi A scenario is a set of time horizons with a carbon reduction pathway. The variations become snakemake wildcards. ```yaml -scenario: +scenario: co2_pathway: ["exp175default"] topology: "current+FCG" planning_horizons: [year_list] @@ -112,7 +112,7 @@ scenario: Emission reduction pathways. `scenario.co2_pathway` entries must be defined here. ```yaml -co2_scenarios: +co2_scenarios: exp175default: # pathway name control: "reduction" pathway: @@ -205,10 +205,10 @@ Configuration for weather data processing: ## Renewable Energy Technologies Atlite -### Wind +### Wind ```yaml renewable: - onwind | offwind: + onwind | offwind: cutout: cutout-name resource: method: wind @@ -350,7 +350,7 @@ Controls nuclear capacity expansion: - **`base_year`**: Reference year for capacity calculations (should be ≤ first planning year) - **`base_capacity`**: (Optional) Base year total capacity in MW. If not set, auto-detected from base_year network -## Sector and component Switches +## Sector and component Switches ```yaml heat_coupling: false @@ -723,7 +723,7 @@ hydro: ## Fossil Fuel Ramping Constraints Operational ramping constraints for fossil fuel power plants: - + ```yaml fossil_ramps: tech: @@ -775,4 +775,3 @@ Also tech costs are now in Euro2015 from DK EA - **`pv_utility_fraction`**: Fraction of solar PV that is utility-scale (100%) This parameter distinguishes between utility-scale and residential/distributed solar installations, affecting cost assumptions and grid integration characteristics. - diff --git a/examples/historical.yml b/examples/historical.yml index cd8c51d0..877dbf59 100644 --- a/examples/historical.yml +++ b/examples/historical.yml @@ -5,7 +5,7 @@ run: name: "reproduce_historical_load" foresight: "overnight" -scenario: +scenario: co2_pathway: ["exp175default"] # co2_scenarios that will be used topology: "current+FCG" # "current" or "FCG" or "current+FCG" or "current+Neighbor" planning_horizons: @@ -22,7 +22,7 @@ subsidies: Guangdong: -10.16 Jiangsu: -10.16 Zhejiang: -10.15 - Beijing: -12.5 + Beijing: -12.5 Tianjin: -12.5 Shanghai: -12.5 Xinjiang: -10 diff --git a/workflow/scripts/_plot_utilities.py b/workflow/scripts/_plot_utilities.py index fda5e610..17454be5 100644 --- a/workflow/scripts/_plot_utilities.py +++ b/workflow/scripts/_plot_utilities.py @@ -12,7 +12,7 @@ import pypsa -def validate_hex_colors(tech_colors: dict[str, str], fill_color = "#999999") -> dict[str, str]: +def validate_hex_colors(tech_colors: dict[str, str], fill_color="#999999") -> dict[str, str]: """Validate and standardize hex color codes in technology color mappings. Ensures all color codes in the technology colors dictionary are valid hexadecimal @@ -461,12 +461,13 @@ def annotate_heatmap( texts.append(text) return texts + def setup_plot_export_hook(plot_accessor_class, export_dir="plot_exports", verbose=True): """Setup a monkey patch to auto-export data to CSV whenever pandas plots are created. Args: plot_accessor_class: The PlotAccessor class to patch (e.g., pandas.plotting.PlotAccessor). - export_dir (str, optional): Directory where CSV exports will be saved. + export_dir (str, optional): Directory where CSV exports will be saved. Defaults to "plot_exports". verbose (bool, optional): Whether to print export messages. Defaults to True. @@ -488,14 +489,14 @@ def setup_plot_export_hook(plot_accessor_class, export_dir="plot_exports", verbo os.makedirs(export_dir, exist_ok=True) # Store original __call__ if not already stored - if not hasattr(plot_accessor_class, '_original_call'): + if not hasattr(plot_accessor_class, "_original_call"): plot_accessor_class._original_call = plot_accessor_class.__call__ def patched_plot_call(self, *args, **kwargs): """Patched __call__ method for PlotAccessor to export data before plotting.""" # Create timestamped filename - if 'fname' in kwargs: - fname = kwargs.pop('fname') + if "fname" in kwargs: + fname = kwargs.pop("fname") else: ts = time.strftime("%Y%m%d_%H%M%S") fname = os.path.join(export_dir, f"plot_export_{ts}.csv") @@ -518,9 +519,9 @@ def patched_plot_call(self, *args, **kwargs): # Return function to remove the patch def remove_hook(): """Remove the plot export hook and restore original behavior.""" - if hasattr(plot_accessor_class, '_original_call'): + if hasattr(plot_accessor_class, "_original_call"): plot_accessor_class.__call__ = plot_accessor_class._original_call - delattr(plot_accessor_class, '_original_call') + delattr(plot_accessor_class, "_original_call") if verbose: print("[pandas-plot-hook] Hook removed, original behavior restored.") diff --git a/workflow/scripts/add_sectors.py b/workflow/scripts/add_sectors.py index ab6bd825..0d6ae070 100644 --- a/workflow/scripts/add_sectors.py +++ b/workflow/scripts/add_sectors.py @@ -27,7 +27,7 @@ def attach_simple_ev( """ Attach electric vehicle demand and charging links to the PyPSA network. - This function implements an EV demand model with a fixed charging profile. + This function implements an EV demand model with a fixed charging profile. For each node, it creates: • an EV load bus, • a Load component representing the EV charging demand time series, @@ -52,9 +52,9 @@ def attach_simple_ev( total_number_evs = total_energy / max(options["annual_consumption"], 1e-6) node_ratio = p_set.sum() / max(total_energy, 1e-6) number_evs = node_ratio * total_number_evs - charge_power = ( - number_evs * options["charge_rate"] * options["share_charger"] - ).clip(lower=0.001) + charge_power = (number_evs * options["charge_rate"] * options["share_charger"]).clip( + lower=0.001 + ) logger.info("EV %s: %s vehicles (direct charging)", ev_type, f"{int(total_number_evs):,}") logger.debug( @@ -143,6 +143,4 @@ def attach_simple_ev( logger.info("Freight EV disabled; skipping.") network.export_to_netcdf(snakemake.output.network) - logger.info( - "Network with EV sectors exported to %s", snakemake.output.network - ) + logger.info("Network with EV sectors exported to %s", snakemake.output.network) diff --git a/workflow/scripts/build_population.py b/workflow/scripts/build_population.py index 6ace0700..830b3fa0 100644 --- a/workflow/scripts/build_population.py +++ b/workflow/scripts/build_population.py @@ -29,7 +29,7 @@ def load_pop_csv(csv_path: os.PathLike) -> pd.DataFrame: ValueError: If the province names do not match expected names """ # Read CSV, skipping comment lines that start with # - df = pd.read_csv(csv_path, index_col=0, header=0, comment='#') + df = pd.read_csv(csv_path, index_col=0, header=0, comment="#") df = df.apply(pd.to_numeric) df = df[POP_YEAR][df.index.isin(PROV_NAMES)] if not sorted(df.index.to_list()) == sorted(PROV_NAMES): diff --git a/workflow/scripts/determine_availability_matrix.py b/workflow/scripts/determine_availability_matrix.py index 1d8f2fe2..87c93d10 100644 --- a/workflow/scripts/determine_availability_matrix.py +++ b/workflow/scripts/determine_availability_matrix.py @@ -60,7 +60,6 @@ excluder = atlite.ExclusionContainer(crs=3035, res=res) if not params["natural_reserves"]: - if technology == "offwind": protected_shp = gpd.read_file(snakemake.input["natural_reserves"]) protected_shape = gpd.tools.overlay( @@ -89,7 +88,7 @@ if codes is None: logger.warning( - f"No land_cover_codes defined for {technology}, " "skipping land cover filtering" + f"No land_cover_codes defined for {technology}, skipping land cover filtering" ) else: logger.info(f"Using Copernicus LC100 land cover codes for {technology}: {codes}") diff --git a/workflow/scripts/functions.py b/workflow/scripts/functions.py index f6ae7408..2ad8bc9a 100644 --- a/workflow/scripts/functions.py +++ b/workflow/scripts/functions.py @@ -24,7 +24,7 @@ def get_poly_center(poly: Polygon): for plotting and spatial analysis in geographic applications. Args: - poly (Polygon): A (shapely) polygon geometry object with a + poly (Polygon): A (shapely) polygon geometry object with a centroid attribute that has x and y coordinate arrays. Returns: diff --git a/workflow/scripts/prepare_network.py b/workflow/scripts/prepare_network.py index 7543e282..fd0ba63a 100644 --- a/workflow/scripts/prepare_network.py +++ b/workflow/scripts/prepare_network.py @@ -623,9 +623,7 @@ def add_voltage_links(network: pypsa.Network, config: dict): line_cost = ( lengths * costs.at["HVDC overhead", "capital_cost"] * FOM_LINES * n_years - ) + costs.at[ - "HVDC inverter pair", "capital_cost" - ] # /MW + ) + costs.at["HVDC inverter pair", "capital_cost"] # /MW # ==== lossy transport model (split into 2) ==== # NB this only works if there is an equalising constraint, which is hidden in solve_ntwk diff --git a/workflow/scripts/readers.py b/workflow/scripts/readers.py index 5d5df80c..b206bc83 100644 --- a/workflow/scripts/readers.py +++ b/workflow/scripts/readers.py @@ -1,7 +1,7 @@ """File reading support functions for PyPSA-China-PIK workflow. This module provides functions for reading and processing yearly load projections -from REMIND data, with support for sector coupling (electric vehicles) and +from REMIND data, with support for sector coupling (electric vehicles) and flexible data format handling. """ @@ -13,9 +13,9 @@ def aggregate_sectoral_loads(yearly_proj: pd.DataFrame, config: dict) -> pd.DataFrame: """Aggregate REMIND load sectors according to the model configuration. - Sectors that are NOT enabled for independent modeling will be aggregated - into the main electricity load. For example, if EV sector is not enabled - as an independent sector (enabled: false), its load will be added to the + Sectors that are NOT enabled for independent modeling will be aggregated + into the main electricity load. For example, if EV sector is not enabled + as an independent sector (enabled: false), its load will be added to the AC load in the aggregation. Args: @@ -76,7 +76,7 @@ def read_yearly_load_projections( ) -> pd.DataFrame: """Read and process yearly load projections from CSV files. - Supports both simple load data and REMIND sector-coupled data with + Supports both simple load data and REMIND sector-coupled data with electric vehicle integration. Automatically detects data format and applies appropriate processing. @@ -122,8 +122,7 @@ def read_yearly_load_projections( if province_col is None: raise ValueError( - f"No province column found in {file_path}. " - f"Expected one of: {province_candidates}" + f"No province column found in {file_path}. Expected one of: {province_candidates}" ) if province_col != "province": diff --git a/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py b/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py index 9894e87b..d927535a 100644 --- a/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py +++ b/workflow/scripts/remind_coupling/ev_refshare_extrapolator.py @@ -185,7 +185,7 @@ def extrapolate_reference(years: list, input_files: dict, output_dir: str, confi - 'ssp2_pop': SSP2 future population projections - 'ssp2_gdp': SSP2 future GDP projections output_dir (str): Output directory for results (CSV files will be saved here) - config (dict, optional): Gompertz model parameters from + config (dict, optional): Gompertz model parameters from sectors.electric_vehicles.gompertz configuration: - 'saturation_level': Maximum vehicles per 1000 people (default: 500) - 'alpha': Fixed Gompertz parameter (default: -5.58) diff --git a/workflow/scripts/remind_coupling/extrapolate_regional_references.py b/workflow/scripts/remind_coupling/extrapolate_regional_references.py index 95116bdd..bcb4ef1e 100644 --- a/workflow/scripts/remind_coupling/extrapolate_regional_references.py +++ b/workflow/scripts/remind_coupling/extrapolate_regional_references.py @@ -5,7 +5,7 @@ shares indicate what fraction of national EV demand belongs to each province. This provides a general framework to coordinate the extrapolation of sector-specific -reference share files. Each sector has its own specialized module implementing the +reference share files. Each sector has its own specialized module implementing the `extrapolate_reference` function. """ @@ -51,7 +51,9 @@ def _load_sector_modules(self): except ImportError as e: logger.warning(f"Could not load {sector} sector module: {e}") - def extrapolate_references(self, years: list[int], input_files: dict[str, str], output_dir: str): + def extrapolate_references( + self, years: list[int], input_files: dict[str, str], output_dir: str + ): """Extrapolate provincial disaggregation shares for all available sectors. Generates reference share files that indicate what fraction of national-level @@ -60,7 +62,7 @@ def extrapolate_references(self, years: list[int], input_files: dict[str, str], Args: years (list[int]): List of target years for projections (e.g., [2020, 2025, 2030]). - input_files (dict[str, str]): Dictionary mapping data types to file paths + input_files (dict[str, str]): Dictionary mapping data types to file paths (e.g., historical GDP, population, sector-specific data). output_dir (str): Directory to save extrapolated reference share files. """ diff --git a/workflow/scripts/solve_network.py b/workflow/scripts/solve_network.py index 74347935..c9637085 100644 --- a/workflow/scripts/solve_network.py +++ b/workflow/scripts/solve_network.py @@ -94,9 +94,7 @@ def add_fuel_subsidies(n: pypsa.Network, subsidy_config: dict, planning_year: in continue # Query generators with matching carrier and location in subsidy provinces - mask = n.generators.query( - "carrier == @carrier and location in @subs.index" - ).index + mask = n.generators.query("carrier == @carrier and location in @subs.index").index if mask.empty: logger.warning( @@ -427,7 +425,6 @@ def add_nuclear_expansion_constraints(n: pypsa.Network): if max_capacity is None: return - nuclear_gens_ext = n.generators[ (n.generators.carrier == "nuclear") & (n.generators.p_nom_extendable == True) ].index From 94e109d4cbabd92ee98236e8dd6382da97f51b4e Mon Sep 17 00:00:00 2001 From: beijingzyl <1772066848@qq.com> Date: Fri, 19 Dec 2025 17:20:18 +0100 Subject: [PATCH 8/9] fix precommit --- workflow/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/workflow/__init__.py b/workflow/__init__.py index 525ef52c..a9b1d403 100644 --- a/workflow/__init__.py +++ b/workflow/__init__.py @@ -1,3 +1,4 @@ """Track version""" + # pypsa-China PIK editions __version__ = "1.3.2" From a13435a5d235beb086cf3dd974cf8b3e41765d82 Mon Sep 17 00:00:00 2001 From: beijingzyl <1772066848@qq.com> Date: Fri, 19 Dec 2025 17:35:35 +0100 Subject: [PATCH 9/9] fix cbc test --- workflow/Snakefile | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/workflow/Snakefile b/workflow/Snakefile index 07f07e9d..b8ab626a 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -55,8 +55,9 @@ include: "rules/postprocess.smk" include: "rules/fetch_data.smk" include: "rules/visualize_inputs.smk" -# Mark calculate_gebco_slope as local rule (always runs locally, not on Slurm) -localrules: calculate_gebco_slope +# Mark calculate_gebco_slope as local rule only when the rule is defined +if not config["run"].get("is_test", False): + localrules: calculate_gebco_slope if config["run"].get("is_test", False): localrules: build_population, dag, fetch_region_shapes @@ -191,12 +192,12 @@ if not config["run"].get("is_test", False): rule calculate_gebco_slope: """ Calculate slope from GEBCO altimetry/bathymetry data. - + Process: 1. Reproject to Mollweide (ESRI:54009) - equal-area projection for accurate slope 2. Calculate slope in percent using gdaldem 3. Reproject back to EPSG:4326 for compatibility with atlite - + Output CRS: EPSG:4326 (WGS84 Geographic) Output format: NetCDF4 with DEFLATE compression """ @@ -208,7 +209,7 @@ if not config["run"].get("is_test", False): "logs/gebco_slope_calculation.log" script: "scripts/calculate_gebco_slope.py" - + rule build_availability_matrix: params: