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Update PowerGenome to new API
ClusterBuilder.from_path now used to load from directory rather than
ResourceGroup objects. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -832,7 +832,7 @@ def add_renewables_clusters(
f"Renewables clusters match multiple NREL ATB technologies: {scenario}"
)
technology = technologies[0]
- builder = ClusterBuilder(SETTINGS["RENEWABLES_CLUSTERS"])
+ builder = ClusterBuilder.from_path(SETTINGS["RENEWABLES_CLUSTERS"])
builder.build_clusters(**scenario, ipm_regions=ipm_regions)
profiles = builder.get_cluster_profiles()
clusters = (
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Update docs and tests for uniques attributes | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -328,6 +328,11 @@ class ResourceGroup:
- `mw`
- `area`
+ - uniques:
+
+ - `ipm_region`
+ - `metro_id`
+
profiles
Variable resource capacity profiles with normalized capacity factors
(from 0 to 1) for every hour of the year (either 8760 or 8784 for a leap year).
@@ -609,9 +614,9 @@ class ClusterBuilder:
>>> builder.build_clusters(region='B', ipm_regions=['B'], min_capacity=2,
... technology='utilitypv', existing=True)
>>> builder.get_cluster_metadata()
- ids mw region technology existing
- 0 (1, 0) 3.0 A utilitypv False
- 1 (1,) 2.0 B utilitypv True
+ ids ipm_region mw region technology existing
+ 0 (1, 0) A 3.0 A utilitypv False
+ 1 (1,) B 2.0 B utilitypv True
>>> builder.get_cluster_profiles()
array([[0.3, 0.3, 0.3, ..., 0.3, 0.3, 0.3],
[0.4, 0.4, 0.4, ..., 0.4, 0.4, 0.4]])
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Add function to group rows by clustered indices | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -1224,3 +1224,35 @@ def cluster_row_trees(
if len(df) > max_rows:
df = cluster_rows(df, by=df[[by]], max_rows=max_rows, **kwargs)
return df[columns]
+
+
+def group_rows(
+ df: pd.DataFrame, ids: Iterable[Iterable]
+) -> pd.core.groupby.DataFrameGroupBy:
+ """
+ Group dataframe rows by index.
+
+ Parameters
+ ----------
+ df
+ Dataframe to group.
+ ids
+ Groups of rows indices.
+
+ Returns
+ -------
+ pd.core.groupby.DataFrameGroupBy
+ Rows of `df` grouped by their membership in each index group.
+
+ Examples
+ --------
+ >>> df = pd.DataFrame({'x': [2, 1, 3]}, index=[2, 1, 3])
+ >>> group_rows(df, [(1, ), (2, 3), (1, 2, 3)]).sum()
+ x
+ 0 1
+ 1 5
+ 2 6
+ """
+ groups = np.repeat(np.arange(len(ids)), [len(x) for x in ids])
+ index = np.concatenate(ids)
+ return df.loc[index].groupby(groups, sort=False)
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Add m_popden to columns to cluster as mean | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -24,6 +24,7 @@ MEANS = [
"site_substation_spur_miles",
"substation_metro_tx_miles",
"site_metro_spur_miles",
+ "m_popden",
]
UNIQUES = ["ipm_region", "metro_id"]
SUMS = ["area", CAPACITY]
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Replace 'rows' with 'records' in pandas to_dict
Short name for 'orient' is deprecated in current pandas vesion. | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -847,7 +847,7 @@ def merge_row_pair(
Examples
--------
>>> df = pd.DataFrame({'mw': [1, 2], 'area': [10, 20], 'lcoe': [0.1, 0.4]})
- >>> a, b = df.to_dict('rows')
+ >>> a, b = df.to_dict('records')
>>> merge_row_pair(a, b, sums=['area', 'mw'], means=['lcoe'], weight='mw')
{'area': 30, 'mw': 3, 'lcoe': 0.3}
>>> merge_row_pair(a, b, sums=['area', 'mw'], means=['lcoe'])
@@ -955,7 +955,7 @@ def cluster_rows(
df.index = index
return df
# Convert dataframe rows to dictionaries
- rows = df.to_dict("rows")
+ rows = df.to_dict("records")
# Preallocate new rows
rows += [None] * drows
# Preallocate new rows
@@ -1050,7 +1050,7 @@ def build_row_tree(
df.index = index
return df
# Convert dataframe rows to dictionaries
- rows = df.to_dict("rows")
+ rows = df.to_dict("records")
# Preallocate new rows
rows += [None] * drows
Z = scipy.cluster.hierarchy.linkage(by, method="ward")
@@ -1194,7 +1194,7 @@ def cluster_row_trees(
# Compute parent
parent = {
# Initial attributes
- **df.loc[[pid], ["_id", "parent_id", "level"]].to_dict("rows")[0],
+ **df.loc[[pid], ["_id", "parent_id", "level"]].to_dict("records")[0],
# Merged children attributes
# NOTE: Needed only if a child is incomplete
**merge_row_pair(df.loc[ids[0]], df.loc[ids[1]], **kwargs),
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Make ResoureGroup profiles optional | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -382,14 +382,14 @@ class ResourceGroup:
required = ["technology"]
if metadata is None:
required.append("metadata")
- if profiles is None:
- required.append("profiles")
missing = [key for key in required if not self.group.get(key)]
if missing:
raise ValueError(
f"Group metadata missing required keys {missing}: {self.group}"
)
self.metadata = Table(df=metadata, path=self.group.get("metadata"))
+ self.profiles = None
+ if profiles is not None or self.group.get("profiles"):
self.profiles = Table(df=profiles, path=self.group.get("profiles"))
@classmethod
@@ -434,6 +434,8 @@ class ResourceGroup:
ValueError
Resource profiles are not either 8760 or 8784 elements.
"""
+ if self.profiles is None:
+ return None
# Cast identifiers to string to match profile columns
ids = self.metadata.read(columns=["id"])["id"].astype(str)
columns = self.profiles.columns
@@ -544,6 +546,8 @@ class ResourceGroup:
np.ndarray
Hourly normalized (0-1) generation profiles (n clusters, m hours).
"""
+ if self.profiles is None:
+ return ValueError("Resource profiles are not available")
# Cast resource identifiers to string to match profile columns
ids = [[str(x) for x in cids] for cids in ids]
metadata = self.metadata.read(columns=["id", CAPACITY]).set_index("id")
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Preserve only merge columns when building tree | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -992,6 +992,9 @@ def build_tree(
drows = nrows - 1
index = [_tuple(x) for x in df.index] + [None] * drows
df = df.reset_index(drop=True)
+ merge = prepare_merge(kwargs, df)
+ columns = get_merge_columns(merge, df)
+ df = df[columns]
if drows < 1:
df.index = index
return df
@@ -1010,11 +1013,11 @@ def build_tree(
mask[link] = False
pid = nrows + i
parent_id[link] = pid
- rows[pid] = merge_row_pair(rows[link[0]], rows[link[1]], **kwargs)
+ rows[pid] = merge_row_pair(rows[link[0]], rows[link[1]], **merge)
index[pid] = index[link[0]] + index[link[1]]
tree = pd.DataFrame([x for x, m in zip(rows, mask) if m])
- # Preserve original column order
- tree = tree[[x for x in df.columns if x in tree]]
+ # Restore original column order
+ tree = tree[columns]
# Normalize ids to 0, ..., n
old_ids = np.where(mask)[0]
new_ids = np.arange(len(old_ids))
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Clarify that ResourceGroup.profiles now optional | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -282,7 +282,7 @@ class ResourceGroup:
- `metadata` : str, optional
Relative path to resource metadata dataset (optional if `metadata` is `None`).
- `profiles` : str, optional
- Relative path to resource profiles dataset (optional if `profiles` is `None`).
+ Relative path to resource profiles dataset.
- ... and any additional (optional) keys.
metadata
@@ -350,7 +350,7 @@ class ResourceGroup:
group : Dict[str, Any]
metadata : Table
Cached interface to resource metadata.
- profiles : Table
+ profiles : Optional[Table]
Cached interface to resource profiles.
Examples
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Update PowerGenome to new ClusterBuilder API | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -835,7 +835,7 @@ def add_renewables_clusters(
builder = ClusterBuilder.from_path(SETTINGS["RENEWABLES_CLUSTERS"])
builder.build_clusters(**scenario, ipm_regions=ipm_regions)
clusters = (
- builder.get_cluster_metadata()
+ builder.get_clusters()
.rename(columns={"mw": "Cap_size", "profile": "variability"})
.assign(technology=technology)
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Improve low capacity warning
Add the region and technology names so users can quickly identify where
they are asking for more capacity than is available.
Also some line modifications by black (remove spaces). | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -454,6 +454,8 @@ class ResourceGroup:
max_lcoe: float = None,
cap_multiplier: float = None,
profiles: bool = True,
+ region: str = None,
+ technology: str = None,
) -> pd.DataFrame:
"""
Compute resource clusters.
@@ -479,6 +481,10 @@ class ResourceGroup:
Multiplier applied to resource capacity before selection by `min_capacity`.
profiles
Whether to include cluster profiles, if available, in column `profile`.
+ region:
+ Name of the model region
+ technology:
+ Name of the resource technology
Returns
-------
@@ -523,7 +529,8 @@ class ResourceGroup:
capacity = df.loc[mask, CAPACITY].sum()
if min_capacity and capacity < min_capacity:
logger.warning(
- f"Selected capacity less than minimum ({capacity} < {min_capacity} MW)"
+ f"Selected technology {technology} capacity in region {region} less "
+ f"than minimum ({capacity} < {min_capacity} MW)"
)
# Apply mask
df = df[mask | ~base] if tree else df[mask]
@@ -688,6 +695,8 @@ class ClusterBuilder:
max_clusters=max_clusters,
max_lcoe=max_lcoe,
cap_multiplier=cap_multiplier,
+ region=region,
+ technology=kwargs.get("technology"),
),
}
self.clusters.append(c)
@@ -900,7 +909,7 @@ def cluster_rows(
def build_tree(
- df: pd.DataFrame, by: Iterable[Iterable], max_level: int = None, **kwargs: Any,
+ df: pd.DataFrame, by: Iterable[Iterable], max_level: int = None, **kwargs: Any
) -> pd.DataFrame:
"""
Build a hierarchical tree of rows in a dataframe.
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Change column name for max capacity
Was previously "Cap_size", but should be "Max_Cap_MW". Cap_size is the
size of a single unit (for commitment purposes). | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -836,7 +836,7 @@ def add_renewables_clusters(
builder.build_clusters(**scenario, ipm_regions=ipm_regions)
clusters = (
builder.get_clusters()
- .rename(columns={"mw": "Cap_size", "profile": "variability"})
+ .rename(columns={"mw": "Max_Cap_MW", "profile": "variability"})
.assign(technology=technology)
)
row = df[df["technology"] == technology].iloc[0]
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No limit on non-renewable new resources
In GenX, a Max_Cap_MW value of 0 means the model can't build more.
A value of -1 means that the model can build as much as it wants. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -719,6 +719,8 @@ def atb_new_generators(atb_costs, atb_hr, settings):
"waccnomtech",
]
new_gen_df = new_gen_df[keep_cols]
+ # Set no capacity limit on new resources that aren't renewables.
+ new_gen_df["Max_Cap_MW"] = -1
regional_cost_multipliers = pd.read_csv(
DATA_PATHS["cost_multipliers"] / "AEO_2020_regional_cost_corrections.csv",
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Move DR concat under if statement
Can't concat dr_rows if the settings parameter doesn't exist. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -1205,7 +1205,7 @@ def add_genx_model_tags(df, settings):
region, tag_col, tech = tag_tuple
tech = re.sub(ignored, "", tech)
mask = technology.str.contains(fr"^{tech}", case=False)
- df.loc[(df["region"] == region) & mask, tag_col,] = tag_value
+ df.loc[(df["region"] == region) & mask, tag_col] = tag_value
return df
@@ -1780,7 +1780,7 @@ def calculate_transmission_inv_cost(resource_df, settings, offshore_spur_costs=N
capex_mw_mile.fillna(0) * resource_df[f"{ttype}_miles"]
)
resource_df[f"{ttype}_inv_mwyr"] = investment_cost_calculator(
- resource_df[f"{ttype}_capex"], params["wacc"], params["investment_years"],
+ resource_df[f"{ttype}_capex"], params["wacc"], params["investment_years"]
)
return resource_df
@@ -2365,7 +2365,6 @@ class GeneratorClusters:
if self.settings.get("demand_response_fn"):
dr_rows = self.create_demand_response_gen_rows()
-
self.new_generators = pd.concat([self.new_generators, dr_rows], sort=False)
self.new_generators["Resource"] = snake_case_col(
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Update renewables_clusters for better testing | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -813,13 +813,26 @@ pv_ac_dc_ratio: 1.3
renewables_clusters:
- region: CA_N
technology: landbasedwind
- max_clusters: 3
- max_lcoe: 250
+ max_clusters: 2
+ max_lcoe: 110
+ min_capacity: 25000
- region: CA_N
technology: offshorewind
turbine_type: floating
pref_site: 1
max_clusters: 3
+ min_capacity: 40000
+ - region: CA_S
+ technology: landbasedwind
+ max_clusters: 4
+ max_lcoe: 100
+ min_capacity: 45000
+ - region: CA_S
+ technology: utilitypv
+ max_clusters: 5
+ max_lcoe: 75
+ min_capacity: 100000
+ cap_multiplier: 0.2
# How much of the theoretical
wind_pv_fraction_developable: 0.5
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Create ClusterBuilder from filename pattern
Replaces ClusterBuilder.from_path with more flexible from_pattern. | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -599,26 +599,24 @@ class ClusterBuilder:
self.clusters: List[dict] = []
@classmethod
- def from_path(cls, path: Union[str, os.PathLike] = ".") -> "ClusterBuilder":
+ def from_pattern(cls, pattern: str = "*.json") -> "ClusterBuilder":
"""
- Load resources from directory.
-
- Reads all files matching pattern '*_group.json'.
+ Load resources from filename pattern.
Parameters
----------
- path
- Path to directory.
+ pattern
+ Pathname pattern for resource JSON metadata files (see :func:`glob.glob`).
Raises
------
FileNotFoundError
- No resource groups found in path.
+ No resource groups found.
"""
- paths = glob.glob(os.path.join(path, "*_group.json"))
+ paths = glob.glob(pattern, recursive=True)
if not paths:
- raise FileNotFoundError(f"No resource groups found in {path}")
- return cls([ResourceGroup.from_json(p) for p in paths])
+ raise FileNotFoundError(f"No resource groups found in {pattern}")
+ return cls([ResourceGroup.from_json(path) for path in paths])
def find_groups(self, **kwargs: Any) -> List[ResourceGroup]:
"""
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Add hydro to NREL ATB technology map | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -32,6 +32,7 @@ NREL_ATB_TECHNOLOGY_MAP = {
("utilitypv", None): {"technology": "utilitypv"},
("landbasedwind", None): {"technology": "landbasedwind"},
("offshorewind", None): {"technology": "offshorewind"},
+ ("hydropower", None): {"technology": "hydro"},
**{
("offshorewind", f"otrg{x}"): {
"technology": "offshorewind",
@@ -95,6 +96,10 @@ def map_nrel_atb_technology(tech: str, detail: str = None) -> Dict[str, Any]:
{'technology': 'offshorewind', 'turbine_type': 'fixed'}
>>> map_nrel_atb_technology('OffShoreWind', 'OTRG7')
{'technology': 'offshorewind', 'turbine_type': 'floating'}
+ >>> map_nrel_atb_technology('Hydropower')
+ {'technology': 'hydro'}
+ >>> map_nrel_atb_technology('Hydropower', 'NSD4')
+ {'technology': 'hydro'}
>>> map_nrel_atb_technology('Unknown')
{}
"""
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Strip underscores from technology names for mapping | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -58,7 +58,7 @@ EIA_TECHNOLOGY_MAP = {
def _normalize(x: Optional[str]) -> Optional[str]:
"""
- Normalize string to lowercase and no whitespace.
+ Normalize string to lowercase, no whitespace, and no underscores.
Examples
--------
@@ -66,12 +66,14 @@ def _normalize(x: Optional[str]) -> Optional[str]:
'offshorewind'
>>> _normalize('OffshoreWind')
'offshorewind'
+ >>> _normalize('Offshore_Wind')
+ 'offshorewind'
>>> _normalize(None) is None
True
"""
if not x:
return x
- return re.sub(r"\s+", "", x.lower())
+ return re.sub(r"\s+|_", "", x.lower())
def map_nrel_atb_technology(tech: str, detail: str = None) -> Dict[str, Any]:
@@ -142,6 +144,8 @@ def map_eia_technology(tech: str) -> Dict[str, Any]:
{'technology': 'offshorewind'}
>>> map_eia_technology('Conventional Hydroelectric')
{'technology': 'hydro'}
+ >>> map_eia_technology('Conventional_Hydroelectric')
+ {'technology': 'hydro'}
>>> map_eia_technology('Unknown')
{}
"""
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Add small hydro to EIA technology map | MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -49,7 +49,8 @@ NREL_ATB_TECHNOLOGY_MAP = {
},
}
EIA_TECHNOLOGY_MAP = {
- "conventionalhydroelectric": {"technology": "hydro"},
+ "conventionalhydroelectric": {"technology": "hydro", "small": False},
+ "smallhydroelectric": {"technology": "hydro", "small": True},
"onshorewindturbine": {"technology": "landbasedwind"},
"offshorewindturbine": {"technology": "offshorewind"},
"solarphotovoltaic": {"technology": "utilitypv"},
@@ -138,14 +139,16 @@ def map_eia_technology(tech: str) -> Dict[str, Any]:
--------
>>> map_eia_technology('Solar Photovoltaic')
{'technology': 'utilitypv'}
+ >>> map_eia_technology('solar_photovoltaic')
+ {'technology': 'utilitypv'}
>>> map_eia_technology('Onshore Wind Turbine')
{'technology': 'landbasedwind'}
>>> map_eia_technology('Offshore Wind Turbine')
{'technology': 'offshorewind'}
>>> map_eia_technology('Conventional Hydroelectric')
- {'technology': 'hydro'}
- >>> map_eia_technology('Conventional_Hydroelectric')
- {'technology': 'hydro'}
+ {'technology': 'hydro', 'small': False}
+ >>> map_eia_technology('Small Hydroelectric')
+ {'technology': 'hydro', 'small': True}
>>> map_eia_technology('Unknown')
{}
"""
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Use existing: False to select new build resources
Add `existing: False` to scenarios in nrelatb.add_newables_clusters(). | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -821,6 +821,7 @@ def add_renewables_clusters(
scenario = scenario.copy()
scenario.pop("region")
scenario["ipm_regions"] = ipm_regions
+ scenario["existing"] = False
cdfs = []
builder = ClusterBuilder.from_pattern(f"{SETTINGS['RENEWABLES_CLUSTERS']}/*.json")
for scenario in scenarios:
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Replace ClusterBuilder.from_pattern with from_json
This is more symmetric with ResourceGroup.from_json and more flexible.
Paths can easily be loaded with glob.glob, Path.glob, or equivalent. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -6,6 +6,7 @@ import copy
import collections
import logging
import operator
+from pathlib import Path
import numpy as np
import pandas as pd
@@ -823,7 +824,8 @@ def add_renewables_clusters(
scenario["ipm_regions"] = ipm_regions
scenario["existing"] = False
cdfs = []
- builder = ClusterBuilder.from_pattern(f"{SETTINGS['RENEWABLES_CLUSTERS']}/*.json")
+ paths = Path(SETTINGS["RENEWABLES_CLUSTERS"]).glob("**/*.json")
+ builder = ClusterBuilder.from_json(paths)
for scenario in scenarios:
# Match cluster technology to NREL ATB technologies
technologies = [
MODIFY
powergenome/renewables_clusters.py
powergenome/renewables_clusters.py
@@ -640,23 +640,23 @@ class ClusterBuilder:
self.groups = groups
@classmethod
- def from_pattern(cls, pattern: str = "*.json") -> "ClusterBuilder":
+ def from_json(cls, paths: Iterable[Union[str, os.PathLike]]) -> "ClusterBuilder":
"""
- Load resources from filename pattern.
+ Load resources from resource group JSON files.
Parameters
----------
- pattern
- Pathname pattern for resource JSON metadata files (see :func:`glob.glob`).
+ paths
+ Paths to resource group JSON files.
Raises
------
- FileNotFoundError
- No resource groups found.
+ ValueError
+ No resource groups specified.
"""
- paths = glob.glob(pattern, recursive=True)
+ paths = list(paths)
if not paths:
- raise FileNotFoundError(f"No resource groups found in {pattern}")
+ raise ValueError(f"No resource groups specified")
return cls([ResourceGroup.from_json(path) for path in paths])
def find_groups(self, **kwargs: Any) -> List[ResourceGroup]:
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Rename RENEWABLE_CLUSTERS param to RESOURCE_GROUPS | MODIFY
powergenome/params.py
powergenome/params.py
@@ -35,10 +35,10 @@ IPM_GEOJSON_PATH = DATA_PATHS["data"] / "ipm_regions_simple.geojson"
SETTINGS = {}
SETTINGS["PUDL_DB"] = os.environ.get("PUDL_DB")
SETTINGS["EIA_API_KEY"] = os.environ.get("EIA_API_KEY")
-SETTINGS["RENEWABLES_CLUSTERS"] = os.environ.get("RENEWABLES_CLUSTERS")
+SETTINGS["RESOURCE_GROUPS"] = os.environ.get("RESOURCE_GROUPS")
CLUSTER_BUILDER = ClusterBuilder.from_json(
- Path(SETTINGS["RENEWABLES_CLUSTERS"]).glob("**/*.json")
+ Path(SETTINGS["RESOURCE_GROUPS"]).glob("**/*.json")
)
# "postgresql://catalyst@127.0.0.1/pudl"
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Rename renewables_clusters.py to resource_clusters.py | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -35,7 +35,7 @@ from powergenome.nrelatb import (
)
from powergenome.params import CLUSTER_BUILDER, DATA_PATHS, IPM_GEOJSON_PATH
from powergenome.price_adjustment import inflation_price_adjustment
-from powergenome.renewables_clusters import map_eia_technology
+from powergenome.resource_clusters import map_eia_technology
from powergenome.util import (
download_save,
map_agg_region_names,
MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -13,7 +13,7 @@ import pandas as pd
from powergenome.params import CLUSTER_BUILDER, DATA_PATHS
from powergenome.price_adjustment import inflation_price_adjustment
-from powergenome.renewables_clusters import map_nrel_atb_technology
+from powergenome.resource_clusters import map_nrel_atb_technology
from powergenome.util import reverse_dict_of_lists
idx = pd.IndexSlice
MODIFY
powergenome/params.py
powergenome/params.py
@@ -7,7 +7,7 @@ from pathlib import Path
from dotenv import find_dotenv, load_dotenv
from powergenome import __file__
-from powergenome.renewables_clusters import ClusterBuilder
+from powergenome.resource_clusters import ClusterBuilder
# Not convinced this is the best way to set folder paths but it works!
powergenome_path = Path(__file__).parent
RENAME
powergenome/renewables_clusters.py
powergenome/resource_clusters.py
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Update call to make_generator_variability | MODIFY
powergenome/run_powergenome_multiple_outputs_cli.py
powergenome/run_powergenome_multiple_outputs_cli.py
@@ -374,9 +374,7 @@ def main():
# include_index=False,
# )
- gen_variability = make_generator_variability(
- gen_clusters, _settings
- )
+ gen_variability = make_generator_variability(gen_clusters)
# write_results_file(
# df=gen_variability,
# folder=case_folder,
@@ -463,9 +461,7 @@ def main():
# file_name="Generators_data.csv",
# )
- gen_variability = make_generator_variability(
- gen_clusters, _settings
- )
+ gen_variability = make_generator_variability(gen_clusters)
# write_results_file(
# df=gen_variability,
# folder=case_folder,
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Use AEO 2020 regional cost multipliers | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -856,7 +856,7 @@ def atb_new_generators(atb_costs, atb_hr, settings):
]
regional_cost_multipliers = pd.read_csv(
- DATA_PATHS["cost_multipliers"] / "EIA regional cost multipliers.csv",
+ DATA_PATHS["cost_multipliers"] / "AEO_2020_regional_cost_corrections.csv",
index_col=0,
)
rev_mult_region_map = reverse_dict_of_lists(settings["cost_multiplier_region_map"])
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Add coal capex to FOM (bug fix) | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -404,7 +404,7 @@ def atb_fixed_var_om_existing(results, atb_costs_df, atb_hr_df, settings):
# ) - (variable * 8760 * 0.59)
_df["Fixed_OM_cost_per_MWyr"] = inflation_price_adjustment(
- fixed, 2017, target_usd_year
+ fixed + annual_capex, 2017, target_usd_year
)
_df["Var_OM_cost_per_MWh"] = simple_o_m["Coal"]["o_m_variable_mwh"]
if "Hydroelectric" in eia_tech:
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Non-sequential index put profiles on wrong lines | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2335,6 +2335,7 @@ class GeneratorClusters:
# Add variable resource profiles
self.results["profile"] = None
+ self.results = self.results.reset_index(drop=True)
for i, row in enumerate(self.results.itertuples()):
params = map_eia_technology(row.technology)
if not params:
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Add demand response profiles to new resources df | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -1972,7 +1972,7 @@ class GeneratorClusters:
_df = pd.DataFrame(
index=self.settings["model_regions"],
- columns=self.settings["generator_columns"],
+ columns=list(self.settings["generator_columns"]) + ["profile"],
)
_df = _df.drop(columns="Resource")
_df["technology"] = resource
@@ -1988,6 +1988,11 @@ class GeneratorClusters:
)
self.demand_response_profiles[resource] = dr_profile
+ dr_cf = dr_profile / dr_profile.max()
+
+ for i, row in enumerate(_df.itertuples()):
+ _df["profile"][i] = dr_cf.iloc[:, i].values
+
dr_capacity = demand_response_resource_capacity(
dr_profile, resource, self.settings
)
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Name gen variability columns
Values out of make_generator_variability are integers. Use region, tech,
and cluster to easily identify in output. | MODIFY
powergenome/run_powergenome_multiple_outputs_cli.py
powergenome/run_powergenome_multiple_outputs_cli.py
@@ -375,6 +375,13 @@ def main():
# )
gen_variability = make_generator_variability(gen_clusters)
+ gen_variability.columns = (
+ gen_clusters["region"]
+ + "_"
+ + gen_clusters["Resource"]
+ + "_"
+ + gen_clusters["cluster"].astype(str)
+ )
# write_results_file(
# df=gen_variability,
# folder=case_folder,
@@ -462,6 +469,13 @@ def main():
# )
gen_variability = make_generator_variability(gen_clusters)
+ gen_variability.columns = (
+ gen_clusters["region"]
+ + "_"
+ + gen_clusters["Resource"]
+ + "_"
+ + gen_clusters["cluster"].astype(str)
+ )
# write_results_file(
# df=gen_variability,
# folder=case_folder,
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Assign unique cluster values for new renewables | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -844,6 +844,7 @@ def add_renewables_clusters(
.rename(columns={"mw": "Max_Cap_MW"})
.assign(technology=technology, region=region)
)
+ clusters["cluster"] = range(1, 1 + len(clusters))
if scenario.get("min_capacity"):
# Warn if total capacity less than expected
capacity = clusters["Max_Cap_MW"].sum()
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Pass df if not small_hydro rather than return None | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -519,7 +519,7 @@ def label_small_hydro(df, settings, by=["plant_id_eia"]):
hydro facilities will have their technology type changed to small hydro.
"""
if not settings.get("small_hydro"):
- return None
+ return df
if "report_date" not in by and "report_date" in df.columns:
# by.append("report_date")
logger.warning("'report_date' is in the df but not used in the groupby")
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Fix coal o&m error
Was accidentally double-counting FOM by adding it to capex | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -388,7 +388,7 @@ def atb_fixed_var_om_existing(results, atb_costs_df, atb_hr_df, settings):
age = settings["model_year"] - _df.operating_date.dt.year
# https://www.eia.gov/analysis/studies/powerplants/generationcost/pdf/full_report.pdf
- annual_capex = (16.53 + (0.126 * age) + (5.68 * 0.5) + 46.01) * 1000
+ annual_capex = (16.53 + (0.126 * age) + (5.68 * 0.5)) * 1000
if plant_capacity < 500:
fixed = 44.21 * 1000
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Fix CC variable O&M | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -266,16 +266,14 @@ def atb_fixed_var_om_existing(results, atb_costs_df, atb_hr_df, settings):
variable = 3.42
else:
fixed = 11.68 * 1000
- # variable = 3.37
+ variable = 3.37
- # fixed = ng_o_m["Combined Cycle"]["o_m_fixed_mw"]
- # variable = ng_o_m["Combined Cycle"]["o_m_variable_mwh"]
_df["Fixed_OM_cost_per_MWyr"] = inflation_price_adjustment(
fixed, 2017, target_usd_year
)
- _df["Var_OM_cost_per_MWh"] = simple_o_m["Combined Cycle"][
- "o_m_variable_mwh"
- ]
+ _df["Var_OM_cost_per_MWh"] = inflation_price_adjustment(
+ variable, 2017, target_usd_year
+ )
if "Combustion Turbine" in eia_tech:
# need to adjust the EIA fixed/variable costs because they have no
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Update getting settings params in clustering | MODIFY
powergenome/cluster_method.py
powergenome/cluster_method.py
"Different ways to cluster plants"
import numpy as np
+import pandas as pd
from sklearn import cluster, preprocessing
@@ -23,14 +24,17 @@ def build_cluster_method_dict(settings):
return cluster_method_dict
-def cluster_kmeans(grouped, region, tech, settings):
+def cluster_kmeans(
+ grouped: pd.DataFrame, region: str, tech: str, settings: dict
+) -> pd.DataFrame:
- if region in settings["alt_num_clusters"]:
- if tech in settings["alt_num_clusters"][region]:
- n_clusters = settings["alt_num_clusters"][region][tech]
+ if region in settings.get("alt_num_clusters", {}):
+ # if tech in settings["alt_num_clusters"][region]:
+ n_clusters = settings["alt_num_clusters"][region].get(tech)
else:
- n_clusters = settings["num_clusters"][tech]
+ n_clusters = settings["num_clusters"].get(tech)
+ if n_clusters:
clusters = cluster.KMeans(n_clusters=n_clusters, random_state=6).fit(
preprocessing.StandardScaler().fit_transform(grouped)
)
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Fill negative heat rates with technology median. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2130,6 +2130,11 @@ class GeneratorClusters:
self.prime_mover_hr_map
)
+ # Set negative heat rates to nan
+ self.units_model.loc[
+ self.units_model.heat_rate_mmbtu_mwh < 0, "heat_rate_mmbtu_mwh"
+ ] = np.nan
+
# Fill any null heat rate values for each tech
for tech in self.units_model["technology_description"]:
self.units_model.loc[
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Remove ownership clustering and upstream data | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -1946,9 +1946,9 @@ class GeneratorClusters:
self.canceled_860m = self.eia_860m["canceled"]
self.retired_860m = self.eia_860m["retired"]
- self.ownership = load_ownership_eia860(self.pudl_engine, self.data_years)
+ # self.ownership = load_ownership_eia860(self.pudl_engine, self.data_years)
self.plants_860 = load_plants_860(self.pudl_engine, self.data_years)
- self.utilities_eia = load_utilities_eia(self.pudl_engine)
+ # self.utilities_eia = load_utilities_eia(self.pudl_engine)
else:
self.existing_resources = pd.DataFrame()
@@ -2221,11 +2221,11 @@ class GeneratorClusters:
]
# gens_860 lost the ownership code... refactor this!
- self.all_gens_860 = load_generator_860_data(self.pudl_engine, self.data_years)
+ # self.all_gens_860 = load_generator_860_data(self.pudl_engine, self.data_years)
# Getting weighted ownership for each unit, which will be used below.
- self.weighted_ownership = weighted_ownership_by_unit(
- self.units_model, self.all_gens_860, self.ownership, self.settings
- )
+ # self.weighted_ownership = weighted_ownership_by_unit(
+ # self.units_model, self.all_gens_860, self.ownership, self.settings
+ # )
# For each group, cluster and calculate the average size/min load/heat rate
# logger.info("Creating technology clusters by region")
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Update to_crs param | MODIFY
powergenome/transmission.py
powergenome/transmission.py
@@ -165,7 +165,7 @@ def transmission_line_distance(
logger.info("Calculating transmission line distance")
ipm_shapefile["geometry"] = ipm_shapefile.buffer(0.01)
model_polygons = ipm_shapefile.dissolve(by="model_region")
- model_polygons = model_polygons.to_crs({"init": "epsg:4326"})
+ model_polygons = model_polygons.to_crs(epsg=4326)
region_centroids = find_centroid(model_polygons)
distances = [
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Remove incorrect ipm region
WEC_SCE doesn't exist | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -393,7 +393,7 @@ model_regions:
region_aggregations:
CA_N: [WEC_CALN, WEC_BANC]
- CA_S: [WEC_SCE, WEC_LADW, WECC_SCE, WEC_SDGE, WECC_IID]
+ CA_S: [WEC_LADW, WECC_SCE, WEC_SDGE, WECC_IID]
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Only add DR rows regions with DR profiles | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2065,6 +2065,8 @@ class GeneratorClusters:
self.demand_response_profiles[resource] = dr_profile
# Add hourly profile to demand response rows
dr_cf = dr_profile / dr_profile.max()
+ dr_regions = dr_cf.columns
+ _df = _df.loc[dr_regions, :]
_df["profile"] = list(dr_cf.values.T)
dr_capacity = demand_response_resource_capacity(
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Bug fix: not all model regions are aggregations
Code was previously looking for IPM regions in region aggregations. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2469,7 +2469,10 @@ class GeneratorClusters:
if group.profiles is None:
# Resource group has no profiles
continue
+ if row.region in self.settings["region_aggregations"]:
ipm_regions = self.settings["region_aggregations"][row.region]
+ else:
+ ipm_regions = [row.region]
metadata = group.metadata.read()
if not metadata["ipm_region"].isin(ipm_regions).any():
# Resource group has no resources in selected IPM regions
MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -924,7 +924,10 @@ def add_renewables_clusters(
df["region"] == region
)
cdfs = []
+ if region in settings["region_aggregations"]:
ipm_regions = settings["region_aggregations"][region]
+ else:
+ ipm_regions = [region]
for scenario in settings.get("renewables_clusters", []):
if scenario["region"] != region:
continue
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Add AZ spur capex, fix capex values | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -93,8 +93,9 @@ transmission_investment_cost:
use_total: true
spur:
capex_mw_mile:
- CA_N: 8904
- CA_S: 8904
+ CA_N: 8775 # 2.5x the rest of WECC
+ CA_S: 8775 # 2.5x the rest of WECC
+ WECC_AZ: 3900
wacc: 0.069
investment_years: 60
offshore_spur:
@@ -104,13 +105,9 @@ transmission_investment_cost:
investment_years: 60
tx:
capex_mw_mile:
- CA_N: 3728
- CA_S: 3728
- WECC_CO: 1457
- WECC_NM: 1457
- WECC_NW: 1457
- WECC_SNV: 1457
- WECC_AZ: 1457
+ CA_N: 3037.5 # 2.5x the rest of WECC
+ CA_S: 3037.5 # 2.5x the rest of WECC
+ WECC_AZ: 1350
wacc: 0.069
investment_years: 60
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Try to fix CI tests | MODIFY
powergenome/params.py
powergenome/params.py
@@ -38,7 +38,7 @@ SETTINGS["EIA_API_KEY"] = os.environ.get("EIA_API_KEY")
SETTINGS["RESOURCE_GROUPS"] = os.environ.get("RESOURCE_GROUPS")
CLUSTER_BUILDER = ClusterBuilder.from_json(
- Path(SETTINGS["RESOURCE_GROUPS"]).glob("**/*.json")
+ Path(SETTINGS.get("RESOURCE_GROUPS"), ".").glob("**/*.json")
)
# "postgresql://catalyst@127.0.0.1/pudl"
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Remove RESOURCE_GROUPS key if None | MODIFY
powergenome/params.py
powergenome/params.py
@@ -36,6 +36,8 @@ SETTINGS = {}
SETTINGS["PUDL_DB"] = os.environ.get("PUDL_DB")
SETTINGS["EIA_API_KEY"] = os.environ.get("EIA_API_KEY")
SETTINGS["RESOURCE_GROUPS"] = os.environ.get("RESOURCE_GROUPS")
+if not SETTINGS["RESOURCE_GROUPS"]:
+ SETTINGS.pop("RESOURCE_GROUPS", None)
CLUSTER_BUILDER = ClusterBuilder.from_json(
Path(SETTINGS.get("RESOURCE_GROUPS"), ".").glob("**/*.json")
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Try again to fix CI error | MODIFY
powergenome/params.py
powergenome/params.py
@@ -37,8 +37,8 @@ SETTINGS["PUDL_DB"] = os.environ.get("PUDL_DB")
SETTINGS["EIA_API_KEY"] = os.environ.get("EIA_API_KEY")
SETTINGS["RESOURCE_GROUPS"] = os.environ.get("RESOURCE_GROUPS")
if not SETTINGS["RESOURCE_GROUPS"]:
- SETTINGS.pop("RESOURCE_GROUPS", None)
-
+ CLUSTER_BUILDER = ClusterBuilder([])
+else:
CLUSTER_BUILDER = ClusterBuilder.from_json(
Path(SETTINGS.get("RESOURCE_GROUPS"), ".").glob("**/*.json")
)
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Fix index, read ferc load curves by default | MODIFY
powergenome/load_profiles.py
powergenome/load_profiles.py
@@ -65,6 +65,7 @@ def make_load_curves(
lc_wide = remove_feb_29(lc_wide)
lc_wide.index.name = "time_index"
+ if lc_wide.index.min() == 0:
lc_wide.index = lc_wide.index + 1
return lc_wide
@@ -181,7 +182,7 @@ def load_usr_demand_profiles(settings):
def make_final_load_curves(
pudl_engine,
settings,
- pudl_table="load_curves_epaipm",
+ pudl_table="load_curves_ferc",
settings_agg_key="region_aggregations",
):
# Check if regional loads are supplied by the user
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Don't use external load curve data | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -27,7 +27,7 @@ region_wind_pv_cap_fn: existing_wind_pv_capacity.csv
demand_segments_fn: test_demand_segments_voll.csv
misc_gen_inputs_fn: test_misc_gen_inputs.csv
genx_settings_fn: GenX_settings.yml # In the same folder as this file, not in "input_folder" subfolder
-regional_load_fn: test_regional_load_profiles.csv #Externally provide load data
+# regional_load_fn: test_regional_load_profiles.csv #Externally provide load data
# If regional load is provided by the user and it already includes demand response
# loads, set this parameter to "true". If the regional hourly loads do not include
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Bugfix
don't use copy.deepcopy when importing deepcopy | MODIFY
powergenome/util.py
powergenome/util.py
@@ -336,7 +336,7 @@ def build_scenario_settings(settings: dict, scenario_definitions: pd.DataFrame)
planning_year_scenario_definitions_dict.pop("year")
for case_id in scenario_definitions["case_id"].unique():
- _settings = copy.deepcopy(settings)
+ _settings = deepcopy(settings)
if "all_cases" in planning_year_settings_management:
new_parameter = planning_year_settings_management["all_cases"]
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Fix crs syntax in geodataframe creation | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -325,7 +325,7 @@ def label_hydro_region(gens_860, pudl_engine, model_regions_gdf):
model_hydro_gdf = gpd.GeoDataFrame(
model_hydro,
geometry=gpd.points_from_xy(model_hydro.longitude, model_hydro.latitude),
- crs={"init": "epsg:4326"},
+ crs="EPSG:4326",
)
if model_hydro_gdf.crs != model_regions_gdf.crs:
@@ -565,7 +565,6 @@ def label_small_hydro(df, settings, by=["plant_id_eia"]):
df.loc[
(df["technology_description"] == "Conventional Hydroelectric")
& (df["model_region"].isin(keep_regions))
-
]
.groupby(by, as_index=False)[cap_col]
.sum()
@@ -1455,9 +1454,8 @@ def import_proposed_generators(planned, settings, model_regions_gdf):
planned_gdf = gpd.GeoDataFrame(
planned.copy(),
geometry=gpd.points_from_xy(planned.longitude.copy(), planned.latitude.copy()),
- crs={"init": "epsg:4326"},
+ crs="EPSG:4326",
)
- # planned_gdf.crs = {"init": "epsg:4326"}
if planned_gdf.crs != model_regions_gdf.crs:
planned_gdf = planned_gdf.to_crs(model_regions_gdf.crs)
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Add AZ solar | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -830,6 +830,11 @@ renewables_clusters:
max_lcoe: 75
min_capacity: 100000
cap_multiplier: 0.2
+ - region: WECC_AZ
+ technology: utilitypv
+ max_clusters: 3
+ min_capacity: 100000
+ cap_multiplier: 0.2
# How much of the theoretical
wind_pv_fraction_developable: 0.5
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Add a short list of scenarios | MODIFY
README.md
README.md
@@ -74,7 +74,7 @@ cpi.update()
### Settings
-Settings are controlled in a YAML file. An example settings file (`test_settings.yml`) and folder with extra user inputs (`extra_inputs`), which set up a small 3-zone model of California and Arizona, are included in the folder `example_system`.
+Settings are controlled in a YAML file. An example settings file (`test_settings.yml`) and folder with extra user inputs (`extra_inputs`), which set up a small 3-zone model of California and Arizona, are included in the folder `example_system`. Scenario options across different planning years are defined in the files `test_scenario_inputs_short.csv` and `test_scenario_inputs.csv` - the "short" version only includes a subset of the full scenario list covered in the settings file and takes much less time to run.
### Example notebooks
MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -17,7 +17,8 @@ model_first_planning_year: [2020, 2031]
# The location and name of additional input files needed to create outputs
input_folder: extra_inputs # Subfolder directly below the location of this settings file
case_id_description_fn: test_case_id_description.csv # Match the case_id with longer case_name
-scenario_definitions_fn: test_scenario_inputs.csv # Define policy/cost scenarios for each case
+scenario_definitions_fn: test_scenario_inputs_short.csv # Define policy/cost scenarios for each case
+# scenario_definitions_fn: test_scenario_inputs.csv # Define policy/cost scenarios for each case
distributed_gen_profiles_fn: test_dg_profiles.csv # Hourly profiles of distributed gen by region
demand_response_fn: test_ev_load_shifting.csv # Load profiles of DR resources by model_year and scenario
emission_policies_fn: test_rps_ces_emission_limits.csv # Emission policies for each model_year/region/case_id
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Fix single-region bug with DR
Squeeze turns a df into a series, but turns a series into a value. Can't map a value. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2070,7 +2070,13 @@ class GeneratorClusters:
dr_capacity = demand_response_resource_capacity(
dr_profile, resource, self.settings
)
+
+ # This is to solve a bug with only one region. Need to come back and solve
+ # in a better fashion.
+ if len(dr_capacity) > 1:
dr_capacity_scenario = dr_capacity.squeeze()
+ else:
+ dr_capacity_scenario = dr_capacity
_df["Existing_Cap_MW"] = _df["region"].map(dr_capacity_scenario)
if not parameters.get("parameter_values"):
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Return empty df if no transmission lines | MODIFY
powergenome/transmission.py
powergenome/transmission.py
@@ -89,6 +89,12 @@ def agg_transmission_constraints(
index=transmission_constraints_table.reindex(combos).dropna().index,
data=0,
)
+
+ if tc_joined.empty:
+ logger.info(f"No transmission lines exist between model regions {combos}")
+ tc_joined["Transmission Path Name"] = None
+ return tc_joined.reset_index(drop=True)
+
tc_joined["Network_lines"] = range(1, len(tc_joined) + 1)
tc_joined["Line_Max_Flow_MW"] = transmission_constraints_table.reindex(
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Minor settings fixes | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -390,6 +390,7 @@ model_regions:
- CA_S
- WECC_AZ
+# Comment out this setting if no aggregated regions are included in the study.
region_aggregations:
CA_N: [WEC_CALN, WEC_BANC]
CA_S: [WEC_LADW, WECC_SCE, WEC_SDGE, WECC_IID]
@@ -877,8 +878,8 @@ cost_multiplier_region_map:
RMRG: [WECC_CO]
BASN: [WECC_ID, WECC_WY, WECC_UT, WECC_NNV]
NWPP: [WECC_PNW, WECC_MT]
- CANO: [WEC_CALN, WEC_BANC, CA_N, CA_S]
- CASO: [WECC_IID, WECC_SCE, WEC_LADW, WEC_SDGE]
+ CANO: [WEC_CALN, WEC_BANC, CA_N]
+ CASO: [WECC_IID, WECC_SCE, WEC_LADW, WEC_SDGE, CA_S]
SRSG: [WECC_AZ, WECC_NM, WECC_SNV]
# The keys are technologies listed in EIA's 2016 capital cost assumptions document
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Version bump
Been forgetting to do this in the settings file! | MODIFY
setup.py
setup.py
@@ -3,7 +3,7 @@ from setuptools import find_packages, setup
setup(
name="powergenome",
packages=find_packages(),
- version="0.2.0",
+ version="0.3.2",
description="Extract PUDL data for use in power system models",
author="Greg Schivley",
entry_points={
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Fix historical load region errors
PJM_Dom wasn't listed
Long island region name was wrong | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -969,7 +969,7 @@ historical_load_region_maps:
MWRCW: [MIS_MAPP, SPP_WAUE, SPP_NEBR, MIS_MIDA, MIS_IA, MIS_MNWI]
NPCCNE: [NENG_ME, NENG_CT, NENGREST]
NPCCNYWE: [NY_Z_J]
- NPCCL: [NY_Z_K]
+ NPCCLI: [NY_Z_K]
NPCCUPNY: [
NY_Z_A,
NY_Z_B,
@@ -978,7 +978,7 @@ historical_load_region_maps:
NY_Z_F,
NY_Z_G-I,
]
- RFCET: [PJM_WMAC, PJM_EMAC, PJM_SMAC, PJM_PENE]
+ RFCET: [PJM_WMAC, PJM_EMAC, PJM_SMAC, PJM_PENE, PJM_Dom]
RFCMI: [MIS_LMI]
RFCWT: [PJM_West, PJM_AP, PJM_ATSI, PJM_COMD]
SERCDLT: [MIS_WOTA, MIS_LA, MIS_AMSO, MIS_AR]
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Bug fix: convert df to series
When some policies apply to all regions and other apply to specific regions
the object stayed as a df rather than a series.
Need a series for policies that apply to all regions. | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -44,6 +44,12 @@ def add_emission_policies(transmission_df, settings, DistrZones=None):
policies = load_policy_scenarios(settings)
year_case_policy = policies.loc[(case_id, model_year), :]
+ # Bug where multiple regions for a case will return this as a df, even if the policy
+ # for this case applies to all regions (code below expects a Series)
+ ycp_shape = year_case_policy.shape
+ if ycp_shape[0] == 1 and len(ycp_shape) > 1:
+ year_case_policy = year_case_policy.squeeze() # convert to series
+
zones = settings["model_regions"]
zone_num_map = {
zone: f"z{number + 1}" for zone, number in zip(zones, range(len(zones)))
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Bug fix: don't need all case ids to appear in every year | MODIFY
powergenome/util.py
powergenome/util.py
@@ -335,7 +335,7 @@ def build_scenario_settings(settings: dict, scenario_definitions: pd.DataFrame)
)
planning_year_scenario_definitions_dict.pop("year")
- for case_id in scenario_definitions["case_id"].unique():
+ for case_id in scenario_definitions.query("year==@year")["case_id"].unique():
_settings = deepcopy(settings)
if "all_cases" in planning_year_settings_management:
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Add missing region to hist load region mapping | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -982,7 +982,7 @@ historical_load_region_maps:
RFCMI: [MIS_LMI]
RFCWT: [PJM_West, PJM_AP, PJM_ATSI, PJM_COMD]
SERCDLT: [MIS_WOTA, MIS_LA, MIS_AMSO, MIS_AR]
- SERCGW: [MIS_MO, S_D_AECI, MIS_IL]
+ SERCGW: [MIS_MO, S_D_AECI, MIS_IL, MIS_INKY]
SERCSOES: [S_SOU]
SERCCNT: [S_C_TVA, S_C_KY]
SERCVC: [S_VACA]
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Ensure gen variability columns are unique
When Resource/cluster are the same across years for different technologies
this causes a bug. | MODIFY
powergenome/run_powergenome_multiple_outputs_cli.py
powergenome/run_powergenome_multiple_outputs_cli.py
@@ -365,6 +365,8 @@ def main():
+ gen_clusters["Resource"]
+ "_"
+ gen_clusters["cluster"].astype(str)
+ + "_"
+ + gen_clusters["R_ID"].astype(str)
)
# write_results_file(
# df=gen_variability,
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Don't ignore spaces in tech names for model tags
Ignoring spaces leads to the same tags for "Natural Gas ..." (EIA) and
"NaturalGas_" (ATB). | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -1210,7 +1210,7 @@ def add_genx_model_tags(df, settings):
dataframe
The original generator cluster results with new columns for each model tag.
"""
- ignored = r"\s+|_"
+ ignored = r"_"
technology = df["technology"].str.replace(ignored, "")
# Create a new dataframe with the same index
default = settings.get("default_model_tag", 0)
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Don't rename Resource when adding misc gen values | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -103,10 +103,12 @@ def add_misc_gen_values(gen_clusters, settings):
# resource_misc_values = misc_values.loc[misc_values["Resource"] == resource, :].dropna()
for col in misc_values.columns:
+ if col == "Resource":
+ continue
value = misc_values.loc[misc_values["Resource"] == resource, col].values[0]
if value != "skip":
gen_clusters.loc[
- gen_clusters["Resource"].str.contains(resource), col
+ gen_clusters["Resource"].str.contains(resource, case=False), col
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return gen_clusters
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bug fix: don't use cols that aren't in df | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -465,6 +465,8 @@ def set_int_cols(df: pd.DataFrame, cols: list = None) -> pd.DataFrame:
if not cols:
cols = INT_COLS
+ cols = [c for c in cols if c in df.columns]
+
for col in cols:
df[col] = df[col].fillna(0).astype(int)
return df
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bug fix: reset heat rates > 35 mmbtu/MWh
This should also check for a cogen flag but is good enough for now. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2185,9 +2185,18 @@ class GeneratorClusters:
self.prime_mover_hr_map
)
+ self.units_model.loc[
+ self.units_model.heat_rate_mmbtu_mwh > 35, "heat_rate_mmbtu_mwh"
+ ] = self.units_model.loc[
+ self.units_model.heat_rate_mmbtu_mwh > 35
+ ].index.map(
+ self.prime_mover_hr_map
+ )
+
# Set negative heat rates to nan
self.units_model.loc[
- self.units_model.heat_rate_mmbtu_mwh < 0, "heat_rate_mmbtu_mwh"
+ (self.units_model.heat_rate_mmbtu_mwh < 0)
+ | (self.units_model.heat_rate_mmbtu_mwh > 35), "heat_rate_mmbtu_mwh"
] = np.nan
# Fill any null heat rate values for each tech
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Add RPS bug fix to settings file. | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -160,6 +160,13 @@ def make_genx_settings_file(pudl_engine, settings, calculated_ces=None):
genx_settings = load_settings(settings["genx_settings_fn"])
policies = load_policy_scenarios(settings)
year_case_policy = policies.loc[(case_id, model_year), :]
+
+ # Bug where multiple regions for a case will return this as a df, even if the policy
+ # for this case applies to all regions (code below expects a Series)
+ ycp_shape = year_case_policy.shape
+ if ycp_shape[0] == 1 and len(ycp_shape) > 1:
+ year_case_policy = year_case_policy.squeeze() # convert to series
+
if settings.get("distributed_gen_profiles_fn"):
dg_generation = make_distributed_gen_profiles(pudl_engine, settings)
total_dg_gen = dg_generation.sum().sum()
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Add extra resource characteristics to tech name | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -967,6 +967,14 @@ def add_renewables_clusters(
+ f" less than minimum ({capacity} < {scenario['min_capacity']} MW)"
)
row = df[df["technology"] == technology].to_dict("records")[0]
+ new_tech_name = "_".join(
+ [
+ str(v)
+ for k, v in scenario.items()
+ if k not in ["region", "technology", "max_clusters", "min_capacity"]
+ ]
+ )
+ clusters["technology"] = clusters["technology"] + "_" + new_tech_name
kwargs = {k: v for k, v in row.items() if k not in clusters}
cdfs.append(clusters.assign(**kwargs))
return pd.concat([df[~mask]] + cdfs, sort=False)
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Only check for duplicate final keys when building scenarios
Previous behavior would check if a top-level key was modified more than
once. New behavior checks if a full key path (all nested keys) is modified,
which allows for more flexible settings management. | MODIFY
powergenome/util.py
powergenome/util.py
@@ -7,6 +7,7 @@ import pudl
import requests
import sqlalchemy as sa
+from flatten_dict import flatten
import yaml
from ruamel.yaml import YAML
from pathlib import Path
@@ -367,7 +368,7 @@ def build_scenario_settings(settings: dict, scenario_definitions: pd.DataFrame)
]
# print(new_parameter)
try:
- settings_keys = list(new_parameter.keys())
+ settings_keys = list(flatten(new_parameter).keys())
except AttributeError:
settings_keys = {}
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Update type hint to show nested scenario dict | MODIFY
powergenome/util.py
powergenome/util.py
@@ -293,7 +293,9 @@ def build_case_id_name_map(settings: dict) -> dict:
return case_id_name_map
-def build_scenario_settings(settings: dict, scenario_definitions: pd.DataFrame) -> dict:
+def build_scenario_settings(
+ settings: dict, scenario_definitions: pd.DataFrame
+) -> Dict[int, Dict[Union[int, str], dict]]:
"""Build a nested dictionary of settings for each planning year/scenario
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Remove unused settings parameters | MODIFY
example_system/test_settings.yml
example_system/test_settings.yml
@@ -693,10 +693,6 @@ alt_atb_cap_recovery_years:
atb_existing_year: 2018
atb_usd_year: 2017 # From Capex figure in atb documentation https://atb.nrel.gov/electricity/2019/summary.html
-# Assume that O&M for existing plants is higher than for new builds by both the ratio
-# of heat rates and a multiplier
-existing_om_multiplier: 1.0
-
# ATB uses an average of conventional and advanced techs. We only want advanced. To use
# standard ATB values set multipliers to 1.
# Heat rate multipliers are an average of "heat rate" and "final heat rate" multipliers.
@@ -765,10 +761,6 @@ eia_atb_tech_map:
Natural Gas Steam Turbine: Coal_newAvgCF # No gas steam turbines in ATB, using coal instead
Solar Thermal with Energy Storage: CSP_Class1 # NEED TO CHECK THIS DEFAULT
-# NEMS has O&M (including capex) for existing generators. Boolean, true or false.
-use_nems_coal_ng_om: true
-
-
# Offshore wind in the west will be floating - OTRG10 maybe.
# Format for each list item is <technology>, <tech_detail>, <cost_case>, <size>
atb_new_gen:
@@ -835,10 +827,6 @@ renewables_clusters:
min_capacity: 100000
cap_multiplier: 0.2
-# How much of the theoretical
-wind_pv_fraction_developable: 0.5
-
-
# Fuel consumption for start-up events (mmbtu/MW) from Lew et al 2013,
# Finding Flexibility: Cycling the Conventional Fleet
startup_fuel_use:
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Bump version number in setup.py | MODIFY
setup.py
setup.py
@@ -3,7 +3,7 @@ from setuptools import find_packages, setup
setup(
name="powergenome",
packages=find_packages(),
- version="0.2.0",
+ version="0.3.3",
description="Extract PUDL data for use in power system models",
author="Greg Schivley",
entry_points={
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Round new min power values
Keep to 3 sig figs so that rounded generation values won't be lower. | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -610,6 +610,6 @@ def fix_min_power_values(
f"{sum(mask)} resources have {min_power_col} larger than hourly generation."
)
- resource_df.loc[mask, min_power_col] = gen_profile_min[mask]
+ resource_df.loc[mask, min_power_col] = gen_profile_min[mask].round(3)
return resource_df
\ No newline at end of file
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Use full ATB string to match startup fuel use
Bug caused NG steam turbine startup fuel to be used for coal plants. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -174,7 +174,7 @@ def startup_fuel(df, settings):
"""
df["Start_fuel_MMBTU_per_MW"] = 0
for eia_tech, fuel_use in (settings.get("startup_fuel_use") or {}).items():
- atb_tech = settings["eia_atb_tech_map"][eia_tech].split("_")[0]
+ atb_tech = settings["eia_atb_tech_map"][eia_tech]
df.loc[df["technology"] == eia_tech, "Start_fuel_MMBTU_per_MW"] = fuel_use
df.loc[
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Use {} when param is None
When no settings are changed for a scenario case, the flatten function
raised an error. Use an empy dict instead of any falsy value. | MODIFY
powergenome/util.py
powergenome/util.py
@@ -368,8 +368,8 @@ def build_scenario_settings(
case_value = case_value_dict[case_id]
new_parameter = planning_year_settings_management[category][
case_value
- ]
- # print(new_parameter)
+ ] or {}
+
try:
settings_keys = list(flatten(new_parameter).keys())
except AttributeError:
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Fix boolean comparison bug with RPS/CES policies | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -192,7 +192,7 @@ def make_genx_settings_file(pudl_engine, settings, calculated_ces=None):
if float(year_case_policy["RPS"]) > 0:
# print(total_dg_gen)
# print(year_case_policy["RPS"])
- if policies.loc[(case_id, model_year), "region"] == "all":
+ if policies.loc[(case_id, model_year), "region"].all() == "all":
genx_settings["RPS"] = 3
genx_settings["RPS_Adjustment"] = float((1 - RPS) * total_dg_gen)
else:
@@ -203,7 +203,7 @@ def make_genx_settings_file(pudl_engine, settings, calculated_ces=None):
genx_settings["RPS_Adjustment"] = 0
if float(year_case_policy["CES"]) > 0:
- if policies.loc[(case_id, model_year), "region"] == "all":
+ if policies.loc[(case_id, model_year), "region"].all() == "all":
genx_settings["CES"] = 3
# This is a little confusing but for partial CES
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Calc line loss even when distances are in km | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -345,10 +345,18 @@ def network_line_loss(transmission: pd.DataFrame, settings: dict) -> pd.DataFram
raise KeyError(
"The parameter 'tx_line_loss_100_miles' is required in your settings file."
)
+ if "distance_mile" in transmission.columns:
+ distance_col = "distance_mile"
+ elif "distance_km" in transmission.columns:
+ distance_col = "distance_km"
+ loss_per_100_miles *= 0.62137
+ logger.info("Line loss per 100 miles was converted to km.")
+ else:
+ raise KeyError("No distance column is available in the transmission dataframe")
loss_per_100_miles = settings["tx_line_loss_100_miles"]
transmission["Line_Loss_Percentage"] = (
- transmission["distance_mile"] / 100 * loss_per_100_miles
- ).round(4)
+ transmission[distance_col] / 100 * loss_per_100_miles
+ )
return transmission
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Prune resource trees with no base resources matching criteria
Fixes incorrect behavior when a precomputed resource tree (e.g. for a metro region)
has no base resources that meet the filter criteria (e.g. max_lcoe).
Previously, the non-base resources were still clustered by cluster_trees().
The entire tree is now removed from the result. | MODIFY
powergenome/resource_clusters.py
powergenome/resource_clusters.py
@@ -569,8 +569,17 @@ class ResourceGroup:
mask[mask] = df.loc[mask, "lcoe"] <= max_lcoe
if not mask.any():
raise ValueError(f"No resources found or selected")
+ if tree:
+ # Only keep trees with one ore more base resources
+ selected = (
+ pd.Series(mask, index=df.index)
+ .groupby(df[tree])
+ .transform(lambda x: x.sum() > 0)
+ )
+ # Add non-base resources to selected trees
+ mask |= selected & ~base
# Apply mask
- df = df[mask | ~base] if tree else df[mask]
+ df = df[mask]
# Prepare merge
merge = copy.deepcopy(MERGE)
# Prepare profiles
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about rounding of certain parameters
removed rounding of up_time, down_time and Max_DSM_delay parameters since we need them as integers | MODIFY
powergenome/GenX.py
powergenome/GenX.py
@@ -29,9 +29,6 @@ INT_COLS = [
COL_ROUND_VALUES = {
"Var_OM_cost_per_MWh": 2,
"Var_OM_cost_per_MWh_in": 2,
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- "Max_DSM_delay": 0,
"Start_cost_per_MW": 0,
"Cost_per_MMBtu": 2,
"CO2_content_tons_per_MMBtu": 5,
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adding unit_id_pudl column
We need the information about thermal units in each cluster for the downscaling purpose. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2357,6 +2357,12 @@ class GeneratorClusters:
if num_clusters[region][tech] != 0:
_df = calc_unit_cluster_values(grouped, self.settings, tech)
_df["region"] = region
+ _df['unit_id_pudl'] = '0'
+ df_1 = df.reset_index(drop=True)
+ EachClusterWeight = [None] * num_clusters[region][tech]
+ for k in range(num_clusters[region][tech]):
+ EachClusterWeight[k] = len(clusters.labels_[clusters.labels_ == k])
+ _df['unit_id_pudl'][k+1] = list(df_1.loc[list(np.where(clusters.labels_==k)[0])]['unit_id_pudl'])
self.cluster_list.append(_df)
# Save some data about individual units for easy access
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saving average CF of wind and solar clusters | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2557,6 +2557,11 @@ class GeneratorClusters:
"Heat_rate_MMBTU_per_MWh"
]
+ self.all_resources['CF']=0.0
+ for i in range(len(self.all_resources['R_ID'])):
+ if isinstance(self.all_resources['profile'][i], (collections.Sequence, np.ndarray)):
+ self.all_resources['CF'][i] = np.mean(self.all_resources['profile'][i].tolist())
+
# Set Min_power of wind/solar to 0
self.all_resources.loc[self.all_resources["DISP"] == 1, "Min_power"] = 0
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fixing index error | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2561,7 +2561,7 @@ class GeneratorClusters:
]
self.all_resources["CF"] = 0.0
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+ for i in range(len(self.all_resources["R_ID"])):
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Remove unused object `EachClusterWeight` | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2358,9 +2358,7 @@ class GeneratorClusters:
_df["region"] = region
_df["unit_id_pudl"] = "0"
df_1 = df.reset_index(drop=True)
- EachClusterWeight = [None] * num_clusters[region][tech]
for k in range(num_clusters[region][tech]):
- EachClusterWeight[k] = len(clusters.labels_[clusters.labels_ == k])
_df["unit_id_pudl"][k + 1] = list(
df_1.loc[list(np.where(clusters.labels_ == k)[0])][
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Add section to clarify data licencing | MODIFY
README.md
README.md
@@ -100,6 +100,10 @@ A folder with extra user inputs is required when using the `run_powergenome_mult
If you have previously installed PowerGenome and the `run_powergenome_multiple` command doesn't work, try reinstalling it using `pip install -e .` as described above. If you downloaded the custom PUDL database before May of 2020, some errors may be resolved by downloading a new version.
+## Licensing
+
+PowerGenome is released under the [MIT License](https://opensource.org/licenses/MIT). Most data inputs are from US government sources (EIA, EPA, FERC, etc), which should not be [subject to copyright in the US](https://www.usa.gov/government-works). Hourly generation profiles for wind and solar resources were created by [Vibrant Clean Energy](https://www.vibrantcleanenergy.com/) and provided without usage restrictions. All PowerGenome data outputs are released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license.
+
## Contributing
Contributions are welcome! There is significant work to do on this project and additional perspective on user needs will help make it better. If you see something that needs to be improved, [open an issue](https://github.com/gschivley/PowerGenome/issues). If you have questions or need assistance, join [PowerGenome on groups.io](https://groups.io/g/powergenome) and post a message there.
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Check to see if model regions are in cost_mult and fuel | MODIFY
powergenome/util.py
powergenome/util.py
import collections
from copy import deepcopy
+import itertools
import logging
import subprocess
from typing import Dict, Tuple, Union
@@ -35,16 +36,40 @@ def check_settings(settings: dict, pudl_engine: sa.engine) -> None:
"region_id_epaipm"
].to_list()
- for model_region, ipm_regions in settings["region_aggregations"].items():
+ cost_mult_regions = list(
+ itertools.chain.from_iterable(settings["cost_multiplier_region_map"].values())
+ )
+
+ aeo_fuel_regions = list(
+ itertools.chain.from_iterable(settings["aeo_fuel_region_map"].values())
+ )
+
+ for agg_region, ipm_regions in settings["region_aggregations"].items():
for ipm_region in ipm_regions:
if ipm_region not in ipm_region_list:
s = f"""
*****************************
- There is no IPM region {ipm_region}, which is listed in {model_region}"
+ There is no IPM region {ipm_region}, which is listed in {agg_region}"
*****************************
"""
logger.warning(s)
+ for model_region in settings["model_regions"]:
+ if model_region not in cost_mult_regions:
+ s = f"""
+ *****************************
+ The model region {model_region} is not included in the settings parameter `cost_multiplier_region_map`"
+ *****************************
+ """
+ logger.warning(s)
+
+ if model_region not in aeo_fuel_regions:
+ s = f"""
+ *****************************
+ The model region {model_region} is not included in the settings parameter `aeo_fuel_region_map`"
+ *****************************
+ """
+ logger.warning(s)
def init_pudl_connection(freq="YS"):
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Add check_settings docstring | MODIFY
powergenome/util.py
powergenome/util.py
@@ -31,6 +31,19 @@ def load_settings(path: Union[str, Path]) -> dict:
def check_settings(settings: dict, pudl_engine: sa.engine) -> None:
+ """Check for user errors in the settings file.
+
+ The YAML settings file is loaded as a dictionary object. It has many different parts
+ that need to have consistent values. This function checks a few (but not all!) of
+ the parameters for common errors or misspelled words.
+
+ Parameters
+ ----------
+ settings : dict
+ Parameters and values from the YAML settings file.
+ pudl_engine : sa.engine
+ Connection to the PUDL sqlite database.
+ """
ipm_region_list = pd.read_sql_table("regions_entity_epaipm", pudl_engine)[
"region_id_epaipm"
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save eia_plant_id data | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2357,6 +2357,7 @@ class GeneratorClusters:
_df = calc_unit_cluster_values(grouped, self.settings, tech)
_df["region"] = region
_df["unit_id_pudl"] = "0"
+ _df["plant_id_eia"] = "0"
df_1 = df.reset_index(drop=True)
for k in range(num_clusters[region][tech]):
_df["unit_id_pudl"][k + 1] = list(
@@ -2364,6 +2365,11 @@ class GeneratorClusters:
"unit_id_pudl"
]
)
+ _df["plant_id_eia"][k + 1] = list(
+ df_1.loc[list(np.where(clusters.labels_ == k)[0])][
+ "plant_id_eia"
+ ]
+ )
self.cluster_list.append(_df)
# Save some data about individual units for easy access
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changing CF to variable_CF | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -2564,7 +2564,7 @@ class GeneratorClusters:
"Heat_rate_MMBTU_per_MWh"
]
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for i in range(len(self.all_resources["R_ID"])):
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Version bump to 0.3.4 | MODIFY
setup.py
setup.py
@@ -3,7 +3,7 @@ from setuptools import find_packages, setup
setup(
name="powergenome",
packages=find_packages(),
- version="0.3.3",
+ version="0.3.4",
description="Extract PUDL data for use in power system models",
author="Greg Schivley",
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Add init_pudl_connection docstring and type hints | MODIFY
powergenome/util.py
powergenome/util.py
@@ -84,8 +84,26 @@ def check_settings(settings: dict, pudl_engine: sa.engine) -> None:
"""
logger.warning(s)
-def init_pudl_connection(freq="YS"):
+def init_pudl_connection(
+ freq: str = "YS",
+) -> Tuple[sa.engine.base.Engine, pudl.output.pudltabl.PudlTabl]:
+ """Initiate a connection object to the sqlite PUDL database and create a pudl
+ object that can quickly access parts of the database.
+
+ Parameters
+ ----------
+ freq : str, optional
+ The time frequency that data should be averaged over in the `pudl_out` object,
+ by default "YS" (annual data).
+
+ Returns
+ -------
+ sa.Engine, pudl.pudltabl
+ A sqlalchemy engine for connecting to the PUDL database, and a pudl PudlTabl
+ object for quickly accessing parts of the database. `pudl_out` is used
+ to access unit heat rates.
+ """
pudl_engine = sa.create_engine(
SETTINGS["PUDL_DB"]
) # pudl.init.connect_db(SETTINGS)
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saving month corresponding to each slot | MODIFY
powergenome/time_reduction.py
powergenome/time_reduction.py
from sklearn.cluster import KMeans
from sklearn.preprocessing import minmax_scale
import numpy as np
+import datetime
import pandas as pd
@@ -201,6 +202,14 @@ def kmeans_time_clustering(
# same CSV file that will be used in GenX
long_duration_storage = long_duration_storage.sort_values(by=["slot"])
+ long_duration_storage = long_duration_storage.reset_index(drop=True)
+
+ #extract month corresponding to each time slot
+ long_duration_storage['Month']=0
+ for slot in long_duration_storage['slot']:
+ dayOfYear = days_in_group * slot
+ d = datetime.datetime.strptime('{} {}'.format(dayOfYear, 2011),'%j %Y')
+ long_duration_storage['Month'][slot-1] = d.month
# Storing selected groupings in a new data frame with appropriate dimensions
# (E.g. load in GW)
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fixing ac-dc multiplier for PV capex | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -79,7 +79,7 @@ def fetch_atb_costs(pudl_engine, settings, offshore_spur_costs=None):
logger.info("Inflating PV costs for DC to AC")
atb_costs.loc[
- atb_costs["technology"].str.contains("PV"), ["o_m_fixed_mw", "o_m_variable_mwh"]
+ atb_costs["technology"].str.contains("PV"), ["capex","o_m_fixed_mw", "o_m_variable_mwh"]
] *= settings["pv_ac_dc_ratio"]
if offshore_spur_costs is not None:
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Load heat rates on class init
Needed for existing plants, not just new ones. | MODIFY
powergenome/generators.py
powergenome/generators.py
@@ -1991,6 +1991,7 @@ class GeneratorClusters:
else:
self.existing_resources = pd.DataFrame()
self.fuel_prices = fetch_fuel_prices(self.settings)
+ self.atb_hr = fetch_atb_heat_rates(self.pudl_engine, self.settings)
def fill_na_heat_rates(self, df):
"""Fill null heat rate values with the median of the series. Not many null
@@ -2504,7 +2505,7 @@ class GeneratorClusters:
self.atb_costs = fetch_atb_costs(
self.pudl_engine, self.settings, self.offshore_spur_costs
)
- self.atb_hr = fetch_atb_heat_rates(self.pudl_engine, self.settings)
+
self.new_generators = atb_new_generators(
self.atb_costs, self.atb_hr, self.settings
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Add annual capex to nuclear FOM
Source is EIA AEO documentation
page 16. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -417,6 +417,7 @@ def atb_fixed_var_om_existing(
"Steam Turbine",
"Hydroelectric",
"Geothermal",
+ "Nuclear",
]
if any(t in eia_tech for t in nems_o_m_techs):
# Change CC and CT O&M to EIA NEMS values, which are much higher for CCs and
@@ -606,6 +607,22 @@ def atb_fixed_var_om_existing(
_df["Var_OM_cost_per_MWh"] = simple_o_m["Pumped Hydro"][
"variable_o_m_mwh"
]
+ if "Nuclear" in eia_tech:
+ age = (settings["model_year"] - _df.operating_date.dt.year).values
+ # EIA, 2020, "Assumptions to Annual Energy Outlook, Electricity Market Module,"
+ # Available: https://www.eia.gov/outlooks/aeo/assumptions/pdf/electricity.pdf
+ fixed = np.ones_like(age)
+ fixed[age < 30] *= 27 * 1000
+ fixed[age >= 30] *= (27+37) * 1000
+
+ _df[
+ "Fixed_OM_cost_per_MWyr"
+ ] = atb_fixed_om_mw_yr + inflation_price_adjustment(
+ fixed, 2019, target_usd_year
+ )
+ _df["Var_OM_cost_per_MWh"] = atb_var_om_mwh * (
+ existing_hr / new_build_hr
+ )
else:
_df["Fixed_OM_cost_per_MWyr"] = atb_fixed_om_mw_yr
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Fill coal/nuclear unit ages where missing
If `operating_date` is null, FOM will be null or wrong. Use mean unit age
at plant, then use a heuristic of 40 year age. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -574,6 +574,8 @@ def atb_fixed_var_om_existing(
assert plant_capacity > 0
age = settings["model_year"] - _df.operating_date.dt.year
+ age = age.fillna(age.mean())
+ age = age.fillna(40)
# https://www.eia.gov/analysis/studies/powerplants/generationcost/pdf/full_report.pdf
annual_capex = (16.53 + (0.126 * age) + (5.68 * 0.5)) * 1000
@@ -612,6 +614,8 @@ def atb_fixed_var_om_existing(
]
if "Nuclear" in eia_tech:
age = (settings["model_year"] - _df.operating_date.dt.year).values
+ age = age.fillna(age.mean())
+ age = age.fillna(40)
# EIA, 2020, "Assumptions to Annual Energy Outlook, Electricity Market Module,"
# Available: https://www.eia.gov/outlooks/aeo/assumptions/pdf/electricity.pdf
fixed = np.ones_like(age)
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Can't fillna on a numpy array | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -614,8 +614,8 @@ def atb_fixed_var_om_existing(
]
if "Nuclear" in eia_tech:
age = (settings["model_year"] - _df.operating_date.dt.year).values
- age = age.fillna(age.mean())
- age = age.fillna(40)
+ # age = age.fillna(age.mean())
+ # age = age.fillna(40)
# EIA, 2020, "Assumptions to Annual Energy Outlook, Electricity Market Module,"
# Available: https://www.eia.gov/outlooks/aeo/assumptions/pdf/electricity.pdf
fixed = np.ones_like(age)
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Drop duplicate atb cost rows.
Duplicates occur when a modified ATB tech and the original tech are both
included in a model e.g. 90% CCS and a modified version for 100% capture.
The df can't be unstacked with duplicate tech/tech_detail/parameter entries. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -184,7 +184,7 @@ def fetch_atb_costs(
# Transform from tidy to wide dataframe, which makes it easier to fill generator
# rows with the correct values.
- atb_costs = df.set_index(
+ atb_costs = df.drop_duplicates().set_index(
[
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Bug fix when no aggregated regions are included | MODIFY
powergenome/util.py
powergenome/util.py
@@ -57,7 +57,7 @@ def check_settings(settings: dict, pudl_engine: sa.engine) -> None:
itertools.chain.from_iterable(settings["aeo_fuel_region_map"].values())
)
- for agg_region, ipm_regions in settings["region_aggregations"].items():
+ for agg_region, ipm_regions in (settings.get("region_aggregations") or {}).items():
for ipm_region in ipm_regions:
if ipm_region not in ipm_region_list:
s = f"""
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Remove R_ID from gen variability column name
Names weren't the same across cases, needed to align. | MODIFY
powergenome/run_powergenome_multiple_outputs_cli.py
powergenome/run_powergenome_multiple_outputs_cli.py
@@ -360,8 +360,6 @@ def main():
+ gen_clusters["Resource"]
+ "_"
+ gen_clusters["cluster"].astype(str)
- + "_"
- + gen_clusters["R_ID"].astype(str)
)
gens = calculate_partial_CES_values(
gen_clusters, fuels, _settings
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Set cluster col to integer
Started as NaN in many new gen cases, so col was float rather than int. | MODIFY
powergenome/nrelatb.py
powergenome/nrelatb.py
@@ -1052,6 +1052,7 @@ def atb_new_generators(atb_costs, atb_hr, settings):
"Fixed_OM_cost_per_MWhyr",
"Inv_cost_per_MWyr",
"Inv_cost_per_MWhyr",
+ "cluster"
]
results = results.fillna(0)
results[int_cols] = results[int_cols].astype(int)
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Shift load from UTC | MODIFY
powergenome/load_profiles.py
powergenome/load_profiles.py
@@ -61,6 +61,10 @@ def make_load_curves(
if len(lc_wide) == 8784:
lc_wide = remove_feb_29(lc_wide)
+ # Shift load from UTC
+ for col in lc_wide:
+ lc_wide[col] = np.roll(lc_wide[col].values, settings.get("utc_offset", 0))
+
lc_wide.index.name = "time_index"
if lc_wide.index.min() == 0:
lc_wide.index = lc_wide.index + 1
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Remove unused shift/wrap function
It's easier/quicker to use np.roll than a function that indexes | MODIFY
powergenome/load_profiles.py
powergenome/load_profiles.py
@@ -9,7 +9,6 @@ import pandas as pd
from powergenome.util import (
regions_to_keep,
reverse_dict_of_lists,
- shift_wrap_profiles,
remove_feb_29,
)
from powergenome.external_data import make_demand_response_profiles
MODIFY
powergenome/util.py
powergenome/util.py
@@ -187,15 +187,6 @@ def download_save(url: str, save_path: Union[str, Path]):
save_path.write_bytes(r.content)
-def shift_wrap_profiles(df, offset):
- "Shift hours to a local offset and append first rows to end"
-
- wrap_rows = df.iloc[:offset, :]
-
- shifted_wrapped_df = pd.concat([df.iloc[offset:, :], wrap_rows], ignore_index=True)
- return shifted_wrapped_df
-
-
def update_dictionary(d: dict, u: dict) -> dict:
"""
Update keys in an existing dictionary (d) with values from u
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network file bug fix for single zone | MODIFY
powergenome/transmission.py
powergenome/transmission.py
@@ -93,6 +93,7 @@ def agg_transmission_constraints(
if tc_joined.empty:
logger.info(f"No transmission lines exist between model regions {combos}")
tc_joined["Transmission Path Name"] = None
+ tc_joined.rename(columns=zone_num_map, inplace=True)
return tc_joined.reset_index(drop=True)
tc_joined["Network_lines"] = range(1, len(tc_joined) + 1)
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Add attribution for hourly demand data processing | MODIFY
README.md
README.md
@@ -102,7 +102,7 @@ If you have previously installed PowerGenome and the `run_powergenome_multiple`
## Licensing
-PowerGenome is released under the [MIT License](https://opensource.org/licenses/MIT). Most data inputs are from US government sources (EIA, EPA, FERC, etc), which should not be [subject to copyright in the US](https://www.usa.gov/government-works). Hourly generation profiles for wind and solar resources were created by [Vibrant Clean Energy](https://www.vibrantcleanenergy.com/) and provided without usage restrictions. All PowerGenome data outputs are released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license.
+PowerGenome is released under the [MIT License](https://opensource.org/licenses/MIT). Most data inputs are from US government sources (EIA, EPA, FERC, etc), which should not be [subject to copyright in the US](https://www.usa.gov/government-works). Hourly FERC demand data has been cleaned using [techniques](https://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Code) developed by Tyler Ruggles and David Farnham, and allocated to IPM regions using [methods developed](https://github.com/catalyst-cooperative/electricity-demand-mapping) by Catalyst Cooperative. Hourly generation profiles for wind and solar resources were created by [Vibrant Clean Energy](https://www.vibrantcleanenergy.com/) and provided without usage restrictions. All PowerGenome data outputs are released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license.
## Contributing
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