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Adding by_charge_key argument to calculate_itemized_cost, and some bug fixes #51

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Merged
fletchapin merged 4 commits into from
Feb 19, 2026
Merged

Adding by_charge_key argument to calculate_itemized_cost, and some bug fixes #51

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b9d0cb4
Adding optional by_charge_key argument to calculate_itemized_cost.py …
dalyw Feb 5, 2026
d7a302f
Adding tests for by_charge_key
dalyw Feb 5, 2026
3254fe3
Removing unneeded type check in test_calculate_itemized_cost_pyo
dalyw Feb 5, 2026
c2c2244
Adding test coverage for reformatted emissions.py line, when emission…
dalyw Feb 5, 2026
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2 changes: 1 addition & 1 deletion README.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ A package for calculating electricity-related emissions and costs for optimizati
Useful Commands
===============

1. ``pip install -e .``
1. ``pip install -e .`` (or ``pip install -e .[test]`` for development)

This will install your package in editable mode.

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121 changes: 66 additions & 55 deletions eeco/costs.py
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Expand Up @@ -124,10 +124,11 @@ def create_charge_array(
# calculate months, days, and hours
weekdays = datetime.dt.weekday.values
months = datetime.dt.month.values
hours = datetime.dt.hour.astype(float).values
# Make sure hours are being incremented by XX-minute increments
minutes = datetime.dt.minute.astype(float).values
hours += minutes / 60
hours = (
datetime.dt.hour.astype(float).values
+ datetime.dt.minute.astype(float).values / 60
)

# create an empty charge array
apply_charge = (
Expand All @@ -153,6 +154,7 @@ def create_charge_array(
# if not specified, assume the old format (imperial units)
if utility == GAS:
# convert from therms to default units of cubic meters
charge = charge.copy()

@fletchapin fletchapin Feb 19, 2026

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What is the goal of copying this dictionary? Was there a bug that you found?

@dalyw dalyw Feb 19, 2026

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This came up when running with pandas 3.0 from flows-mpc, which had the older billing format in tests.

It was raising a ChainedAssignmentError: https://pandas.pydata.org/docs/user_guide/copy_on_write.html#chained-assignment

Instead of making a copy, we could possibly use .loc (like the linked example) if modifying limit_charges directly instead of charge, but that would probably be more complicated.

I ended up changing the flows-mpc billing csvs to the new format so it's not actually hitting this code anymore. Think it's worth adding a test case for this "except" section?

charge[CHARGE] /= 2.83168 # therm per cubic meter of CH4
charge_array = apply_charge * charge[CHARGE]
return charge_array
Expand Down Expand Up @@ -246,10 +248,6 @@ def get_charge_dict(start_dt, end_dt, rate_data, resolution="15m"):
),
columns=[DATETIME],
)
hours = datetime[DATETIME].dt.hour.astype(float).values
# Make sure hours are being incremented by XX-minute increments
minutes = datetime[DATETIME].dt.minute.astype(float).values
hours += minutes / 60

for utility in [GAS, ELECTRIC]:
for charge_type in [CUSTOMER, ENERGY, DEMAND, EXPORT]:
Expand Down Expand Up @@ -1522,6 +1520,7 @@ def calculate_itemized_cost(
demand_scale_factor=1,
model=None,
decomposition_type=None,
by_charge_key=False,
varstr_alias_func=default_varstr_alias_func,
):
"""Calculates itemized costs as a nested dictionary
Expand Down Expand Up @@ -1582,40 +1581,49 @@ def calculate_itemized_cost(
The model object associated with the problem.
Only used in the case of Pyomo, so `None` by default.

decomposition_type : str or None
Type of decomposition to use for consumption data.
- "absolute_value": Linear problem using absolute value
- "binary_variable": `NotImplementedError`
- None (default): No decomposition, treats all consumption as imports

by_charge_key : bool
Whether to further itemize the results by charge key. Default is False.

Returns
-------
dict
{

"electric": {

"customer": `float`
"customer": `float` or dict (key -> cost) if by_charge_key

"energy": `float`
"energy": `float` or dict (key -> cost) if by_charge_key

"demand": `float`
"demand": `float` or dict (key -> cost) if by_charge_key

"export": `float`
"export": `float` or dict (key -> cost) if by_charge_key

"total": `float`
"total": `float` or dict (key -> cost) if by_charge_key

}

"gas": {

"customer": `float`,
"customer": `float` or dict (key -> cost) if by_charge_key

"energy": `float`
"energy": `float` or dict (key -> cost) if by_charge_key

"demand": `float`
"demand": `float` or dict (key -> cost) if by_charge_key

"export": `float`
"export": `float` or dict (key -> cost) if by_charge_key

"total": `float`
"total": `float` or dict (key -> cost) if by_charge_key

}

"total": `float`
"total": `float` or dict (key -> cost) if by_charge_key

}

Expand All @@ -1638,19 +1646,25 @@ def calculate_itemized_cost(
consumption_data_dict, conversion_factors, decomposition_type, model
)

total_cost = 0
results_dict = {}

if desired_utility is None:
for utility in [ELECTRIC, GAS]:
results_dict[utility] = {}
total_utility_cost = 0
for charge_type in [CUSTOMER, ENERGY, DEMAND, EXPORT]:
utilities = [ELECTRIC, GAS] if desired_utility is None else [desired_utility]
charge_types = [CUSTOMER, ENERGY, DEMAND, EXPORT]
charge_items = list(charge_dict.items()) if by_charge_key else [(None, charge_dict)]

for utility in utilities:
results_dict[utility] = {}
for charge_type in charge_types:
if by_charge_key:
results_dict[utility][charge_type] = {}

for charge_key, charges in charge_items:
charge_input = charges if charge_key is None else {charge_key: charges}
cost, model = calculate_cost(
charge_dict,
charge_input,
consumption_data_dict,
resolution=resolution,
prev_demand_dict=prev_demand_dict,
prev_consumption_dict=prev_consumption_dict,
consumption_estimate=consumption_estimate,
desired_utility=utility,
desired_charge_type=charge_type,
Expand All @@ -1659,38 +1673,35 @@ def calculate_itemized_cost(
decomposition_type=decomposition_type,
varstr_alias_func=varstr_alias_func,
)

results_dict[utility][charge_type] = cost
total_utility_cost += cost

results_dict[utility]["total"] = total_utility_cost
total_cost += total_utility_cost
else:
results_dict[desired_utility] = {}
total_utility_cost = 0
for charge_type in [CUSTOMER, ENERGY, DEMAND, EXPORT]:
cost, model = calculate_cost(
charge_dict,
consumption_data_dict,
resolution=resolution,
prev_demand_dict=prev_demand_dict,
prev_consumption_dict=prev_consumption_dict,
consumption_estimate=consumption_estimate,
desired_utility=desired_utility,
desired_charge_type=charge_type,
demand_scale_factor=demand_scale_factor,
model=model,
decomposition_type=decomposition_type,
varstr_alias_func=varstr_alias_func,
if by_charge_key:
results_dict[utility][charge_type][charge_key] = cost
else:
results_dict[utility][charge_type] = cost

if by_charge_key:
results_dict[utility]["total"] = {}
for charge_type in charge_types:
for charge_key, cost in results_dict[utility][charge_type].items():
results_dict[utility]["total"][charge_key] = (
results_dict[utility]["total"].get(charge_key, 0) + cost
)
else:
results_dict[utility]["total"] = sum(
results_dict[utility][charge_type] for charge_type in charge_types
)

results_dict[desired_utility][charge_type] = cost
total_utility_cost += cost

results_dict[desired_utility]["total"] = total_utility_cost
total_cost += total_utility_cost
if by_charge_key:
results_dict["total"] = {}
for utility in utilities:
for charge_key, cost in results_dict[utility]["total"].items():
results_dict["total"][charge_key] = (
results_dict["total"].get(charge_key, 0) + cost
)
else:
results_dict["total"] = sum(
results_dict[utility]["total"] for utility in utilities
)

results_dict["total"] = total_cost
return results_dict, model


Expand Down
50 changes: 50 additions & 0 deletions eeco/data/emissions_datetime_local.csv
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Original file line number Diff line number Diff line change
@@ -0,0 +1,50 @@
datetime_local,co2_eq_kg_per_MWh
2024-07-31 00:00:00,395.532224
2024-07-31 01:00:00,415.03668
2024-07-31 02:00:00,424.10852
2024-07-31 03:00:00,427.737256
2024-07-31 04:00:00,425.015704
2024-07-31 05:00:00,410.50076
2024-07-31 06:00:00,393.264264
2024-07-31 07:00:00,366.955928
2024-07-31 08:00:00,355.616128
2024-07-31 09:00:00,351.987392
2024-07-31 10:00:00,350.626616
2024-07-31 11:00:00,352.440984
2024-07-31 12:00:00,353.348168
2024-07-31 13:00:00,346.99788
2024-07-31 14:00:00,339.740408
2024-07-31 15:00:00,337.018856
2024-07-31 16:00:00,337.92604
2024-07-31 17:00:00,346.99788
2024-07-31 18:00:00,357.430496
2024-07-31 19:00:00,359.698456
2024-07-31 20:00:00,360.152048
2024-07-31 21:00:00,362.420008
2024-07-31 22:00:00,369.223888
2024-07-31 23:00:00,375.574176
2024-08-01 00:00:00,414.583088
2024-08-01 01:00:00,433.18036
2024-08-01 02:00:00,439.98424
2024-08-01 03:00:00,443.612976
2024-08-01 04:00:00,443.159384
2024-08-01 05:00:00,419.119008
2024-08-01 06:00:00,405.96484
2024-08-01 07:00:00,378.295728
2024-08-01 08:00:00,366.955928
2024-08-01 09:00:00,364.234376
2024-08-01 10:00:00,362.420008
2024-08-01 11:00:00,364.234376
2024-08-01 12:00:00,364.234376
2024-08-01 13:00:00,355.162536
2024-08-01 14:00:00,347.451472
2024-08-01 15:00:00,345.637104
2024-08-01 16:00:00,346.99788
2024-08-01 17:00:00,358.791272
2024-08-01 18:00:00,365.595152
2024-08-01 19:00:00,366.048744
2024-08-01 20:00:00,369.223888
2024-08-01 21:00:00,376.934952
2024-08-01 22:00:00,385.099608
2024-08-01 23:00:00,393.264264
2024-08-02 00:00:00,414.583088
34 changes: 23 additions & 11 deletions eeco/emissions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,8 +217,8 @@ def get_carbon_intensity(
rollover = (
end_month != start_month
) # should we extend this for multiple months/years?
old_end_day = end_day
if rollover:
old_end_day = end_day
end_day = calendar.monthrange(start_year, start_month)[1]
old_end_hour = end_hour
end_hour = 24
Expand Down Expand Up @@ -275,17 +275,27 @@ def get_carbon_intensity(
# reset start index to first hour of the month
# can assume start hour is zero since we are rolling over
start_hour = 0
end_month_day = i + 1
if no_day_var:
start_index = emissions_data.loc[
(emissions_data[HOUR_VARNAME] == start_hour)
& (emissions_data[MONTH_VARNAME] == end_month)
].idxmax()[0]
start_index = (
emissions_data.loc[
(emissions_data[HOUR_VARNAME] == start_hour)
& (emissions_data[MONTH_VARNAME] == end_month)
]
.idxmax()
.iloc[0]
)
else:
start_index = emissions_data.loc[
(emissions_data[HOUR_VARNAME] == start_hour)
& (emissions_data[DAY_VARNAME] == start_day)
& (emissions_data[MONTH_VARNAME] == end_month)
].idxmax()[0]
start_index = (
emissions_data.loc[
(emissions_data[HOUR_VARNAME] == start_hour)
# & (emissions_data[DAY_VARNAME] == start_day)
& (emissions_data[DAY_VARNAME] == end_month_day)
& (emissions_data[MONTH_VARNAME] == end_month)
]
.idxmax()
.iloc[0]
)
if i == int(old_end_day - 1):
# don't include last hour as it may not be a full hour
for j in range(0, int(end_hour)):
Expand All @@ -310,6 +320,8 @@ def get_carbon_intensity(
leftover = (ntsteps - num_first_min) % n_per_hour
if leftover == 0:
leftover = n_per_hour
# use last day of range if there is rollover
last_day = old_end_day if rollover else end_day
if no_day_var:
end_index = (
emissions_data.loc[
Expand All @@ -323,7 +335,7 @@ def get_carbon_intensity(
end_index = (
emissions_data.loc[
(emissions_data[HOUR_VARNAME] == end_hour)
& (emissions_data[DAY_VARNAME] == end_day)
& (emissions_data[DAY_VARNAME] == last_day)
& (emissions_data[MONTH_VARNAME] == end_month)
]
.idxmax()
Expand Down
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