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refactor: simplify trip data names
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fix: missed_vehicles concatenation

refactor: refine filtering; unneeded print statements

fix: add nd_len back where needed

refactor: parameter specification, indexing refinement

chore: linter complaints resolved
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@dmuldrew
dmuldrew committed Feb 9, 2023
1 parent f6189ed commit 69f1e0d
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Showing 2 changed files with 81 additions and 112 deletions.
18 changes: 3 additions & 15 deletions prereise/gather/demanddata/transportation_electrification/immediate.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -136,35 +136,26 @@ def immediate_charging(
trips = data_helper.load_hdv_data("hhdv", filepath)

# filter for cyclical trips
nd_len = len(trips)
filtered_census_data = pd.DataFrame(columns=const.nhts_census_column_names)
i = 0
while i < nd_len:
while i < len(trips):
total_trips = int(trips.iloc[i, trips.columns.get_loc("total_trips")])
# copy one vehicle information to the block
individual = trips.iloc[i : i + total_trips].copy()
if (
individual["why_from"].head(1).values[0]
== individual["dwell_location"].tail(1).values[0]
):
if individual["why_from"].iloc[0] == individual["dwell_location"].iloc[-1]:
filtered_census_data = pd.concat(
[filtered_census_data, individual], ignore_index=True
)
i += total_trips
del trips
trips = filtered_census_data

print(f"updated data length:{nd_len}")
#####

# Constants
kwhmi = data_helper.get_kwhmi(model_year, veh_type, veh_range)
battery_capacity = kwhmi * veh_range

if input_day is None:
input_day = data_helper.get_input_day(
data_helper.get_model_year_dti(model_year)
)
input_day = data_helper.get_input_day(data_helper.get_model_year_dti(model_year))

# updates the weekend and weekday values in the nhts data
trips = data_helper.update_if_weekend(trips)
Expand Down Expand Up @@ -266,9 +257,6 @@ def immediate_charging(
np.sum(daily_resampled_profiles["weekend"]),
np.sum(daily_resampled_profiles["weekday"]),
]
print(np.sum(daily_resampled_profiles["weekend"]))
print(np.sum(daily_resampled_profiles["weekday"]))
print(output_load_sum_list)

return summed_profile, output_load_sum_list, trips

Expand Down
175 changes: 78 additions & 97 deletions prereise/gather/demanddata/transportation_electrification/smart_charging.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@ def smart_charging(
kwhmi=None,
trip_strategy=1,
input_day=None,
debug_printout=False,
):
"""Smart charging function
Expand All @@ -74,29 +75,23 @@ def smart_charging(
last trip (2)
:param numpy.ndarray input_day: daily list which specifies 1 if the day is a weekend,
and 2 if the day is a weekday
:param bool debug_printout: specify if additional model parameters will be printed
:return: (*numpy.ndarray*) -- charging profiles.
"""

# for code developer debugging printouts (1 = yes, 0 = no)
debug_printout = 0

# load NHTS data from function
if veh_type.lower() == "ldv":
newdata1 = data_helper.remove_ldt(
data_helper.load_data(census_region, filepath)
)
newdata = data_helper.remove_ldt(data_helper.load_data(census_region, filepath))
# updates the weekend and weekday values in the nhts data
newdata1 = data_helper.update_if_weekend(newdata1)
newdata = data_helper.update_if_weekend(newdata)
elif veh_type.lower() == "ldt":
newdata1 = data_helper.remove_ldv(
data_helper.load_data(census_region, filepath)
)
newdata = data_helper.remove_ldv(data_helper.load_data(census_region, filepath))
# updates the weekend and weekday values in the nhts data
newdata1 = data_helper.update_if_weekend(newdata1)
newdata = data_helper.update_if_weekend(newdata)
elif veh_type.lower() == "mdv":
newdata1 = data_helper.load_hdv_data("mhdv", filepath)
newdata = data_helper.load_hdv_data("mhdv", filepath)
elif veh_type.lower() == "hdv":
newdata1 = data_helper.load_hdv_data("hhdv", filepath)
newdata = data_helper.load_hdv_data("hhdv", filepath)

new_columns = [
"trip start battery charge",
Expand All @@ -108,18 +103,16 @@ def smart_charging(
"Battery charge",
"trip_number",
]
newdata1 = newdata1.reindex(
list(newdata1.columns) + new_columns, axis=1, fill_value=0
)
newdata = newdata.reindex(list(newdata.columns) + new_columns, axis=1, fill_value=0)

newdata1["trip_number"] = newdata1.groupby("vehicle_number").cumcount() + 1
newdata["trip_number"] = newdata.groupby("vehicle_number").cumcount() + 1

if input_day is None:
input_day = data_helper.get_input_day(
data_helper.get_model_year_dti(model_year)
)

external_signal = -min(external_signal) + external_signal
external_signal -= min(external_signal)

if kwhmi is None:
kwhmi = data_helper.get_kwhmi(model_year, veh_type, veh_range)
Expand All @@ -131,78 +124,75 @@ def smart_charging(

# Add booleans for whether the location allows charging
if location_strategy == 3:
newdata1["location_allowed"] = True
newdata["location_allowed"] = True
else:
if veh_type.lower() in {"ldv", "ldt"}:
allowed = const.ldv_location_allowed[location_strategy]
newdata1["location_allowed"] = newdata1["dwell_location"].isin(allowed)
newdata["location_allowed"] = newdata["dwell_location"].isin(allowed)
elif veh_type.lower() in {"mdv", "hdv"}:
allowed = const.hdv_location_allowed[location_strategy]
newdata1["location_allowed"] = newdata1["dwell_location"].isin(allowed)
newdata["location_allowed"] = newdata["dwell_location"].isin(allowed)

# Add booleans for whether the trip_number (compared to total trips) allows charging
if trip_strategy == 1:
newdata1["trip_allowed"] = True
newdata["trip_allowed"] = True
elif trip_strategy == 2:
newdata1["trip_allowed"] = newdata1["trip_number"] == newdata1["total_trips"]
newdata["trip_allowed"] = newdata["trip_number"] == newdata["total_trips"]
else:
raise ValueError("trip_strategy parameter is not valid, i.e. either 1 or 2")

# Add booleans for whether the dwell time is long enough to allow charging
newdata1["dwell_allowed"] = newdata1["dwell_time"] > 0.2
newdata["dwell_allowed"] = newdata["dwell_time"] > 0.2

# Add boolean for whether this trip allows charging
allowed_cols = [
"location_allowed",
"trip_allowed",
"dwell_allowed",
]
newdata1["charging_allowed"] = newdata1[allowed_cols].apply(all, axis=1)
newdata["charging_allowed"] = newdata[allowed_cols].apply(all, axis=1)

newdata1["dwell_charging"] = (
newdata1["charging_allowed"]
* newdata1["dwell_time"]
newdata["dwell_charging"] = (
newdata["charging_allowed"]
* newdata["dwell_time"]
* power
* charging_efficiency
)

grouped_trips = newdata1.groupby("vehicle_number")
grouped_trips = newdata.groupby("vehicle_number")
for vehicle_num, group in grouped_trips:
newdata1.loc[group.index, "max_charging"] = newdata1.loc[
newdata.loc[group.index, "max_charging"] = newdata.loc[
group.index, "dwell_charging"
].sum()
newdata1.loc[group.index, "required_charging"] = (
newdata1.loc[group.index, "trip_miles"].sum() * kwhmi
newdata.loc[group.index, "required_charging"] = (
newdata.loc[group.index, "trip_miles"].sum() * kwhmi
)

# Filter for whenever available charging is insufficient to meet required charging
newdata = newdata1.loc[
(newdata1["required_charging"] <= newdata1["max_charging"])
newdata = newdata.loc[
(newdata["required_charging"] <= newdata["max_charging"])
].copy()

# Filter for vehicle's battery range
newdata = newdata.loc[(newdata["total vehicle miles traveled"] < veh_range)]

nd_len = len(newdata)

# LDV and LDT filter for cyclical trips
if veh_type.lower() in {"ldv", "ldt"}:
filtered_census_data = pd.DataFrame(columns=const.nhts_census_column_names)
i = 0
while i < nd_len:
while i < len(newdata):
total_trips = int(newdata.iloc[i, newdata.columns.get_loc("total_trips")])
# copy one vehicle information to the block
individual = newdata.iloc[i : i + total_trips].copy()
if (
individual["why_from"].head(1).values[0]
== individual["dwell_location"].tail(1).values[0]
):
if individual["why_from"].iloc[0] == individual["dwell_location"].iloc[-1]:
filtered_census_data = pd.concat(
[filtered_census_data, individual], ignore_index=True
)
i += total_trips
del newdata
gc.collect()
newdata = filtered_census_data

nd_len = len(newdata)

model_year_profile = np.zeros(24 * len(input_day))
if veh_type.lower() in {"ldv", "ldt"}:
data_day = data_helper.get_data_day(newdata)
Expand All @@ -213,8 +203,6 @@ def smart_charging(
newdata, input_day, veh_type.lower()
)

nd_len = len(newdata)

if veh_type.lower() in {"ldv", "ldt"}:
location_allowed = const.ldv_location_allowed
use_data_row = ldv_weekday_weekend_check
Expand Down Expand Up @@ -267,23 +255,21 @@ def smart_charging(
flag_3_fail = 0
flag_4_fail = 0
missed_vmt = 0
# if veh_type.lower() in {"ldv", "ldt"}:
# missed_vehicles = pd.DataFrame(
# columns=const.nhts_census_column_names
# ).astype("int64")
# elif veh_type.lower() in {"mdv", "hdv"}:
# missed_vehicles = pd.DataFrame(
# columns=list(newdata.columns)
# + [
# "power_allowed",
# "charging consumption",
# "seg",
# "power",
# "segcum",
# "energy limit",
# "rates",
# ]
# )
if veh_type.lower() in {"ldv", "ldt"}:
missed_vehicles = pd.DataFrame(columns=const.nhts_census_column_names)
elif veh_type.lower() in {"mdv", "hdv"}:
missed_vehicles = pd.DataFrame(
columns=list(newdata.columns)
+ [
"power_allowed",
"charging consumption",
"seg",
"power",
"segcum",
"energy limit",
"rates",
]
)

i = 0

Expand All @@ -296,13 +282,9 @@ def smart_charging(
individual = newdata.iloc[i : i + total_trips].copy()

individual_vmt += float(
individual.iloc[
0, individual.columns.get_loc("total vehicle miles traveled")
]
)
individual_trip_miles += float(
individual.iloc[:, individual.columns.get_loc("trip_miles")].sum()
individual["total vehicle miles traveled"].iloc[0]
)
individual_trip_miles += float(individual["trip_miles"].sum())

individual["rates"] = individual.apply(
lambda d: dwelling.get_rates(
Expand Down Expand Up @@ -444,14 +426,13 @@ def smart_charging(
else:
# collected for debugging if desired
optimization_fail += 1
# agg_col_set = set(missed_vehicles.columns.to_list())
# ind_col_set = set(individual.columns.to_list())
# print(ind_col_set - agg_col_set)
# print(missed_vehicles.index)
# print(individual.index)
# missed_vehicles = pd.concat(
# [missed_vehicles, individual], ignore_index=True
# )
missed_vehicles = pd.concat(
[
missed_vehicles,
individual.reset_index(inplace=True, drop=True),
],
ignore_index=True,
)
missed_vmt += individual["trip_miles"].sum()
if exitflag == 1:
flag_1_fail += 1
Expand Down Expand Up @@ -483,7 +464,7 @@ def smart_charging(
* bev_vmt
)

if debug_printout == 1:
if debug_printout is True:
output_check_array = (
outputelectricload
/ kwhmi
Expand Down Expand Up @@ -513,26 +494,26 @@ def smart_charging(
print(f"Exit Flag 3: {flag_3_fail}")
print(f"Exit Flag 4: {flag_4_fail}")

# print(
# missed_vehicles[
# [
# "dwell_time",
# "total vehicle miles traveled",
# "dwell_location",
# "vehicle_number",
# "location_allowed",
# "trip_allowed",
# "dwell_allowed",
# "charging_allowed",
# "dwell_charging",
# "required_charging",
# "max_charging",
# # ,"trip_number","Date","Day of Week",
# # "If Weekend","Trip start time (HHMM)","Trip end time (HHMM)","Travel Minutes",
# # "trip_miles","Vehicle miles traveled","why_from",
# ]
# ]
# )
print(
missed_vehicles[
[
"dwell_time",
"total vehicle miles traveled",
"dwell_location",
"vehicle_number",
"location_allowed",
"trip_allowed",
"dwell_allowed",
"charging_allowed",
"dwell_charging",
"required_charging",
"max_charging",
# ,"trip_number","Date","Day of Week",
# "If Weekend","Trip start time (HHMM)","Trip end time (HHMM)","Travel Minutes",
# "trip_miles","Vehicle miles traveled","why_from",
]
]
)

model_year_profile[trip_window_indices] += scaled_output

Expand Down

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