import os
import numpy as np
import pandas as pd
from prereise.gather.demanddata.bldg_electrification import const
[docs]def calculate_cop(temp_c, model):
cop_base, cr_base = _calculate_cop_base_cr_base(temp_c, model)
eaux = [max(0.75 - i, 0) for i in cr_base]
sumlist = [
(cr_base[i] + eaux[i]) / (cr_base[i] / cop_base[i] + eaux[i])
if cr_base[i] != 0
else 1
for i in range(len(cr_base))
]
cop = [1 if cr_base[i] == 0 else max(sumlist[i], 1) for i in range(len(cr_base))]
return cop
def _calculate_cop_base_cr_base(temp_c, model):
temp_k = [i + 273.15 for i in temp_c]
cop_base = [0] * len(temp_c)
cr_base = [0] * len(temp_c)
model_params = const.hp_param.set_index("model").loc[model]
T1_K = model_params.loc["T1_K"] # noqa: N806
COP1 = model_params.loc["COP1"] # noqa: N806
T2_K = model_params.loc["T2_K"] # noqa: N806
COP2 = model_params.loc["COP2"] # noqa: N806
T3_K = model_params.loc["T3_K"] # noqa: N806
COP3 = model_params.loc["COP3"] # noqa: N806
CR3 = model_params.loc["CR3"] # noqa: N806
a = model_params.loc["a"]
b = model_params.loc["b"]
c = model_params.loc["c"]
for i, temp in enumerate(temp_k):
if temp + b > 0:
cr_base[i] = a * np.log(temp + b) + c
if temp > T2_K:
cop_base[i] = ((COP1 - COP2) / (T1_K - T2_K)) * temp + (
COP2 * T1_K - COP1 * T2_K
) / (T1_K - T2_K)
if T3_K < temp <= T2_K:
cop_base[i] = ((COP2 - COP3) / (T2_K - T3_K)) * temp + (
COP3 * T2_K - COP2 * T3_K
) / (T2_K - T3_K)
if temp <= T3_K:
cop_base[i] = (cr_base[i] / CR3) * COP3
return cop_base, cr_base
[docs]def htg_to_cop(temp_c, model):
if model == "futurehp":
cop = calculate_cop(temp_c, model)
adv_cop = calculate_cop(temp_c, "advperfhp")
cop_final = [max(cop[i], adv_cop[i]) for i in range(len(cop))]
return cop_final
else:
return calculate_cop(temp_c, model)
[docs]def generate_htg_profiles(
yr_temps=const.base_year,
states=None,
bldg_class="res",
hp_model="advperfhp",
output_folder="Profiles",
):
"""Generate and write profiles on dist.
Create time series for electricity loads from converting
fossil fuel heating to electric heat pumps.
:param int yr_temps: year for temperature, defaults to ``const.base_year``.
:param list states: list of states to loop through, defaults to None, in which
case ``const.state_list`` is used.
:param str bldg_class: type of building.
:param str hp_model: type of heat pump.
:param str output_folder: location to store profiles (will be created if necessary).
:raises TypeError:
if ``yr_temps`` is not an int.
if ``bldg_class`` and ``hp_model`` are not str.
:raises ValueError:
if ``yr_temps`` is not one of the available temperature data year
if ``bldg_class`` is not 'res' or 'com'
if ``hp_model`` is not 'advperfhp', 'midperfhp' or 'futurehp'
"""
if not isinstance(yr_temps, int):
raise TypeError("yr_temps must be an int")
if states is None:
states = const.state_list
if not isinstance(bldg_class, str):
raise TypeError("bldg_class must be a str")
if not isinstance(hp_model, str):
raise TypeError("hp_model must be a str")
if yr_temps not in const.yr_temps_all:
raise ValueError(
"yr_temps must be among available temperature years: "
"{const.yr_temps_first}-{const.yr_temps_last}"
)
if bldg_class not in ["res", "com"]:
raise ValueError(
"bldg_class must be one of: \n", "res: residential \n", "com: commercial"
)
if hp_model not in ["advperfhp", "midperfhp", "futurehp"]:
raise ValueError(
"hp_model must be one of: \n",
"midperfhp: mid-performance cold climate heat pump \n",
"advperfhp: advanced performance cold climate heat pump \n",
"futurehp: future performance heat pump",
)
os.makedirs(output_folder, exist_ok=True)
# parse user data
temp_ref_it = const.temp_ref[bldg_class]
dir_path = os.path.dirname(os.path.abspath(__file__))
puma_slopes = pd.read_csv(
os.path.join(dir_path, "data", f"puma_slopes_ff_{bldg_class}.csv"),
index_col="puma",
)
# Loop through states to create profile outputs
for state in states:
# Load and subset relevant data for the state
puma_data_it = const.puma_data.query("state == @state")
puma_slopes_it = puma_slopes.query("state == @state")
temps_pumas_it = pd.read_csv(
f"https://besciences.blob.core.windows.net/datasets/bldg_el/pumas/{yr_temps}/temps/temps_pumas_{state}_{yr_temps}.csv"
)
# Compute electric HP loads from fossil fuel conversion
elec_htg_ff2hp_puma_mw_it_ref_temp = temps_pumas_it.applymap(
lambda x: max(temp_ref_it - x, 0)
)
elec_htg_ff2hp_puma_mw_it_func = temps_pumas_it.apply(
lambda x: np.reciprocal(htg_to_cop(x, hp_model))
)
elec_htg_ff2hp_puma_mw_it = elec_htg_ff2hp_puma_mw_it_ref_temp.multiply(
elec_htg_ff2hp_puma_mw_it_func
)
pumalist = (
puma_slopes_it[f"htg_slope_{bldg_class}_mmbtu_m2_degC"]
* puma_data_it[f"{bldg_class}_area_{const.base_year}_m2"]
* puma_data_it[f"frac_ff_sh_{bldg_class}_{const.base_year}"]
* (const.conv_mmbtu_to_kwh * const.conv_kw_to_mw)
* const.eff_htg_ff_base
)
elec_htg_ff2hp_puma_mw_it *= pumalist
# Export profile file as CSV
elec_htg_ff2hp_puma_mw_it.to_csv(
os.path.join(
os.path.dirname(__file__),
"Profiles",
f"elec_htg_ff2hp_{bldg_class}_{state}_{yr_temps}_{hp_model}_mw.csv",
),
index=False,
)
