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components.py
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components.py
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import logging
import numpy as np
import pandas as pd
from mswh.comm.label_map import SwhLabels
from mswh.tools.unit_converters import UnitConv
log = logging.getLogger(__name__)
class Converter(object):
"""Contains energy converter models, such as
solar collectors, electric resistance heaters, gas burners,
photovoltaic panels, and heat pumps. Depending on the intended
usage, the models can be used to determine either a time period
of component operation (for example an entire year), or a single
timestep of component performance.
Parameters:
params: pd df
Component performance parameters per project
Default: None (default model parameters will get used)
weather: pd df
Weather data timeseries with columns: amb. temp,
solar irradiation. Number of rows equals the number of timesteps.
Default: None (constant values will be set - use for
a single timestep calculation, or if passing arguments
directly to static methods)
sizes: pd df
Component sizes per project.
Default: 1. (see individual components for specifics)
log_level: None or python logger logging level,
Default: logging.DEBUG
This applies for a subset of the class functionality, mostly
used to deprecate logger messages for certain calculations.
For Example: log_level = logging.ERROR will only throw error
messages and ignore INFO, DEBUG and WARNING.
Note:
If more than one of the same component is a part of the
system, a separate instance of the converter should
be created for each instance of the component.
Each component is also implemented as a static method that
can be used outside of this framework.
Examples:
See :func:`mswh.system.tests.test_components <mswh.system.tests.test_components>` module and
:func:`scripts/Project Level MSWH System Tool.ipynb <scripts/Project Level MSWH System Tool.ipynb>`
for examples on how to use the methods as stand alone and
in a system model simulation.
"""
def __init__(
self, params=None, weather=None, sizes=1.0, log_level=logging.DEBUG
):
# log level (e.g. only partial functionality of the class
# is being used and one does not desire to see all infos)
self.log_level = log_level
logging.getLogger().setLevel(log_level)
# extract labels
self.c = SwhLabels().set_hous_labels()
self.s = SwhLabels().set_prod_labels()
self.r = SwhLabels().set_res_labels()
if isinstance(params, pd.DataFrame):
self.use_defaults = False
# extract components and their performance parameters
self.components = []
# extract components provided in params
components = params[self.s["comp"]].unique().tolist()
if self.s["sol_col"] in components:
self.components.append(self.s["sol_col"])
self.params_sol_col = dict()
# this method of collector model selection prefers the
# model under ```try:``` as long as the parameters were
# found in the parameter table
try: # HWB
self.params_sol_col[self.s["interc_hwb"]] = params.loc[
params[self.s["param"]] == self.s["interc_hwb"],
self.s["param_value"],
].values[0]
self.params_sol_col[self.s["slope_hwb"]] = params.loc[
params[self.s["param"]] == self.s["slope_hwb"],
self.s["param_value"],
].values[0]
self.solar_model = "HWB"
except: # CD
self.params_sol_col[self.s["interc_cd"]] = params.loc[
params[self.s["param"]] == self.s["interc_cd"],
self.s["param_value"],
].values[0]
self.params_sol_col[self.s["a1_cd"]] = params.loc[
params[self.s["param"]] == self.s["a1_cd"],
self.s["param_value"],
].values[0]
self.params_sol_col[self.s["a2_cd"]] = params.loc[
params[self.s["param"]] == self.s["a2_cd"],
self.s["param_value"],
].values[0]
self.solar_model = "CD"
if self.s["pv"] in components:
self.components.append(self.s["pv"])
self.params_pv = dict()
# Extract the model parameters
self.params_pv[self.s["eta_pv"]] = params.loc[
params[self.s["param"]] == self.s["eta_pv"],
self.s["param_value"],
].values[0]
self.params_pv[self.s["f_act"]] = params.loc[
params[self.s["param"]] == self.s["f_act"],
self.s["param_value"],
].values[0]
self.params_pv[self.s["irrad_ref"]] = params.loc[
params[self.s["param"]] == self.s["irrad_ref"],
self.s["param_value"],
].values[0]
msg = "Photovoltaic is setup."
log.info(msg)
if self.s["inv"] in components:
self.components.append(self.s["inv"])
self.params_inv = dict()
# extract the total dc-ac conversion efficiency
self.params_inv[self.s["eta_dc_ac"]] = params.loc[
params[self.s["param"]] == self.s["eta_dc_ac"],
self.s["param_value"],
].values[0]
msg = "Inverter is setup."
log.info(msg)
if self.s["hp"] in components:
self.components.append(self.s["hp"])
self.params_hp = dict()
# Extract the model parameters
self.params_hp[self.s["c1_cop"]] = params.loc[
params[self.s["param"]] == self.s["c1_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c2_cop"]] = params.loc[
params[self.s["param"]] == self.s["c2_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c3_cop"]] = params.loc[
params[self.s["param"]] == self.s["c3_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c4_cop"]] = params.loc[
params[self.s["param"]] == self.s["c4_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c5_cop"]] = params.loc[
params[self.s["param"]] == self.s["c5_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c6_cop"]] = params.loc[
params[self.s["param"]] == self.s["c6_cop"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c1_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c1_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c2_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c2_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c3_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c3_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c4_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c4_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c5_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c5_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["c6_heat_cap"]] = params.loc[
params[self.s["param"]] == self.s["c6_heat_cap"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["heat_cap_rated"]] = params.loc[
params[self.s["param"]] == self.s["heat_cap_rated"],
self.s["param_value"],
].values[0]
self.params_hp[self.s["cop_rated"]] = params.loc[
params[self.s["param"]] == self.s["cop_rated"],
self.s["param_value"],
].values[0]
msg = "Heat pump is setup."
log.info(msg)
if self.s["el_res"] in components:
self.components.append(self.s["el_res"])
self.params_el_res = dict()
# Extract electric resistance parameters
self.params_el_res[self.s["eta_el_res"]] = params.loc[
params[self.s["param"]] == self.s["eta_el_res"],
self.s["param_value"],
].values[0]
if self.s["gas_burn"] in components:
self.components.append(self.s["gas_burn"])
self.params_gas_burn = dict()
# Extract gas burner parameters
self.params_gas_burn[self.s["comb_eff"]] = params.loc[
params[self.s["param"]] == self.s["comb_eff"],
self.s["param_value"],
].values[0]
# when adding components, extract parameters similarly
elif not isinstance(params, pd.DataFrame):
self.use_defaults = True
# extract component size/capacity (see setter for details)
self.size = sizes
# extract weather and irradiation data
self.weather = weather
@property
def weather(self):
return self.__weather
@weather.setter
def weather(self, value):
"""Re-extracts weather timeseries if a new weather dataset
is assigned to an instantiated class object
"""
self.__weather = value
if isinstance(value, pd.DataFrame):
self.t_amb = UnitConv(
self.weather[self.c["t_amb_C"]].values
).degC_K(unit_in="degC")
self.inc_rad = self.weather[self.c["irrad_on_tilt"]].values
msg = "Assigned weather data timeseries."
log.info(msg)
elif value is None:
self.t_amb = 293.15 # K
self.inc_rad = 800 # W
msg = (
"No weather data got passed to converters. "
"Setting default scalar values for ambient temperature, "
"{}, and solar irradiation, {}."
)
log.info(msg.format(self.t_amb, self.inc_rad))
@property
def size(self):
return self.__size
@size.setter
def size(self, value):
"""Re-extracts sizes from a dataframe"""
set_sizes = dict()
if (not isinstance(value, pd.DataFrame)) and (value == 1.0):
# assign unit size
set_sizes = value
elif isinstance(value, pd.DataFrame):
if self.s["gas_tank"] in self.components:
set_sizes[self.s["gas_tank"]] = value.loc[
value[self.s["comp"]] == self.s["gas_tank"], self.s["cap"]
].values[0]
if self.s["sol_col"] in self.components:
set_sizes[self.s["sol_col"]] = value.loc[
value[self.s["comp"]] == self.s["sol_col"], self.s["cap"]
].values[0]
if self.s["pv"] in self.components:
set_sizes[self.s["pv"]] = value.loc[
value[self.s["comp"]] == self.s["pv"], self.s["cap"]
].values[0]
if self.s["hp"] in self.components:
set_sizes[self.s["hp"]] = value.loc[
value[self.s["comp"]] == self.s["hp"], self.s["cap"]
].values[0]
if self.s["el_res"] in self.components:
set_sizes[self.s["el_res"]] = value.loc[
value[self.s["comp"]] == self.s["el_res"], self.s["cap"]
].values[0]
if self.s["gas_burn"] in self.components:
try:
set_sizes[self.s["gas_burn"]] = value.loc[
value[self.s["comp"]] == self.s["gas_burn"],
self.s["cap"],
].values[0]
except:
set_sizes[self.s["gas_burn"]] = None
msg = (
"Could not find the size for the "
"gas instantaneous water heater, "
"Setting size to infinite."
)
log.info(msg)
else:
msg = "Provided sizes format is not supported."
log.error(msg)
raise ValueError
self.__size = set_sizes
def heat_pump(self, T_wet_bulb, T_tank):
"""Returns the current heating performance and electricity usage
in the current conditions depending on wet bulb temperature,
average tank water temperature, and the rated heating performance.
Rated conditions are: wet bulb = 14 degC, tank = 48.9 degC
Parameters:
T_wet_bulb: real, array
Inlet air wet bulb temperature [K]
T_tank: real, array
Water temperature in the storage tank [K]
C1: real
Coefficient 1, either for normalized COP or heating
capacity curve [-]
C2: real
Coefficient 2, either for normalized COP or heating
capacity curve [1/degC]
C3: real
Coefficient 3, either for normalized COP or heating
capacity curve [1/degC2]
C4: real
Coefficient 4, either for normalized COP or heating
capacity curve [1/degC]
C5: real
Coefficient 5, either for normalized COP or heating
capacity curve [1/degC2]
C6: real
Coefficient 6, either for normalized COP or heating
capacity curve [1/degC2]
Returns:
performance: dict
* 'cop': current Coefficient Of Performance (COP), [-]
* 'heat_cap': current heating capacity of heat pump, [W]
* 'el_use': current electricity use of heat pump [W]
"""
# Set rated heating capacity
heat_cap_rated = self.params_hp[self.s["heat_cap_rated"]]
# Set rated COP (coefficient of performance)
cop_rated = self.params_hp[self.s["cop_rated"]]
# Calculate actual heating capacity under current conditions
# (T_wet_bulb and T_tank)
heat_cap = heat_cap_rated * self._heat_pump(
T_wet_bulb,
T_tank,
self.params_hp[self.s["c1_heat_cap"]],
self.params_hp[self.s["c2_heat_cap"]],
self.params_hp[self.s["c3_heat_cap"]],
self.params_hp[self.s["c4_heat_cap"]],
self.params_hp[self.s["c5_heat_cap"]],
self.params_hp[self.s["c6_heat_cap"]],
)
# if the temperature difference between the tank and the
# ambient is large (e.g. an outside tank in a cold climate)
# negative heat_cap values may occur based on the
# equation in _heat_pump. Assuming that the device is
# disabled at those times, we impose a lower limit at 0:
if isinstance(heat_cap, np.ndarray):
heat_cap[heat_cap < 0.0] = 0.0
elif isinstance(heat_cap, float):
heat_cap = abs(heat_cap * (heat_cap > 0))
# Calculate actual COP under current conditions
# (T_wet_bulb and T_tank)
cop = cop_rated * self._heat_pump(
T_wet_bulb,
T_tank,
self.params_hp[self.s["c1_cop"]],
self.params_hp[self.s["c2_cop"]],
self.params_hp[self.s["c3_cop"]],
self.params_hp[self.s["c4_cop"]],
self.params_hp[self.s["c5_cop"]],
self.params_hp[self.s["c6_cop"]],
)
# Dictionary containing the results
res = {}
res["cop"] = cop
res["heat_cap"] = heat_cap
res["el_use"] = heat_cap / cop
return res
@staticmethod
def _heat_pump(
T_wet_bulb,
T_tank,
C1=1.229e00,
C2=5.549e-02,
C3=1.139e-04,
C4=-1.128e-02,
C5=-3.570e-06,
C6=-7.234e-04,
):
"""Heat pump model. Source:
B. Sparn, K. Hudon, and D. Christensen, “Laboratory Performance Evaluation of Residential Integrated Heat Pump Water Heaters,” Renew. Energy, p. 77, 2014.
https://www1.eere.energy.gov/buildings/publications/pdfs/building_america/evaluation_hpwh.pdf
Parameters:
T_wet_bulb: real, array
Inlet air wet bulb temperature [K]
T_tank: real, array
Water temperature in the storage tank [K]
C1: real
Coefficient 1, either for normalized COP or heating capacity
curve [-]
C2: real
Coefficient 2, either for normalized COP or heating capacity
curve [1/degC]
C3: real
Coefficient 3, either for normalized COP or heating capacity
curve [1/degC^2]
C4: real
Coefficient 4, either for normalized COP or heating capacity
curve [1/deg^C]
C5: real
Coefficient 5, either for normalized COP or heating capacity
curve [1/degC^2]
C6: real
Coefficient 6, either for normalized COP or heating capacity
curve [1/degC^2]
Returns:
performance: real
Performance factor
"""
# The formula needs temperatures in Celsius
T_wet_bulb_C = UnitConv(T_wet_bulb).degC_K(unit_in="K")
T_tank_C = UnitConv(T_tank).degC_K(unit_in="K")
# Calculate performance factor
performance = (
C1
+ C2 * T_wet_bulb_C
+ C3 * T_wet_bulb_C * T_wet_bulb_C
+ C4 * T_tank_C
+ C5 * T_tank_C * T_tank_C
+ C6 * T_wet_bulb_C * T_tank_C
)
return performance
def electric_resistance(self, Q_dem):
"""Electric resistance heater model. Can be
used both as an instantaneous electric WH and as
an auxiliary heater within the thermal tank.
Parameters:
Q_dem: float or array like, [W]
Heat demand
Returns:
res: dict
* self.r['q_del_bckp'] : float,
array - delivered heat rate, [W]
* self.r['q_el_use'] : float,
array - electricity use, [W]
* self.r['q_unmet'] : float,
array - unmet demand heat rate, [W]
"""
# return the heat rates for:
# delivered heat, electricity use, and unmet demand
Q_del, P_el_use, Q_unmet = self._heater(
Q_dem,
Q_nom=self.size[self.s["el_res"]],
eff=self.params_el_res[self.s["eta_el_res"]],
)
# return the heat rate of heat delivered and gas consumed
res = {
self.r["q_del_bckp"]: Q_del,
self.r["el_use"]: P_el_use,
self.r["q_unmet"]: Q_unmet,
}
return res
def gas_burner(self, Q_dem):
"""Gas burner model. Used both
as an instantaneous gas WH and as a
gas backup for solar thermal.
Parameters:
Q_dem: float or array like, W
Heat demand
Returns:
res: dict
* self.r['q_del_bckp'] : float,
array - delivered heat rate, [W]
* self.r['q_gas_use'] : float, array - gas use heat rate, [W]
* self.r['q_unmet'] : float, array -
unmet demand heat rate, [W]
Any further unit conversion should be performed
using unit_converters.Utility class
"""
# return the heat rates for:
# delivered heat, gas use, and unmet demand
Q_del, Q_en_use, Q_unmet = self._heater(
Q_dem,
eff=self.params_gas_burn[self.s["comb_eff"]],
Q_nom=self.size[self.s["gas_burn"]],
)
# return the heat rate of heat delivered and gas consumed
res = {
self.r["q_del_bckp"]: Q_del,
self.r["gas_use"]: Q_en_use,
self.r["q_unmet"]: Q_unmet,
}
return res
@staticmethod
def _heater(Q_dem, eff=0.85, Q_nom=None):
"""Simplified efficiency based model that can be
used for an in-tank main or auxiliary gas and
electric resistance heater.
Parameters:
Q_dem: float or array like, W
Heat demand
eff: float
Energy conversion efficiency, such as
combustion or electric resistance
Q_nom: float, W
Nominal capacity.
Default: None - infinite capacity
so that the heater can cover any load
Returns:
Q_del: float, array
Delivered heat rate, [W]
Q_gas_use: float, array
Energy (gas, electricity) use heat rate, [W]
Q_unmet: float, array
Unmet demand heat rate, [W]
"""
# start with assuming the heater capacity is infinite
Q_del = Q_dem + 0.0
# limit the delivery if the heater has a limited capacity
if Q_nom is not None:
if not np.isscalar(Q_dem):
Q_del[Q_del > Q_nom] = Q_nom
elif np.isscalar(Q_dem):
Q_del = min(Q_dem, Q_nom)
else:
msg = "Heater demand data type {} seems not supported."
log.error(msg.format(type(Q_dem)))
raise ValueError
# Unmet demand
Q_unmet = Q_dem - Q_del
# Gas consumption (heat rate in W, use unit_converters.Utility class
# for further conversions)
Q_en_use = Q_del / eff
return Q_del, Q_en_use, Q_unmet
def solar_collector(self, t_in, t_amb=None, inc_rad=None):
"""Two commonly used empirical instantaneous collector
efficiency models based on test data from standard
test procedures (SRCC, ISO9806), found in
J. A. Duffie and W. A. Beckman, Solar engineering of thermal processes, 3rd ed. Hoboken, N.J: Wiley, 2006., are:
* Cooper and Dunkle (CD model, eq 6.17.7)
* Hottel-Whillier-Bliss (HWB model, eq 6.16.1, 6.7.6)
Parameters:
t_in: float, array
Collector inlet temperature (timeseries) [K]
t_amb: float, array
Ambient temperature (timeseries) [K]
Default: None (to use data extracted from the weather df)
inc_rad: float, array
Incident radiation (timeseries) [W]
Default: None (to use data extracted from the weather df)
Returns:
res: dict or floats or arrays
{'Q_gain' : Solar gains from the gross collector area, [W]
'eff' : Efficiency of solar to heat conversion, [-]
"""
try:
gross_area = self.size[self.s["sol_col"]]
except:
gross_area = 1.0
msg = "Could not extract collector size. " "Setting it to {}."
log.info(msg.format(gross_area))
# if t_in is output of the tank model, solar collector
# model needs to be simulated step by step. In that
# case the timestep ambient temperature and incident solar
# radiation should be passed directly to this method
if t_amb is None:
msg = (
"Using ambient temperature array to get solar "
"collector gains. This will result in an array calculation."
)
log.info(msg)
t_amb = self.t_amb
if inc_rad is None:
msg = (
"Using irradiation array to get solar collector"
" gains. This will result in an array calculation."
)
log.info(msg)
inc_rad = self.inc_rad
if self.use_defaults:
msg = (
"Solar collector parameters have not been passed to the"
" component model. Using HWB model with default parameters."
)
log.info(msg)
self.sol_col_gain, self.sol_col_eff = self._hwb_solar_collector(
gross_area, inc_rad, t_amb, t_in
)
# based on the keywords in self.params call one or the other method
elif self.solar_model == "HWB":
self.sol_col_gain, self.sol_col_eff = self._hwb_solar_collector(
gross_area,
inc_rad,
t_amb,
t_in,
intercept=self.params_sol_col[self.s["interc_hwb"]],
slope=self.params_sol_col[self.s["slope_hwb"]],
)
elif self.solar_model == "CD":
self.sol_col_gain, self.sol_col_eff = self._cd_solar_collector(
gross_area,
inc_rad,
t_amb,
t_in,
intercept=self.params_sol_col[self.s["interc_cd"]],
a_1=self.params_sol_col[self.s["a1_cd"]],
a_2=self.params_sol_col[self.s["a2_cd"]],
)
if not isinstance(t_in, float):
msg = "\nCalculated solar collector gain time series.\n"
log.info(msg)
res = {"Q_gain": self.sol_col_gain, "eff": self.sol_col_eff}
return res
@staticmethod
def _hwb_solar_collector(
gross_area, inc_rad, t_amb, t_in, intercept=0.753, slope=-4.025
):
"""HWB based model as applied in test procedures
used in SRCC Standard 100-2006-09 (ASHRAE 93)
Default parameters: Heliodyne, Inc, GOBI 410 001 Plus
Parameters:
gross_area: float
Gross collector area [m2]
inc_rad: float or array like
Global solar radiation on 1 m2 of the
collector tilted surface [W/m2]
t_amb: float or array like
Ambient temperature (timeseries) [K or degC]
t_in: float or array like
Collector inlet temperature (timeseries)
[use same unit as t_amb]
intercept: float
Rating parameter
slope: float
Rating parameter
Returns:
solar_gain: float, array
Solar gains from the gross collector area [W]
conversion_efficiency: float, array
Conversion efficiency [-]
"""
# msg = 'Allow div 0.'
# log.debug(msg)
# avoid division by zero by creating a copy
# of the irradiation data with infinity
# instead of zero (see efficiency formula)
if not np.isscalar(inc_rad):
inc_rad_mod = inc_rad
inc_rad_mod[inc_rad == 0] = -np.inf
elif np.isscalar(inc_rad):
if inc_rad == 0.0:
inc_rad_mod = -np.inf
else:
inc_rad_mod = inc_rad
else:
msg = "Solar irradiation data type {} seems not supported."
log.error(msg.format(type(inc_rad)))
raise ValueError
# instantaneous collector efficiency, [-]
eta = intercept * (inc_rad != 0.0) + slope * (
(t_in - t_amb) / inc_rad_mod
)
# instantaneous solar gain, [W]
calc_gain = inc_rad * gross_area * eta
# set negative gains that the model may yield at
# cold weather to zero
if isinstance(calc_gain, np.ndarray):
calc_gain[calc_gain < 0.0] = 0.0
gain = calc_gain
elif isinstance(calc_gain, float):
gain = calc_gain * (calc_gain > 0)
return gain, eta
@staticmethod
def _cd_solar_collector(
gross_area,
inc_rad,
t_amb,
t_in,
intercept=0.75,
a_1=-3.688,
a_2=-0.0055,
):
"""CD based model as applied in test procedures
used in SRCC Standard 100-2006-09 (ISO 12975 with dT = Tin - Tamb)
Default parameters: `Heliodyne, Inc, GOBI 410 001 Plus <https://secure.solar-rating.org/Certification/Ratings/RatingsReport.aspx?device=6931&units=METRICS>`_
Parameters:
gross_area: float
Gross collector area [m2]
inc_rad: float, array
Global solar radiation on 1 m2 of the
collector tilted surface [W/m2]
t_amb: float, array
Ambient temperature (timeseries) [K or degC]
t_in: float, array
Collector inlet temperature (timeseries)
[use same unit as t_amb]
intercept: float
Rating parameter
a_1: float
Rating parameter
a_2: float
Rating parameter
"""
# avoid division by zero by creating a copy
# of the irradiation data with infinity
# instead of zero (see efficiency formula)
if not np.isscalar(inc_rad):
inc_rad_mod = inc_rad
inc_rad_mod[inc_rad == 0] = -np.inf
elif np.isscalar(inc_rad):
if inc_rad == 0.0:
inc_rad_mod = -np.inf
else:
inc_rad_mod = inc_rad
else:
msg = "Solar irradiation data type {} seems not supported."
log.error(msg.format(type(inc_rad)))
raise ValueError
# instantaneous collector efficiency, [-]
eta = (
intercept * (inc_rad != 0.0)
+ a_1 * ((t_in - t_amb) / inc_rad_mod)
+ a_2 * ((t_in - t_amb) / inc_rad_mod ** 2)
)
# instantaneous solar gain, [W]
calc_gain = inc_rad * gross_area * np.nan_to_num(eta)
# set negative gains that the model may yield at
# cold weather to zero
if isinstance(calc_gain, np.ndarray):
calc_gain[calc_gain < 0.0] = 0.0
gain = calc_gain
elif isinstance(calc_gain, float):
gain = calc_gain * (calc_gain > 0)
return gain, eta
def photovoltaic(self, use_p_peak=True, inc_rad=None):
"""Photovoltaic model
Parameters:
use_p_peak: boolean
Boolean flag determining if peak power is used for sizing
the pv panel (instead of area and efficiency)
Returns:
self.pv_power: dict of floats
Generated power [W]
* 'ac' : AC
* 'dc' : DC
"""
try:
panel_size = self.size[self.s["pv"]]
except:
# default to 1000. kW_peak or it's equivalent in m2 for
# default efficiency
panel_size = 1000.0 if use_p_peak else 6.25
log.info(
"Could not get panel size. Setting it to {}".format(panel_size)
)
# Set panel size according to use_p_peak value
if use_p_peak:
p_peak = panel_size
panel_area = None
# Uncomment this line, since it creates too much output
# for system level simulation
# log.info('Using peak power as a PV size parameter.')
else:
p_peak = None
panel_area = panel_size
# Uncomment this line, since it creates too much output
# for system level simulation
# log.info('Using area as a PV size parameter.')
if inc_rad is None:
msg = (
"Using irradiation array to get photovoltaic"
" gains. This will result in an array calculation."
)
log.info(msg)
inc_rad = self.inc_rad
# if no input parameters have been passed to the class
if self.use_defaults:
msg = (
"Photovoltaic parameters have not been passed to the"
" component model. Using default parameters."
)
log.info(msg)
self.pv_power = self._simple_photovoltaic(
irrad=inc_rad, panel_area=panel_area, p_peak=p_peak
)
# pass parameters from the param input dataframe
else:
self.pv_power = self._simple_photovoltaic(
irrad=inc_rad,
panel_area=panel_area,
f_act=self.params_pv[self.s["f_act"]],
eta_pv=self.params_pv[self.s["eta_pv"]],
eta_dc_ac=self.params_inv[self.s["eta_dc_ac"]],
irrad_ref=self.params_pv[self.s["irrad_ref"]],
p_peak=p_peak,
)
return self.pv_power
@staticmethod
def _simple_photovoltaic(
irrad,
p_peak=None,
panel_area=None,
f_act=1.0,
eta_pv=0.16,
eta_dc_ac=0.85,
irrad_ref=1000.0,
):
"""Simple photovoltaic model based on
http://simulationresearch.lbl.gov/modelica/releases/latest/help/Buildings_Electrical_AC_OnePhase_Sources.html#Buildings.Electrical.AC.OnePhase.Sources.PVSimple
Parameters:
irrad: float
Total solar irradiation (direct and diffuse) [W/m2]
panel_area: float or None
Panel area (area of active cells) [m2].
Set to None if using the peak power as a PV sizing variable.
p_peak: float or None
Peak power of the photovoltaic panel
(also: nominal power, nameplate size) [W]
Set to None if using the panel area as a PV sizing variable.
irrad_ref: float
Reference irradiation of the photovoltaic panel
(default: 1000 W/m2) [W/m2]