def __init__(self):
# Integrate simulatable class for time indexing
Simulatable.__init__(self)
# Integrate load demand data_loader for csv load profile integration
self.load_demand = data_loader.LoadDemand()
#Initialize power of load profile
self.power = 0
self.load_data = None
def __init__(self,
timestep,
peak_power,
controller_type,
env,
file_path=None):
# Read component parameters from json file
if file_path:
self.load(file_path)
else:
print('Attention: No json file for photovoltaic model specified')
self.controller_method = "mppt" # [-] Specification of photvoltaic charge controller (needed to call apropriate power method)
self.temperature_a = -3.47 # [0] Thermal model: Numeric parameter a of Sandia PV Array Performance Model
self.temperature_b = -0.0594 # [0] Thermal model: Numeric parameter b of Sandia PV Array Performance Model
self.temperature_deltaT = 3 # [0] Thermal model: Numeric parameter dT of Sandia PV Array Performance Model
self.params_name = "A10Green_Technology_A10J_S72_185" # [-] Photovoltaic module specification
self.params_V_mp_ref = 36.72 # [V] Photovoltaic voltage at MPP
self.params_alpha_sc = 0.002253 # [A/C] Short-circuit current temperature coefficient
self.params_a_ref = 1.98482 # [V] Product of the usual diode ideality factor (n, unitless), number of cells in series (Ns), and cell thermal voltage at reference conditions
self.params_I_L_ref = 5.43568 # [A] The light-generated current (or photocurrent) at reference conditions
self.params_I_o_ref = 1.16164e-09 # [A] The dark or diode reverse saturation current at reference conditions
self.params_R_sh_ref = 298.424 # [Ohm] The shunt resistance at reference conditions
self.params_R_s = 0.311962 # [Ohm] The series resistance at reference conditions
self.params_pdc0 = 184.7016 # [W] Photovoltaic power of the modules at 1000 W/m2 and cell reference temperature
self.params_gamma_pdc = -0.005 # [1/C] The temperature coefficient. Typically -0.002 to -0.005
self.degradation_pv = 1.58154e-10 # [1/s] Photovoltaic degradation per second: deg_yearly=0.5% --> 0.005 / (8760*3600)
self.investment_costs_specific = 0.8 # [$/Wp] Photovoltaic specific investment costs
# Integrate simulatable class for time indexing
Simulatable.__init__(self)
# Integrate environment class
self.env = env
# Charge controller type
self.controller_type = controller_type
# [s] Timestep
self.timestep = timestep
## Basic parameters
self.peak_power = peak_power
# [W] Current PV peak power dependent on aging
self.peak_power_current = self.peak_power
# [V] Battery voltage for PWM PV model
self.battery_voltage = 12
## PV aging model
# [W] End-of-Life condition of PV module
self.end_of_life_photovoltaic = 0.8 * self.peak_power #eingelesen
## Economic model
# Nominal installed pv size for economic calculation
self.size_nominal = self.peak_power
def __init__(self,
input_link_1,
input_link_2,
load):
# Integrate simulatable class and connected component classes
Simulatable.__init__(self)
self.load = load
self.input_link_1 = input_link_1
self.input_link_2 = input_link_2
# Initialize power flow of junction
self.power = 0
def __init__(self,
timestep,
peak_power,
env,
file_path=None):
# Read component parameters of wind turbine from json file
if file_path:
self.load(file_path)
else:
print('Attention: No json file for wind turbine model specified')
self.turbine_type = "Enair 30PRO" # [-] Wind turbine specification
self.hub_height = 13 # [m] The height of the hub
self.diameter = 3.8 # [m] The diameter of the rotor
self.nominal_power = 2500 # [m] Nominal turbine power
self.power_curve_data = {"value": [0.0, 10.0, 100.0, 300.0, 1000.0, 1450.0, 1850.0, 2100.0, 2300.0, 2500.0, 2500.0, 2500.0, 2500.0],
"wind_speed": [2.0, 3.0, 4.0, 5.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]}
self.degradation = 5.0736e-10 # [1/s] Wind turbine degradation per second: deg_yearly=1.6%
self.investment_costs_specific = 2.5168 # [$/Wp] Wind turbine specific investment costs
# Transfer power curve data into DataFrame
self.power_curve = pd.DataFrame(self.power_curve_data)
# Integrate simulatable class for time indexing
Simulatable.__init__(self)
# [s] Timestep
self.timestep = timestep
# Integrate environment class
self.env = env
## Basic parameters
self.peak_power = peak_power
# [W] Current PV peak power dependent on aging
self.peak_power_current = self.peak_power
## Wind turbine aging model
# [W] End-of-Life condition of wind turbine module
self.end_of_life_wind_turbine = 0.7 * self.peak_power
## Wind turbine economic model
# Nominal installed wind turbine size for economic calculation
self.size_nominal = self.peak_power
def __init__(self, timestep, power_nominal, input_link, file_path=None):
# Read component parameters from json file
if file_path:
self.load(file_path)
else:
print(
'Attention: No json file for power component efficiency specified'
)
self.specification = "MPPT_HQST_40A_12V_34V" # [-] Specification of power component
self.efficiency_nominal = 0.951 # [1] Nominal power component efficeincy
self.voltage_loss = 0.009737 # [-] Dimensionless parameter for component model
self.resistance_loss = 0.031432 # [-] Dimensionless parameter for component model
self.power_self_consumption = 0.002671 # [-] Dimensionless parameter for component model
self.end_of_life_power_components = 315360000 # [s] End of life time in seconds
self.investment_costs_specific = 0.036 # [$/Wp] Specific investment costs
# Integrate Serializable for serialization of component parameters
Serializable.__init__(self) # not needed !?
# Integrate Simulatable class for time indexing
Simulatable.__init__(self) # not needed !?
# Integrate input power
self.input_link = input_link
# [s] Timestep
self.timestep = timestep
#Calculate star parameters of efficeincy curve
self.voltage_loss_star = self.voltage_loss
self.resistance_loss_star = self.resistance_loss / self.efficiency_nominal
self.power_self_consumption_star = self.power_self_consumption * self.efficiency_nominal
## Power model
# Initialize power
self.power = 0
# set nominal power of power component
self.power_nominal = power_nominal
## Economic model
# Nominal installed component size for economic calculation
self.size_nominal = power_nominal
def __init__(self, timestep, power_nominal, input_link, file_path=None):
'''
Parameters
----------
timestep: int. Simulation timestep in seconds
power_nominal : int. Nominal power of power component in watt [W]
input_link : class. Class of component which supplies input power
file_path : json file to load power component parameters
'''
# Read component parameters from json file
if file_path:
self.load(file_path)
else:
print(
'Attention: No json file for power component efficiency specified'
)
self.specification = "Generic system controller" # [-] Specification of power component
self.end_of_life_power_components = 315360000 # [s] End of life time in seconds
self.investment_costs_specific = 0.036 # [$/Wp] Specific investment costs
# Integrate Serializable for serialization of component parameters
Serializable.__init__(self) # not needed !?
# Integrate Simulatable class for time indexing
Simulatable.__init__(self) # not needed !?
# Integrate input power
self.input_link = input_link
# [s] Timestep
self.timestep = timestep
## Power model
# Initialize power
self.power = 0
# set nominal power of power component
self.power_nominal = power_nominal
## Economic model
# Nominal installed component size for economic calculation
self.size_nominal = power_nominal
def __init__(self, simulation_steps, timestep, pv_peak_power,
wt_nominal_power, battery_capacity):
'''
Parameters can be defined externally or inside class
----------
pv_peak_power : int. Installed phovoltaic peak power in Watt peak [Wp]
battery capacity : int. Installed nominal battery capacity in Watthours [Wh]
pv_orientation : tuble of floats. PV oriantation with:
1. tuble entry pv azimuth in degrees from north [°] (0°=north, 90°=east, 180°=south, 270°=west).
2. tuble entry pv inclination in degrees from horizontal [°]
system_location : tuble of floats. System location coordinates:
1. tuble entry system longitude in degrees [°]
2. tuble entry system latitude in degrees [°]
simulation_steps : int. Number of simulation steps
timestep: int. Simulation timestep in seconds
'''
#%% Define simulation settings
# System specifications
# [Wp] Installed PV power
self.pv_peak_power = pv_peak_power
# [Wp] Installed nominal Wind power
self.wt_nominal_power = wt_nominal_power
# [Wh] Installed battery capacity
self.battery_capacity = battery_capacity
# PV orientation : tuble of floats. PV oriantation with:
# 1. pv azimuth in degrees [°] (0°=north, 90°=east, 180°=south, 270°=west). & 2. pv inclination in degrees [°]
self.pv_orientation = (0, 0)
# System location
# Latitude: Positive north of equator, negative south of equator.
# Longitude: Positive east of prime meridian, negative west of prime meridian.
self.system_location = pvlib.location.Location(
latitude=-3.386925,
longitude=36.682995,
tz='Africa/Dar_es_Salaam',
altitude=1400)
## Define simulation time parameters
# Number of simulation timesteps
self.simulation_steps = simulation_steps
# [s] Simulation timestep
self.timestep = timestep
#%% Initialize classes
# load class
self.load = Load()
## Photovoltaic system componnets
# Environment class
self.env = Environment(timestep=self.timestep,
system_orientation=self.pv_orientation,
system_location=self.system_location)
# Photovoltaic panel
self.pv = Photovoltaic(
timestep=self.timestep,
peak_power=self.pv_peak_power,
controller_type='mppt',
env=self.env,
file_path='data/components/photovoltaic_resonix_120Wp.json')
# Photovoltaic charge controller
self.pv_charger = Power_Component(
timestep=self.timestep,
power_nominal=self.pv_peak_power,
input_link=self.pv,
file_path='data/components/power_component_mppt_pv.json')
## Windturbine system components
# Environment class - WIND
#self.env_wt = Environment_Wind(timestep=self.timestep)
# Windturbine
self.wt = Wind_Turbine(
timestep,
peak_power=self.wt_nominal_power,
env=self.env,
file_path='data/components/windturbine_Enair_30PRO.json')
self.wt_charger = Power_Component(
timestep=self.timestep,
power_nominal=self.wt_nominal_power,
input_link=self.wt,
file_path='data/components/power_component_mppt_wt.json')
## Central power knot
self.power_junction = Power_Junction(input_link_1=self.pv_charger,
input_link_2=self.wt_charger,
load=self.load)
## Battery system components
self.battery_management = Power_Component(
timestep=self.timestep,
power_nominal=self.pv_peak_power,
input_link=self.power_junction,
file_path='data/components/power_component_bms.json')
self.battery = Battery(timestep=self.timestep,
capacity_nominal_wh=self.battery_capacity,
input_link=self.battery_management,
env=self.env,
file_path='data/components/battery_lfp.json')
## Initialize Simulatable class and define needs_update initially to True
self.needs_update = True
Simulatable.__init__(self, self.load, self.env, self.pv,
self.pv_charger, self.wt, self.wt_charger,
self.power_junction, self.battery_management,
self.battery)
def __init__(self,
timestep,
capacity_nominal_wh,
input_link,
env,
file_path=None):
# Read component parameters from json file
if file_path:
self.load(file_path)
else:
print('Attention: No json file for battery model specified')
self.specification = "lithium_lfp" # [-] Specification of battery
self.voltage_nominal = 3.2 # [V] Nominal battery voltage
self.power_self_discharge_rate = 3.8072e-09 # [1/s] Battery self discharge rate, e.g. 1%/monat = 1/(30.4*24*3600)
self.energy_density_kg = 256.0 # [Wh/kg] Batery mass specific energy density
self.energy_density_m2 = 5843.0 # [Wh/m2] Battery surface specific energy density
self.charge_power_efficiency_a = -0.0224 # [1] Battery charge efficiency parameter - gradient of linear function
self.charge_power_efficiency_b = 1.0 # [1] Battery charge efficiency parameter - intercept of linear function
self.discharge_power_efficiency_a = -0.0281 # [1] Battery discharge efficiency parameter - gradient of linear function
self.discharge_power_efficiency_b = 1.0 # [1] Battery discharge efficiency parameter - intercept of linear function
self.end_of_discharge_a = 0.0394 # [1] Battery end of discharge parameter - gradient of linear function
self.end_of_discharge_b = -0.0211 # [1] Battery end of discharge parameter - itercept of linear function
self.end_of_charge_a = -0.0361 # [1] Battery end of charge parameter - gradient of linear function
self.end_of_charge_b = 1.1410 # [1] Battery end of charge parameter - itercept of linear function
self.temperature_operation_min = 233.15 # [K] Minimal battery temperature
self.temperature_operation_max: 343.15 # [K] Maximum battery temperature
self.heat_transfer_coefficient = 2 # [W/m2K] Heat transfer coefficeint battery - environment
self.heat_capacity = 850 # [J/kgK] Battery heat capacity
self.counter_mc = 0 # [1] Aging model: Initialization counter for
self.energy_mc = 0 # [Wh] Aging model: Initialization of energy of micro cacle
self.depth_of_discharge_mc = 0 # [1] Aging model: Initialization of depth of discharge of micro cycle
self.temperature_mc = 0 # [1] Aging model: Initialization of temperature of micro cycle
self.cycle_life_loss = 0 # [Wh] Aging model: Initialization capacity loss
self.cycle_aging_p4 = 0.0 # [1] Cycle aging model: polynomial parameter 4th degree
self.cycle_aging_p3 = -19047.619 # [1] Cycle aging model: polynomial parameter 3th degree
self.cycle_aging_p2 = 47142.8571 # [1] Cycle aging model: polynomial parameter 2th degree
self.cycle_aging_p1 = -43380.9523 # [1] Cycle aging model: polynomial parameter 1th degree
self.cycle_aging_p0 = 17285.7142 # [1] Cycle aging model: polynomial parameter 0th degree
self.cycle_aging_pl0 = 1.0 # [1] Cycle aging model: linear temperature function - intersection
self.cycle_aging_pl1 = 0.0 # [1] Cycle aging model: linear temperature function - gradient
self.calendric_aging_p5 = 0.0 # [1] Calendric aging model: polynomial parameter 5th degree
self.calendric_aging_p3 = 0.0 # [1] Calendric aging model: polynomial parameter 3th degree
self.calendric_aging_p1 = 0.0 # [1] Calendric aging model: polynomial parameter 1th degree
self.calendric_aging_p0 = 0.0 # [1] Calendric aging model: polynomial parameter 0th degree
self.voltage_charge_a = -0.50580 # [1] Voltage model: charge case parameter a
self.voltage_charge_b = -7.92750e-07 # [1] Voltage model: charge case parameter b
self.voltage_charge_c = 0.00021 # [1] Voltage model: charge case parameter c
self.voltage_charge_d = 0.73013 # [1] Voltage model: charge case parameter d
self.voltage_charge_e = 0.00079 # [1] Voltage model: charge case parameter e
self.voltage_charge_f = 3.17644 # [1] Voltage model: charge case parameter f
self.voltage_discharge_a = -0.41009 # [1] Voltage model: discharge case parameter a
self.voltage_discharge_b = 2.13015e-06 # [1] Voltage model: discharge case parameter b
self.voltage_discharge_c = -0.00013 # [1] Voltage model: discharge case parameter c
self.voltage_discharge_d = 0.69962 # [1] Voltage model: discharge case parameter d
self.voltage_discharge_e = -0.00161 # [1] Voltage model: discharge case parameter e
self.voltage_discharge_f = 3.03234 # [1] Voltage model: discharge case parameter f
self.investment_costs_specific = 0.16 # [$/Wh] Battery specific investment costs
# Integrate simulatable class for time indexing
Simulatable.__init__(self)
# Integrate environment class
self.env = env
# Integrate input power
self.input_link = input_link
# [s] Timestep
self.timestep = timestep
##Basic parameters
##Power model
# [Wh] Battery nominal capacity at nominal C-Rate
self.capacity_nominal_wh = capacity_nominal_wh
# [Wh] Current battery nominal capacity at nominal C-Rate
self.capacity_current_wh = capacity_nominal_wh
# Initialize initial paremeters
self.state_of_charge = 0.8
## Temperature model
# [kg] Mass of the battery
self.mass = self.capacity_nominal_wh / self.energy_density_kg
# [m^2] Battery area
self.surface = self.capacity_nominal_wh / self.energy_density_m2
# Initialize initial parameters
self.temperature = 298.15
self.power_loss = 0.
## Aging model
# [Wh] End-of-Life condition of battery
self.end_of_life_battery_wh = 0.8 * self.capacity_nominal_wh
## Economic model
self.size_nominal = self.capacity_nominal_wh