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412 lines
14 KiB
Python
412 lines
14 KiB
Python
import warnings
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from functools import partial, lru_cache
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from numbers import Number
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from itertools import count
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import numpy as np
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from numpy.polynomial import Polynomial
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from scipy.optimize import minimize_scalar
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# from .converters import *
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class Asset:
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"""Generic class for producing/consuming assets. Specific asset classes can
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inherit from this class.
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Parameters:
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-----------
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max_power : int/float
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Maximum asset power in MW electric
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min_power : int/float
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Minimium asset load in MW electric
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Usage:
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------
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Use the set_load and get_load methods to set and get asset status in MW.
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Convention is negative values for inputs (consumption) and positive
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values for outputs (production).
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"""
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_freq_to_multiplier = {"H": 1, "15T": (1 / 4), "1T": (1 / 60)}
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_ids = count(0)
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def __init__(self, name, max_power, min_power):
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if min_power > max_power:
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raise ValueError("'min_power' can not be larger than 'max_power'.")
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self.name = name
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self.id = next(self._ids)
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self.max_power = max_power
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self.min_power = min_power
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self.modes = {"max": max_power, "min": min_power}
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def __repr__(self):
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return f"{self.__class__.__name__}(self, max_power={self.max_power}, min_power={self.min_power})"
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def set_load(self, load):
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"""Set Asset load in MW.
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Convention is negative value for consumption and positive value
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for production. Subclasses might use a different convention if
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this seems more intiutive.
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Returns the load that is set in MW.
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"""
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if load < self.min_power or load > self.max_power:
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warnings.warn(
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f"Chosen Asset load for {self.name} is out of range. "
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f"Should be between {self.min_power} and {self.max_power}. "
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f"Function will return boundary load level for now."
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)
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load = min(max(load, self.min_power), self.max_power)
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return load
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def set_mode(self, mode):
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""" """
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load = self.modes[mode]
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return self.set_load(load)
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def MW_to_MWh(self, MW):
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"""Performs conversion from MW to MWh using the time_factor variable."""
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return MW * self.time_factor
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def MWh_to_MW(self, MWh):
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"""Performs conversion from MWh to MW using the time_factor variable."""
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return MWh / self.time_factor
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def set_freq(self, freq):
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"""
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Function that aligns time frequency between Model and Asset.
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Can be '1T', '15T' or 'H'
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The time_factor variable is used in subclasses to perform MW to MWh conversions.
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"""
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self.freq = freq
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self.time_factor = Asset._freq_to_multiplier[freq]
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def set_financials(self, capex, opex, devex, lifetime=None, depreciate=True, salvage_value=0):
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"""Set financial data of the asset."""
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self.capex = capex
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self.opex = opex
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self.devex = devex
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self.lifetime = lifetime
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self.depreciate = depreciate
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self.salvage_value = salvage_value
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class WaterStorage(Asset):
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def __init__(
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self,
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name,
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max_power,
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min_power,
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roundtrip_eff,
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capacity_per_volume,
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volume,
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lifetime,
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temperature,
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min_storagelevel,
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initial_storagelevel=None
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):
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if min_power > max_power:
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raise ValueError("'min_power' can not be larger than 'max_power'.")
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super().__init__(name, max_power, min_power)
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self.roundtrip_eff = roundtrip_eff
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self.volume = volume
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self.lifetime = lifetime
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self.temperature = temperature
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self.capacity_per_volume = capacity_per_volume #MWh/m3
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self.max_storage_capacity = self.volume * self.capacity_per_volume #MWh
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self.max_storagelevel = self.max_storage_capacity * 0.95
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self.min_storagelevel = min_storagelevel
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if initial_storagelevel:
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self.storagelevel = initial_storagelevel
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else:
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self.storagelevel = self.min_storagelevel
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def set_storagelevel(self, storagelevel):
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"""Set the storagelevel in MWh."""
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if storagelevel < self.min_storagelevel or storagelevel > self.max_storagelevel:
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raise ValueError(
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f"Tried to set Storage Level to {storagelevel}. "
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f"Storage Level must be a value between "
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f"{self.min_storagelevel} and {self.max_storagelevel} (in MWh)"
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)
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self.storagelevel = storagelevel
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@property
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def charging_power_limit(self):
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max_charging_energy = self.max_storagelevel - self.storagelevel
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max_charging_power = min(self.MWh_to_MW(max_charging_energy), -self.min_power)
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return max_charging_power
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@property
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def discharging_power_limit(self):
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max_discharging_energy = self.storagelevel - self.min_storagelevel
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max_discharging_power = min(self.MWh_to_MW(max_discharging_energy), self.max_power)
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return max_discharging_power
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def charge(self, power):
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energy = self.MW_to_MWh(power)
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max_charging = self.max_storagelevel - self.storagelevel
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bounded_energy = min (energy, max_charging)
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# print('bounded_energy', bounded_energy)
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new_cl = self.storagelevel + bounded_energy
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# print('new_cl', new_cl)
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self.set_storagelevel(new_cl)
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power = self.MWh_to_MW(bounded_energy)
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return power
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def discharge(self, power):
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energy = self.MW_to_MWh(power)
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max_discharging = self.storagelevel - self.min_storagelevel
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bounded_energy = min(energy, max_discharging)
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# print('bounded_energy', bounded_energy)
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new_cl = self.storagelevel - bounded_energy
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# print('new_cl', new_cl)
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self.set_storagelevel(new_cl)
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power = self.MWh_to_MW(bounded_energy)
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return power
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def get_soc(self, storagelevel, max_storage_capacity):
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"""Get the SoC in % (decimal value)"""
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return self.storagelevel / self.max_storage_capacity
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def set_financials(self,
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capex_per_MW,
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capex_per_MWh,
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opex,
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devex,
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lifetime=None,
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depreciate=True, salvage_value=0):
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total_capex = (
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capex_per_MW * self.max_power + capex_per_MWh * self.max_storage_capacity
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)
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super().set_financials(
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total_capex,
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opex,
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devex,
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lifetime=None,
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depreciate=True,
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salvage_value=0
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)
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def __repr__(self):
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return (
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f"{self.__class__.__name__}(name={self.name}, max_power={self.max_power}, "
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f"min_power={self.min_power}, roundtrip_eff={self.roundtrip_eff}, capacity_per_volume={self.capacity_per_volume}, volume = {self.volume}),"
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f"lifetime={self.lifetime}, temperature = {self.temperature}, min_storagelevel = {self.min_storagelevel})"
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)
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class Heatpump(Asset):
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"""Subclass for a Heatpump.
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Use cop parameter to set fixed COP (float/int) or COP curve (func).
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COP curve should take load in MWhe and return COP.
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Parameters:
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-----------
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max_th_power : numeric
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Maximum thermal output in MW (positive value)
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cop_curve : numeric or list or function
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3 ways to set the COP of the Heatpump:
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(1) Fixed COP based on [numeric] value.
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(2) Polynomial with coefficients based on [list] input.
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Input coeficients in format [c0, c1, c2, ..., c(n)],
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will generate Polynomial p(x) = c0 + c1*x + c2*x^2 ... cn*x^n,
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where x = % thermal load (in % of thermal capacity) as decimal value.
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Example:
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cop=[1, 2, 3, 4] will result in following COP curve:
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p(x) = 1 + 2x + 3x**2 + 4x**3,
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(3) [function] in format func(*args, **kwargs)
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Function should return a Polynomial that takes 'load_perc' as parameter.
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min_th_power : numeric
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Minimum thermal output in MW (positive value)
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Notes:
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------
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Sign convention:
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Thermal power outputs have positive values
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Electric power inputs have negative values
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"""
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def __init__(
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self,
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name,
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max_th_power,
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cop_curve,
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min_th_power=0,
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):
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if max_th_power < 0 or min_th_power < 0:
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raise ValueError("Thermal power can not have negative values.")
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if min_th_power > max_th_power:
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raise ValueError("'min_th_power' can not be larger than 'max_th_power'.")
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self.name = name
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self.max_th_power = max_th_power
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self.min_th_power = min_th_power
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self.cop_curve = self._set_cop_curve(cop_curve=cop_curve)
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def __repr__(self):
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return (
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f"{self.__class__.__name__}(name='{self.name}', max_thermal_power={self.max_th_power}, "
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f"cop_curve={self.cop_curve}, min_th_power={self.min_th_power})"
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)
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# Is turning everything into a Polynomial the best solution here?
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def _set_cop_curve(self, cop_curve):
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"""Generate COP curve function based on different inputtypes.
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Returns a function that takes *args **kwargs and returns a Polynomial.
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"""
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if isinstance(cop_curve, list):
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def func(*args, **kwargs):
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return Polynomial(cop_curve)
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return func
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return cop_curve
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@lru_cache(maxsize=None)
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def get_cop(self, heat_output, Tsink=None, Tsource=None):
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"""Get COP corresponding to certain load.
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Parameters:
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-----------
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heat_output : numeric
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Thermal load in MW
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Tsink : numeric
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Sink temperature in degrees celcius
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Tsource : numeric
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Source temperature in degrees celcius
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Notes:
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------
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Sign convention:
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Positive values for thermal load
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Negative values for electric load
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"""
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load_perc = heat_output / self.max_th_power
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cop_curve = self.cop_curve
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if not callable(cop_curve):
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return cop_curve
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else:
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return cop_curve(Tsink=Tsink, Tsource=Tsource)(load_perc)
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def th_to_el_power(self, heat_output, Tsink=None, Tsource=None):
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if not self.min_th_power <= heat_output <= self.max_th_power:
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warnings.warn(
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f"Chosen heat output is out of range [{self.min_th_power} - {self.max_th_power}]. "
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"Heat output is being limited to the closest boundary."
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)
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heat_output = min(max(heat_output, self.min_th_power), self.max_th_power)
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cop = self.get_cop(heat_output=heat_output, Tsink=Tsink, Tsource=Tsource)
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return -heat_output / cop
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def set_load(self, *args, **kwargs):
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raise NotImplementedError(
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"Directly setting the electric load of the heatpump is not possible (yet). "
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"Functionality will be implemented if there is a specific usecase for it."
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)
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@lru_cache(maxsize=None)
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def set_heat_output(self, heat_output, Tsink=None, Tsource=None):
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"""Set heat output in MWth, returns load of heatpump as tuple (MWe, MWth)"""
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if not self.min_th_power <= heat_output <= self.max_th_power:
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warnings.warn(
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f"Chosen heat output is out of range [{self.min_th_power} - {self.max_th_power}]. "
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"Heat output is being limited to the closest boundary."
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)
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heat_output = min(max(heat_output, self.min_th_power), self.max_th_power)
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# if Tsink is not None and Tsource is not None and Tsink <= Tsource:
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# raise ValueError(f"Tsource '{Tsource}' can not be higher than '{Tsink}'.")
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cop = self.get_cop(heat_output=heat_output, Tsink=Tsink, Tsource=Tsource)
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e_load = -heat_output / cop
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return e_load, heat_output
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def _cost_function(self, x, c1, c2, c3, Tsink=None, Tsource=None):
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"""Objective function for set_opt_load function.
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x = heatpump thermal load in MW
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c1 = electricity_cost
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c2 = alt_heat_price
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c3 = demand
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"""
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return (
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x / self.get_cop(heat_output=x, Tsink=Tsink, Tsource=Tsource) * c1
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+ (c3 - x) * c2
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)
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@lru_cache(maxsize=None)
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def set_opt_load(
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self,
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electricity_cost,
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alt_heat_price,
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demand,
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Tsink=None,
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Tsource=None,
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tolerance=0.01,
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):
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"""Set optimal load of Heatpump with minimal total heat costs.
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Function uses np.minimize_scalar to minimize cost function.
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Parameters:
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-----------
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electricity_cost:
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Cost of input electricity in €/MWh(e)
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alt_heat_price:
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Price of heat from alternative source in €/MWh(th)
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demand:
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Heat demand in MW(th)
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Returns:
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--------
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Optimal load of heatpump as tuple (MWe, MWth)
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"""
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c1 = electricity_cost
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c2 = alt_heat_price
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c3 = demand
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cop_curve = self.cop_curve
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if isinstance(cop_curve, Number):
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if c1 / cop_curve <= c2:
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return self.max_th_power
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else:
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return self.min_th_power
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obj_func = partial(
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self._cost_function, c1=c1, c2=c2, c3=c3, Tsink=Tsink, Tsource=Tsource
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)
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low_bound = 0
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up_bound = min(c3, self.max_th_power)
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opt_th_load = minimize_scalar(
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obj_func,
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bounds=(low_bound, up_bound),
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method="bounded",
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options={"xatol": tolerance},
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).x
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opt_e_load, opt_th_load = self.set_heat_output(
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opt_th_load, Tsink=Tsink, Tsource=Tsource
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)
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return opt_e_load, opt_th_load |