mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
264 lines
12 KiB
Python
264 lines
12 KiB
Python
from datetime import datetime
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from typing import Any, Dict, Optional, Union
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import numpy as np
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from pydantic import BaseModel, Field, field_validator, model_validator
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from typing_extensions import Self
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from akkudoktoreos.config import EOSConfig
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from akkudoktoreos.devices.battery import PVAkku
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from akkudoktoreos.devices.generic import HomeAppliance
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from akkudoktoreos.devices.inverter import Wechselrichter
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from akkudoktoreos.utils.utils import NumpyEncoder
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class EnergieManagementSystemParameters(BaseModel):
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pv_prognose_wh: list[float] = Field(
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description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals."
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)
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strompreis_euro_pro_wh: list[float] = Field(
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description="An array of floats representing the electricity price in euros per watt-hour for different time intervals."
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)
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einspeiseverguetung_euro_pro_wh: list[float] | float = Field(
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description="A float or array of floats representing the feed-in compensation in euros per watt-hour."
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)
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preis_euro_pro_wh_akku: float
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gesamtlast: list[float] = Field(
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description="An array of floats representing the total load (consumption) in watts for different time intervals."
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)
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@model_validator(mode="after")
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def validate_list_length(self) -> Self:
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pv_prognose_length = len(self.pv_prognose_wh)
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if (
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pv_prognose_length != len(self.strompreis_euro_pro_wh)
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or pv_prognose_length != len(self.gesamtlast)
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or (
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isinstance(self.einspeiseverguetung_euro_pro_wh, list)
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and pv_prognose_length != len(self.einspeiseverguetung_euro_pro_wh)
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)
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):
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raise ValueError("Input lists have different lengths")
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return self
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class SimulationResult(BaseModel):
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"""This object contains the results of the simulation and provides insights into various parameters over the entire forecast period."""
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Last_Wh_pro_Stunde: list[Optional[float]] = Field(description="TBD")
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EAuto_SoC_pro_Stunde: list[Optional[float]] = Field(
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description="The state of charge of the EV for each hour."
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)
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Einnahmen_Euro_pro_Stunde: list[Optional[float]] = Field(
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description="The revenue from grid feed-in or other sources in euros per hour."
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)
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Gesamt_Verluste: float = Field(
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description="The total losses in watt-hours over the entire period."
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)
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Gesamtbilanz_Euro: float = Field(
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description="The total balance of revenues minus costs in euros."
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)
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Gesamteinnahmen_Euro: float = Field(description="The total revenues in euros.")
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Gesamtkosten_Euro: float = Field(description="The total costs in euros.")
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Home_appliance_wh_per_hour: list[Optional[float]] = Field(
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description="The energy consumption of a household appliance in watt-hours per hour."
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)
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Kosten_Euro_pro_Stunde: list[Optional[float]] = Field(
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description="The costs in euros per hour."
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)
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Netzbezug_Wh_pro_Stunde: list[Optional[float]] = Field(
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description="The grid energy drawn in watt-hours per hour."
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)
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Netzeinspeisung_Wh_pro_Stunde: list[Optional[float]] = Field(
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description="The energy fed into the grid in watt-hours per hour."
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)
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Verluste_Pro_Stunde: list[Optional[float]] = Field(
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description="The losses in watt-hours per hour."
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)
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akku_soc_pro_stunde: list[Optional[float]] = Field(
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description="The state of charge of the battery (not the EV) in percentage per hour."
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)
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Electricity_price: list[Optional[float]] = Field(
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description="Used Electricity Price, including predictions"
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)
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@field_validator(
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"Last_Wh_pro_Stunde",
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"Netzeinspeisung_Wh_pro_Stunde",
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"akku_soc_pro_stunde",
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"Netzbezug_Wh_pro_Stunde",
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"Kosten_Euro_pro_Stunde",
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"Einnahmen_Euro_pro_Stunde",
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"EAuto_SoC_pro_Stunde",
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"Verluste_Pro_Stunde",
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"Home_appliance_wh_per_hour",
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"Electricity_price",
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mode="before",
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)
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def convert_numpy(cls, field: Any) -> Any:
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return NumpyEncoder.convert_numpy(field)[0]
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class EnergieManagementSystem:
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def __init__(
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self,
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config: EOSConfig,
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parameters: EnergieManagementSystemParameters,
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wechselrichter: Wechselrichter,
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eauto: Optional[PVAkku] = None,
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home_appliance: Optional[HomeAppliance] = None,
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):
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self.akku = wechselrichter.akku
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self.gesamtlast = np.array(parameters.gesamtlast, float)
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self.pv_prognose_wh = np.array(parameters.pv_prognose_wh, float)
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self.strompreis_euro_pro_wh = np.array(parameters.strompreis_euro_pro_wh, float)
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self.einspeiseverguetung_euro_pro_wh_arr = (
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parameters.einspeiseverguetung_euro_pro_wh
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if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list)
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else np.full(len(self.gesamtlast), parameters.einspeiseverguetung_euro_pro_wh, float)
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)
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self.eauto = eauto
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self.home_appliance = home_appliance
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self.wechselrichter = wechselrichter
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self.ac_charge_hours = np.full(config.prediction_hours, 0)
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self.dc_charge_hours = np.full(config.prediction_hours, 1)
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self.ev_charge_hours = np.full(config.prediction_hours, 0)
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def set_akku_discharge_hours(self, ds: np.ndarray) -> None:
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self.akku.set_discharge_per_hour(ds)
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def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None:
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self.ac_charge_hours = ds
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def set_akku_dc_charge_hours(self, ds: np.ndarray) -> None:
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self.dc_charge_hours = ds
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def set_ev_charge_hours(self, ds: np.ndarray) -> None:
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self.ev_charge_hours = ds
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def set_home_appliance_start(self, start_hour: int, global_start_hour: int = 0) -> None:
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assert self.home_appliance is not None
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self.home_appliance.set_starting_time(start_hour, global_start_hour=global_start_hour)
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def reset(self) -> None:
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if self.eauto:
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self.eauto.reset()
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self.akku.reset()
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def simuliere_ab_jetzt(self) -> dict[str, Any]:
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jetzt = datetime.now()
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start_stunde = jetzt.hour
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return self.simuliere(start_stunde)
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def simuliere(self, start_hour: int) -> dict[str, Any]:
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"""hour.
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akku_soc_pro_stunde begin of the hour, initial hour state!
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last_wh_pro_stunde integral of last hour (end state)
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"""
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lastkurve_wh = self.gesamtlast
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assert (
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len(lastkurve_wh) == len(self.pv_prognose_wh) == len(self.strompreis_euro_pro_wh)
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), f"Array sizes do not match: Load Curve = {len(lastkurve_wh)}, PV Forecast = {len(self.pv_prognose_wh)}, Electricity Price = {len(self.strompreis_euro_pro_wh)}"
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# Optimized total hours calculation
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end_hour = len(lastkurve_wh)
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total_hours = end_hour - start_hour
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# Pre-allocate arrays for the results, optimized for speed
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loads_energy_per_hour = np.full((total_hours), np.nan)
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feedin_energy_per_hour = np.full((total_hours), np.nan)
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consumption_energy_per_hour = np.full((total_hours), np.nan)
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costs_per_hour = np.full((total_hours), np.nan)
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revenue_per_hour = np.full((total_hours), np.nan)
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soc_per_hour = np.full((total_hours), np.nan) # Hour End State
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soc_ev_per_hour = np.full((total_hours), np.nan)
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losses_wh_per_hour = np.full((total_hours), np.nan)
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home_appliance_wh_per_hour = np.full((total_hours), np.nan)
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electricity_price_per_hour = np.full((total_hours), np.nan)
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# Set initial state
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soc_per_hour[0] = self.akku.ladezustand_in_prozent()
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if self.eauto:
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soc_ev_per_hour[0] = self.eauto.ladezustand_in_prozent()
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# All States
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for hour in range(start_hour, end_hour):
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hour_since_now = hour - start_hour
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# save begin states
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soc_per_hour[hour_since_now] = self.akku.ladezustand_in_prozent()
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if self.eauto:
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soc_ev_per_hour[hour_since_now] = self.eauto.ladezustand_in_prozent()
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# Accumulate loads and PV generation
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consumption = self.gesamtlast[hour]
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losses_wh_per_hour[hour_since_now] = 0.0
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if self.home_appliance:
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ha_load = self.home_appliance.get_load_for_hour(hour)
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consumption += ha_load
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home_appliance_wh_per_hour[hour_since_now] = ha_load
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# E-Auto handling
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if self.eauto and self.ev_charge_hours[hour] > 0:
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loaded_energy_ev, verluste_eauto = self.eauto.energie_laden(
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None, hour, relative_power=self.ev_charge_hours[hour]
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)
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consumption += loaded_energy_ev
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losses_wh_per_hour[hour_since_now] += verluste_eauto
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# Process inverter logic
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energy_produced = self.pv_prognose_wh[hour]
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self.akku.set_charge_allowed_for_hour(self.dc_charge_hours[hour], hour)
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energy_feedin_grid_actual, energy_consumption_grid_actual, losses, eigenverbrauch = (
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self.wechselrichter.energie_verarbeiten(energy_produced, consumption, hour)
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)
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# AC PV Battery Charge
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if self.ac_charge_hours[hour] > 0.0:
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self.akku.set_charge_allowed_for_hour(1, hour)
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battery_charged_energy_actual, battery_losses_actual = self.akku.energie_laden(
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None, hour, relative_power=self.ac_charge_hours[hour]
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)
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# print(stunde, " ", geladene_menge, " ",self.ac_charge_hours[stunde]," ",self.akku.ladezustand_in_prozent())
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consumption += battery_charged_energy_actual
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consumption += battery_losses_actual
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energy_consumption_grid_actual += battery_charged_energy_actual
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energy_consumption_grid_actual += battery_losses_actual
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losses_wh_per_hour[hour_since_now] += battery_losses_actual
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feedin_energy_per_hour[hour_since_now] = energy_feedin_grid_actual
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consumption_energy_per_hour[hour_since_now] = energy_consumption_grid_actual
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losses_wh_per_hour[hour_since_now] += losses
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loads_energy_per_hour[hour_since_now] = consumption
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electricity_price_per_hour[hour_since_now] = self.strompreis_euro_pro_wh[hour]
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# Financial calculations
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costs_per_hour[hour_since_now] = (
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energy_consumption_grid_actual * self.strompreis_euro_pro_wh[hour]
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)
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revenue_per_hour[hour_since_now] = (
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energy_feedin_grid_actual * self.einspeiseverguetung_euro_pro_wh_arr[hour]
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)
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# Total cost and return
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gesamtkosten_euro = np.nansum(costs_per_hour) - np.nansum(revenue_per_hour)
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# Prepare output dictionary
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out: Dict[str, Union[np.ndarray, float]] = {
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"Last_Wh_pro_Stunde": loads_energy_per_hour,
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"Netzeinspeisung_Wh_pro_Stunde": feedin_energy_per_hour,
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"Netzbezug_Wh_pro_Stunde": consumption_energy_per_hour,
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"Kosten_Euro_pro_Stunde": costs_per_hour,
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"akku_soc_pro_stunde": soc_per_hour,
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"Einnahmen_Euro_pro_Stunde": revenue_per_hour,
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"Gesamtbilanz_Euro": gesamtkosten_euro,
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"EAuto_SoC_pro_Stunde": soc_ev_per_hour,
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"Gesamteinnahmen_Euro": np.nansum(revenue_per_hour),
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"Gesamtkosten_Euro": np.nansum(costs_per_hour),
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"Verluste_Pro_Stunde": losses_wh_per_hour,
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"Gesamt_Verluste": np.nansum(losses_wh_per_hour),
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"Home_appliance_wh_per_hour": home_appliance_wh_per_hour,
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"Electricity_price": electricity_price_per_hour,
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}
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return out
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