From 3599088dce478369e4c61060003344c4db4d73a4 Mon Sep 17 00:00:00 2001 From: Bobby Noelte Date: Sat, 8 Nov 2025 15:42:18 +0100 Subject: [PATCH] chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte --------- Signed-off-by: Bobby Noelte --- docs/_generated/config.md | 42 +- openapi.json | 39 +- src/akkudoktoreos/devices/devices.py | 52 +- src/akkudoktoreos/devices/genetic/battery.py | 223 ++++- .../devices/genetic/homeappliance.py | 35 +- .../optimization/genetic/genetic.py | 357 +++++--- .../optimization/genetic/geneticdevices.py | 10 +- .../optimization/genetic/geneticparams.py | 4 +- .../optimization/genetic/geneticsolution.py | 113 ++- .../optimization/optimization.py | 2 + src/akkudoktoreos/server/dash/plan.py | 130 ++- tests/test_battery.py | 98 ++- tests/test_config.py | 39 +- tests/test_geneticsimulation.py | 40 +- tests/test_geneticsimulation2.py | 66 +- tests/testdata/optimize_result_1.json | 234 ++--- tests/testdata/optimize_result_2.json | 818 +++++++++--------- tests/testdata/optimize_result_2_full.json | 812 ++++++++--------- 18 files changed, 1769 insertions(+), 1345 deletions(-) diff --git a/docs/_generated/config.md b/docs/_generated/config.md index 42fffeb..00c0e20 100644 --- a/docs/_generated/config.md +++ b/docs/_generated/config.md @@ -195,7 +195,7 @@ Properties: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -222,7 +222,7 @@ Properties: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -264,7 +264,7 @@ Properties: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -291,7 +291,7 @@ Properties: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -601,7 +601,7 @@ Properties: | levelized_cost_of_storage_kwh | `float` | `rw` | `0.0` | Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [€/kWh]. | | max_charge_power_w | `Optional[float]` | `rw` | `5000` | Maximum charging power [W]. | | min_charge_power_w | `Optional[float]` | `rw` | `50` | Minimum charging power [W]. | -| charge_rates | `Optional[list[float]]` | `rw` | `None` | Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available. | +| charge_rates | `Optional[numpydantic.vendor.npbase_meta_classes.NDArray]` | `rw` | `[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]` | Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates. | | min_soc_percentage | `int` | `rw` | `0` | Minimum state of charge (SOC) as percentage of capacity [%]. This is the target SoC for charging | | max_soc_percentage | `int` | `rw` | `100` | Maximum state of charge (SOC) as percentage of capacity [%]. | | measurement_key_soc_factor | `str` | `ro` | `N/A` | Measurement key for the battery state of charge (SoC) as factor of total capacity [0.0 ... 1.0]. | @@ -630,13 +630,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": [ - 0.0, - 0.25, - 0.5, - 0.75, - 1.0 - ], + "charge_rates": "[0. 0.25 0.5 0.75 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100 }, @@ -648,7 +642,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100 }, @@ -660,7 +654,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100 }, @@ -672,7 +666,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100 } @@ -697,13 +691,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": [ - 0.0, - 0.25, - 0.5, - 0.75, - 1.0 - ], + "charge_rates": "[0. 0.25 0.5 0.75 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -727,7 +715,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100, "measurement_key_soc_factor": "ev1-soc-factor", @@ -751,7 +739,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100, "measurement_key_soc_factor": "inverter1-soc-factor", @@ -775,7 +763,7 @@ Battery SoC, power. | "levelized_cost_of_storage_kwh": 0.12, "max_charge_power_w": 5000.0, "min_charge_power_w": 50.0, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 10, "max_soc_percentage": 100, "measurement_key_soc_factor": "dishwasher-soc-factor", @@ -1821,7 +1809,7 @@ Validators: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", @@ -1848,7 +1836,7 @@ Validators: "levelized_cost_of_storage_kwh": 0.0, "max_charge_power_w": 5000, "min_charge_power_w": 50, - "charge_rates": null, + "charge_rates": "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]", "min_soc_percentage": 0, "max_soc_percentage": 100, "measurement_key_soc_factor": "battery1-soc-factor", diff --git a/openapi.json b/openapi.json index 6dd25c3..3f55be3 100644 --- a/openapi.json +++ b/openapi.json @@ -2122,7 +2122,7 @@ } ], "title": "Charge Rates", - "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", + "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates.", "examples": [ [ 0.0, @@ -2263,7 +2263,7 @@ } ], "title": "Charge Rates", - "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", + "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates.", "examples": [ [ 0.0, @@ -5921,6 +5921,15 @@ "title": "Total Costs Amt", "description": "The total costs [money amount]." }, + "fitness_score": { + "items": { + "type": "number" + }, + "type": "array", + "uniqueItems": true, + "title": "Fitness Score", + "description": "The fitness score as a set of fitness values." + }, "prediction": { "$ref": "#/components/schemas/PydanticDateTimeDataFrame", "description": "Datetime data frame with time series prediction data per optimization interval:- pv_energy_wh: PV energy prediction (positive) in wh- elec_price_amt_kwh: Electricity price prediction in money per kwh- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh- weather_temp_air_celcius: Temperature in \u00b0C- loadforecast_energy_wh: Load mean energy prediction in wh- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh" @@ -5937,6 +5946,7 @@ "total_losses_energy_wh", "total_revenues_amt", "total_costs_amt", + "fitness_score", "prediction", "solution" ], @@ -7585,6 +7595,31 @@ "title": "Max Soc Percentage", "description": "An integer representing the maximum state of charge (SOC) of the battery in percentage.", "default": 100 + }, + "charge_rates": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Charge Rates", + "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", + "examples": [ + [ + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ], + null + ] } }, "additionalProperties": false, diff --git a/src/akkudoktoreos/devices/devices.py b/src/akkudoktoreos/devices/devices.py index 67dddbd..37f5729 100644 --- a/src/akkudoktoreos/devices/devices.py +++ b/src/akkudoktoreos/devices/devices.py @@ -1,10 +1,13 @@ """General configuration settings for simulated devices for optimization.""" import json +import re from typing import Any, Optional, TextIO, cast +import numpy as np from loguru import logger -from pydantic import Field, computed_field, model_validator +from numpydantic import NDArray, Shape +from pydantic import Field, computed_field, field_validator, model_validator from akkudoktoreos.config.configabc import SettingsBaseModel from akkudoktoreos.core.cache import CacheFileStore @@ -14,6 +17,9 @@ from akkudoktoreos.core.pydantic import ConfigDict, PydanticBaseModel from akkudoktoreos.devices.devicesabc import DevicesBaseSettings from akkudoktoreos.utils.datetimeutil import DateTime, TimeWindowSequence, to_datetime +# Default charge rates for battery +BATTERY_DEFAULT_CHARGE_RATES = np.linspace(0.0, 1.0, 11) # 0.0, 0.1, ..., 1.0 + class BatteriesCommonSettings(DevicesBaseSettings): """Battery devices base settings.""" @@ -61,9 +67,12 @@ class BatteriesCommonSettings(DevicesBaseSettings): examples=[50], ) - charge_rates: Optional[list[float]] = Field( - default=None, - description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", + charge_rates: Optional[NDArray[Shape["*"], float]] = Field( + default=BATTERY_DEFAULT_CHARGE_RATES, + description=( + "Charge rates as factor of maximum charging power [0.00 ... 1.00]. " + "None triggers fallback to default charge-rates." + ), examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None], ) @@ -71,7 +80,10 @@ class BatteriesCommonSettings(DevicesBaseSettings): default=0, ge=0, le=100, - description="Minimum state of charge (SOC) as percentage of capacity [%]. This is the target SoC for charging", + description=( + "Minimum state of charge (SOC) as percentage of capacity [%]. " + "This is the target SoC for charging" + ), examples=[10], ) @@ -83,6 +95,36 @@ class BatteriesCommonSettings(DevicesBaseSettings): examples=[100], ) + @field_validator("charge_rates", mode="before") + def validate_and_sort_charge_rates(cls, v: Any) -> NDArray[Shape["*"], float]: + # None means fallback to default values + if v is None: + return BATTERY_DEFAULT_CHARGE_RATES.copy() + + # Convert to numpy array + if isinstance(v, str): + # Remove brackets and split by comma or whitespace + numbers = re.split(r"[,\s]+", v.strip("[]")) + + # Filter out any empty strings and convert to floats + arr = np.array([float(x) for x in numbers if x]) + else: + arr = np.array(v, dtype=float) + + # Must not be empty + if arr.size == 0: + raise ValueError("charge_rates must contain at least one value.") + + # Enforce bounds: 0.0 ≤ x ≤ 1.0 + if (arr < 0.0).any() or (arr > 1.0).any(): + raise ValueError("charge_rates must be within [0.0, 1.0].") + + # Remove duplicates + sort + arr = np.unique(arr) + arr.sort() + + return arr + @computed_field # type: ignore[prop-decorator] @property def measurement_key_soc_factor(self) -> str: diff --git a/src/akkudoktoreos/devices/genetic/battery.py b/src/akkudoktoreos/devices/genetic/battery.py index ff120d8..51aac50 100644 --- a/src/akkudoktoreos/devices/genetic/battery.py +++ b/src/akkudoktoreos/devices/genetic/battery.py @@ -1,7 +1,8 @@ -from typing import Any, Optional +from typing import Any, Iterator, Optional import numpy as np +from akkudoktoreos.devices.devices import BATTERY_DEFAULT_CHARGE_RATES from akkudoktoreos.optimization.genetic.geneticdevices import ( BaseBatteryParameters, SolarPanelBatteryParameters, @@ -17,12 +18,20 @@ class Battery: self._setup() def _setup(self) -> None: - """Sets up the battery parameters based on configuration or provided parameters.""" + """Sets up the battery parameters based on provided parameters.""" self.capacity_wh = self.parameters.capacity_wh self.initial_soc_percentage = self.parameters.initial_soc_percentage self.charging_efficiency = self.parameters.charging_efficiency self.discharging_efficiency = self.parameters.discharging_efficiency + # Charge rates, in case of None use default + self.charge_rates = BATTERY_DEFAULT_CHARGE_RATES + if self.parameters.charge_rates: + charge_rates = np.array(self.parameters.charge_rates, dtype=float) + charge_rates = np.unique(charge_rates) + charge_rates.sort() + self.charge_rates = charge_rates + # Only assign for storage battery self.min_soc_percentage = ( self.parameters.min_soc_percentage @@ -36,12 +45,30 @@ class Battery: self.max_charge_power_w = self.parameters.max_charge_power_w else: self.max_charge_power_w = self.capacity_wh # TODO this should not be equal capacity_wh - self.discharge_array = np.full(self.prediction_hours, 1) - self.charge_array = np.full(self.prediction_hours, 1) + self.discharge_array = np.full(self.prediction_hours, 0) + self.charge_array = np.full(self.prediction_hours, 0) self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh + def _lower_charge_rates_desc(self, start_rate: float) -> Iterator[float]: + """Yield all charge rates lower than a given rate in descending order. + + Args: + charge_rates (np.ndarray): Sorted 1D array of available charge rates. + start_rate (float): The reference charge rate. + + Yields: + float: Charge rates lower than `start_rate`, in descending order. + """ + charge_rates_fast = self.charge_rates + + # Find the insertion index for start_rate (left-most position) + idx = np.searchsorted(charge_rates_fast, start_rate, side="left") + + # Yield values before idx in reverse (descending) + return (charge_rates_fast[j] for j in range(idx - 1, -1, -1)) + def to_dict(self) -> dict[str, Any]: """Converts the object to a dictionary representation.""" return { @@ -61,8 +88,8 @@ class Battery: """Resets the battery state to its initial values.""" self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh) - self.discharge_array = np.full(self.prediction_hours, 1) - self.charge_array = np.full(self.prediction_hours, 1) + self.discharge_array = np.full(self.prediction_hours, 0) + self.charge_array = np.full(self.prediction_hours, 0) def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None: """Sets the discharge values for each hour.""" @@ -80,70 +107,172 @@ class Battery: ) self.charge_array = np.array(charge_array) - def set_charge_allowed_for_hour(self, charge: float, hour: int) -> None: - """Sets the charge for a specific hour.""" - if hour >= self.prediction_hours: - raise ValueError( - f"Hour {hour} is out of range. Must be less than {self.prediction_hours}." - ) - self.charge_array[hour] = charge - def current_soc_percentage(self) -> float: """Calculates the current state of charge in percentage.""" return (self.soc_wh / self.capacity_wh) * 100 def discharge_energy(self, wh: float, hour: int) -> tuple[float, float]: - """Discharges energy from the battery.""" + """Discharge energy from the battery. + + Discharge is limited by: + * Requested delivered energy + * Remaining energy above minimum SoC + * Maximum discharge power + * Discharge efficiency + + Args: + wh (float): Requested delivered energy in watt-hours. + hour (int): Time index. If `self.discharge_array[hour] == 0`, + no discharge occurs. + + Returns: + tuple[float, float]: + delivered_wh (float): Actual delivered energy [Wh]. + losses_wh (float): Conversion losses [Wh]. + + """ if self.discharge_array[hour] == 0: return 0.0, 0.0 - max_possible_discharge_wh = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency - max_possible_discharge_wh = max(max_possible_discharge_wh, 0.0) + # Raw extractable energy above minimum SoC + raw_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0) - max_possible_discharge_wh = min( - max_possible_discharge_wh, self.max_charge_power_w - ) # TODO make a new cfg variable max_discharge_power_w + # Maximum raw discharge due to power limit + max_raw_wh = self.max_charge_power_w # TODO rename to max_discharge_power_w - actual_discharge_wh = min(wh, max_possible_discharge_wh) - actual_withdrawal_wh = ( - actual_discharge_wh / self.discharging_efficiency - if self.discharging_efficiency > 0 - else 0.0 - ) + # Actual raw withdrawal (internal) + raw_withdrawal_wh = min(raw_available_wh, max_raw_wh) - self.soc_wh -= actual_withdrawal_wh + # Convert raw to delivered + max_deliverable_wh = raw_withdrawal_wh * self.discharging_efficiency + + # Cap by requested delivered energy + delivered_wh = min(wh, max_deliverable_wh) + + # Effective raw withdrawal based on what is delivered + raw_used_wh = delivered_wh / self.discharging_efficiency + + # Update SoC + self.soc_wh -= raw_used_wh self.soc_wh = max(self.soc_wh, self.min_soc_wh) - losses_wh = actual_withdrawal_wh - actual_discharge_wh - return actual_discharge_wh, losses_wh + # Losses + losses_wh = raw_used_wh - delivered_wh + + return delivered_wh, losses_wh def charge_energy( - self, wh: Optional[float], hour: int, relative_power: float = 0.0 + self, + wh: Optional[float], + hour: int, + charge_factor: float = 0.0, ) -> tuple[float, float]: - """Charges energy into the battery.""" + """Charge energy into the battery. + + Two **exclusive** modes: + + Mode 1: + - `wh is not None` and `charge_factor == 0` + → The raw requested charge energy is `wh` (pre-efficiency). + → If remaining capacity is insufficient, charging is automatically limited. + → No exception is raised due to capacity limits. + + Mode 2: + - `wh is None` and `charge_factor > 0` + → The raw requested energy is `max_charge_power_w * charge_factor`. + → If the request exceeds remaining capacity, the algorithm tries to + find a lower charge_factor that is compatible. If such a charge factor + exists, this hour’s charge_factor is replaced. + → If no charge factor can accommodate charging, the request is ignored + (`(0.0, 0.0)` is returned) and a penalty is applied elsewhere. + + Charging is constrained by: + • Available SoC headroom (max_soc_wh − soc_wh) + • max_charge_power_w + • charging_efficiency + + Args: + wh (float | None): + Requested raw energy [Wh] before efficiency. + Must be provided only for Mode 1 (charge_factor must be 0). + + hour (int): + Time index. If charging is disabled at this hour (charge_array[hour] == 0), + returns `(0.0, 0.0)`. + + charge_factor (float): + Fraction (0–1) of max charge power. + Must be >0 only in Mode 2 (`wh is None`). + + Returns: + tuple[float, float]: + stored_wh : float + Energy stored after efficiency [Wh]. + losses_wh : float + Conversion losses [Wh]. + + Raises: + ValueError: + - If the mode is ambiguous (neither Mode 1 nor Mode 2). + - If the final new SoC would exceed capacity_wh. + + Notes: + stored_wh = raw_input_wh * charging_efficiency + losses_wh = raw_input_wh − stored_wh + """ + # Charging allowed in this hour? if hour is not None and self.charge_array[hour] == 0: - return 0.0, 0.0 # Charging not allowed in this hour + return 0.0, 0.0 - if relative_power > 0.0: - wh = self.max_charge_power_w * relative_power + # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access + soc_wh_fast = self.soc_wh + max_charge_power_w_fast = self.max_charge_power_w + charging_efficiency_fast = self.charging_efficiency - wh = wh if wh is not None else self.max_charge_power_w + # Decide mode & determine raw_request_wh and raw_charge_wh + if wh is not None and charge_factor == 0.0: # mode 1 + raw_request_wh = wh + raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast + elif wh is None and charge_factor > 0.0: # mode 2 + raw_request_wh = max_charge_power_w_fast * charge_factor + raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast + if raw_request_wh > raw_charge_wh: + # Use a lower charge factor + lower_charge_factors = self._lower_charge_rates_desc(charge_factor) + for charge_factor in lower_charge_factors: + raw_request_wh = max_charge_power_w_fast * charge_factor + if raw_request_wh <= raw_charge_wh: + self.charge_array[hour] = charge_factor + break + if raw_request_wh > raw_charge_wh: + # ignore request - penalty for missing SoC will be applied + self.charge_array[hour] = 0 + return 0.0, 0.0 + else: + raise ValueError( + f"{self.parameters.device_id}: charge_energy must be called either " + "with wh != None and charge_factor == 0, or with wh == None and charge_factor > 0." + ) - max_possible_charge_wh = ( - (self.max_soc_wh - self.soc_wh) / self.charging_efficiency - if self.charging_efficiency > 0 - else 0.0 - ) - max_possible_charge_wh = max(max_possible_charge_wh, 0.0) + # Remaining capacity + max_raw_wh = min(raw_charge_wh, max_charge_power_w_fast) - effective_charge_wh = min(wh, max_possible_charge_wh) - charged_wh = effective_charge_wh * self.charging_efficiency + # Actual raw intake + raw_input_wh = raw_request_wh if raw_request_wh < max_raw_wh else max_raw_wh - self.soc_wh += charged_wh - self.soc_wh = min(self.soc_wh, self.max_soc_wh) + # Apply efficiency + stored_wh = raw_input_wh * charging_efficiency_fast + new_soc = soc_wh_fast + stored_wh - losses_wh = effective_charge_wh - charged_wh - return charged_wh, losses_wh + if new_soc > self.capacity_wh: + raise ValueError( + f"{self.parameters.device_id}: SoC {new_soc} Wh exceeds capacity {self.capacity_wh} Wh" + ) + + self.soc_wh = new_soc + losses_wh = raw_input_wh - stored_wh + + return stored_wh, losses_wh def current_energy_content(self) -> float: """Returns the current usable energy in the battery.""" diff --git a/src/akkudoktoreos/devices/genetic/homeappliance.py b/src/akkudoktoreos/devices/genetic/homeappliance.py index d2c1ecd..4045599 100644 --- a/src/akkudoktoreos/devices/genetic/homeappliance.py +++ b/src/akkudoktoreos/devices/genetic/homeappliance.py @@ -1,5 +1,3 @@ -from typing import Optional - import numpy as np from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters @@ -28,7 +26,6 @@ class HomeAppliance: self.load_curve = np.zeros(self.prediction_hours) # Initialize the load curve with zeros self.duration_h = self.parameters.duration_h self.consumption_wh = self.parameters.consumption_wh - self.appliance_start: Optional[int] = None # setup possible start times if self.parameters.time_windows is None: self.parameters.time_windows = TimeWindowSequence( @@ -59,33 +56,32 @@ class HomeAppliance: else: self.start_latest = 23 - def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> None: + def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> int: """Sets the start time of the device and generates the corresponding load curve. :param start_hour: The hour at which the device should start. """ - self.reset_load_curve() - - # Check if the duration of use is within the available time windows if not self.start_allowed[start_hour]: - # No available time window to start home appliance - # Use the earliest one - start_hour = self.start_earliest + # It is not allowed (by the time windows) to start the application at this time + if global_start_hour <= self.start_latest: + # There is a time window left to start the appliance. Use it + start_hour = self.start_latest + else: + # There is no time window left to run the application + # Set the start into tomorrow + start_hour = self.start_earliest + 24 - # Check if it is possibility to start the appliance - if start_hour < global_start_hour: - # Start is before current time - # Use the latest one - start_hour = self.start_latest + self.reset_load_curve() # Calculate power per hour based on total consumption and duration power_per_hour = self.consumption_wh / self.duration_h # Convert to watt-hours # Set the power for the duration of use in the load curve array - self.load_curve[start_hour : start_hour + self.duration_h] = power_per_hour + if start_hour < len(self.load_curve): + end_hour = min(start_hour + self.duration_h, self.prediction_hours) + self.load_curve[start_hour:end_hour] = power_per_hour - # Set the selected start hour - self.appliance_start = start_hour + return start_hour def reset_load_curve(self) -> None: """Resets the load curve.""" @@ -107,6 +103,3 @@ class HomeAppliance: ) return self.load_curve[hour] - - def get_appliance_start(self) -> Optional[int]: - return self.appliance_start diff --git a/src/akkudoktoreos/optimization/genetic/genetic.py b/src/akkudoktoreos/optimization/genetic/genetic.py index 2bfb4ce..a759c43 100644 --- a/src/akkudoktoreos/optimization/genetic/genetic.py +++ b/src/akkudoktoreos/optimization/genetic/genetic.py @@ -69,7 +69,16 @@ class GeneticSimulation(PydanticBaseModel): ac_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD") dc_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD") + bat_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field( + default=None, description="TBD" + ) ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD") + ev_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field( + default=None, description="TBD" + ) + home_appliance_start_hour: Optional[int] = Field( + default=None, description="Home appliance start hour - None denotes no start." + ) def prepare( self, @@ -100,8 +109,11 @@ class GeneticSimulation(PydanticBaseModel): self.home_appliance = home_appliance self.inverter = inverter self.ac_charge_hours = np.full(self.prediction_hours, 0.0) - self.dc_charge_hours = np.full(self.prediction_hours, 1.0) + self.dc_charge_hours = np.full(self.prediction_hours, 0.0) + self.bat_discharge_hours = np.full(self.prediction_hours, 0.0) self.ev_charge_hours = np.full(self.prediction_hours, 0.0) + self.ev_discharge_hours = np.full(self.prediction_hours, 0.0) + self.home_appliance_start_hour = None """Prepare simulation runs.""" self.load_energy_array = np.array(parameters.gesamtlast, float) self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float) @@ -114,28 +126,12 @@ class GeneticSimulation(PydanticBaseModel): ) ) - def set_akku_discharge_hours(self, ds: np.ndarray) -> None: - if self.battery: - self.battery.set_discharge_per_hour(ds) - - def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None: - self.ac_charge_hours = ds - - def set_akku_dc_charge_hours(self, ds: np.ndarray) -> None: - self.dc_charge_hours = ds - - def set_ev_charge_hours(self, ds: np.ndarray) -> None: - self.ev_charge_hours = ds - - def set_home_appliance_start(self, ds: int, global_start_hour: int = 0) -> None: - if self.home_appliance: - self.home_appliance.set_starting_time(ds, global_start_hour=global_start_hour) - def reset(self) -> None: if self.ev: self.ev.reset() if self.battery: self.battery.reset() + self.home_appliance_start_hour = None def simulate(self, start_hour: int) -> dict[str, Any]: """Simulate energy usage and costs for the given start hour. @@ -146,45 +142,66 @@ class GeneticSimulation(PydanticBaseModel): # Remember start hour self.start_hour = start_hour - # Check for simulation integrity - required_attrs = [ - "load_energy_array", - "pv_prediction_wh", - "elect_price_hourly", - "ev_charge_hours", - "ac_charge_hours", - "dc_charge_hours", - "elect_revenue_per_hour_arr", - ] - missing_data = [ - attr.replace("_", " ").title() for attr in required_attrs if getattr(self, attr) is None - ] + # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access + load_energy_array_fast = self.load_energy_array + ev_charge_hours_fast = self.ev_charge_hours + ev_discharge_hours_fast = self.ev_discharge_hours + ac_charge_hours_fast = self.ac_charge_hours + dc_charge_hours_fast = self.dc_charge_hours + bat_discharge_hours_fast = self.bat_discharge_hours + elect_price_hourly_fast = self.elect_price_hourly + elect_revenue_per_hour_arr_fast = self.elect_revenue_per_hour_arr + pv_prediction_wh_fast = self.pv_prediction_wh + battery_fast = self.battery + ev_fast = self.ev + home_appliance_fast = self.home_appliance + inverter_fast = self.inverter - if missing_data: - logger.error("Mandatory data missing - %s", ", ".join(missing_data)) - raise ValueError(f"Mandatory data missing: {', '.join(missing_data)}") + # Check for simulation integrity (in a way that mypy understands) + if ( + load_energy_array_fast is None + or pv_prediction_wh_fast is None + or elect_price_hourly_fast is None + or ev_charge_hours_fast is None + or ac_charge_hours_fast is None + or dc_charge_hours_fast is None + or elect_revenue_per_hour_arr_fast is None + or bat_discharge_hours_fast is None + or ev_discharge_hours_fast is None + ): + missing = [] + if load_energy_array_fast is None: + missing.append("Load Energy Array") + if pv_prediction_wh_fast is None: + missing.append("PV Prediction Wh") + if elect_price_hourly_fast is None: + missing.append("Electricity Price Hourly") + if ev_charge_hours_fast is None: + missing.append("EV Charge Hours") + if ac_charge_hours_fast is None: + missing.append("AC Charge Hours") + if dc_charge_hours_fast is None: + missing.append("DC Charge Hours") + if elect_revenue_per_hour_arr_fast is None: + missing.append("Electricity Revenue Per Hour") + if bat_discharge_hours_fast is None: + missing.append("Battery Discharge Hours") + if ev_discharge_hours_fast is None: + missing.append("EV Discharge Hours") + msg = ", ".join(missing) + logger.error("Mandatory data missing - %s", msg) + raise ValueError(f"Mandatory data missing: {msg}") - # Pre-fetch data - load_energy_array = np.array(self.load_energy_array) - pv_prediction_wh = np.array(self.pv_prediction_wh) - elect_price_hourly = np.array(self.elect_price_hourly) - ev_charge_hours = np.array(self.ev_charge_hours) - ac_charge_hours = np.array(self.ac_charge_hours) - dc_charge_hours = np.array(self.dc_charge_hours) - elect_revenue_per_hour_arr = np.array(self.elect_revenue_per_hour_arr) - - # Fetch objects - battery = self.battery - ev = self.ev - home_appliance = self.home_appliance - inverter = self.inverter - - if not (len(load_energy_array) == len(pv_prediction_wh) == len(elect_price_hourly)): - error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(pv_prediction_wh)}, Electricity Price = {len(elect_price_hourly)}" + if not ( + len(load_energy_array_fast) + == len(pv_prediction_wh_fast) + == len(elect_price_hourly_fast) + ): + error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array_fast)}, PV Forecast = {len(pv_prediction_wh_fast)}, Electricity Price = {len(elect_price_hourly_fast)}" logger.error(error_msg) raise ValueError(error_msg) - end_hour = len(load_energy_array) + end_hour = len(load_energy_array_fast) total_hours = end_hour - start_hour # Pre-allocate arrays for the results, optimized for speed @@ -200,82 +217,104 @@ class GeneticSimulation(PydanticBaseModel): electricity_price_per_hour = np.full((total_hours), np.nan) # Set initial state - if battery: - soc_per_hour[0] = battery.current_soc_percentage() - if ev: - soc_ev_per_hour[0] = ev.current_soc_percentage() + if battery_fast: + soc_per_hour[0] = battery_fast.current_soc_percentage() + # Fill the charge array of the battery + dc_charge_hours_fast[0:start_hour] = 0 + dc_charge_hours_fast[end_hour:] = 0 + ac_charge_hours_fast[0:start_hour] = 0 + dc_charge_hours_fast[end_hour:] = 0 + battery_fast.charge_array = np.where( + ac_charge_hours_fast != 0, ac_charge_hours_fast, dc_charge_hours_fast + ) + # Fill the discharge array of the battery + bat_discharge_hours_fast[0:start_hour] = 0 + bat_discharge_hours_fast[end_hour:] = 0 + battery_fast.discharge_array = bat_discharge_hours_fast + + if ev_fast: + soc_ev_per_hour[0] = ev_fast.current_soc_percentage() + # Fill the charge array of the ev + ev_charge_hours_fast[0:start_hour] = 0 + ev_charge_hours_fast[end_hour:] = 0 + ev_fast.charge_array = ev_charge_hours_fast + # Fill the discharge array of the ev + ev_discharge_hours_fast[0:start_hour] = 0 + ev_discharge_hours_fast[end_hour:] = 0 + ev_fast.discharge_array = ev_discharge_hours_fast + + if home_appliance_fast and self.home_appliance_start_hour: + home_appliance_enabled = True + self.home_appliance_start_hour = home_appliance_fast.set_starting_time( + self.home_appliance_start_hour, start_hour + ) + else: + home_appliance_enabled = False for hour in range(start_hour, end_hour): hour_idx = hour - start_hour - # save begin states - if battery: - soc_per_hour[hour_idx] = battery.current_soc_percentage() - if ev: - soc_ev_per_hour[hour_idx] = ev.current_soc_percentage() - # Accumulate loads and PV generation - consumption = load_energy_array[hour] + consumption = load_energy_array_fast[hour] losses_wh_per_hour[hour_idx] = 0.0 # Home appliances - if home_appliance: - ha_load = home_appliance.get_load_for_hour(hour) + if home_appliance_enabled: + ha_load = home_appliance_fast.get_load_for_hour(hour) # type: ignore[union-attr] consumption += ha_load home_appliance_wh_per_hour[hour_idx] = ha_load # E-Auto handling - if ev and ev_charge_hours[hour] > 0: - loaded_energy_ev, verluste_eauto = ev.charge_energy( - None, hour, relative_power=ev_charge_hours[hour] - ) - consumption += loaded_energy_ev - losses_wh_per_hour[hour_idx] += verluste_eauto + if ev_fast: + soc_ev_per_hour[hour_idx] = ev_fast.current_soc_percentage() # save begin state + if ev_charge_hours_fast[hour] > 0: + loaded_energy_ev, verluste_eauto = ev_fast.charge_energy( + wh=None, hour=hour, charge_factor=ev_charge_hours_fast[hour] + ) + consumption += loaded_energy_ev + losses_wh_per_hour[hour_idx] += verluste_eauto # Process inverter logic energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = ( 0.0 ) - hour_ac_charge = ac_charge_hours[hour] - hour_dc_charge = dc_charge_hours[hour] - hourly_electricity_price = elect_price_hourly[hour] - hourly_energy_revenue = elect_revenue_per_hour_arr[hour] - - if battery: - battery.set_charge_allowed_for_hour(hour_dc_charge, hour) - - if inverter: - energy_produced = pv_prediction_wh[hour] + if inverter_fast: + energy_produced = pv_prediction_wh_fast[hour] ( energy_feedin_grid_actual, energy_consumption_grid_actual, losses, eigenverbrauch, - ) = inverter.process_energy(energy_produced, consumption, hour) + ) = inverter_fast.process_energy(energy_produced, consumption, hour) # AC PV Battery Charge - if battery and hour_ac_charge > 0.0: - battery.set_charge_allowed_for_hour(1, hour) - battery_charged_energy_actual, battery_losses_actual = battery.charge_energy( - None, hour, relative_power=hour_ac_charge - ) + if battery_fast: + soc_per_hour[hour_idx] = battery_fast.current_soc_percentage() # save begin state + hour_ac_charge = ac_charge_hours_fast[hour] + if hour_ac_charge > 0.0: + battery_charged_energy_actual, battery_losses_actual = ( + battery_fast.charge_energy(None, hour, charge_factor=hour_ac_charge) + ) - total_battery_energy = battery_charged_energy_actual + battery_losses_actual - consumption += total_battery_energy - energy_consumption_grid_actual += total_battery_energy - losses_wh_per_hour[hour_idx] += battery_losses_actual + total_battery_energy = battery_charged_energy_actual + battery_losses_actual + consumption += total_battery_energy + energy_consumption_grid_actual += total_battery_energy + losses_wh_per_hour[hour_idx] += battery_losses_actual # Update hourly arrays feedin_energy_per_hour[hour_idx] = energy_feedin_grid_actual consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual losses_wh_per_hour[hour_idx] += losses loads_energy_per_hour[hour_idx] = consumption + hourly_electricity_price = elect_price_hourly_fast[hour] electricity_price_per_hour[hour_idx] = hourly_electricity_price # Financial calculations costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price - revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_energy_revenue + revenue_per_hour[hour_idx] = ( + energy_feedin_grid_actual * elect_revenue_per_hour_arr_fast[hour] + ) total_cost = np.nansum(costs_per_hour) total_losses = np.nansum(losses_wh_per_hour) @@ -289,7 +328,7 @@ class GeneticSimulation(PydanticBaseModel): "Kosten_Euro_pro_Stunde": costs_per_hour, "akku_soc_pro_stunde": soc_per_hour, "Einnahmen_Euro_pro_Stunde": revenue_per_hour, - "Gesamtbilanz_Euro": total_cost - total_revenue, + "Gesamtbilanz_Euro": total_cost - total_revenue, # Fitness score ("FitnessMin") "EAuto_SoC_pro_Stunde": soc_ev_per_hour, "Gesamteinnahmen_Euro": total_revenue, "Gesamtkosten_Euro": total_cost, @@ -574,27 +613,33 @@ class GeneticOptimization(OptimizationBase): discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( individual ) + if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int: - self.simulation.set_home_appliance_start( - washingstart_int, global_start_hour=self.ems.start_datetime.hour - ) + # Set start hour for appliance + self.simulation.home_appliance_start_hour = washingstart_int - ac, dc, discharge = self.decode_charge_discharge(discharge_hours_bin) + ac_charge_hours, dc_charge_hours, discharge = self.decode_charge_discharge( + discharge_hours_bin + ) - self.simulation.set_akku_discharge_hours(discharge) + self.simulation.bat_discharge_hours = discharge # Set DC charge hours only if DC optimization is enabled if self.optimize_dc_charge: - self.simulation.set_akku_dc_charge_hours(dc) - self.simulation.set_akku_ac_charge_hours(ac) + self.simulation.dc_charge_hours = dc_charge_hours + else: + self.simulation.dc_charge_hours = np.full(self.config.prediction.hours, 1) + self.simulation.ac_charge_hours = ac_charge_hours if eautocharge_hours_index is not None: eautocharge_hours_float = np.array( [self.ev_possible_charge_values[i] for i in eautocharge_hours_index], float, ) - self.simulation.set_ev_charge_hours(eautocharge_hours_float) + # discharge is set to 0 by default + self.simulation.ev_charge_hours = eautocharge_hours_float else: - self.simulation.set_ev_charge_hours(np.full(self.config.prediction.hours, 0)) + # discharge is set to 0 by default + self.simulation.ev_charge_hours = np.full(self.config.prediction.hours, 0) # Do the simulation and return result. return self.simulation.simulate(self.ems.start_datetime.hour) @@ -606,21 +651,57 @@ class GeneticOptimization(OptimizationBase): start_hour: int, worst_case: bool, ) -> tuple[float]: - """Evaluate the fitness of an individual solution based on the simulation results.""" + """Evaluate the fitness score of a single individual in the DEAP genetic algorithm. + + This method runs a simulation based on the provided individual genome and + optimization parameters. The resulting performance is converted into a + fitness score compatible with DEAP (i.e., returned as a 1-tuple). + + Args: + individual (list[int]): + The genome representing one candidate solution. + parameters (GeneticOptimizationParameters): + Optimization parameters that influence simulation behavior, + constraints, and scoring logic. + start_hour (int): + The simulation start hour (0–23 or domain-specific). + Used to initialize time-based scheduling or constraints. + worst_case (bool): + If True, evaluates the solution under worst-case assumptions + (e.g., pessimistic forecasts or boundary conditions). + If False, uses nominal assumptions. + + Returns: + tuple[float]: + A single-element tuple containing the computed fitness score. + Lower score is better: "FitnessMin". + + Raises: + ValueError: If input arguments are invalid or the individual structure + is not compatible with the simulation. + RuntimeError: If the simulation fails or cannot produce results. + + Notes: + The resulting score should match DEAP's expected format: a tuple, even + if only a single scalar fitness value is returned. + """ try: - o = self.evaluate_inner(individual) + simulation_result = self.evaluate_inner(individual) except Exception as e: - return (100000.0,) # Return a high penalty in case of an exception + # Return bad fitness score ("FitnessMin") in case of an exception + return (100000.0,) - gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0) - - discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( - individual - ) + gesamtbilanz = simulation_result["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0) # EV 100% & charge not allowed if self.optimize_ev: - eauto_soc_per_hour = np.array(o.get("EAuto_SoC_pro_Stunde", [])) # Beispielkey + discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( + individual + ) + + eauto_soc_per_hour = np.array( + simulation_result.get("EAuto_SoC_pro_Stunde", []) + ) # Beispielkey if eauto_soc_per_hour is None or eautocharge_hours_index is None: raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None") @@ -686,8 +767,8 @@ class GeneticOptimization(OptimizationBase): # More metrics individual.extra_data = ( # type: ignore[attr-defined] - o["Gesamtbilanz_Euro"], - o["Gesamt_Verluste"], + simulation_result["Gesamtbilanz_Euro"], + simulation_result["Gesamt_Verluste"], parameters.eauto.min_soc_percentage - self.simulation.ev.current_soc_percentage() if parameters.eauto and self.simulation.ev else 0, @@ -701,7 +782,7 @@ class GeneticOptimization(OptimizationBase): ) gesamtbilanz += -restwert_akku - if self.optimize_ev: + if self.optimize_ev and parameters.eauto and self.simulation.ev: try: penalty = self.config.optimization.genetic.penalties["ev_soc_miss"] except: @@ -710,16 +791,14 @@ class GeneticOptimization(OptimizationBase): logger.error( "Penalty function parameter `ev_soc_miss` not configured, using {}.", penalty ) - gesamtbilanz += max( - 0, - ( - parameters.eauto.min_soc_percentage - - self.simulation.ev.current_soc_percentage() - if parameters.eauto and self.simulation.ev - else 0 + ev_soc_percentage = self.simulation.ev.current_soc_percentage() + if ( + ev_soc_percentage < parameters.eauto.min_soc_percentage + or ev_soc_percentage > parameters.eauto.max_soc_percentage + ): + gesamtbilanz += ( + abs(parameters.eauto.min_soc_percentage - ev_soc_percentage) * penalty ) - * penalty, - ) return (gesamtbilanz,) @@ -825,7 +904,7 @@ class GeneticOptimization(OptimizationBase): parameters.pv_akku, prediction_hours=self.config.prediction.hours, ) - akku.set_charge_per_hour(np.full(self.config.prediction.hours, 1)) + akku.set_charge_per_hour(np.full(self.config.prediction.hours, 0)) eauto: Optional[Battery] = None if parameters.eauto: @@ -917,7 +996,7 @@ class GeneticOptimization(OptimizationBase): ) # home appliance may have choosen a different appliance start hour if self.simulation.home_appliance: - washingstart_int = self.simulation.home_appliance.get_appliance_start() + washingstart_int = self.simulation.home_appliance_start_hour eautocharge_hours_float = ( [self.ev_possible_charge_values[i] for i in eautocharge_hours_index] @@ -925,12 +1004,28 @@ class GeneticOptimization(OptimizationBase): else None ) - ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin) + # Simulation may have changed something, use simulation values + ac_charge_hours = self.simulation.ac_charge_hours + if ac_charge_hours is None: + ac_charge_hours = [] + else: + ac_charge_hours = ac_charge_hours.tolist() + dc_charge_hours = self.simulation.dc_charge_hours + if dc_charge_hours is None: + dc_charge_hours = [] + else: + dc_charge_hours = dc_charge_hours.tolist() + discharge = self.simulation.bat_discharge_hours + if discharge is None: + discharge = [] + else: + discharge = discharge.tolist() + # Visualize the results visualize = { - "ac_charge": ac_charge.tolist(), - "dc_charge": dc_charge.tolist(), - "discharge_allowed": discharge.tolist(), + "ac_charge": ac_charge_hours, + "dc_charge": dc_charge_hours, + "discharge_allowed": discharge, "eautocharge_hours_float": eautocharge_hours_float, "result": simulation_result, "eauto_obj": self.simulation.ev.to_dict() if self.simulation.ev else None, @@ -946,8 +1041,8 @@ class GeneticOptimization(OptimizationBase): return GeneticSolution( **{ - "ac_charge": ac_charge, - "dc_charge": dc_charge, + "ac_charge": ac_charge_hours, + "dc_charge": dc_charge_hours, "discharge_allowed": discharge, "eautocharge_hours_float": eautocharge_hours_float, "result": GeneticSimulationResult(**simulation_result), diff --git a/src/akkudoktoreos/optimization/genetic/geneticdevices.py b/src/akkudoktoreos/optimization/genetic/geneticdevices.py index 00fca1b..13ccf2a 100644 --- a/src/akkudoktoreos/optimization/genetic/geneticdevices.py +++ b/src/akkudoktoreos/optimization/genetic/geneticdevices.py @@ -74,6 +74,11 @@ class BaseBatteryParameters(DeviceParameters): le=100, description="An integer representing the maximum state of charge (SOC) of the battery in percentage.", ) + charge_rates: Optional[list[float]] = Field( + default=None, + description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", + examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None], + ) class SolarPanelBatteryParameters(BaseBatteryParameters): @@ -90,11 +95,6 @@ class ElectricVehicleParameters(BaseBatteryParameters): initial_soc_percentage: int = initial_soc_percentage_field( "An integer representing the current state of charge (SOC) of the battery in percentage." ) - charge_rates: Optional[list[float]] = Field( - default=None, - description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.", - examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None], - ) class HomeApplianceParameters(DeviceParameters): diff --git a/src/akkudoktoreos/optimization/genetic/geneticparams.py b/src/akkudoktoreos/optimization/genetic/geneticparams.py index 600a68a..bc8103a 100644 --- a/src/akkudoktoreos/optimization/genetic/geneticparams.py +++ b/src/akkudoktoreos/optimization/genetic/geneticparams.py @@ -457,7 +457,7 @@ class GeneticOptimizationParameters( { "device_id": "ev11", "capacity_wh": 50000, - "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], + "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], "min_soc_percentage": 70, } ] @@ -483,7 +483,7 @@ class GeneticOptimizationParameters( { "device_id": "ev12", "capacity_wh": 50000, - "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], + "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], "min_soc_percentage": 70, } ] diff --git a/src/akkudoktoreos/optimization/genetic/geneticsolution.py b/src/akkudoktoreos/optimization/genetic/geneticsolution.py index 2ac6fc7..fe2d665 100644 --- a/src/akkudoktoreos/optimization/genetic/geneticsolution.py +++ b/src/akkudoktoreos/optimization/genetic/geneticsolution.py @@ -6,7 +6,9 @@ import pandas as pd from loguru import logger from pydantic import Field, field_validator -from akkudoktoreos.config.config import get_config +from akkudoktoreos.core.coreabc import ( + ConfigMixin, +) from akkudoktoreos.core.emplan import ( DDBCInstruction, EnergyManagementPlan, @@ -109,7 +111,7 @@ class GeneticSimulationResult(GeneticParametersBaseModel): return NumpyEncoder.convert_numpy(field)[0] -class GeneticSolution(GeneticParametersBaseModel): +class GeneticSolution(ConfigMixin, GeneticParametersBaseModel): """**Note**: The first value of "Last_Wh_per_hour", "Netzeinspeisung_Wh_per_hour", and "Netzbezug_Wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged.""" ac_charge: list[float] = Field( @@ -228,18 +230,20 @@ class GeneticSolution(GeneticParametersBaseModel): """ from akkudoktoreos.core.ems import get_ems - config = get_config() start_datetime = get_ems().start_datetime + start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour interval_hours = 1 power_to_energy_per_interval_factor = 1.0 # --- Create index based on list length and interval --- - n_points = len(self.result.Kosten_Euro_pro_Stunde) + # Ensure we only use the minimum of results and commands if differing + periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_hour) time_index = pd.date_range( start=start_datetime, - periods=n_points, + periods=periods, freq=f"{interval_hours}h", ) + n_points = len(time_index) end_datetime = start_datetime.add(hours=n_points) # Fill solution into dataframe with correct column names @@ -256,26 +260,42 @@ class GeneticSolution(GeneticParametersBaseModel): solution = pd.DataFrame( { "date_time": time_index, - "load_energy_wh": self.result.Last_Wh_pro_Stunde, - "grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde, - "grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde, - "elec_price_prediction_amt_kwh": [v * 1000 for v in self.result.Electricity_price], - "costs_amt": self.result.Kosten_Euro_pro_Stunde, - "revenue_amt": self.result.Einnahmen_Euro_pro_Stunde, - "losses_energy_wh": self.result.Verluste_Pro_Stunde, + # result starts at start_day_hour + "load_energy_wh": self.result.Last_Wh_pro_Stunde[:n_points], + "grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde[:n_points], + "grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde[:n_points], + "costs_amt": self.result.Kosten_Euro_pro_Stunde[:n_points], + "revenue_amt": self.result.Einnahmen_Euro_pro_Stunde[:n_points], + "losses_energy_wh": self.result.Verluste_Pro_Stunde[:n_points], }, index=time_index, ) # Add battery data - solution["battery1_soc_factor"] = [v / 100 for v in self.result.akku_soc_pro_stunde] - operation: dict[str, list[float]] = {} - for hour, rate in enumerate(self.ac_charge): - if hour >= n_points: + solution["battery1_soc_factor"] = [ + v / 100 + for v in self.result.akku_soc_pro_stunde[:n_points] # result starts at start_day_hour + ] + operation: dict[str, list[float]] = { + "genetic_ac_charge_factor": [], + "genetic_dc_charge_factor": [], + "genetic_discharge_allowed_factor": [], + } + # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day + for hour_idx, rate in enumerate(self.ac_charge): + if hour_idx < start_day_hour: + continue + if hour_idx >= start_day_hour + n_points: break + ac_charge_hour = self.ac_charge[hour_idx] + dc_charge_hour = self.dc_charge[hour_idx] + discharge_allowed_hour = bool(self.discharge_allowed[hour_idx]) operation_mode, operation_mode_factor = self._battery_operation_from_solution( - self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour]) + ac_charge_hour, dc_charge_hour, discharge_allowed_hour ) + operation["genetic_ac_charge_factor"].append(ac_charge_hour) + operation["genetic_dc_charge_factor"].append(dc_charge_hour) + operation["genetic_discharge_allowed_factor"].append(discharge_allowed_hour) for mode in BatteryOperationMode: mode_key = f"battery1_{mode.lower()}_op_mode" factor_key = f"battery1_{mode.lower()}_op_factor" @@ -289,15 +309,22 @@ class GeneticSolution(GeneticParametersBaseModel): operation[mode_key].append(0.0) operation[factor_key].append(0.0) for key in operation.keys(): + if len(operation[key]) != n_points: + error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}" + logger.error(error_msg) + raise ValueError(error_msg) solution[key] = operation[key] # Add EV battery solution + # eautocharge_hours_float start at hour 0 of start day + # result.EAuto_SoC_pro_Stunde start at start_datetime.hour if self.eauto_obj: if self.eautocharge_hours_float is None: # Electric vehicle is full enough. No load times. solution[f"{self.eauto_obj.device_id}_soc_factor"] = [ self.eauto_obj.initial_soc_percentage / 100.0 ] * n_points + solution["genetic_ev_charge_factor"] = [0.0] * n_points # operation modes operation_mode = BatteryOperationMode.IDLE for mode in BatteryOperationMode: @@ -311,12 +338,17 @@ class GeneticSolution(GeneticParametersBaseModel): solution[factor_key] = [0.0] * n_points else: solution[f"{self.eauto_obj.device_id}_soc_factor"] = [ - v / 100 for v in self.result.EAuto_SoC_pro_Stunde + v / 100 for v in self.result.EAuto_SoC_pro_Stunde[:n_points] ] - operation = {} - for hour, rate in enumerate(self.eautocharge_hours_float): - if hour >= n_points: + operation = { + "genetic_ev_charge_factor": [], + } + for hour_idx, rate in enumerate(self.eautocharge_hours_float): + if hour_idx < start_day_hour: + continue + if hour_idx >= start_day_hour + n_points: break + operation["genetic_ev_charge_factor"].append(rate) operation_mode, operation_mode_factor = self._battery_operation_from_solution( rate, 0.0, False ) @@ -333,11 +365,16 @@ class GeneticSolution(GeneticParametersBaseModel): operation[mode_key].append(0.0) operation[factor_key].append(0.0) for key in operation.keys(): + if len(operation[key]) != n_points: + error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}" + logger.error(error_msg) + raise ValueError(error_msg) solution[key] = operation[key] # Add home appliance data if self.washingstart: - solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour + # result starts at start_day_hour + solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour[:n_points] # Fill prediction into dataframe with correct column names # - pvforecast_ac_energy_wh_energy_wh: PV energy prediction (positive) in wh @@ -445,10 +482,13 @@ class GeneticSolution(GeneticParametersBaseModel): generated_at=to_datetime(), comment="Optimization solution derived from GeneticSolution.", valid_from=start_datetime, - valid_until=start_datetime.add(hours=config.optimization.horizon_hours), + valid_until=start_datetime.add(hours=self.config.optimization.horizon_hours), total_losses_energy_wh=self.result.Gesamt_Verluste, total_revenues_amt=self.result.Gesamteinnahmen_Euro, total_costs_amt=self.result.Gesamtkosten_Euro, + fitness_score={ + self.result.Gesamtkosten_Euro, + }, prediction=PydanticDateTimeDataFrame.from_dataframe(prediction), solution=PydanticDateTimeDataFrame.from_dataframe(solution), ) @@ -460,6 +500,7 @@ class GeneticSolution(GeneticParametersBaseModel): from akkudoktoreos.core.ems import get_ems start_datetime = get_ems().start_datetime + start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour plan = EnergyManagementPlan( id=f"plan-genetic@{to_datetime(as_string=True)}", generated_at=to_datetime(), @@ -471,10 +512,15 @@ class GeneticSolution(GeneticParametersBaseModel): last_operation_mode: Optional[str] = None last_operation_mode_factor: Optional[float] = None resource_id = "battery1" - logger.debug("BAT: {} - {}", resource_id, self.ac_charge) - for hour, rate in enumerate(self.ac_charge): + # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day + logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_hour:]) + for hour_idx, rate in enumerate(self.ac_charge): + if hour_idx < start_day_hour: + continue operation_mode, operation_mode_factor = self._battery_operation_from_solution( - self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour]) + self.ac_charge[hour_idx], + self.dc_charge[hour_idx], + bool(self.discharge_allowed[hour_idx]), ) if ( operation_mode == last_operation_mode @@ -484,7 +530,7 @@ class GeneticSolution(GeneticParametersBaseModel): continue last_operation_mode = operation_mode last_operation_mode_factor = operation_mode_factor - execution_time = start_datetime.add(hours=hour) + execution_time = start_datetime.add(hours=hour_idx - start_day_hour) plan.add_instruction( FRBCInstruction( resource_id=resource_id, @@ -496,6 +542,7 @@ class GeneticSolution(GeneticParametersBaseModel): ) # Add EV battery instructions (fill rate based control) + # eautocharge_hours_float start at hour 0 of start day if self.eauto_obj: resource_id = self.eauto_obj.device_id if self.eautocharge_hours_float is None: @@ -513,8 +560,12 @@ class GeneticSolution(GeneticParametersBaseModel): else: last_operation_mode = None last_operation_mode_factor = None - logger.debug("EV: {} - {}", resource_id, self.eauto_obj.charge_array) - for hour, rate in enumerate(self.eautocharge_hours_float): + logger.debug( + "EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:] + ) + for hour_idx, rate in enumerate(self.eautocharge_hours_float): + if hour_idx < start_day_hour: + continue operation_mode, operation_mode_factor = self._battery_operation_from_solution( rate, 0.0, False ) @@ -526,7 +577,7 @@ class GeneticSolution(GeneticParametersBaseModel): continue last_operation_mode = operation_mode last_operation_mode_factor = operation_mode_factor - execution_time = start_datetime.add(hours=hour) + execution_time = start_datetime.add(hours=hour_idx - start_day_hour) plan.add_instruction( FRBCInstruction( resource_id=resource_id, @@ -542,7 +593,7 @@ class GeneticSolution(GeneticParametersBaseModel): resource_id = "homeappliance1" operation_mode = ApplianceOperationMode.RUN # type: ignore[assignment] operation_mode_factor = 1.0 - execution_time = start_datetime.add(hours=self.washingstart) + execution_time = start_datetime.add(hours=self.washingstart - start_day_hour) plan.add_instruction( DDBCInstruction( resource_id=resource_id, diff --git a/src/akkudoktoreos/optimization/optimization.py b/src/akkudoktoreos/optimization/optimization.py index 3644b8d..636a452 100644 --- a/src/akkudoktoreos/optimization/optimization.py +++ b/src/akkudoktoreos/optimization/optimization.py @@ -110,6 +110,8 @@ class OptimizationSolution(PydanticBaseModel): total_costs_amt: float = Field(description="The total costs [money amount].") + fitness_score: set[float] = Field(description="The fitness score as a set of fitness values.") + prediction: PydanticDateTimeDataFrame = Field( description=( "Datetime data frame with time series prediction data per optimization interval:" diff --git a/src/akkudoktoreos/server/dash/plan.py b/src/akkudoktoreos/server/dash/plan.py index 4831a7a..be6eacc 100644 --- a/src/akkudoktoreos/server/dash/plan.py +++ b/src/akkudoktoreos/server/dash/plan.py @@ -54,8 +54,21 @@ color_palette = { "pink-500": "#EC4899", # pink-500 "rose-500": "#F43F5E", # rose-500 } +# Color names colors = list(color_palette.keys()) +# Colums that are exclude from the the solution card display +# They are currently not used or are covered by others +solution_excludes = [ + "date_time", + "_op_mode", + "_fault_", + "_outage_supply_", + "_reserve_backup_", + "_ramp_rate_control_", + "_frequency_regulation_", +] + # Current state of solution displayed solution_visible: dict[str, bool] = { "pv_energy_wh": True, @@ -122,7 +135,9 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti instruction_columns = [ instruction for instruction in solution_columns - if instruction.endswith("op_mode") or instruction.endswith("op_factor") + if instruction.endswith("op_mode") + or instruction.endswith("op_factor") + or instruction.startswith("genetic_") ] solution_columns = [x for x in solution_columns if x not in instruction_columns] @@ -140,13 +155,26 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti prediction_columns_to_join = prediction_df.columns.difference(df.columns) df = df.join(prediction_df[prediction_columns_to_join], how="inner") - # Remove time offset from UTC to get naive local time and make bokey plot in local time + # Exclude columns that currently do not have a value + excludes = solution_excludes + for instruction in instruction_columns: + if instruction.endswith("op_mode") and df[instruction].eq(0).all(): + # Exclude op_mode and op_factor if all op_mode is 0 + excludes.append(instruction) + excludes.append(f"{instruction[:-4]}factor") + + # Make bokey plot in local time at location + # Determine daylight saving time change dst_offsets = df.index.map(lambda x: x.dst().total_seconds() / 3600) + # Determine desired timezone if config.general is None or config.general.timezone is None: date_time_tz = "Europe/Berlin" else: date_time_tz = config.general.timezone - df["date_time"] = pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz) + # Ensure original date_time is parsed as UTC and convert to local time + df["date_time_local"] = ( + pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz).dt.tz_localize(None) + ) # There is a special case if we have daylight saving time change in the time series if dst_offsets.nunique() > 1: @@ -241,21 +269,12 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti # Create line renderers for each column renderers = {} + # Have an index for the colors of predictions, solutions and instructions. + prediction_color_idx = 0 + solution_color_idx = int(len(colors) * 0.33) + 1 + instruction_color_idx = int(len(colors) * 0.66) + 1 for i, col in enumerate(sorted(df.columns)): # Exclude some columns that are currently not used or are covered by others - excludes = [ - "date_time", - "_op_mode", - "_fault_", - "_forced_discharge_", - "_outage_supply_", - "_reserve_backup_", - "_ramp_rate_control_", - "_frequency_regulation_", - "_grid_support_export_", - "_peak_shaving_", - ] - # excludes = ["date_time"] if any(exclude in col for exclude in excludes): continue if col in solution_visible: @@ -265,73 +284,85 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti solution_visible[col] = visible if col in solution_color: color = solution_color[col] - elif col == "pv_energy_wh": - color = "yellow-500" - solution_color[col] = color - elif col == "elec_price_amt_kwh": - color = "red-500" - solution_color[col] = color else: - color = colors[i % len(colors)] + if col in prediction_columns: + color = colors[prediction_color_idx % len(colors)] + prediction_color_idx += 3 + elif col in solution_columns: + color = colors[solution_color_idx % len(colors)] + solution_color_idx += 3 + else: + color = colors[instruction_color_idx % len(colors)] + instruction_color_idx += 3 + # Remember the color of this column solution_color[col] = color + if col in prediction_columns: + line_dash = "dotted" + else: + line_dash = "solid" if visible: - if col == "pv_energy_wh": - r = plot.vbar( - x="date_time", - top=col, - source=source, - width=BAR_WIDTH_1HOUR * 0.8, - legend_label=col, - color=color_palette[color], - level="underlay", - ) - elif col.endswith("energy_wh"): + if col.endswith("energy_wh"): r = plot.step( - x="date_time", + x="date_time_local", y=col, - mode="before", + mode="after", source=source, legend_label=col, color=color_palette[color], + line_dash=line_dash, + ) + elif col.endswith("soc_factor"): + r = plot.line( + x="date_time_local", + y=col, + source=source, + legend_label=col, + color=color_palette[color], + line_dash=line_dash, + y_range_name="factor", ) elif col.endswith("factor"): r = plot.step( - x="date_time", + x="date_time_local", y=col, - mode="before", + mode="after", source=source, legend_label=col, color=color_palette[color], + line_dash=line_dash, y_range_name="factor", ) elif col.endswith("mode"): r = plot.step( - x="date_time", + x="date_time_local", y=col, - mode="before", + mode="after", source=source, legend_label=col, color=color_palette[color], + line_dash=line_dash, y_range_name="factor", ) elif col.endswith("amt_kwh"): r = plot.step( - x="date_time", + x="date_time_local", y=col, - mode="before", + mode="after", source=source, legend_label=col, color=color_palette[color], + line_dash=line_dash, y_range_name="amt_kwh", ) elif col.endswith("amt"): r = plot.step( - x="date_time", + x="date_time_local", y=col, - mode="before", + mode="after", source=source, legend_label=col, color=color_palette[color], + line_dash=line_dash, y_range_name="amt", ) else: @@ -430,7 +461,16 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti ) return Grid( - Bokeh(plot), + Grid( + Bokeh(plot), + Card( + P(f"Total revenues: {solution.total_revenues_amt}"), + P(f"Total costs: {solution.total_costs_amt}"), + P(f"Total losses: {solution.total_losses_energy_wh / 1000} kWh"), + P(f"Fitness score: {solution.fitness_score}"), + ), + cols=1, + ), Checkbox, cls="w-full space-y-3 space-x-3", ) diff --git a/tests/test_battery.py b/tests/test_battery.py index 23b489b..0021a62 100644 --- a/tests/test_battery.py +++ b/tests/test_battery.py @@ -6,20 +6,53 @@ from akkudoktoreos.devices.genetic.battery import Battery, SolarPanelBatteryPara @pytest.fixture def setup_pv_battery(): + device_id="battery1" + capacity_wh=10000 + initial_soc_percentage=50 + charging_efficiency=0.88 + discharging_efficiency=0.88 + min_soc_percentage=20 + max_soc_percentage=80 + max_charge_power_w=8000 + hours=24 + params = SolarPanelBatteryParameters( - device_id="battery1", - capacity_wh=10000, - initial_soc_percentage=50, - min_soc_percentage=20, - max_soc_percentage=80, - max_charge_power_w=8000, - hours=24, + device_id=device_id, + capacity_wh=capacity_wh, + initial_soc_percentage=initial_soc_percentage, + charging_efficiency=charging_efficiency, + discharging_efficiency=discharging_efficiency, + min_soc_percentage=min_soc_percentage, + max_soc_percentage=max_soc_percentage, + max_charge_power_w=max_charge_power_w, + hours=hours, ) battery = Battery( params, prediction_hours=48, ) battery.reset() + + assert battery.parameters.device_id==device_id + assert battery.capacity_wh==capacity_wh + assert battery.initial_soc_percentage==initial_soc_percentage + assert battery.charging_efficiency==charging_efficiency + assert battery.initial_soc_percentage==initial_soc_percentage + assert battery.discharging_efficiency==discharging_efficiency + assert battery.max_soc_percentage==max_soc_percentage + assert battery.max_charge_power_w==max_charge_power_w + assert battery.soc_wh==float((initial_soc_percentage / 100) * capacity_wh) + assert battery.min_soc_wh==float((min_soc_percentage / 100) * capacity_wh) + assert battery.max_soc_wh==float((max_soc_percentage / 100) * capacity_wh) + assert np.all(battery.charge_array == 0) + assert np.all(battery.discharge_array == 0) + + # Init for test + battery.charge_array = np.full(battery.prediction_hours, 1) + battery.discharge_array = np.full(battery.prediction_hours, 1) + assert np.all(battery.charge_array == 1) + assert np.all(battery.discharge_array == 1) + return battery @@ -164,17 +197,47 @@ def test_charge_energy_not_allowed_hour(setup_pv_battery): ), "SOC should remain unchanged" -def test_charge_energy_relative_power(setup_pv_battery): +@pytest.mark.parametrize( + "wh, charge_factor, expected_raises", + [ + (None, 0.5, False), # Expected to work normally (if capacity allows) + (None, 1.0, False), # Often still OK, depending on fixture capacity + (None, 2.0, False), # Exceeds max charge → always ValueError + (1000, 0, False), + (1000, 1.0, True), + ], +) +def test_charge_energy_with_charge_factor(setup_pv_battery, wh, charge_factor, expected_raises): battery = setup_pv_battery + hour = 4 - relative_power = 0.5 # 50% of max charge power - charged_wh, losses_wh = battery.charge_energy(wh=None, hour=4, relative_power=relative_power) + if wh is not None and charge_factor == 0.0: # mode 1 + raw_request_wh = wh + else: + raw_request_wh = battery.max_charge_power_w * charge_factor + raw_capacity_wh = max(battery.max_soc_wh - battery.soc_wh, 0.0) - assert charged_wh > 0, "Charging should occur with relative power" - assert losses_wh >= 0, "Losses should not be negative" - assert charged_wh <= battery.max_charge_power_w * relative_power, ( - "Charging should respect relative power limit" + if expected_raises: + # Should raise + with pytest.raises(ValueError): + battery.charge_energy( + wh=wh, + hour=hour, + charge_factor=charge_factor, + ) + return + + # Should NOT raise + charged_wh, losses_wh = battery.charge_energy( + wh=wh, + hour=hour, + charge_factor=charge_factor, ) + + # Expectations + assert charged_wh > 0, "Charging should occur with charge factor" + assert losses_wh >= 0, "Losses must not be negative" + assert charged_wh <= raw_request_wh, "Charging must not exceed request" assert battery.soc_wh > 0, "SOC should increase after charging" @@ -198,6 +261,13 @@ def setup_car_battery(): prediction_hours=48, ) battery.reset() + + # Init for test + battery.charge_array = np.full(battery.prediction_hours, 1) + battery.discharge_array = np.full(battery.prediction_hours, 1) + assert np.all(battery.charge_array == 1) + assert np.all(battery.discharge_array == 1) + return battery diff --git a/tests/test_config.py b/tests/test_config.py index acae32b..3dbaff5 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -3,11 +3,24 @@ from pathlib import Path from typing import Any, Optional, Union from unittest.mock import patch +import numpy as np import pytest from loguru import logger from pydantic import IPvAnyAddress, ValidationError from akkudoktoreos.config.config import ConfigEOS, GeneralSettings +from akkudoktoreos.devices.devices import BATTERY_DEFAULT_CHARGE_RATES + + +def assert_values_equal(actual, expected): + """Compare values, handling lists and numpy arrays.""" + if isinstance(actual, (list, np.ndarray)) or isinstance(expected, (list, np.ndarray)): + # Convert both to numpy arrays for comparison + actual_arr = np.array(actual) + expected_arr = np.array(expected) + assert np.array_equal(actual_arr, expected_arr), f"Expected {expected_arr}, but got {actual_arr}" + else: + assert actual == expected, f"Expected {expected}, but got {actual}" # overwrite config_mixin fixture from conftest @@ -352,7 +365,7 @@ def test_config_common_settings_timezone_none_when_coordinates_missing(): [ ( "devices.electric_vehicles[0].charge_rates", - [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], + BATTERY_DEFAULT_CHARGE_RATES, ) ], ValueError, @@ -364,10 +377,10 @@ def test_config_common_settings_timezone_none_when_coordinates_missing(): [ ( "devices.electric_vehicles[0].charge_rates", - [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], + BATTERY_DEFAULT_CHARGE_RATES, ) ], - TypeError, + KeyError, ), # Invalid index (no number) ( @@ -376,10 +389,10 @@ def test_config_common_settings_timezone_none_when_coordinates_missing(): [ ( "devices.electric_vehicles[0].charge_rates", - [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0], + BATTERY_DEFAULT_CHARGE_RATES, ) ], - IndexError, + KeyError, ), # Unset value (set None) ( @@ -388,7 +401,7 @@ def test_config_common_settings_timezone_none_when_coordinates_missing(): [ ( "devices.electric_vehicles[0].charge_rates", - None, + BATTERY_DEFAULT_CHARGE_RATES, ) ], None, @@ -400,17 +413,13 @@ def test_set_nested_key(path, value, expected, exception, config_eos): config_eos.set_nested_value(path, value) for expected_path, expected_value in expected: actual_value = eval(f"config_eos.{expected_path}") - assert actual_value == expected_value, ( - f"Expected {expected_value} at {expected_path}, but got {actual_value}" - ) + assert_values_equal(actual_value, expected_value) else: try: config_eos.set_nested_value(path, value) for expected_path, expected_value in expected: actual_value = eval(f"config_eos.{expected_path}") - assert actual_value == expected_value, ( - f"Expected {expected_value} at {expected_path}, but got {actual_value}" - ) + assert_values_equal(actual_value, expected_value) pytest.fail( f"Expected exception {exception} but none was raised. Set '{expected_path}' to '{actual_value}'" ) @@ -508,19 +517,19 @@ def test_merge_settings_partial(config_eos): config_eos.merge_settings_from_dict(partial_settings) assert config_eos.devices.max_electric_vehicles == 1 assert len(config_eos.devices.electric_vehicles) == 1 - assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0] + assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]) # Assure re-apply generates the same config config_eos.merge_settings_from_dict(partial_settings) assert config_eos.devices.max_electric_vehicles == 1 assert len(config_eos.devices.electric_vehicles) == 1 - assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0] + assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]) # Assure update keeps same values config_eos.update() assert config_eos.devices.max_electric_vehicles == 1 assert len(config_eos.devices.electric_vehicles) == 1 - assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0] + assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]) def test_merge_settings_empty(config_eos): diff --git a/tests/test_geneticsimulation.py b/tests/test_geneticsimulation.py index 57c1778..fbcf3ed 100644 --- a/tests/test_geneticsimulation.py +++ b/tests/test_geneticsimulation.py @@ -65,7 +65,6 @@ def genetic_simulation(config_eos) -> GeneticSimulation: optimization_hours = config_eos.optimization.horizon_hours, prediction_hours = config_eos.prediction.hours, ) - home_appliance.set_starting_time(2) # Example initialization of electric car battery eauto = Battery( @@ -250,6 +249,17 @@ def genetic_simulation(config_eos) -> GeneticSimulation: home_appliance=home_appliance, ) + # Init for test + assert simulation.ac_charge_hours is not None + assert simulation.dc_charge_hours is not None + assert simulation.bat_discharge_hours is not None + assert simulation.ev_charge_hours is not None + simulation.ac_charge_hours[start_hour] = 1.0 + simulation.dc_charge_hours[start_hour] = 1.0 + simulation.bat_discharge_hours[start_hour] = 1.0 + simulation.ev_charge_hours[start_hour] = 1.0 + simulation.home_appliance_start_hour = 2 + return simulation @@ -321,34 +331,34 @@ def test_simulation(genetic_simulation): # Verify that the value at index 0 is 'None' # Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent - assert result["Netzbezug_Wh_pro_Stunde"][1] == 0, ( - "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 0." + assert result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13, ( + "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13." ) # Verify the total balance - assert abs(result["Gesamtbilanz_Euro"] - 1.958185274567674) < 1e-5, ( - "Total balance should be 1.958185274567674." + assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, ( + "Total balance should be 6.612835813556755." ) # Check total revenue and total costs - assert abs(result["Gesamteinnahmen_Euro"] - 1.168863124510214) < 1e-5, ( - "Total revenue should be 1.168863124510214." + assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, ( + "Total revenue should be 1.964301131937134." ) - assert abs(result["Gesamtkosten_Euro"] - 3.127048399077888) < 1e-5, ( - "Total costs should be 3.127048399077888 ." + assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, ( + "Total costs should be 8.577136945493889 ." ) # Check the losses - assert abs(result["Gesamt_Verluste"] - 2871.5330639359036) < 1e-5, ( - "Total losses should be 2871.5330639359036 ." + assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, ( + "Total losses should be 1620.0 ." ) # Check the values in 'akku_soc_pro_stunde' - assert result["akku_soc_pro_stunde"][-1] == 42.151590909090906, ( - "The value at index -1 of 'akku_soc_pro_stunde' should be 42.151590909090906." + assert result["akku_soc_pro_stunde"][-1] == 98.0, ( + "The value at index -1 of 'akku_soc_pro_stunde' should be 98.0." ) - assert result["akku_soc_pro_stunde"][1] == 60.08659090909091, ( - "The value at index 1 of 'akku_soc_pro_stunde' should be 60.08659090909091." + assert result["akku_soc_pro_stunde"][1] == 98.0, ( + "The value at index 1 of 'akku_soc_pro_stunde' should be 98.0." ) # Check home appliances diff --git a/tests/test_geneticsimulation2.py b/tests/test_geneticsimulation2.py index a5749d7..e539f0e 100644 --- a/tests/test_geneticsimulation2.py +++ b/tests/test_geneticsimulation2.py @@ -65,7 +65,6 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation: optimization_hours = config_eos.optimization.horizon_hours, prediction_hours = config_eos.prediction.hours, ) - home_appliance.set_starting_time(2) # Example initialization of electric car battery eauto = Battery( @@ -158,10 +157,11 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation: ac = np.full(config_eos.prediction.hours, 0.0) ac[20] = 1 - simulation.set_akku_ac_charge_hours(ac) + simulation.ac_charge_hours = ac dc = np.full(config_eos.prediction.hours, 0.0) dc[11] = 1 - simulation.set_akku_dc_charge_hours(dc) + simulation.dc_charge_hours = dc + simulation.home_appliance_start_hour = 2 return simulation @@ -242,26 +242,26 @@ def test_simulation(genetic_simulation_2): ) # Verfify DC and AC Charge Bins - assert abs(result["akku_soc_pro_stunde"][2] - 44.70681818181818) < 1e-5, ( - "'akku_soc_pro_stunde[2]' should be 44.70681818181818." + assert abs(result["akku_soc_pro_stunde"][2] - 80.0) < 1e-5, ( + "'akku_soc_pro_stunde[2]' should be 80.0." ) - assert abs(result["akku_soc_pro_stunde"][10] - 10.0) < 1e-5, ( - "'akku_soc_pro_stunde[10]' should be 10." + assert abs(result["akku_soc_pro_stunde"][10] - 80.0) < 1e-5, ( + "'akku_soc_pro_stunde[10]' should be 80." ) assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][10] - 3946.93) < 1e-3, ( "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 3946.93." ) - assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 0.0) < 1e-3, ( - "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 0.0." + assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 2799.7263636361786) < 1e-3, ( + "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 2799.7263636361786." ) - assert abs(result["akku_soc_pro_stunde"][20] - 10) < 1e-5, ( - "'akku_soc_pro_stunde[20]' should be 10." + assert abs(result["akku_soc_pro_stunde"][20] - 100) < 1e-5, ( + "'akku_soc_pro_stunde[20]' should be 100." ) - assert abs(result["Last_Wh_pro_Stunde"][20] - 6050.98) < 1e-3, ( - "'Last_Wh_pro_Stunde[20]' should be 6050.98." + assert abs(result["Last_Wh_pro_Stunde"][20] - 1050.98) < 1e-3, ( + "'Last_Wh_pro_Stunde[20]' should be 1050.98." ) print("All tests passed successfully.") @@ -280,46 +280,6 @@ def test_set_parameters(genetic_simulation_2): ) -def test_set_akku_discharge_hours(genetic_simulation_2): - """Test the set_akku_discharge_hours method of EnergyManagement.""" - simulation = genetic_simulation_2 - discharge_hours = np.full(simulation.prediction_hours, 1.0) - simulation.set_akku_discharge_hours(discharge_hours) - assert np.array_equal(simulation.battery.discharge_array, discharge_hours), ( - "Discharge hours should be set correctly" - ) - - -def test_set_akku_ac_charge_hours(genetic_simulation_2): - """Test the set_akku_ac_charge_hours method of EnergyManagement.""" - simulation = genetic_simulation_2 - ac_charge_hours = np.full(simulation.prediction_hours, 1.0) - simulation.set_akku_ac_charge_hours(ac_charge_hours) - assert np.array_equal(simulation.ac_charge_hours, ac_charge_hours), ( - "AC charge hours should be set correctly" - ) - - -def test_set_akku_dc_charge_hours(genetic_simulation_2): - """Test the set_akku_dc_charge_hours method of EnergyManagement.""" - simulation = genetic_simulation_2 - dc_charge_hours = np.full(simulation.prediction_hours, 1.0) - simulation.set_akku_dc_charge_hours(dc_charge_hours) - assert np.array_equal(simulation.dc_charge_hours, dc_charge_hours), ( - "DC charge hours should be set correctly" - ) - - -def test_set_ev_charge_hours(genetic_simulation_2): - """Test the set_ev_charge_hours method of EnergyManagement.""" - simulation = genetic_simulation_2 - ev_charge_hours = np.full(simulation.prediction_hours, 1.0) - simulation.set_ev_charge_hours(ev_charge_hours) - assert np.array_equal(simulation.ev_charge_hours, ev_charge_hours), ( - "EV charge hours should be set correctly" - ) - - def test_reset(genetic_simulation_2): """Test the reset method of EnergyManagement.""" simulation = genetic_simulation_2 diff --git a/tests/testdata/optimize_result_1.json b/tests/testdata/optimize_result_1.json index e381f98..03a7428 100644 --- a/tests/testdata/optimize_result_1.json +++ b/tests/testdata/optimize_result_1.json @@ -2,14 +2,14 @@ "ac_charge": [ 0.0, 0.0, - 0.875, 0.0, - 0.875, 0.0, - 0.625, - 0.75, 0.0, - 0.375, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, 0.0, 0.0, 0.0, @@ -50,16 +50,16 @@ 0.0 ], "dc_charge": [ - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, 1.0, 1.0, 1.0, @@ -103,9 +103,9 @@ 0, 0, 0, - 1, 0, - 1, + 0, + 0, 0, 0, 0, @@ -476,7 +476,6 @@ 0.0 ], "akku_soc_pro_stunde": [ - 80.0, 80.0, 79.91107093663912, 79.91107093663912, @@ -504,7 +503,7 @@ 76.64231728537023, 77.71252293120729, 81.22045681594867, - 87.86517878095492, + 81.61517878095492, 100.0, 100.0, 100.0, @@ -513,6 +512,7 @@ 100.0, 100.0, 100.0, + 96.84060778236915, 96.84060778236915 ], "Electricity_price": [ @@ -558,108 +558,108 @@ }, "eauto_obj": { "device_id": "ev1", + "hours": 48, "charge_array": [ - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0 + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 ], "discharge_array": [ - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1 + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 ], "discharging_efficiency": 1.0, - "hours": 48, "capacity_wh": 60000, "charging_efficiency": 0.95, "max_charge_power_w": 11040, @@ -717,4 +717,4 @@ 5.0 ], "washingstart": null -} +} \ No newline at end of file diff --git a/tests/testdata/optimize_result_2.json b/tests/testdata/optimize_result_2.json index cb6962f..17d5946 100644 --- a/tests/testdata/optimize_result_2.json +++ b/tests/testdata/optimize_result_2.json @@ -1,18 +1,20 @@ { "ac_charge": [ - 0.5, 0.0, 0.0, 0.0, 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.375, + 0.0, + 0.0, 0.75, 0.0, - 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2500.0, - 2500.0, 0.0, + 2500.0, + 2500.0, 0.0, 0.0, 0.0, @@ -365,16 +365,16 @@ 0.0 ], "Kosten_Euro_pro_Stunde": [ - 0.0, - 0.004569992000000018, - 2.750373626, - 2.1541494859999997, - 1.0128942300000001, - 0.0, + 2.54785212, + 1.061242392, + 1.0445786259999998, + 2.1770732859999997, + 0.53097063, + 1.6959351, 1.4118501319999999, 0.0, + 0.061530097476751734, 0.0, - 0.55162953, 0.0, 0.0, 0.0, @@ -385,36 +385,36 @@ 0.199865757, 0.182970359, 0.162995926, - 1.4005233899999998, - 0.26411047, - 0.24530383800000005, + 0.0, + 0.0, + 0.0, 0.08545095, 0.007989913613567745, 0.028255713342252034, 0.025392879919306634, - 0.0, - 0.0, + 0.010682755832597498, + 4.174095896658514e-14, 0.0003442778967139274, - 0.6945180797975209, 0.0, - 0.04565364324294593, + 0.3686370794492921, + 0.0, 0.08231598, 0.174597189, 0.293043269, - 0.214398479, + 0.0, 0.16484566 ], "Netzbezug_Wh_pro_Stunde": [ - 0.0, - 20.660000000000082, - 13140.82, - 11464.339999999998, - 5510.85, - 0.0, + 11174.789999999999, + 4797.66, + 4990.82, + 11586.34, + 2888.8500000000004, + 8462.75, 6423.339999999999, 0.0, + 205.30563055305882, 0.0, - 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