Merge pull request #251 from fboundy/patch

3.15.4

README.md

@@ -1,4 +1,4 @@
-# PV Opt: Home Assistant Solar/Battery Optimiser v3.15.3
+# PV Opt: Home Assistant Solar/Battery Optimiser v3.15.4
 
 
 Solar / Battery Charging Optimisation for Home Assistant. This appDaemon application attempts to optimise charging and discharging of a home solar/battery system to minimise cost electricity cost on a daily basis using freely available solar forecast data from SolCast. This is particularly beneficial for Octopus Agile but is also benefeficial for other time-of-use tariffs such as Octopus Flux or simple Economy 7.

apps/pv_opt/pv_opt.py

@@ -12,7 +12,7 @@
 from numpy import nan
 import re
 
-VERSION = "3.15.3"
+VERSION = "3.15.4"
 
 
 OCTOPUS_PRODUCT_URL = r"https://api.octopus.energy/v1/products/"
@@ -717,6 +717,8 @@ def _load_pv_system_model(self):
         self.battery_model = pv.BatteryModel(
             capacity=self.get_config("battery_capacity_wh"),
             max_dod=self.get_config("maximum_dod_percent") / 100,
+            current_limit_amps=self.get_config("battery_current_limit_amps", default=100),
+            voltage=self.get_config("battery_voltage", default=50),
         )
         self.pv_system = pv.PVsystemModel("PV_Opt", self.inverter_model, self.battery_model, host=self)
 

apps/pv_opt/pvpy.py

@@ -8,6 +8,8 @@
 from datetime import datetime
 
 OCTOPUS_PRODUCT_URL = r"https://api.octopus.energy/v1/products/"
+AGILE_PREDICT_URL = r"https://agilepredict.com/api/"
+
 TIME_FORMAT = "%d/%m %H:%M %Z"
 MAX_ITERS = 3
 
@@ -76,6 +78,7 @@ def __init__(
         self.eco7 = eco7
         self.area = kwargs.get("area", None)
         self.day_ahead = None
+        self.agile_predict = None
         self.eco7_start = pd.Timestamp(eco7_start, tz="UTC")
 
         if octopus:
@@ -200,12 +203,12 @@ def to_df(self, start=None, end=None, **kwargs):
             if "AGILE" in self.name and use_day_ahead:
                 if self.day_ahead is not None and df.index[-1].day == end.day:
                     # reset the day ahead forecasts if we've got a forecast going into tomorrow
+                    self.agile_predict = None
                     self.day_ahead = None
                     self.log("")
                     self.log(f"Cleared day ahead forecast for tariff {self.name}")
 
                 if pd.Timestamp.now(tz=self.tz).hour > 11 and df.index[-1].day != end.day:
-
                     # if it is after 11 but we don't have new Agile prices yet, check for a day-ahead forecast
                     if self.day_ahead is None:
                         self.day_ahead = self.get_day_ahead(df.index[0])
@@ -222,7 +225,9 @@ def to_df(self, start=None, end=None, **kwargs):
                         else:
                             factors = AGILE_FACTORS["import"][self.area]
 
-                        mask = (self.day_ahead.index.hour >= 16) & (self.day_ahead.index.hour < 19)
+                        mask = (self.day_ahead.index.tz_convert("GB").hour >= 16) & (
+                            self.day_ahead.index.tz_convert("GB").hour < 19
+                        )
 
                         agile = (
                             pd.concat(
@@ -237,7 +242,13 @@ def to_df(self, start=None, end=None, **kwargs):
                         )
 
                         df = pd.concat([df, agile])
-                        # self.log(df)
+                else:
+                    # Otherwise download the latest forecast from AgilePredict
+                    if self.agile_predict is None:
+                        self.agile_predict = self._get_agile_predict()
+
+                    if self.agile_predict is not None:
+                        df = pd.concat([df, self.agile_predict.loc[df.index[-1] + pd.Timedelta("30min") : end]])
 
             # If the index frequency >30 minutes so we need to just extend it:
             if (len(df) > 1 and ((df.index[-1] - df.index[-2]).total_seconds() / 60) > 30) or len(df) == 1:
@@ -285,6 +296,19 @@ def to_df(self, start=None, end=None, **kwargs):
 
         return df
 
+    def _get_agile_predict(self):
+        url = f"{AGILE_PREDICT_URL}{self.area}?days=2&high_low=false"
+        try:
+            r = requests.get(url)
+            r.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
+
+        except requests.exceptions.RequestException as e:
+            return
+
+        df = pd.DataFrame(r.json()[0]["prices"]).set_index("date_time")
+        df.index = pd.to_datetime(df.index).tz_convert("UTC")
+        return df["agile_pred"]
+
     def get_day_ahead(self, start):
         url = "https://www.nordpoolgroup.com/api/marketdata/page/325?currency=GBP"
 
@@ -371,13 +395,34 @@ def __init__(
 
 
 class BatteryModel:
-    def __init__(self, capacity: int, max_dod: float = 0.15) -> None:
+    """Describes the battery system attached to the inverter
+
+    Attributes:
+        capacity: An integer describing the Wh capacity of the battery.
+        max_dod: A float describing the maximum depth of discharge of the battery.
+        current_limit_amps: An int describing the maximum amps at which the battery can charge/discharge.
+        voltage: An int describing the voltage of the battery system.
+    """
+
+    def __init__(self, capacity: int, max_dod: float = 0.15, current_limit_amps: int = 100, voltage: int = 50) -> None:
         self.capacity = capacity
         self.max_dod = max_dod
+        self.current_limit_amps = current_limit_amps
+        self.voltage = voltage
 
     def __str__(self):
         pass
 
+    @property
+    def max_charge_power(self) -> int:
+        """returns the maximum watts at which the battery can charge."""
+        return self.current_limit_amps * self.voltage
+
+    @property
+    def max_discharge_power(self) -> int:
+        """returns the maximum watts at which the battery can discharge."""
+        return self.max_charge_power
+
 
 class OctopusAccount:
     def __init__(self, account_number, api_key) -> None:
@@ -904,7 +949,7 @@ def optimised_force(self, initial_soc, static_flows, contract: Contract, **kwarg
                     str_log += f"SOC: {x.loc[start_window]['soc']:5.1f}%->{x.loc[start_window]['soc_end']:5.1f}% "
 
                     forced_charge = min(
-                        self.inverter.charger_power
+                        min(self.battery.max_charge_power, self.inverter.charger_power)
                         - x["forced"].loc[start_window]
                         - x[cols["solar"]].loc[start_window],
                         ((100 - x["soc_end"].loc[start_window]) / 100 * self.battery.capacity) * 2 * factor,
@@ -1016,7 +1061,8 @@ def optimised_force(self, initial_soc, static_flows, contract: Contract, **kwarg
                         slot = (
                             start_window,
                             -min(
-                                self.inverter.inverter_power - x[kwargs.get("solar", "solar")].loc[start_window],
+                                min(self.battery.max_discharge_power, self.inverter.inverter_power),
+                                -x[kwargs.get("solar", "solar")].loc[start_window],
                                 ((x["soc_end"].loc[start_window] - self.battery.max_dod) / 100 * self.battery.capacity)
                                 * 2
                                 * factor,