feat: Enhance Tibber Prices integration with new configuration options and improved data handling

- Added new configuration options for minimum distance from average price for best and peak prices.
- Updated default values for best and peak price flexibility.
- Improved coordinator to handle midnight turnover and data rotation more effectively.
- Refactored entity initialization to streamline device information retrieval.
- Updated sensor attributes to use more descriptive names for price values.
- Enhanced translations for new configuration options in English and German.
- Improved unit tests for coordinator functionality, ensuring proper cleanup and async handling.

custom_components/tibber_prices/__init__.py

@@ -120,6 +120,4 @@ async def async_reload_entry(
     entry: TibberPricesConfigEntry,
 ) -> None:
     """Reload config entry."""
-    LOGGER.debug(f"[tibber_prices] async_reload_entry called for entry_id={entry.entry_id}")
-    await async_unload_entry(hass, entry)
-    await async_setup_entry(hass, entry)
+    await hass.config_entries.async_reload(entry.entry_id)

custom_components/tibber_prices/binary_sensor.py

@@ -28,11 +28,15 @@ if TYPE_CHECKING:
 
 from .const import (
     CONF_BEST_PRICE_FLEX,
+    CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
     CONF_EXTENDED_DESCRIPTIONS,
     CONF_PEAK_PRICE_FLEX,
+    CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
     DEFAULT_BEST_PRICE_FLEX,
+    DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
     DEFAULT_EXTENDED_DESCRIPTIONS,
     DEFAULT_PEAK_PRICE_FLEX,
+    DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
     async_get_entity_description,
     get_entity_description,
 )
@@ -280,7 +284,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         # Calculate difference from average price for the day
         avg_diff = new_interval["price"] - annotation_ctx["avg_price"]
         new_interval["price_diff_from_avg"] = round(avg_diff, 4)
-        new_interval["price_diff_from_avg_ct"] = round(avg_diff * 100, 2)
+        new_interval["price_diff_from_avg_minor"] = round(avg_diff * 100, 2)
         avg_diff_percent = (
             ((new_interval["price"] - annotation_ctx["avg_price"]) / annotation_ctx["avg_price"]) * 100
             if annotation_ctx["avg_price"] != 0
@@ -291,22 +295,24 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         trailing_avg = annotation_ctx.get("trailing_24h_avg", 0.0)
         trailing_avg_diff = new_interval["price"] - trailing_avg
         new_interval["price_diff_from_trailing_24h_avg"] = round(trailing_avg_diff, 4)
-        new_interval["price_diff_from_trailing_24h_avg_ct"] = round(trailing_avg_diff * 100, 2)
+        new_interval["price_diff_from_trailing_24h_avg_minor"] = round(trailing_avg_diff * 100, 2)
         trailing_avg_diff_percent = (
             ((new_interval["price"] - trailing_avg) / trailing_avg) * 100 if trailing_avg != 0 else 0.0
         )
         new_interval["price_diff_from_trailing_24h_avg_" + PERCENTAGE] = round(trailing_avg_diff_percent, 2)
         new_interval["trailing_24h_avg_price"] = round(trailing_avg, 4)
+        new_interval["trailing_24h_avg_price_minor"] = round(trailing_avg * 100, 2)
         # Calculate difference from leading 24-hour average
         leading_avg = annotation_ctx.get("leading_24h_avg", 0.0)
         leading_avg_diff = new_interval["price"] - leading_avg
         new_interval["price_diff_from_leading_24h_avg"] = round(leading_avg_diff, 4)
-        new_interval["price_diff_from_leading_24h_avg_ct"] = round(leading_avg_diff * 100, 2)
+        new_interval["price_diff_from_leading_24h_avg_minor"] = round(leading_avg_diff * 100, 2)
         leading_avg_diff_percent = (
             ((new_interval["price"] - leading_avg) / leading_avg) * 100 if leading_avg != 0 else 0.0
         )
         new_interval["price_diff_from_leading_24h_avg_" + PERCENTAGE] = round(leading_avg_diff_percent, 2)
         new_interval["leading_24h_avg_price"] = round(leading_avg, 4)
+        new_interval["leading_24h_avg_price_minor"] = round(leading_avg * 100, 2)
         return new_interval
 
     def _annotate_period_intervals(
@@ -330,15 +336,15 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             reference_type = "ref"
         if reference_type == "min":
             diff_key = "price_diff_from_min"
-            diff_ct_key = "price_diff_from_min_ct"
+            diff_ct_key = "price_diff_from_min_minor"
             diff_pct_key = "price_diff_from_min_" + PERCENTAGE
         elif reference_type == "max":
             diff_key = "price_diff_from_max"
-            diff_ct_key = "price_diff_from_max_ct"
+            diff_ct_key = "price_diff_from_max_minor"
             diff_pct_key = "price_diff_from_max_" + PERCENTAGE
         else:
             diff_key = "price_diff"
-            diff_ct_key = "price_diff_ct"
+            diff_ct_key = "price_diff_minor"
             diff_pct_key = "price_diff_" + PERCENTAGE
         result = []
         period_count = len(periods)
@@ -415,8 +421,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
     def _build_periods(
         self,
         all_prices: list[dict],
-        ref_prices: dict,
-        flex: float,
+        price_context: dict,
         *,
         reverse_sort: bool,
     ) -> list[list[dict]]:
@@ -424,7 +429,24 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         Build periods, allowing periods to cross midnight (day boundary).
 
         Strictly enforce flex threshold by percent diff, matching attribute calculation.
+        Additionally enforces:
+        1. Cap at daily average to prevent overlap between best and peak periods
+        2. Minimum distance from average to ensure meaningful price difference
+
+        Args:
+            all_prices: All price data points
+            price_context: Dict with ref_prices, avg_prices, flex, and min_distance_from_avg
+            reverse_sort: True for peak price (descending), False for best price (ascending)
+
+        Returns:
+            List of periods, each period is a list of interval dicts
+
         """
+        ref_prices = price_context["ref_prices"]
+        avg_prices = price_context["avg_prices"]
+        flex = price_context["flex"]
+        min_distance_from_avg = price_context["min_distance_from_avg"]
+
         periods: list[list[dict]] = []
         current_period: list[dict] = []
         last_ref_date = None
@@ -435,18 +457,34 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             starts_at = dt_util.as_local(starts_at)
             date = starts_at.date()
             ref_price = ref_prices[date]
+            avg_price = avg_prices[date]
             price = float(price_data["total"])
             percent_diff = ((price - ref_price) / ref_price) * 100 if ref_price != 0 else 0.0
             percent_diff = round(percent_diff, 2)
             # For best price (flex >= 0): percent_diff <= flex*100 (prices up to flex% above reference)
             # For peak price (flex <= 0): percent_diff >= flex*100 (prices down to |flex|% below reference)
             in_flex = percent_diff <= flex * 100 if not reverse_sort else percent_diff >= flex * 100
+            # Cap at daily average to prevent overlap between best and peak periods
+            # Best price: only prices below average
+            # Peak price: only prices above average
+            within_avg_boundary = price <= avg_price if not reverse_sort else price >= avg_price
+            # Enforce minimum distance from average (in percentage terms)
+            # Best price: price must be at least min_distance_from_avg% below average
+            # Peak price: price must be at least min_distance_from_avg% above average
+            if not reverse_sort:
+                # Best price: price <= avg * (1 - min_distance_from_avg/100)
+                min_distance_threshold = avg_price * (1 - min_distance_from_avg / 100)
+                meets_min_distance = price <= min_distance_threshold
+            else:
+                # Peak price: price >= avg * (1 + min_distance_from_avg/100)
+                min_distance_threshold = avg_price * (1 + min_distance_from_avg / 100)
+                meets_min_distance = price >= min_distance_threshold
             # Split period if day changes
             if last_ref_date is not None and date != last_ref_date and current_period:
                 periods.append(current_period)
                 current_period = []
             last_ref_date = date
-            if in_flex:
+            if in_flex and within_avg_boundary and meets_min_distance:
                 current_period.append(
                     {
                         "interval_hour": starts_at.hour,
@@ -527,10 +565,19 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             CONF_BEST_PRICE_FLEX if not reverse_sort else CONF_PEAK_PRICE_FLEX,
             DEFAULT_BEST_PRICE_FLEX if not reverse_sort else DEFAULT_PEAK_PRICE_FLEX,
         )
+        min_distance_from_avg = self._get_flex_option(
+            CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG if not reverse_sort else CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
+            DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG if not reverse_sort else DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
+        )
+        price_context = {
+            "ref_prices": ref_prices,
+            "avg_prices": avg_price_by_day,
+            "flex": flex,
+            "min_distance_from_avg": min_distance_from_avg,
+        }
         periods = self._build_periods(
             all_prices,
-            ref_prices,
-            flex,
+            price_context,
             reverse_sort=reverse_sort,
         )
         self._add_interval_ends(periods)

custom_components/tibber_prices/config_flow.py

@@ -39,13 +39,17 @@ from .api import (
 )
 from .const import (
     CONF_BEST_PRICE_FLEX,
+    CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
     CONF_EXTENDED_DESCRIPTIONS,
     CONF_PEAK_PRICE_FLEX,
+    CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
     CONF_PRICE_RATING_THRESHOLD_HIGH,
     CONF_PRICE_RATING_THRESHOLD_LOW,
     DEFAULT_BEST_PRICE_FLEX,
+    DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
     DEFAULT_EXTENDED_DESCRIPTIONS,
     DEFAULT_PEAK_PRICE_FLEX,
+    DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
     DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
     DEFAULT_PRICE_RATING_THRESHOLD_LOW,
     DOMAIN,
@@ -80,7 +84,7 @@ class TibberPricesFlowHandler(ConfigFlow, domain=DOMAIN):
 
     @staticmethod
     @callback
-    def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:  # noqa: ARG004
+    def async_get_options_flow(_config_entry: ConfigEntry) -> OptionsFlow:
         """Create an options flow for this configentry."""
         return TibberPricesOptionsFlowHandler()
 
@@ -211,8 +215,8 @@ class TibberPricesFlowHandler(ConfigFlow, domain=DOMAIN):
     def _get_home_title(home: dict) -> str:
         """Generate a user-friendly title for a home."""
         title = home.get("appNickname")
-        if title:
-            return title
+        if title and title.strip():
+            return title.strip()
 
         address = home.get("address", {})
         if address:
@@ -362,8 +366,8 @@ class TibberPricesSubentryFlowHandler(ConfigSubentryFlow):
     def _get_home_title(self, home: dict) -> str:
         """Generate a user-friendly title for a home."""
         title = home.get("appNickname")
-        if title:
-            return title
+        if title and title.strip():
+            return title.strip()
 
         address = home.get("address", {})
         if address:
@@ -406,13 +410,19 @@ class TibberPricesSubentryFlowHandler(ConfigSubentryFlow):
 class TibberPricesOptionsFlowHandler(OptionsFlow):
     """Handle options for tibber_prices entries."""
 
+    def __init__(self) -> None:
+        """Initialize options flow."""
+        self._options: dict[str, Any] = {}
+
     async def async_step_init(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Manage the options."""
+        """Manage the options - General Settings."""
+        # Initialize options from config_entry on first call
+        if not self._options:
+            self._options = dict(self.config_entry.options)
+
         if user_input is not None:
-            return self.async_create_entry(
-                title="",
-                data=user_input,
-            )
+            self._options.update(user_input)
+            return await self.async_step_price_rating()
 
         return self.async_show_form(
             step_id="init",
@@ -424,38 +434,23 @@ class TibberPricesOptionsFlowHandler(OptionsFlow):
                             CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS
                         ),
                     ): BooleanSelector(),
-                    vol.Optional(
-                        CONF_BEST_PRICE_FLEX,
-                        default=int(
-                            self.config_entry.options.get(
-                                CONF_BEST_PRICE_FLEX,
-                                DEFAULT_BEST_PRICE_FLEX,
+                }
+            ),
+            description_placeholders={
+                "user_login": self.config_entry.data.get("user_login", "N/A"),
+            },
         )
-                        ),
-                    ): NumberSelector(
-                        NumberSelectorConfig(
-                            min=0,
-                            max=100,
-                            step=1,
-                            mode=NumberSelectorMode.SLIDER,
-                        ),
-                    ),
-                    vol.Optional(
-                        CONF_PEAK_PRICE_FLEX,
-                        default=int(
-                            self.config_entry.options.get(
-                                CONF_PEAK_PRICE_FLEX,
-                                DEFAULT_PEAK_PRICE_FLEX,
-                            )
-                        ),
-                    ): NumberSelector(
-                        NumberSelectorConfig(
-                            min=-100,
-                            max=0,
-                            step=1,
-                            mode=NumberSelectorMode.SLIDER,
-                        ),
-                    ),
+
+    async def async_step_price_rating(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
+        """Configure price rating thresholds."""
+        if user_input is not None:
+            self._options.update(user_input)
+            return await self.async_step_best_price()
+
+        return self.async_show_form(
+            step_id="price_rating",
+            data_schema=vol.Schema(
+                {
                     vol.Optional(
                         CONF_PRICE_RATING_THRESHOLD_LOW,
                         default=int(
@@ -490,8 +485,96 @@ class TibberPricesOptionsFlowHandler(OptionsFlow):
                     ),
                 }
             ),
-            description_placeholders={
-                "user_login": self.config_entry.data.get("user_login", "N/A"),
-                "unique_id": self.config_entry.unique_id or "unknown",
-            },
+        )
+
+    async def async_step_best_price(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
+        """Configure best price period settings."""
+        if user_input is not None:
+            self._options.update(user_input)
+            return await self.async_step_peak_price()
+
+        return self.async_show_form(
+            step_id="best_price",
+            data_schema=vol.Schema(
+                {
+                    vol.Optional(
+                        CONF_BEST_PRICE_FLEX,
+                        default=int(
+                            self.config_entry.options.get(
+                                CONF_BEST_PRICE_FLEX,
+                                DEFAULT_BEST_PRICE_FLEX,
+                            )
+                        ),
+                    ): NumberSelector(
+                        NumberSelectorConfig(
+                            min=0,
+                            max=100,
+                            step=1,
+                            mode=NumberSelectorMode.SLIDER,
+                        ),
+                    ),
+                    vol.Optional(
+                        CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
+                        default=int(
+                            self.config_entry.options.get(
+                                CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
+                                DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
+                            )
+                        ),
+                    ): NumberSelector(
+                        NumberSelectorConfig(
+                            min=0,
+                            max=50,
+                            step=1,
+                            mode=NumberSelectorMode.SLIDER,
+                        ),
+                    ),
+                }
+            ),
+        )
+
+    async def async_step_peak_price(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
+        """Configure peak price period settings."""
+        if user_input is not None:
+            self._options.update(user_input)
+            return self.async_create_entry(title="", data=self._options)
+
+        return self.async_show_form(
+            step_id="peak_price",
+            data_schema=vol.Schema(
+                {
+                    vol.Optional(
+                        CONF_PEAK_PRICE_FLEX,
+                        default=int(
+                            self.config_entry.options.get(
+                                CONF_PEAK_PRICE_FLEX,
+                                DEFAULT_PEAK_PRICE_FLEX,
+                            )
+                        ),
+                    ): NumberSelector(
+                        NumberSelectorConfig(
+                            min=-100,
+                            max=0,
+                            step=1,
+                            mode=NumberSelectorMode.SLIDER,
+                        ),
+                    ),
+                    vol.Optional(
+                        CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
+                        default=int(
+                            self.config_entry.options.get(
+                                CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
+                                DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
+                            )
+                        ),
+                    ): NumberSelector(
+                        NumberSelectorConfig(
+                            min=0,
+                            max=50,
+                            step=1,
+                            mode=NumberSelectorMode.SLIDER,
+                        ),
+                    ),
+                }
+            ),
         )

custom_components/tibber_prices/const.py

@@ -23,6 +23,8 @@ DOMAIN = "tibber_prices"
 CONF_EXTENDED_DESCRIPTIONS = "extended_descriptions"
 CONF_BEST_PRICE_FLEX = "best_price_flex"
 CONF_PEAK_PRICE_FLEX = "peak_price_flex"
+CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG = "best_price_min_distance_from_avg"
+CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG = "peak_price_min_distance_from_avg"
 CONF_PRICE_RATING_THRESHOLD_LOW = "price_rating_threshold_low"
 CONF_PRICE_RATING_THRESHOLD_HIGH = "price_rating_threshold_high"
 
@@ -31,8 +33,10 @@ ATTRIBUTION = "Data provided by Tibber"
 # Integration name should match manifest.json
 DEFAULT_NAME = "Tibber Price Information & Ratings"
 DEFAULT_EXTENDED_DESCRIPTIONS = False
-DEFAULT_BEST_PRICE_FLEX = 20  # 20% flexibility for best price (user-facing, percent)
-DEFAULT_PEAK_PRICE_FLEX = -20  # 20% flexibility for peak price (user-facing, percent)
+DEFAULT_BEST_PRICE_FLEX = 15  # 15% flexibility for best price (user-facing, percent)
+DEFAULT_PEAK_PRICE_FLEX = -15  # 15% flexibility for peak price (user-facing, percent)
+DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG = 2  # 2% minimum distance from daily average for best price
+DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG = 2  # 2% minimum distance from daily average for peak price
 DEFAULT_PRICE_RATING_THRESHOLD_LOW = -10  # Default rating threshold low percentage
 DEFAULT_PRICE_RATING_THRESHOLD_HIGH = 10  # Default rating threshold high percentage
 

custom_components/tibber_prices/coordinator.py

@@ -3,13 +3,14 @@
 from __future__ import annotations
 
 import logging
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
 from typing import TYPE_CHECKING, Any
 
 from homeassistant.const import CONF_ACCESS_TOKEN
-from homeassistant.core import HomeAssistant, callback
+from homeassistant.core import CALLBACK_TYPE, HomeAssistant, callback
 from homeassistant.exceptions import ConfigEntryAuthFailed
 from homeassistant.helpers import aiohttp_client
+from homeassistant.helpers.event import async_track_utc_time_change
 from homeassistant.helpers.storage import Store
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
 from homeassistant.util import dt as dt_util
@@ -40,12 +41,18 @@ _LOGGER = logging.getLogger(__name__)
 # Storage version for storing data
 STORAGE_VERSION = 1
 
-# Update interval - fetch data every 15 minutes
+# Update interval - fetch data every 15 minutes (when data is incomplete)
 UPDATE_INTERVAL = timedelta(minutes=15)
 
+# Update interval when all data is available - every 4 hours (reduce API calls)
+UPDATE_INTERVAL_COMPLETE = timedelta(hours=4)
+
 # Quarter-hour boundaries for entity state updates (minutes: 00, 15, 30, 45)
 QUARTER_HOUR_BOUNDARIES = (0, 15, 30, 45)
 
+# Hour after which tomorrow's price data is expected (13:00 local time)
+TOMORROW_DATA_CHECK_HOUR = 13
+
 
 class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
     """Enhanced coordinator with main/subentry pattern and comprehensive caching."""
@@ -82,66 +89,131 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self._cached_price_data: dict[str, Any] | None = None
         self._last_price_update: datetime | None = None
 
+        # Track the last date we checked for midnight turnover
+        self._last_midnight_check: datetime | None = None
+
         # Track if this is the main entry (first one created)
         self._is_main_entry = not self._has_existing_main_coordinator()
 
-        # Quarter-hour entity refresh timer
-        self._quarter_hour_timer_handle: Any = None
+        # Log prefix for identifying this coordinator instance
+        self._log_prefix = f"[{config_entry.title}]"
+
+        # Quarter-hour entity refresh timer (runs at :00, :15, :30, :45)
+        self._quarter_hour_timer_cancel: CALLBACK_TYPE | None = None
         self._schedule_quarter_hour_refresh()
 
+    def _log(self, level: str, message: str, *args: Any, **kwargs: Any) -> None:
+        """Log with coordinator-specific prefix."""
+        prefixed_message = f"{self._log_prefix} {message}"
+        getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
+
     def _schedule_quarter_hour_refresh(self) -> None:
-        """Schedule the next quarter-hour entity refresh."""
-        now = dt_util.utcnow()
-        current_minute = now.minute
-
-        # Find the next quarter-hour boundary
-        for boundary in QUARTER_HOUR_BOUNDARIES:
-            if boundary > current_minute:
-                minutes_to_wait = boundary - current_minute
-                break
-        else:
-            # All boundaries passed, go to first boundary of next hour
-            minutes_to_wait = (60 - current_minute) + QUARTER_HOUR_BOUNDARIES[0]
-
-        # Calculate the exact time of the next boundary
-        next_refresh = now + timedelta(minutes=minutes_to_wait)
-        next_refresh = next_refresh.replace(second=0, microsecond=0)
-
+        """Schedule the next quarter-hour entity refresh using Home Assistant's time tracking."""
         # Cancel any existing timer
-        if self._quarter_hour_timer_handle:
-            self._quarter_hour_timer_handle.cancel()
+        if self._quarter_hour_timer_cancel:
+            self._quarter_hour_timer_cancel()
+            self._quarter_hour_timer_cancel = None
 
-        # Schedule the refresh
-        self._quarter_hour_timer_handle = self.hass.loop.call_at(
-            self.hass.loop.time() + (next_refresh - now).total_seconds(),
+        # Use Home Assistant's async_track_utc_time_change to trigger exactly at quarter-hour boundaries
+        # This ensures we trigger at :00, :15, :30, :45 seconds=1 to avoid triggering too early
+        self._quarter_hour_timer_cancel = async_track_utc_time_change(
+            self.hass,
             self._handle_quarter_hour_refresh,
+            minute=QUARTER_HOUR_BOUNDARIES,
+            second=1,
         )
 
-        _LOGGER.debug(
-            "Scheduled entity refresh at %s (in %d minutes)",
-            next_refresh.isoformat(),
-            minutes_to_wait,
+        self._log(
+            "debug",
+            "Scheduled quarter-hour refresh for boundaries: %s (at second=1)",
+            QUARTER_HOUR_BOUNDARIES,
         )
 
     @callback
-    def _handle_quarter_hour_refresh(self) -> None:
-        """Handle quarter-hour entity refresh by triggering async state updates."""
-        _LOGGER.debug("Quarter-hour refresh triggered at %s", dt_util.utcnow().isoformat())
+    def _handle_quarter_hour_refresh(self, _now: datetime | None = None) -> None:
+        """Handle quarter-hour entity refresh - check for midnight turnover and update entities."""
+        now = dt_util.now()
+        self._log("debug", "Quarter-hour refresh triggered at %s", now.isoformat())
 
-        # Notify all listeners to update their state without fetching fresh data
+        # Check if midnight has passed since last check
+        midnight_turnover_performed = self._check_and_handle_midnight_turnover(now)
+
+        if midnight_turnover_performed:
+            self._log("info", "Midnight turnover detected and performed during quarter-hour refresh")
+            # Schedule cache save asynchronously (we're in a callback)
+            self.hass.async_create_task(self._store_cache())
+            # Entity update already done in _check_and_handle_midnight_turnover
+            # Skip the regular update to avoid double-update
+        else:
+            # Regular quarter-hour refresh - notify listeners to update their state
             # This causes entity state properties to be re-evaluated with the current time
             # Using async_update_listeners() instead of async_set_updated_data() to avoid
             # interfering with the coordinator's update timing
             self.async_update_listeners()
 
-        # Schedule the next quarter-hour refresh
-        self._schedule_quarter_hour_refresh()
+    @callback
+    def _check_and_handle_midnight_turnover(self, now: datetime) -> bool:
+        """
+        Check if midnight has passed and perform data rotation if needed.
+
+        This is called by the quarter-hour timer to ensure timely rotation
+        without waiting for the next API update cycle.
+
+        Returns:
+            True if midnight turnover was performed, False otherwise
+
+        """
+        current_date = now.date()
+
+        # First time check - initialize
+        if self._last_midnight_check is None:
+            self._last_midnight_check = now
+            return False
+
+        last_check_date = self._last_midnight_check.date()
+
+        # Check if we've crossed into a new day
+        if current_date > last_check_date:
+            self._log(
+                "debug",
+                "Midnight crossed: last_check=%s, current=%s",
+                last_check_date,
+                current_date,
+            )
+
+            # Perform rotation on cached data if available
+            if self._cached_price_data and "homes" in self._cached_price_data:
+                for home_id, home_data in self._cached_price_data["homes"].items():
+                    if "price_info" in home_data:
+                        price_info = home_data["price_info"]
+                        rotated = self._perform_midnight_turnover(price_info)
+                        home_data["price_info"] = rotated
+                        self._log("debug", "Rotated price data for home %s", home_id)
+
+                # Update coordinator's data with enriched rotated data
+                if self.data:
+                    # Re-transform data to ensure enrichment is applied to rotated data
+                    if self.is_main_entry():
+                        self.data = self._transform_data_for_main_entry(self._cached_price_data)
+                    else:
+                        # For subentry, we need to get data from main coordinator
+                        # but we can update the timestamp to trigger entity refresh
+                        self.data["timestamp"] = now
+
+                    # Notify listeners about the updated data after rotation
+                    self.async_update_listeners()
+
+            self._last_midnight_check = now
+            return True
+
+        self._last_midnight_check = now
+        return False
 
     async def async_shutdown(self) -> None:
         """Shut down the coordinator and clean up timers."""
-        if self._quarter_hour_timer_handle:
-            self._quarter_hour_timer_handle.cancel()
-            self._quarter_hour_timer_handle = None
+        if self._quarter_hour_timer_cancel:
+            self._quarter_hour_timer_cancel()
+            self._quarter_hour_timer_cancel = None
 
     def _has_existing_main_coordinator(self) -> bool:
         """Check if there's already a main coordinator in hass.data."""
@@ -179,7 +251,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         ) as err:
             # Use cached data as fallback if available
             if self._cached_price_data is not None:
-                _LOGGER.warning("API error, using cached data: %s", err)
+                self._log("warning", "API error, using cached data: %s", err)
                 return self._merge_cached_data()
             msg = f"Error communicating with API: {err}"
             raise UpdateFailed(msg) from err
@@ -199,16 +271,17 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             # Transform for main entry: provide aggregated view
             return self._transform_data_for_main_entry(raw_data)
 
-        # Use cached data
+        # Use cached data if available
         if self._cached_price_data is not None:
             return self._transform_data_for_main_entry(self._cached_price_data)
 
-        # No cached data, fetch new
-        raw_data = await self._fetch_all_homes_data()
-        self._cached_price_data = raw_data
-        self._last_price_update = current_time
-        await self._store_cache()
-        return self._transform_data_for_main_entry(raw_data)
+        # Fallback: no cache and no update needed (shouldn't happen)
+        self._log("warning", "No cached data available and update not triggered - returning empty data")
+        return {
+            "timestamp": current_time,
+            "homes": {},
+            "priceInfo": {},
+        }
 
     async def _handle_subentry_update(self) -> dict[str, Any]:
         """Handle update for subentry - get data from main coordinator."""
@@ -217,7 +290,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
     async def _fetch_all_homes_data(self) -> dict[str, Any]:
         """Fetch data for all homes (main coordinator only)."""
-        _LOGGER.debug("Fetching data for all homes")
+        self._log("debug", "Fetching data for all homes")
 
         # Get price data for all homes
         price_data = await self.api.async_get_price_info()
@@ -226,6 +299,8 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         homes_list = price_data.get("homes", {})
 
         for home_id, home_price_data in homes_list.items():
+            # Store raw price data without enrichment
+            # Enrichment will be done dynamically when data is transformed
             home_data = {
                 "price_info": home_price_data,
             }
@@ -276,19 +351,21 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                     self._last_price_update = dt_util.parse_datetime(last_price_update)
                 if last_user_update := stored.get("last_user_update"):
                     self._last_user_update = dt_util.parse_datetime(last_user_update)
+                if last_midnight_check := stored.get("last_midnight_check"):
+                    self._last_midnight_check = dt_util.parse_datetime(last_midnight_check)
 
                 # Validate cache: check if price data is from a previous day
                 if not self._is_cache_valid():
-                    _LOGGER.info("Cached price data is from a previous day, clearing cache to fetch fresh data")
+                    self._log("info", "Cached price data is from a previous day, clearing cache to fetch fresh data")
                     self._cached_price_data = None
                     self._last_price_update = None
                     await self._store_cache()
                 else:
-                    _LOGGER.debug("Cache loaded successfully")
+                    self._log("debug", "Cache loaded successfully")
             else:
-                _LOGGER.debug("No cache found, will fetch fresh data")
+                self._log("debug", "No cache found, will fetch fresh data")
         except OSError as ex:
-            _LOGGER.warning("Failed to load cache: %s", ex)
+            self._log("warning", "Failed to load cache: %s", ex)
 
     def _is_cache_valid(self) -> bool:
         """
@@ -307,7 +384,8 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         last_update_local_date = dt_util.as_local(self._last_price_update).date()
 
         if current_local_date != last_update_local_date:
-            _LOGGER.debug(
+            self._log(
+                "debug",
                 "Cache date mismatch: cached=%s, current=%s",
                 last_update_local_date,
                 current_local_date,
@@ -350,11 +428,12 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                     prices_need_rotation = first_today_price_date < current_local_date
 
         if prices_need_rotation:
-            _LOGGER.info("Performing midnight turnover: today→yesterday, tomorrow→today")
+            self._log("info", "Performing midnight turnover: today→yesterday, tomorrow→today")
             return {
                 "yesterday": today_prices,
                 "today": tomorrow_prices,
                 "tomorrow": [],
+                "currency": price_info.get("currency", "EUR"),
             }
 
         return price_info
@@ -366,11 +445,12 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             "user_data": self._cached_user_data,
             "last_price_update": (self._last_price_update.isoformat() if self._last_price_update else None),
             "last_user_update": (self._last_user_update.isoformat() if self._last_user_update else None),
+            "last_midnight_check": (self._last_midnight_check.isoformat() if self._last_midnight_check else None),
         }
 
         try:
             await self._store.async_save(data)
-            _LOGGER.debug("Cache stored successfully")
+            self._log("debug", "Cache stored successfully")
         except OSError:
             _LOGGER.exception("Failed to store cache")
 
@@ -378,39 +458,103 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         """Update user data if needed (daily check)."""
         if self._last_user_update is None or current_time - self._last_user_update >= self._user_update_interval:
             try:
-                _LOGGER.debug("Updating user data")
+                self._log("debug", "Updating user data")
                 user_data = await self.api.async_get_viewer_details()
                 self._cached_user_data = user_data
                 self._last_user_update = current_time
-                _LOGGER.debug("User data updated successfully")
+                self._log("debug", "User data updated successfully")
             except (
                 TibberPricesApiClientError,
                 TibberPricesApiClientCommunicationError,
             ) as ex:
-                _LOGGER.warning("Failed to update user data: %s", ex)
+                self._log("warning", "Failed to update user data: %s", ex)
 
     @callback
     def _should_update_price_data(self, current_time: datetime) -> bool:
-        """Check if price data should be updated."""
+        """
+        Check if price data should be updated from the API.
+
+        Updates occur when:
+        1. No cached data exists
+        2. Cache is invalid (from previous day)
+        3. It's after 13:00 local time and tomorrow's data is missing or invalid
+        4. Regular update interval has passed
+
+        """
         if self._cached_price_data is None:
-            _LOGGER.debug("Should update: No cached price data")
+            self._log("debug", "Should update: No cached price data")
             return True
         if self._last_price_update is None:
-            _LOGGER.debug("Should update: No last price update timestamp")
+            self._log("debug", "Should update: No last price update timestamp")
             return True
 
-        time_since_update = current_time - self._last_price_update
-        should_update = time_since_update >= UPDATE_INTERVAL
+        now_local = dt_util.as_local(current_time)
+        tomorrow_date = (now_local + timedelta(days=1)).date()
 
-        _LOGGER.debug(
-            "Should update price data: %s (time since last update: %s, interval: %s)",
+        # Check if after 13:00 and tomorrow data is missing or invalid
+        if (
+            now_local.hour >= TOMORROW_DATA_CHECK_HOUR
+            and self._cached_price_data
+            and "homes" in self._cached_price_data
+            and self._needs_tomorrow_data(tomorrow_date)
+        ):
+            self._log("debug", "Should update: After %s:00 and valid tomorrow data missing", TOMORROW_DATA_CHECK_HOUR)
+            return True
+
+        # Check regular update interval
+        time_since_update = current_time - self._last_price_update
+
+        # Determine appropriate interval based on data completeness
+        has_tomorrow_data = self._has_valid_tomorrow_data(tomorrow_date)
+        interval = UPDATE_INTERVAL_COMPLETE if has_tomorrow_data else UPDATE_INTERVAL
+        should_update = time_since_update >= interval
+
+        if should_update:
+            self._log(
+                "debug",
+                "Should update price data: %s (time since last update: %s, interval: %s, has_tomorrow: %s)",
                 should_update,
                 time_since_update,
-            UPDATE_INTERVAL,
+                interval,
+                has_tomorrow_data,
             )
 
         return should_update
 
+    def _needs_tomorrow_data(self, tomorrow_date: date) -> bool:
+        """Check if tomorrow data is missing or invalid."""
+        if not self._cached_price_data or "homes" not in self._cached_price_data:
+            return False
+
+        for home_data in self._cached_price_data["homes"].values():
+            price_info = home_data.get("price_info", {})
+            tomorrow_prices = price_info.get("tomorrow", [])
+
+            # Check if tomorrow data is missing
+            if not tomorrow_prices:
+                return True
+
+            # Check if tomorrow data is actually for tomorrow (validate date)
+            first_price = tomorrow_prices[0]
+            if starts_at := first_price.get("startsAt"):
+                price_time = dt_util.parse_datetime(starts_at)
+                if price_time:
+                    price_date = dt_util.as_local(price_time).date()
+                    if price_date != tomorrow_date:
+                        self._log(
+                            "debug",
+                            "Tomorrow data has wrong date: expected=%s, actual=%s",
+                            tomorrow_date,
+                            price_date,
+                        )
+                        return True
+
+        return False
+
+    def _has_valid_tomorrow_data(self, tomorrow_date: date) -> bool:
+        """Check if we have valid tomorrow data (inverse of _needs_tomorrow_data)."""
+        return not self._needs_tomorrow_data(tomorrow_date)
+
     @callback
     def _merge_cached_data(self) -> dict[str, Any]:
         """Merge cached data into the expected format for main entry."""
@@ -445,10 +589,15 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         # Perform midnight turnover if needed (handles day transitions)
         price_info = self._perform_midnight_turnover(price_info)
 
-        # Get threshold percentages for enrichment
-        thresholds = self._get_threshold_percentages()
+        # Ensure all required keys exist (API might not return tomorrow data yet)
+        price_info.setdefault("yesterday", [])
+        price_info.setdefault("today", [])
+        price_info.setdefault("tomorrow", [])
+        price_info.setdefault("currency", "EUR")
 
-        # Enrich price info with calculated differences (trailing 24h averages)
+        # Enrich price info dynamically with calculated differences and rating levels
+        # This ensures enrichment is always up-to-date, especially after midnight turnover
+        thresholds = self._get_threshold_percentages()
         price_info = enrich_price_info_with_differences(
             price_info,
             threshold_low=thresholds["low"],
@@ -481,10 +630,15 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         # Perform midnight turnover if needed (handles day transitions)
         price_info = self._perform_midnight_turnover(price_info)
 
-        # Get threshold percentages for enrichment
-        thresholds = self._get_threshold_percentages()
+        # Ensure all required keys exist (API might not return tomorrow data yet)
+        price_info.setdefault("yesterday", [])
+        price_info.setdefault("today", [])
+        price_info.setdefault("tomorrow", [])
+        price_info.setdefault("currency", "EUR")
 
-        # Enrich price info with calculated differences (trailing 24h averages)
+        # Enrich price info dynamically with calculated differences and rating levels
+        # This ensures enrichment is always up-to-date, especially after midnight turnover
+        thresholds = self._get_threshold_percentages()
         price_info = enrich_price_info_with_differences(
             price_info,
             threshold_low=thresholds["low"],

custom_components/tibber_prices/entity.py

@@ -19,57 +19,8 @@ class TibberPricesEntity(CoordinatorEntity[TibberPricesDataUpdateCoordinator]):
         """Initialize."""
         super().__init__(coordinator)
 
-        # Get user profile information from coordinator
-        user_profile = self.coordinator.get_user_profile()
-
-        # Check if this is a main entry or subentry
-        is_subentry = bool(self.coordinator.config_entry.data.get("home_id"))
-
-        # Initialize variables
-        home_name = "Tibber Home"
-        home_id = self.coordinator.config_entry.unique_id
-        home_type = None
-
-        if is_subentry:
-            # For subentries, show specific home information
-            home_data = self.coordinator.config_entry.data.get("home_data", {})
-            home_id = self.coordinator.config_entry.data.get("home_id")
-
-            # Get home details
-            address = home_data.get("address", {})
-            address1 = address.get("address1", "")
-            city = address.get("city", "")
-            app_nickname = home_data.get("appNickname", "")
-            home_type = home_data.get("type", "")
-
-            # Compose home name
-            home_name = app_nickname or address1 or f"Tibber Home {home_id}"
-            if city:
-                home_name = f"{home_name}, {city}"
-
-            # Add user information if available
-            if user_profile and user_profile.get("name"):
-                home_name = f"{home_name} ({user_profile['name']})"
-        elif user_profile:
-            # For main entry, show user profile information
-            user_name = user_profile.get("name", "Tibber User")
-            user_email = user_profile.get("email", "")
-            home_name = f"Tibber - {user_name}"
-            if user_email:
-                home_name = f"{home_name} ({user_email})"
-        elif coordinator.data:
-            # Fallback to original logic if user data not available yet
-            try:
-                address1 = str(coordinator.data.get("address", {}).get("address1", ""))
-                city = str(coordinator.data.get("address", {}).get("city", ""))
-                app_nickname = str(coordinator.data.get("appNickname", ""))
-                home_type = str(coordinator.data.get("type", ""))
-                # Compose a nice name
-                home_name = "Tibber " + (app_nickname or address1 or "Home")
-                if city:
-                    home_name = f"{home_name}, {city}"
-            except (KeyError, IndexError, TypeError):
-                home_name = "Tibber Home"
+        # Get device information
+        home_name, home_id, home_type = self._get_device_info()
 
         # Get translated home type using the configured language
         language = coordinator.hass.config.language or "en"
@@ -90,3 +41,83 @@ class TibberPricesEntity(CoordinatorEntity[TibberPricesDataUpdateCoordinator]):
             serial_number=home_id if home_id else None,
             configuration_url="https://developer.tibber.com/explorer",
         )
+
+    def _get_device_info(self) -> tuple[str, str | None, str | None]:
+        """Get device name, ID and type."""
+        user_profile = self.coordinator.get_user_profile()
+        is_subentry = bool(self.coordinator.config_entry.data.get("home_id"))
+        home_id = self.coordinator.config_entry.unique_id
+        home_type = None
+
+        if is_subentry:
+            home_name, home_id, home_type = self._get_subentry_device_info()
+            # Add user information if available
+            if user_profile and user_profile.get("name"):
+                home_name = f"{home_name} ({user_profile['name']})"
+        elif user_profile:
+            home_name = self._get_main_entry_device_info(user_profile)
+        else:
+            home_name, home_type = self._get_fallback_device_info()
+
+        return home_name, home_id, home_type
+
+    def _get_subentry_device_info(self) -> tuple[str, str | None, str | None]:
+        """Get device info for subentry."""
+        home_data = self.coordinator.config_entry.data.get("home_data", {})
+        home_id = self.coordinator.config_entry.data.get("home_id")
+
+        # Get home details
+        address = home_data.get("address", {})
+        address1 = address.get("address1", "")
+        city = address.get("city", "")
+        app_nickname = home_data.get("appNickname", "")
+        home_type = home_data.get("type", "")
+
+        # Compose home name
+        if app_nickname and app_nickname.strip():
+            # If appNickname is set, use it as-is (don't add city)
+            home_name = app_nickname.strip()
+        elif address1:
+            # If no appNickname, use address and optionally add city
+            home_name = address1
+            if city:
+                home_name = f"{home_name}, {city}"
+        else:
+            # Fallback to home ID
+            home_name = f"Tibber Home {home_id}"
+
+        return home_name, home_id, home_type
+
+    def _get_main_entry_device_info(self, user_profile: dict) -> str:
+        """Get device info for main entry."""
+        user_name = user_profile.get("name", "Tibber User")
+        user_email = user_profile.get("email", "")
+        home_name = f"Tibber - {user_name}"
+        if user_email:
+            home_name = f"{home_name} ({user_email})"
+        return home_name
+
+    def _get_fallback_device_info(self) -> tuple[str, str | None]:
+        """Get fallback device info if user data not available yet."""
+        if not self.coordinator.data:
+            return "Tibber Home", None
+
+        try:
+            address1 = str(self.coordinator.data.get("address", {}).get("address1", ""))
+            city = str(self.coordinator.data.get("address", {}).get("city", ""))
+            app_nickname = str(self.coordinator.data.get("appNickname", ""))
+            home_type = str(self.coordinator.data.get("type", ""))
+
+            # Compose a nice name
+            if app_nickname and app_nickname.strip():
+                home_name = f"Tibber {app_nickname.strip()}"
+            elif address1:
+                home_name = f"Tibber {address1}"
+                if city:
+                    home_name = f"{home_name}, {city}"
+            else:
+                home_name = "Tibber Home"
+        except (KeyError, IndexError, TypeError):
+            return "Tibber Home", None
+        else:
+            return home_name, home_type

custom_components/tibber_prices/sensor.py

@@ -1027,7 +1027,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
                             "interval_start": starts_at.isoformat(),
                             "interval_end": interval_end.isoformat(),
                             "price": float(price_data["total"]),
-                            "price_cents": round(float(price_data["total"]) * 100, 2),
+                            "price_minor": round(float(price_data["total"]) * 100, 2),
                             "level": price_data.get("level", "NORMAL"),
                             "rating": price_data.get("difference", None),
                             "rating_level": price_data.get("rating_level"),
@@ -1076,7 +1076,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             interval_data = {
                 "minute": starts_at.minute,
                 "price": interval["price"],
-                "price_cents": interval["price_cents"],
+                "price_minor": interval["price_minor"],
                 "level": interval["level"],  # Price level from priceInfo
                 "time": starts_at.strftime("%H:%M"),
             }
@@ -1408,12 +1408,15 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             else:
                 # Fallback: use the first timestamp of the appropriate day
                 day_key = "tomorrow" if "tomorrow" in key else "today"
-                attributes["timestamp"] = price_info.get(day_key, [{}])[0].get("startsAt")
+                day_data = price_info.get(day_key, [])
+                if day_data:
+                    attributes["timestamp"] = day_data[0].get("startsAt")
         else:
             # Fallback: use the first timestamp of the appropriate day
             day_key = "tomorrow" if "tomorrow" in key else "today"
-            first_timestamp = price_info.get(day_key, [{}])[0].get("startsAt")
-            attributes["timestamp"] = first_timestamp
+            day_data = price_info.get(day_key, [])
+            if day_data:
+                attributes["timestamp"] = day_data[0].get("startsAt")
 
     def _add_average_price_attributes(self, attributes: dict) -> None:
         """Add attributes for trailing and leading average price sensors."""

custom_components/tibber_prices/translations/de.json

@@ -68,17 +68,35 @@
     "options": {
         "step": {
             "init": {
-                "description": "Benutzer: {user_login}",
+                "title": "Allgemeine Einstellungen",
+                "description": "Konfiguration allgemeiner Einstellungen für Tibber Preisinformationen & Bewertungen.\n\nBenutzer: {user_login}",
                 "data": {
-                    "access_token": "API-Zugriffstoken",
-                    "extended_descriptions": "Erweiterte Beschreibungen in Entitätsattributen anzeigen",
-                    "best_price_flex": "Flexibilität für Bestpreis (%)",
-                    "peak_price_flex": "Flexibilität für Spitzenpreis (%)",
-                    "price_rating_threshold_low": "Preisbewertungs-Schwellenwert Niedrig (% vs. Durchschnitt)",
-                    "price_rating_threshold_high": "Preisbewertungs-Schwellenwert Hoch (% vs. Durchschnitt)"
+                    "extended_descriptions": "Erweiterte Beschreibungen in Entity-Attributen anzeigen"
+                }
             },
-                "title": "Optionen für Tibber Preisinformationen & Bewertungen",
-                "submit": "Optionen speichern"
+            "price_rating": {
+                "title": "Preisbewertungs-Schwellwerte",
+                "description": "Konfiguration der Schwellwerte für Preisbewertungsstufen (NIEDRIG/NORMAL/HOCH) basierend auf dem Vergleich mit dem gleitenden 24-Stunden-Durchschnitt.",
+                "data": {
+                    "price_rating_threshold_low": "Schwellwert für niedrige Bewertung (% unter gleitendem Durchschnitt)",
+                    "price_rating_threshold_high": "Schwellwert für hohe Bewertung (% über gleitendem Durchschnitt)"
+                }
+            },
+            "best_price": {
+                "title": "Bestpreis-Periode Einstellungen",
+                "description": "Konfiguration für den Bestpreis-Periode Binärsensor. Dieser Sensor ist während der Zeiträume mit den niedrigsten Strompreisen aktiv.",
+                "data": {
+                    "best_price_flex": "Flexibilität: Maximale % über dem Mindestpreis",
+                    "best_price_min_distance_from_avg": "Mindestabstand: Erforderliche % unter dem Tagesdurchschnitt"
+                }
+            },
+            "peak_price": {
+                "title": "Spitzenpreis-Periode Einstellungen",
+                "description": "Konfiguration für den Spitzenpreis-Periode Binärsensor. Dieser Sensor ist während der Zeiträume mit den höchsten Strompreisen aktiv.",
+                "data": {
+                    "peak_price_flex": "Flexibilität: Maximale % unter dem Höchstpreis (negativer Wert)",
+                    "peak_price_min_distance_from_avg": "Mindestabstand: Erforderliche % über dem Tagesdurchschnitt"
+                }
             }
         },
         "error": {

custom_components/tibber_prices/translations/en.json

@@ -68,17 +68,35 @@
     "options": {
         "step": {
             "init": {
-                "description": "User: {user_login}",
+                "title": "General Settings",
+                "description": "Configure general settings for Tibber Price Information & Ratings.\n\nUser: {user_login}",
                 "data": {
-                    "access_token": "API access token",
-                    "extended_descriptions": "Show extended descriptions in entity attributes",
-                    "best_price_flex": "Best Price Flexibility (%)",
-                    "peak_price_flex": "Peak Price Flexibility (%)",
-                    "price_rating_threshold_low": "Price Rating Threshold Low (% vs trailing average)",
-                    "price_rating_threshold_high": "Price Rating Threshold High (% vs trailing average)"
+                    "extended_descriptions": "Show extended descriptions in entity attributes"
+                }
             },
-                "title": "Options for Tibber Price Information & Ratings",
-                "submit": "Save Options"
+            "price_rating": {
+                "title": "Price Rating Thresholds",
+                "description": "Configure thresholds for price rating levels (LOW/NORMAL/HIGH) based on comparison with trailing 24-hour average.",
+                "data": {
+                    "price_rating_threshold_low": "Low Rating Threshold (% below trailing average)",
+                    "price_rating_threshold_high": "High Rating Threshold (% above trailing average)"
+                }
+            },
+            "best_price": {
+                "title": "Best Price Period Settings",
+                "description": "Configure settings for the Best Price Period binary sensor. This sensor is active during periods with the lowest electricity prices.",
+                "data": {
+                    "best_price_flex": "Flexibility: Maximum % above minimum price",
+                    "best_price_min_distance_from_avg": "Minimum Distance: Required % below daily average"
+                }
+            },
+            "peak_price": {
+                "title": "Peak Price Period Settings",
+                "description": "Configure settings for the Peak Price Period binary sensor. This sensor is active during periods with the highest electricity prices.",
+                "data": {
+                    "peak_price_flex": "Flexibility: Maximum % below maximum price (negative value)",
+                    "peak_price_min_distance_from_avg": "Minimum Distance: Required % above daily average"
+                }
             }
         },
         "error": {

tests/test_coordinator_basic.py

@@ -1,22 +1,28 @@
-"""Test basic coordinator functionality with the enhanced coordinator."""
+"""Test basic coordinator functions."""
 
+from __future__ import annotations
+
+import asyncio  # noqa: TC003
+from typing import TYPE_CHECKING
 from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
-from custom_components.tibber_prices.coordinator import (
-    TibberPricesDataUpdateCoordinator,
-)
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+from custom_components.tibber_prices.coordinator import TibberPricesDataUpdateCoordinator
 
 
 class TestBasicCoordinator:
-    """Test basic coordinator functionality."""
+    """Test basic coordinator operations."""
 
     @pytest.fixture
-    def mock_hass(self) -> Mock:
+    def mock_hass(self, event_loop: asyncio.AbstractEventLoop) -> Mock:
         """Create a mock Home Assistant instance."""
         hass = Mock()
         hass.data = {}
+        hass.loop = event_loop
         return hass
 
     @pytest.fixture
@@ -26,6 +32,7 @@ class TestBasicCoordinator:
         config_entry.unique_id = "test_home_123"
         config_entry.entry_id = "test_entry"
         config_entry.data = {"access_token": "test_token"}
+        config_entry.title = "Test Home"
         return config_entry
 
     @pytest.fixture
@@ -36,7 +43,7 @@ class TestBasicCoordinator:
     @pytest.fixture
     def coordinator(
         self, mock_hass: Mock, mock_config_entry: Mock, mock_session: Mock
-    ) -> TibberPricesDataUpdateCoordinator:
+    ) -> Generator[TibberPricesDataUpdateCoordinator]:
         """Create a coordinator instance."""
         with (
             patch(
@@ -50,12 +57,20 @@ class TestBasicCoordinator:
             mock_store.async_save = AsyncMock()
             mock_store_class.return_value = mock_store
 
-            return TibberPricesDataUpdateCoordinator(mock_hass, mock_config_entry)
+            coord = TibberPricesDataUpdateCoordinator(mock_hass, mock_config_entry)
+
+            # Ensure cleanup after test
+            yield coord
+
+            # Clean up the timer
+            if coord._quarter_hour_timer_cancel:  # noqa: SLF001
+                coord._quarter_hour_timer_cancel()  # noqa: SLF001
+                coord._quarter_hour_timer_cancel = None  # noqa: SLF001
 
     def test_coordinator_creation(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
         """Test that coordinator can be created."""
         assert coordinator is not None  # noqa: S101
-        assert hasattr(coordinator, "get_current_interval_data")  # noqa: S101
+        assert hasattr(coordinator, "get_current_interval")  # noqa: S101
         assert hasattr(coordinator, "get_all_intervals")  # noqa: S101
         assert hasattr(coordinator, "get_user_profile")  # noqa: S101
 
@@ -76,7 +91,7 @@ class TestBasicCoordinator:
 
     def test_get_current_interval_data_no_data(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
         """Test getting current interval data when no data is available."""
-        current_data = coordinator.get_current_interval_data()
+        current_data = coordinator.get_current_interval()
         assert current_data is None  # noqa: S101
 
     def test_get_all_intervals_no_data(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:

tests/test_coordinator_enhanced.py

@@ -3,9 +3,11 @@
 from __future__ import annotations
 
 import asyncio
+from typing import TYPE_CHECKING
 from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
+import pytest_asyncio
 
 from custom_components.tibber_prices.api import TibberPricesApiClientCommunicationError
 from custom_components.tibber_prices.const import DOMAIN
@@ -13,6 +15,9 @@ from custom_components.tibber_prices.coordinator import (
     TibberPricesDataUpdateCoordinator,
 )
 
+if TYPE_CHECKING:
+    from collections.abc import AsyncGenerator
+
 
 class TestEnhancedCoordinator:
     """Test enhanced coordinator functionality."""
@@ -24,6 +29,7 @@ class TestEnhancedCoordinator:
         config_entry.unique_id = "test_home_id_123"
         config_entry.entry_id = "test_entry_id"
         config_entry.data = {"access_token": "test_token"}
+        config_entry.options = {}  # Add options dict for threshold lookups
         return config_entry
 
     @pytest.fixture
@@ -54,10 +60,10 @@ class TestEnhancedCoordinator:
         api.async_get_monthly_price_rating = AsyncMock(return_value={"homes": {}})
         return api
 
-    @pytest.fixture
-    def coordinator(
+    @pytest_asyncio.fixture
+    async def coordinator(
         self, mock_hass: Mock, mock_config_entry: Mock, mock_store: Mock, mock_api: Mock
-    ) -> TibberPricesDataUpdateCoordinator:
+    ) -> AsyncGenerator[TibberPricesDataUpdateCoordinator]:
         """Create a coordinator for testing."""
         mock_session = Mock()
         with (
@@ -76,7 +82,12 @@ class TestEnhancedCoordinator:
             )
             # Replace the API instance with our mock
             coordinator.api = mock_api
-            return coordinator
+
+            # Yield for testing
+            yield coordinator
+
+            # Clean up timer on teardown
+            await coordinator.async_shutdown()
 
     @pytest.mark.asyncio
     async def test_main_subentry_pattern(self, mock_hass: Mock, mock_store: Mock) -> None:
@@ -86,6 +97,7 @@ class TestEnhancedCoordinator:
         main_config_entry.unique_id = "main_home_id"
         main_config_entry.entry_id = "main_entry_id"
         main_config_entry.data = {"access_token": "test_token"}
+        main_config_entry.options = {}  # Add options dict for threshold lookups
 
         mock_session = Mock()
         with (
@@ -111,6 +123,7 @@ class TestEnhancedCoordinator:
         sub_config_entry.unique_id = "sub_home_id"
         sub_config_entry.entry_id = "sub_entry_id"
         sub_config_entry.data = {"access_token": "test_token", "home_id": "sub_home_id"}
+        sub_config_entry.options = {}  # Add options dict for threshold lookups
 
         # Set up domain data to simulate main coordinator being already registered
         mock_hass.data[DOMAIN] = {"main_entry_id": main_coordinator}
@@ -133,6 +146,10 @@ class TestEnhancedCoordinator:
         # Verify subentry coordinator is not marked as main entry
         assert not sub_coordinator.is_main_entry()  # noqa: S101
 
+        # Clean up coordinators
+        await main_coordinator.async_shutdown()
+        await sub_coordinator.async_shutdown()
+
     @pytest.mark.asyncio
     async def test_user_data_functionality(self, coordinator: TibberPricesDataUpdateCoordinator) -> None:
         """Test user data related functionality."""