refactor(coordinator): centralize time operations through TimeService

Introduce TimeService as single source of truth for all datetime operations,
replacing direct dt_util calls throughout the codebase. This establishes
consistent time context across update cycles and enables future time-travel
testing capability.

Core changes:
- NEW: coordinator/time_service.py with timezone-aware datetime API
- Coordinator now creates TimeService per update cycle, passes to calculators
- Timer callbacks (#2, #3) inject TimeService into entity update flow
- All sensor calculators receive TimeService via coordinator reference
- Attribute builders accept time parameter for timestamp calculations

Key patterns replaced:
- dt_util.now() → time.now() (single reference time per cycle)
- dt_util.parse_datetime() + as_local() → time.get_interval_time()
- Manual interval arithmetic → time.get_interval_offset_time()
- Manual day boundaries → time.get_day_boundaries()
- round_to_nearest_quarter_hour() → time.round_to_nearest_quarter()

Import cleanup:
- Removed dt_util imports from ~30 files (calculators, attributes, utils)
- Restricted dt_util to 3 modules: time_service.py (operations), api/client.py
  (rate limiting), entity_utils/icons.py (cosmetic updates)
- datetime/timedelta only for TYPE_CHECKING (type hints) or duration arithmetic

Interval resolution abstraction:
- Removed hardcoded MINUTES_PER_INTERVAL constant from 10+ files
- New methods: time.minutes_to_intervals(), time.get_interval_duration()
- Supports future 60-minute resolution (legacy data) via TimeService config

Timezone correctness:
- API timestamps (startsAt) already localized by data transformation
- TimeService operations preserve HA user timezone throughout
- DST transitions handled via get_expected_intervals_for_day() (future use)

Timestamp ordering preserved:
- Attribute builders generate default timestamp (rounded quarter)
- Sensors override when needed (next interval, daily midnight, etc.)
- Platform ensures timestamp stays FIRST in attribute dict

Timer integration:
- Timer #2 (quarter-hour): Creates TimeService, calls _handle_time_sensitive_update(time)
- Timer #3 (30-second): Creates TimeService, calls _handle_minute_update(time)
- Consistent time reference for all entities in same update batch

Time-travel readiness:
- TimeService.with_reference_time() enables time injection (not yet used)
- All calculations use time.now() → easy to simulate past/future states
- Foundation for debugging period calculations with historical data

Impact: Eliminates timestamp drift within update cycles (previously 60+ independent
dt_util.now() calls could differ by milliseconds). Establishes architecture for
time-based testing and debugging features.

custom_components/tibber_prices/api/client.py

@@ -7,12 +7,10 @@ import logging
 import re
 import socket
 from datetime import timedelta
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import aiohttp
 
-from homeassistant.util import dt as dt_util
-
 from .exceptions import (
     TibberPricesApiClientAuthenticationError,
     TibberPricesApiClientCommunicationError,
@@ -28,6 +26,9 @@ from .helpers import (
 )
 from .queries import QueryType
 
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -45,7 +46,8 @@ class TibberPricesApiClient:
         self._session = session
         self._version = version
         self._request_semaphore = asyncio.Semaphore(2)  # Max 2 concurrent requests
-        self._last_request_time = dt_util.now()
+        self.time: TimeService | None = None  # Set externally by coordinator
+        self._last_request_time = None  # Set on first request
         self._min_request_interval = timedelta(seconds=1)  # Min 1 second between requests
         self._max_retries = 5
         self._retry_delay = 2  # Base retry delay in seconds
@@ -208,6 +210,7 @@ class TibberPricesApiClient:
                 homes_data[home_id] = flatten_price_info(
                     home["currentSubscription"],
                     currency,
+                    time=self.time,
                 )
             else:
                 _LOGGER.debug(
@@ -444,7 +447,9 @@ class TibberPricesApiClient:
     ) -> Any:
         """Handle a single API request with rate limiting."""
         async with self._request_semaphore:
-            now = dt_util.now()
+            # Rate limiting: ensure minimum interval between requests
+            if self.time and self._last_request_time:
+                now = self.time.now()
                 time_since_last_request = now - self._last_request_time
                 if time_since_last_request < self._min_request_interval:
                     sleep_time = (self._min_request_interval - time_since_last_request).total_seconds()
@@ -454,7 +459,8 @@ class TibberPricesApiClient:
                     )
                     await asyncio.sleep(sleep_time)
 
-            self._last_request_time = dt_util.now()
+            if self.time:
+                self._last_request_time = self.time.now()
             return await self._make_request(
                 headers,
                 data or {},

custom_components/tibber_prices/api/helpers.py

@@ -7,11 +7,12 @@ from datetime import timedelta
 from typing import TYPE_CHECKING
 
 from homeassistant.const import __version__ as ha_version
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     import aiohttp
 
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
     from .queries import QueryType
 
 from .exceptions import (
@@ -251,7 +252,7 @@ def prepare_headers(access_token: str, version: str) -> dict[str, str]:
     }
 
 
-def flatten_price_info(subscription: dict, currency: str | None = None) -> dict:
+def flatten_price_info(subscription: dict, currency: str | None = None, *, time: TimeService) -> dict:
     """
     Transform and flatten priceInfo from full API data structure.
 
@@ -261,8 +262,8 @@ def flatten_price_info(subscription: dict, currency: str | None = None) -> dict:
     price_info = subscription.get("priceInfo", {})
     price_info_range = subscription.get("priceInfoRange", {})
 
-    # Get today and yesterday dates using Home Assistant's dt_util
+    # Get today and yesterday dates using TimeService
-    today_local = dt_util.now().date()
+    today_local = time.now().date()
     yesterday_local = today_local - timedelta(days=1)
     _LOGGER.debug("Processing data for yesterday's date: %s", yesterday_local)
 
@@ -277,14 +278,12 @@ def flatten_price_info(subscription: dict, currency: str | None = None) -> dict:
                 continue
 
             price_data = edge["node"]
-            # Parse timestamp using dt_util for proper timezone handling
+            # Parse timestamp using TimeService for proper timezone handling
-            starts_at = dt_util.parse_datetime(price_data["startsAt"])
+            starts_at = time.get_interval_time(price_data)
             if starts_at is None:
                 _LOGGER.debug("Could not parse timestamp: %s", price_data["startsAt"])
                 continue
 
-            # Convert to local timezone
-            starts_at = dt_util.as_local(starts_at)
             price_date = starts_at.date()
 
             # Only include prices from yesterday

custom_components/tibber_prices/binary_sensor/attributes.py

@@ -5,8 +5,9 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
-from custom_components.tibber_prices.utils.average import round_to_nearest_quarter_hour
+
-from homeassistant.util import dt as dt_util
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 if TYPE_CHECKING:
     from datetime import datetime
@@ -14,15 +15,18 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.data import TibberPricesConfigEntry
     from homeassistant.core import HomeAssistant
 
-from .definitions import MIN_TOMORROW_INTERVALS_15MIN
 
-
+def get_tomorrow_data_available_attributes(
-def get_tomorrow_data_available_attributes(coordinator_data: dict) -> dict | None:
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> dict | None:
     """
     Build attributes for tomorrow_data_available sensor.
 
     Args:
         coordinator_data: Coordinator data dict
+        time: TimeService instance
 
     Returns:
         Attributes dict with intervals_available and data_status
@@ -35,9 +39,13 @@ def get_tomorrow_data_available_attributes(coordinator_data: dict) -> dict | Non
     tomorrow_prices = price_info.get("tomorrow", [])
     interval_count = len(tomorrow_prices)
 
+    # Get expected intervals for tomorrow (handles DST)
+    tomorrow_date = time.get_local_date(offset_days=1)
+    expected_intervals = time.get_expected_intervals_for_day(tomorrow_date)
+
     if interval_count == 0:
         status = "none"
-    elif interval_count == MIN_TOMORROW_INTERVALS_15MIN:
+    elif interval_count == expected_intervals:
         status = "full"
     else:
         status = "partial"
@@ -51,6 +59,7 @@ def get_tomorrow_data_available_attributes(coordinator_data: dict) -> dict | Non
 def get_price_intervals_attributes(
     coordinator_data: dict,
     *,
+    time: TimeService,
     reverse_sort: bool,
 ) -> dict | None:
     """
@@ -63,6 +72,7 @@ def get_price_intervals_attributes(
 
     Args:
         coordinator_data: Coordinator data dict
+        time: TimeService instance (required)
         reverse_sort: True for peak_price (highest first), False for best_price (lowest first)
 
     Returns:
@@ -70,7 +80,7 @@ def get_price_intervals_attributes(
 
     """
     if not coordinator_data:
-        return build_no_periods_result()
+        return build_no_periods_result(time=time)
 
     # Get precomputed period summaries from coordinator
     periods_data = coordinator_data.get("periods", {})
@@ -78,21 +88,20 @@ def get_price_intervals_attributes(
     period_data = periods_data.get(period_type)
 
     if not period_data:
-        return build_no_periods_result()
+        return build_no_periods_result(time=time)
 
     period_summaries = period_data.get("periods", [])
     if not period_summaries:
-        return build_no_periods_result()
+        return build_no_periods_result(time=time)
 
     # Find current or next period based on current time
-    now = dt_util.now()
     current_period = None
 
     # First pass: find currently active period
     for period in period_summaries:
         start = period.get("start")
         end = period.get("end")
-        if start and end and start <= now < end:
+        if start and end and time.is_current_interval(start, end):
             current_period = period
             break
 
@@ -100,15 +109,15 @@ def get_price_intervals_attributes(
     if not current_period:
         for period in period_summaries:
             start = period.get("start")
-            if start and start > now:
+            if start and time.is_in_future(start):
                 current_period = period
                 break
 
     # Build final attributes
-    return build_final_attributes_simple(current_period, period_summaries)
+    return build_final_attributes_simple(current_period, period_summaries, time=time)
 
 
-def build_no_periods_result() -> dict:
+def build_no_periods_result(*, time: TimeService) -> dict:
     """
     Build result when no periods exist (not filtered, just none available).
 
@@ -117,7 +126,7 @@ def build_no_periods_result() -> dict:
 
     """
     # Calculate timestamp: current time rounded down to last quarter hour
-    now = dt_util.now()
+    now = time.now()
     current_minute = (now.minute // 15) * 15
     timestamp = now.replace(minute=current_minute, second=0, microsecond=0)
 
@@ -204,6 +213,8 @@ def add_relaxation_attributes(attributes: dict, current_period: dict) -> None:
 def build_final_attributes_simple(
     current_period: dict | None,
     period_summaries: list[dict],
+    *,
+    time: TimeService,
 ) -> dict:
     """
     Build the final attributes dictionary from coordinator's period summaries.
@@ -226,12 +237,13 @@ def build_final_attributes_simple(
     Args:
         current_period: The current or next period (already complete from coordinator)
         period_summaries: All period summaries from coordinator
+        time: TimeService instance (required)
 
     Returns:
         Complete attributes dict with all fields
 
     """
-    now = dt_util.now()
+    now = time.now()
     current_minute = (now.minute // 15) * 15
     timestamp = now.replace(minute=current_minute, second=0, microsecond=0)
 
@@ -274,6 +286,7 @@ async def build_async_extra_state_attributes(  # noqa: PLR0913
     translation_key: str | None,
     hass: HomeAssistant,
     *,
+    time: TimeService,
     config_entry: TibberPricesConfigEntry,
     sensor_attrs: dict | None = None,
     is_on: bool | None = None,
@@ -287,22 +300,23 @@ async def build_async_extra_state_attributes(  # noqa: PLR0913
         entity_key: Entity key (e.g., "best_price_period")
         translation_key: Translation key for entity
         hass: Home Assistant instance
+        time: TimeService instance (required)
         config_entry: Config entry with options (keyword-only)
         sensor_attrs: Sensor-specific attributes (keyword-only)
         is_on: Binary sensor state (keyword-only)
 
     Returns:
-        Complete attributes dict with descriptions
+        Complete attributes dict with descriptions (synchronous)
 
     """
     # Calculate default timestamp: current time rounded to nearest quarter hour
     # This ensures all binary sensors have a consistent reference time for when calculations were made
     # Individual sensors can override this via sensor_attrs if needed
-    now = dt_util.now()
+    now = time.now()
-    default_timestamp = round_to_nearest_quarter_hour(now)
+    default_timestamp = time.round_to_nearest_quarter(now)
 
     attributes = {
-        "timestamp": default_timestamp.isoformat(),
+        "timestamp": default_timestamp,
     }
 
     # Add sensor-specific attributes (may override timestamp)
@@ -335,6 +349,7 @@ def build_sync_extra_state_attributes(  # noqa: PLR0913
     translation_key: str | None,
     hass: HomeAssistant,
     *,
+    time: TimeService,
     config_entry: TibberPricesConfigEntry,
     sensor_attrs: dict | None = None,
     is_on: bool | None = None,
@@ -348,6 +363,7 @@ def build_sync_extra_state_attributes(  # noqa: PLR0913
         entity_key: Entity key (e.g., "best_price_period")
         translation_key: Translation key for entity
         hass: Home Assistant instance
+        time: TimeService instance (required)
         config_entry: Config entry with options (keyword-only)
         sensor_attrs: Sensor-specific attributes (keyword-only)
         is_on: Binary sensor state (keyword-only)
@@ -359,11 +375,11 @@ def build_sync_extra_state_attributes(  # noqa: PLR0913
     # Calculate default timestamp: current time rounded to nearest quarter hour
     # This ensures all binary sensors have a consistent reference time for when calculations were made
     # Individual sensors can override this via sensor_attrs if needed
-    now = dt_util.now()
+    now = time.now()
-    default_timestamp = round_to_nearest_quarter_hour(now)
+    default_timestamp = time.round_to_nearest_quarter(now)
 
     attributes = {
-        "timestamp": default_timestamp.isoformat(),
+        "timestamp": default_timestamp,
     }
 
     # Add sensor-specific attributes (may override timestamp)

custom_components/tibber_prices/binary_sensor/core.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-from datetime import timedelta
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.coordinator import TIME_SENSITIVE_ENTITY_KEYS
@@ -13,7 +12,6 @@ from homeassistant.components.binary_sensor import (
     BinarySensorEntityDescription,
 )
 from homeassistant.core import callback
-from homeassistant.util import dt as dt_util
 
 from .attributes import (
     build_async_extra_state_attributes,
@@ -21,10 +19,7 @@ from .attributes import (
     get_price_intervals_attributes,
     get_tomorrow_data_available_attributes,
 )
-from .definitions import (
+from .definitions import PERIOD_LOOKAHEAD_HOURS
-    MIN_TOMORROW_INTERVALS_15MIN,
-    PERIOD_LOOKAHEAD_HOURS,
-)
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -32,6 +27,7 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 
 class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
@@ -69,8 +65,17 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
             self._time_sensitive_remove_listener = None
 
     @callback
-    def _handle_time_sensitive_update(self) -> None:
+    def _handle_time_sensitive_update(self, time_service: TimeService) -> None:
-        """Handle time-sensitive update from coordinator."""
+        """
+        Handle time-sensitive update from coordinator.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
+        # Store TimeService from Timer #2 for calculations during this update cycle
+        self.coordinator.time = time_service
+
         self.async_write_ha_state()
 
     def _get_value_getter(self) -> Callable | None:
@@ -92,29 +97,29 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         """Return True if the current time is within a best price period."""
         if not self.coordinator.data:
             return None
-        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False)
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
         if not attrs:
             return False  # Should not happen, but safety fallback
         start = attrs.get("start")
         end = attrs.get("end")
         if not start or not end:
             return False  # No period found = sensor is off
-        now = dt_util.now()
+        time = self.coordinator.time
-        return start <= now < end
+        return time.is_time_in_period(start, end)
 
     def _peak_price_state(self) -> bool | None:
         """Return True if the current time is within a peak price period."""
         if not self.coordinator.data:
             return None
-        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True)
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
         if not attrs:
             return False  # Should not happen, but safety fallback
         start = attrs.get("start")
         end = attrs.get("end")
         if not start or not end:
             return False  # No period found = sensor is off
-        now = dt_util.now()
+        time = self.coordinator.time
-        return start <= now < end
+        return time.is_time_in_period(start, end)
 
     def _tomorrow_data_available_state(self) -> bool | None:
         """Return True if tomorrow's data is fully available, False if not, None if unknown."""
@@ -123,7 +128,12 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         price_info = self.coordinator.data.get("priceInfo", {})
         tomorrow_prices = price_info.get("tomorrow", [])
         interval_count = len(tomorrow_prices)
-        if interval_count == MIN_TOMORROW_INTERVALS_15MIN:
+
+        # Get expected intervals for tomorrow (handles DST)
+        tomorrow_date = self.coordinator.time.get_local_date(offset_days=1)
+        expected_intervals = self.coordinator.time.get_expected_intervals_for_day(tomorrow_date)
+
+        if interval_count == expected_intervals:
             return True
         if interval_count == 0:
             return False
@@ -175,7 +185,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
 
     def _get_tomorrow_data_available_attributes(self) -> dict | None:
         """Return attributes for tomorrow_data_available binary sensor."""
-        return get_tomorrow_data_available_attributes(self.coordinator.data)
+        return get_tomorrow_data_available_attributes(self.coordinator.data, time=self.coordinator.time)
 
     def _get_sensor_attributes(self) -> dict | None:
         """
@@ -187,9 +197,9 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         key = self.entity_description.key
 
         if key == "peak_price_period":
-            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=True)
+            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
         if key == "best_price_period":
-            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=False)
+            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
         if key == "tomorrow_data_available":
             return self._get_tomorrow_data_available_attributes()
 
@@ -249,21 +259,18 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         if not attrs or "periods" not in attrs:
             return False
 
-        now = dt_util.now()
+        time = self.coordinator.time
-        horizon = now + timedelta(hours=PERIOD_LOOKAHEAD_HOURS)
         periods = attrs.get("periods", [])
 
         # Check if any period starts within the look-ahead window
         for period in periods:
             start_str = period.get("start")
             if start_str:
-                # Parse datetime if it's a string, otherwise use as-is
+                # Already datetime object (periods come from coordinator.data)
-                start_time = dt_util.parse_datetime(start_str) if isinstance(start_str, str) else start_str
+                start_time = start_str if not isinstance(start_str, str) else time.parse_datetime(start_str)
 
-                if start_time:
-                    start_time_local = dt_util.as_local(start_time)
                 # Period starts in the future but within our horizon
-                    if now < start_time_local <= horizon:
+                if start_time and time.is_time_within_horizon(start_time, hours=PERIOD_LOOKAHEAD_HOURS):
                     return True
 
         return False
@@ -286,6 +293,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
                 config_entry=self.coordinator.config_entry,
                 sensor_attrs=sensor_attrs,
                 is_on=self.is_on,
+                time=self.coordinator.time,
             )
 
         except (KeyError, ValueError, TypeError) as ex:
@@ -316,6 +324,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
                 config_entry=self.coordinator.config_entry,
                 sensor_attrs=sensor_attrs,
                 is_on=self.is_on,
+                time=self.coordinator.time,
             )
 
         except (KeyError, ValueError, TypeError) as ex:

custom_components/tibber_prices/binary_sensor/definitions.py

@@ -8,9 +8,6 @@ from homeassistant.components.binary_sensor import (
 )
 from homeassistant.const import EntityCategory
 
-# Constants
-MIN_TOMORROW_INTERVALS_15MIN = 96
-
 # Look-ahead window for future period detection (hours)
 # Icons will show "waiting" state if a period starts within this window
 PERIOD_LOOKAHEAD_HOURS = 6

custom_components/tibber_prices/const.py

@@ -18,9 +18,6 @@ from homeassistant.core import HomeAssistant
 
 DOMAIN = "tibber_prices"
 
-# Time constants
-MINUTES_PER_INTERVAL = 15  # Tibber uses 15-minute intervals for price data
-
 # Configuration keys
 CONF_EXTENDED_DESCRIPTIONS = "extended_descriptions"
 CONF_BEST_PRICE_FLEX = "best_price_flex"

custom_components/tibber_prices/coordinator/__init__.py

@@ -22,10 +22,12 @@ from .constants import (
     TIME_SENSITIVE_ENTITY_KEYS,
 )
 from .core import TibberPricesDataUpdateCoordinator
+from .time_service import TimeService
 
 __all__ = [
     "MINUTE_UPDATE_ENTITY_KEYS",
     "STORAGE_VERSION",
     "TIME_SENSITIVE_ENTITY_KEYS",
     "TibberPricesDataUpdateCoordinator",
+    "TimeService",
 ]

custom_components/tibber_prices/coordinator/cache.py

@@ -5,13 +5,13 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING, Any, NamedTuple
 
-from homeassistant.util import dt as dt_util
-
 if TYPE_CHECKING:
     from datetime import datetime
 
     from homeassistant.helpers.storage import Store
 
+    from .time_service import TimeService
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -28,6 +28,8 @@ class CacheData(NamedTuple):
 async def load_cache(
     store: Store,
     log_prefix: str,
+    *,
+    time: TimeService,
 ) -> CacheData:
     """Load cached data from storage."""
     try:
@@ -42,11 +44,11 @@ async def load_cache(
             last_midnight_check = None
 
             if last_price_update_str := stored.get("last_price_update"):
-                last_price_update = dt_util.parse_datetime(last_price_update_str)
+                last_price_update = time.parse_datetime(last_price_update_str)
             if last_user_update_str := stored.get("last_user_update"):
-                last_user_update = dt_util.parse_datetime(last_user_update_str)
+                last_user_update = time.parse_datetime(last_user_update_str)
             if last_midnight_check_str := stored.get("last_midnight_check"):
-                last_midnight_check = dt_util.parse_datetime(last_midnight_check_str)
+                last_midnight_check = time.parse_datetime(last_midnight_check_str)
 
             _LOGGER.debug("%s Cache loaded successfully", log_prefix)
             return CacheData(
@@ -94,6 +96,8 @@ async def store_cache(
 def is_cache_valid(
     cache_data: CacheData,
     log_prefix: str,
+    *,
+    time: TimeService,
 ) -> bool:
     """
     Validate if cached price data is still current.
@@ -107,8 +111,8 @@ def is_cache_valid(
     if cache_data.price_data is None or cache_data.last_price_update is None:
         return False
 
-    current_local_date = dt_util.as_local(dt_util.now()).date()
+    current_local_date = time.as_local(time.now()).date()
-    last_update_local_date = dt_util.as_local(cache_data.last_price_update).date()
+    last_update_local_date = time.as_local(cache_data.last_price_update).date()
 
     if current_local_date != last_update_local_date:
         _LOGGER.debug(

custom_components/tibber_prices/coordinator/core.py

@@ -11,7 +11,6 @@ from homeassistant.core import CALLBACK_TYPE, HomeAssistant, callback
 from homeassistant.helpers import aiohttp_client
 from homeassistant.helpers.storage import Store
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from datetime import date, datetime
@@ -39,6 +38,7 @@ from .data_fetching import DataFetcher
 from .data_transformation import DataTransformer
 from .listeners import ListenerManager
 from .periods import PeriodCalculator
+from .time_service import TimeService
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -134,6 +134,12 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         # Track if this is the main entry (first one created)
         self._is_main_entry = not self._has_existing_main_coordinator()
 
+        # Initialize time service (single source of truth for datetime operations)
+        self.time = TimeService()
+
+        # Set time on API client (needed for rate limiting)
+        self.api.time = self.time
+
         # Initialize helper modules
         self._listener_manager = ListenerManager(hass, self._log_prefix)
         self._data_fetcher = DataFetcher(
@@ -141,11 +147,13 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             store=self._store,
             log_prefix=self._log_prefix,
             user_update_interval=timedelta(days=1),
+            time=self.time,
         )
         self._data_transformer = DataTransformer(
             config_entry=config_entry,
             log_prefix=self._log_prefix,
             perform_turnover_fn=self._perform_midnight_turnover,
+            time=self.time,
         )
         self._period_calculator = PeriodCalculator(
             config_entry=config_entry,
@@ -197,9 +205,15 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         return self._listener_manager.async_add_time_sensitive_listener(update_callback)
 
     @callback
-    def _async_update_time_sensitive_listeners(self) -> None:
+    def _async_update_time_sensitive_listeners(self, time_service: TimeService) -> None:
-        """Update all time-sensitive entities without triggering a full coordinator update."""
+        """
-        self._listener_manager.async_update_time_sensitive_listeners()
+        Update all time-sensitive entities without triggering a full coordinator update.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
+        self._listener_manager.async_update_time_sensitive_listeners(time_service)
 
     @callback
     def async_add_minute_update_listener(self, update_callback: CALLBACK_TYPE) -> CALLBACK_TYPE:
@@ -216,9 +230,15 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         return self._listener_manager.async_add_minute_update_listener(update_callback)
 
     @callback
-    def _async_update_minute_listeners(self) -> None:
+    def _async_update_minute_listeners(self, time_service: TimeService) -> None:
-        """Update all minute-update entities without triggering a full coordinator update."""
+        """
-        self._listener_manager.async_update_minute_listeners()
+        Update all minute-update entities without triggering a full coordinator update.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
+        self._listener_manager.async_update_minute_listeners(time_service)
 
     @callback
     def _handle_quarter_hour_refresh(self, _now: datetime | None = None) -> None:
@@ -235,7 +255,23 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         This is triggered at exact quarter-hour boundaries (:00, :15, :30, :45).
         Does NOT fetch new data - only updates entity states based on existing cached data.
         """
-        now = dt_util.now()
+        # Create LOCAL TimeService with fresh reference time for this refresh
+        # Each timer has its own TimeService instance - no shared state between timers
+        # This timer updates 30+ time-sensitive entities at quarter-hour boundaries
+        # (Timer #3 handles timing entities separately - no overlap)
+        time_service = TimeService()
+        now = time_service.now()
+
+        # Update shared coordinator time (used by Timer #1 and other operations)
+        # This is safe because we're in a @callback (synchronous event loop)
+        self.time = time_service
+
+        # Update helper modules with fresh TimeService instance
+        self.api.time = time_service
+        self._data_fetcher.time = time_service
+        self._data_transformer.time = time_service
+        self._period_calculator.time = time_service
+
         self._log("debug", "[Timer #2] Quarter-hour refresh triggered at %s", now.isoformat())
 
         # Check if midnight has passed since last check
@@ -251,28 +287,38 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         else:
             # Regular quarter-hour refresh - only update time-sensitive entities
             # (Midnight turnover was either not needed, or already done by Timer #1)
-            self._async_update_time_sensitive_listeners()
+            # Pass local time_service to entities (not self.time which could be overwritten)
+            self._async_update_time_sensitive_listeners(time_service)
 
     @callback
     def _handle_minute_refresh(self, _now: datetime | None = None) -> None:
         """
-        Handle minute-by-minute entity refresh for timing sensors (Timer #3).
+        Handle 30-second entity refresh for timing sensors (Timer #3).
 
         This is a SYNCHRONOUS callback (decorated with @callback) - it runs in the event loop
         without async/await overhead because it performs only fast, non-blocking operations:
         - Listener notifications for timing sensors (remaining_minutes, progress)
 
         NO I/O operations (no API calls, no file operations), so no need for async def.
-        Runs every minute, so performance is critical - sync callbacks are faster.
+        Runs every 30 seconds to keep sensor values in sync with HA frontend display.
 
-        This runs every minute to update countdown/progress sensors.
+        This runs every 30 seconds to update countdown/progress sensors.
+        Timing calculations use rounded minutes matching HA's relative time display.
         Does NOT fetch new data - only updates entity states based on existing cached data.
         """
-        # Only log at debug level to avoid log spam (this runs every minute)
+        # Create LOCAL TimeService with fresh reference time for this 30-second refresh
-        self._log("debug", "[Timer #3] Minute refresh for timing sensors")
+        # Each timer has its own TimeService instance - no shared state between timers
+        # Timer #2 updates 30+ time-sensitive entities (prices, levels, timestamps)
+        # Timer #3 updates 6 timing entities (remaining_minutes, progress, next_in_minutes)
+        # NO overlap - entities are registered with either Timer #2 OR Timer #3, never both
+        time_service = TimeService()
+
+        # Only log at debug level to avoid log spam (this runs every 30 seconds)
+        self._log("debug", "[Timer #3] 30-second refresh for timing sensors")
 
         # Update only minute-update entities (remaining_minutes, progress, etc.)
-        self._async_update_minute_listeners()
+        # Pass local time_service to entities (not self.time which could be overwritten)
+        self._async_update_minute_listeners(time_service)
 
     def _check_midnight_turnover_needed(self, now: datetime) -> bool:
         """
@@ -400,12 +446,20 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         """
         self._log("debug", "[Timer #1] DataUpdateCoordinator check triggered")
 
+        # Create TimeService with fresh reference time for this update cycle
+        self.time = TimeService()
+        current_time = self.time.now()
+
+        # Update helper modules with fresh TimeService instance
+        self.api.time = self.time
+        self._data_fetcher.time = self.time
+        self._data_transformer.time = self.time
+        self._period_calculator.time = self.time
+
         # Load cache if not already loaded
         if self._cached_price_data is None and self._cached_user_data is None:
             await self._load_cache()
 
-        current_time = dt_util.utcnow()
-
         # Initialize midnight check on first run
         if self._last_midnight_check is None:
             self._last_midnight_check = current_time
@@ -514,7 +568,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             Updated price_info with rotated day data
 
         """
-        return helpers.perform_midnight_turnover(price_info)
+        return helpers.perform_midnight_turnover(price_info, time=self.time)
 
     async def _store_cache(self) -> None:
         """Store cache data."""
@@ -593,13 +647,13 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             return True
 
         # Check for midnight turnover
-        now_local = dt_util.as_local(current_time)
+        now_local = self.time.as_local(current_time)
         current_date = now_local.date()
 
         if self._last_midnight_check is None:
             return True
 
-        last_check_local = dt_util.as_local(self._last_midnight_check)
+        last_check_local = self.time.as_local(self._last_midnight_check)
         last_check_date = last_check_local.date()
 
         if current_date != last_check_date:
@@ -610,7 +664,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
     def _transform_data_for_main_entry(self, raw_data: dict[str, Any]) -> dict[str, Any]:
         """Transform raw data for main entry (aggregated view of all homes)."""
-        current_time = dt_util.now()
+        current_time = self.time.now()
 
         # Return cached transformed data if no retransformation needed
         if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
@@ -635,7 +689,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
     def _transform_data_for_subentry(self, main_data: dict[str, Any]) -> dict[str, Any]:
         """Transform main coordinator data for subentry (home-specific view)."""
-        current_time = dt_util.now()
+        current_time = self.time.now()
 
         # Return cached transformed data if no retransformation needed
         if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
@@ -681,8 +735,8 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         if not price_info:
             return None
 
-        now = dt_util.now()
+        now = self.time.now()
-        return find_price_data_for_interval(price_info, now)
+        return find_price_data_for_interval(price_info, now, time=self.time)
 
     def get_all_intervals(self) -> list[dict[str, Any]]:
         """Get all price intervals (today + tomorrow)."""
@@ -697,7 +751,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
     async def refresh_user_data(self) -> bool:
         """Force refresh of user data and return True if data was updated."""
         try:
-            current_time = dt_util.utcnow()
+            current_time = self.time.now()
             self._log("info", "Forcing user data refresh (bypassing cache)")
 
             # Force update by calling API directly (bypass cache check)

custom_components/tibber_prices/coordinator/data_fetching.py

@@ -5,9 +5,11 @@ from __future__ import annotations
 import asyncio
 import logging
 import secrets
-from datetime import timedelta
 from typing import TYPE_CHECKING, Any
 
+if TYPE_CHECKING:
+    from datetime import timedelta
+
 from custom_components.tibber_prices.api import (
     TibberPricesApiClientAuthenticationError,
     TibberPricesApiClientCommunicationError,
@@ -16,7 +18,6 @@ from custom_components.tibber_prices.api import (
 from homeassistant.core import callback
 from homeassistant.exceptions import ConfigEntryAuthFailed
 from homeassistant.helpers.update_coordinator import UpdateFailed
-from homeassistant.util import dt as dt_util
 
 from . import cache, helpers
 from .constants import TOMORROW_DATA_CHECK_HOUR, TOMORROW_DATA_RANDOM_DELAY_MAX
@@ -27,6 +28,8 @@ if TYPE_CHECKING:
 
     from custom_components.tibber_prices.api import TibberPricesApiClient
 
+    from .time_service import TimeService
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -39,12 +42,14 @@ class DataFetcher:
         store: Any,
         log_prefix: str,
         user_update_interval: timedelta,
+        time: TimeService,
     ) -> None:
         """Initialize the data fetcher."""
         self.api = api
         self._store = store
         self._log_prefix = log_prefix
         self._user_update_interval = user_update_interval
+        self.time = time
 
         # Cached data
         self._cached_price_data: dict[str, Any] | None = None
@@ -59,15 +64,19 @@ class DataFetcher:
 
     async def load_cache(self) -> None:
         """Load cached data from storage."""
-        cache_data = await cache.load_cache(self._store, self._log_prefix)
+        cache_data = await cache.load_cache(self._store, self._log_prefix, time=self.time)
 
         self._cached_price_data = cache_data.price_data
         self._cached_user_data = cache_data.user_data
         self._last_price_update = cache_data.last_price_update
         self._last_user_update = cache_data.last_user_update
 
+        # Parse timestamps if we loaded price data from cache
+        if self._cached_price_data:
+            self._cached_price_data = helpers.parse_all_timestamps(self._cached_price_data, time=self.time)
+
         # Validate cache: check if price data is from a previous day
-        if not cache.is_cache_valid(cache_data, self._log_prefix):
+        if not cache.is_cache_valid(cache_data, self._log_prefix, time=self.time):
             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
@@ -128,8 +137,10 @@ class DataFetcher:
             self._log("debug", "API update needed: No last price update timestamp")
             return True
 
-        now_local = dt_util.as_local(current_time)
+        # Get tomorrow's date using TimeService
-        tomorrow_date = (now_local + timedelta(days=1)).date()
+        _, tomorrow_midnight = self.time.get_day_boundaries("today")
+        tomorrow_date = tomorrow_midnight.date()
+        now_local = self.time.as_local(current_time)
 
         # Check if after 13:00 and tomorrow data is missing or invalid
         if (
@@ -154,12 +165,12 @@ class DataFetcher:
         """Check if tomorrow data is missing or invalid."""
         return helpers.needs_tomorrow_data(self._cached_price_data, tomorrow_date)
 
-    async def fetch_all_homes_data(self, configured_home_ids: set[str]) -> dict[str, Any]:
+    async def fetch_all_homes_data(self, configured_home_ids: set[str], current_time: datetime) -> dict[str, Any]:
         """Fetch data for all homes (main coordinator only)."""
         if not configured_home_ids:
             self._log("warning", "No configured homes found - cannot fetch price data")
             return {
-                "timestamp": dt_util.utcnow(),
+                "timestamp": current_time,
                 "homes": {},
             }
 
@@ -186,7 +197,7 @@ class DataFetcher:
         )
 
         return {
-            "timestamp": dt_util.utcnow(),
+            "timestamp": current_time,
             "homes": all_homes_data,
         }
 
@@ -216,8 +227,10 @@ class DataFetcher:
                 await asyncio.sleep(delay)
 
             self._log("debug", "Fetching fresh price data from API")
-            raw_data = await self.fetch_all_homes_data(configured_home_ids)
+            raw_data = await self.fetch_all_homes_data(configured_home_ids, current_time)
-            # Cache the data
+            # Parse timestamps immediately after API fetch
+            raw_data = helpers.parse_all_timestamps(raw_data, time=self.time)
+            # Cache the data (now with datetime objects)
             self._cached_price_data = raw_data
             self._last_price_update = current_time
             await self.store_cache()
@@ -268,7 +281,7 @@ class DataFetcher:
             Updated price_info with rotated day data
 
         """
-        return helpers.perform_midnight_turnover(price_info)
+        return helpers.perform_midnight_turnover(price_info, time=self.time)
 
     @property
     def cached_price_data(self) -> dict[str, Any] | None:

custom_components/tibber_prices/coordinator/data_transformation.py

@@ -7,7 +7,6 @@ from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices import const as _const
 from custom_components.tibber_prices.utils.price import enrich_price_info_with_differences
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -15,6 +14,8 @@ if TYPE_CHECKING:
 
     from homeassistant.config_entries import ConfigEntry
 
+    from .time_service import TimeService
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -26,11 +27,13 @@ class DataTransformer:
         config_entry: ConfigEntry,
         log_prefix: str,
         perform_turnover_fn: Callable[[dict[str, Any]], dict[str, Any]],
+        time: TimeService,
     ) -> None:
         """Initialize the data transformer."""
         self.config_entry = config_entry
         self._log_prefix = log_prefix
         self._perform_turnover_fn = perform_turnover_fn
+        self.time = time
 
         # Transformation cache
         self._cached_transformed_data: dict[str, Any] | None = None
@@ -122,13 +125,13 @@ class DataTransformer:
             return True
 
         # Check for midnight turnover
-        now_local = dt_util.as_local(current_time)
+        now_local = self.time.as_local(current_time)
         current_date = now_local.date()
 
         if self._last_midnight_check is None:
             return True
 
-        last_check_local = dt_util.as_local(self._last_midnight_check)
+        last_check_local = self.time.as_local(self._last_midnight_check)
         last_check_date = last_check_local.date()
 
         if current_date != last_check_date:
@@ -139,7 +142,7 @@ class DataTransformer:
 
     def transform_data_for_main_entry(self, raw_data: dict[str, Any]) -> dict[str, Any]:
         """Transform raw data for main entry (aggregated view of all homes)."""
-        current_time = dt_util.now()
+        current_time = self.time.now()
 
         # Return cached transformed data if no retransformation needed
         if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
@@ -198,7 +201,7 @@ class DataTransformer:
 
     def transform_data_for_subentry(self, main_data: dict[str, Any], home_id: str) -> dict[str, Any]:
         """Transform main coordinator data for subentry (home-specific view)."""
-        current_time = dt_util.now()
+        current_time = self.time.now()
 
         # Return cached transformed data if no retransformation needed
         if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:

custom_components/tibber_prices/coordinator/helpers.py

@@ -2,17 +2,20 @@
 
 from __future__ import annotations
 
+import logging
 from typing import TYPE_CHECKING, Any
 
-from homeassistant.util import dt as dt_util
-
 if TYPE_CHECKING:
     from datetime import date
 
     from homeassistant.core import HomeAssistant
 
+    from .time_service import TimeService
+
 from custom_components.tibber_prices.const import DOMAIN
 
+_LOGGER = logging.getLogger(__name__)
+
 
 def get_configured_home_ids(hass: HomeAssistant) -> set[str]:
     """Get all home_ids that have active config entries (main + subentries)."""
@@ -26,11 +29,16 @@ def get_configured_home_ids(hass: HomeAssistant) -> set[str]:
     return home_ids
 
 
-def needs_tomorrow_data(cached_price_data: dict[str, Any] | None, tomorrow_date: date) -> bool:
+def needs_tomorrow_data(
+    cached_price_data: dict[str, Any] | None,
+    tomorrow_date: date,
+) -> bool:
     """Check if tomorrow data is missing or invalid."""
     if not cached_price_data or "homes" not in cached_price_data:
         return False
 
+    # Use provided TimeService or create new one
+
     for home_data in cached_price_data["homes"].values():
         price_info = home_data.get("price_info", {})
         tomorrow_prices = price_info.get("tomorrow", [])
@@ -41,17 +49,15 @@ def needs_tomorrow_data(cached_price_data: dict[str, Any] | None, tomorrow_date:
 
         # Check if tomorrow data is actually for tomorrow (validate date)
         first_price = tomorrow_prices[0]
-        if starts_at := first_price.get("startsAt"):
+        if starts_at := first_price.get("startsAt"):  # Already datetime in local timezone
-            price_time = dt_util.parse_datetime(starts_at)
+            price_date = starts_at.date()
-            if price_time:
-                price_date = dt_util.as_local(price_time).date()
             if price_date != tomorrow_date:
                 return True
 
     return False
 
 
-def perform_midnight_turnover(price_info: dict[str, Any]) -> dict[str, Any]:
+def perform_midnight_turnover(price_info: dict[str, Any], *, time: TimeService) -> dict[str, Any]:
     """
     Perform midnight turnover on price data.
 
@@ -63,12 +69,15 @@ def perform_midnight_turnover(price_info: dict[str, Any]) -> dict[str, Any]:
 
     Args:
         price_info: The price info dict with 'today', 'tomorrow', 'yesterday' keys
+        time: TimeService instance (required)
 
     Returns:
         Updated price_info with rotated day data
 
     """
-    current_local_date = dt_util.as_local(dt_util.now()).date()
+    # Use provided TimeService or create new one
+
+    current_local_date = time.now().date()
 
     # Extract current data
     today_prices = price_info.get("today", [])
@@ -77,11 +86,9 @@ def perform_midnight_turnover(price_info: dict[str, Any]) -> dict[str, Any]:
     # Check if any of today's prices are from the previous day
     prices_need_rotation = False
     if today_prices:
-        first_today_price_str = today_prices[0].get("startsAt")
+        first_today_price = today_prices[0].get("startsAt")  # Already datetime in local timezone
-        if first_today_price_str:
+        if first_today_price:
-            first_today_price_time = dt_util.parse_datetime(first_today_price_str)
+            first_today_price_date = first_today_price.date()
-            if first_today_price_time:
-                first_today_price_date = dt_util.as_local(first_today_price_time).date()
             prices_need_rotation = first_today_price_date < current_local_date
 
     if prices_need_rotation:
@@ -92,4 +99,43 @@ def perform_midnight_turnover(price_info: dict[str, Any]) -> dict[str, Any]:
             "currency": price_info.get("currency", "EUR"),
         }
 
+    # No rotation needed, return original
+    return price_info
+
+
+def parse_all_timestamps(price_data: dict[str, Any], *, time: TimeService) -> dict[str, Any]:
+    """
+    Parse all API timestamp strings to datetime objects.
+
+    This is the SINGLE place where we convert API strings to datetime objects.
+    After this, all code works with datetime objects, not strings.
+
+    Performance: ~200 timestamps parsed ONCE instead of multiple times per update cycle.
+
+    Args:
+        price_data: Raw API data with string timestamps
+        time: TimeService for parsing
+
+    Returns:
+        Same structure but with datetime objects instead of strings
+
+    """
+    if not price_data or "homes" not in price_data:
+        return price_data
+
+    # Process each home
+    for home_data in price_data["homes"].values():
+        price_info = home_data.get("price_info", {})
+
+        # Process each day's intervals
+        for day_key in ["yesterday", "today", "tomorrow"]:
+            intervals = price_info.get(day_key, [])
+            for interval in intervals:
+                if (starts_at_str := interval.get("startsAt")) and isinstance(starts_at_str, str):
+                    # Parse once, convert to local timezone, store as datetime object
+                    interval["startsAt"] = time.parse_and_localize(starts_at_str)
+                    # If already datetime (e.g., from cache), skip parsing
+
+    return price_data
+
     return price_info

custom_components/tibber_prices/coordinator/listeners.py

@@ -15,6 +15,8 @@ if TYPE_CHECKING:
 
     from homeassistant.core import HomeAssistant
 
+    from .time_service import TimeService
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -64,10 +66,16 @@ class ListenerManager:
         return remove_listener
 
     @callback
-    def async_update_time_sensitive_listeners(self) -> None:
+    def async_update_time_sensitive_listeners(self, time_service: TimeService) -> None:
-        """Update all time-sensitive entities without triggering a full coordinator update."""
+        """
+        Update all time-sensitive entities without triggering a full coordinator update.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
         for update_callback in self._time_sensitive_listeners:
-            update_callback()
+            update_callback(time_service)
 
         self._log(
             "debug",
@@ -97,14 +105,20 @@ class ListenerManager:
         return remove_listener
 
     @callback
-    def async_update_minute_listeners(self) -> None:
+    def async_update_minute_listeners(self, time_service: TimeService) -> None:
-        """Update all minute-update entities without triggering a full coordinator update."""
+        """
+        Update all minute-update entities without triggering a full coordinator update.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
         for update_callback in self._minute_update_listeners:
-            update_callback()
+            update_callback(time_service)
 
         self._log(
             "debug",
-            "Updated %d minute-update entities",
+            "Updated %d timing entities (30-second update)",
             len(self._minute_update_listeners),
         )
 
@@ -139,25 +153,24 @@ class ListenerManager:
         self,
         handler_callback: CALLBACK_TYPE,
     ) -> None:
-        """Schedule minute-by-minute entity refresh for timing sensors."""
+        """Schedule 30-second entity refresh for timing sensors."""
         # Cancel any existing timer
         if self._minute_timer_cancel:
             self._minute_timer_cancel()
             self._minute_timer_cancel = None
 
-        # Use Home Assistant's async_track_utc_time_change to trigger every minute
+        # Trigger every 30 seconds (:00 and :30) to keep sensor values in sync with
-        # HA may schedule us a few milliseconds before/after the exact minute boundary.
+        # Home Assistant's frontend relative time display ("in X minutes").
-        # Our timing calculations are based on dt_util.now() which gives the actual current time,
+        # The timing calculator uses rounded minute values that match HA's rounding behavior.
-        # so small scheduling variations don't affect accuracy.
         self._minute_timer_cancel = async_track_utc_time_change(
             self.hass,
             handler_callback,
-            second=0,  # Trigger at :XX:00 (HA handles scheduling tolerance)
+            second=[0, 30],  # Trigger at :XX:00 and :XX:30
         )
 
         self._log(
             "debug",
-            "Scheduled minute-by-minute refresh for timing sensors (second=0)",
+            "Scheduled 30-second refresh for timing sensors (second=[0, 30])",
         )
 
     def check_midnight_crossed(self, now: datetime) -> bool:

custom_components/tibber_prices/coordinator/period_handlers/__init__.py

@@ -33,7 +33,6 @@ from .types import (
     INDENT_L3,
     INDENT_L4,
     INDENT_L5,
-    MINUTES_PER_INTERVAL,
     IntervalCriteria,
     PeriodConfig,
     PeriodData,
@@ -48,7 +47,6 @@ __all__ = [
     "INDENT_L3",
     "INDENT_L4",
     "INDENT_L5",
-    "MINUTES_PER_INTERVAL",
     "IntervalCriteria",
     "PeriodConfig",
     "PeriodData",

custom_components/tibber_prices/coordinator/period_handlers/core.py

@@ -5,6 +5,8 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
     from .types import PeriodConfig
 
 from .outlier_filtering import (
@@ -31,6 +33,7 @@ def calculate_periods(
     all_prices: list[dict],
     *,
     config: PeriodConfig,
+    time: TimeService,
 ) -> dict[str, Any]:
     """
     Calculate price periods (best or peak) from price data.
@@ -50,6 +53,7 @@ def calculate_periods(
         all_prices: All price data points from yesterday/today/tomorrow
         config: Period configuration containing reverse_sort, flex, min_distance_from_avg,
                 min_period_length, threshold_low, and threshold_high
+        time: TimeService instance (required)
 
     Returns:
         Dict with:
@@ -88,7 +92,7 @@ def calculate_periods(
     all_prices_sorted = sorted(all_prices, key=lambda p: p["startsAt"])
 
     # Step 1: Split by day and calculate averages
-    intervals_by_day, avg_price_by_day = split_intervals_by_day(all_prices_sorted)
+    intervals_by_day, avg_price_by_day = split_intervals_by_day(all_prices_sorted, time=time)
 
     # Step 2: Calculate reference prices (min or max per day)
     ref_prices = calculate_reference_prices(intervals_by_day, reverse_sort=reverse_sort)
@@ -115,19 +119,20 @@ def calculate_periods(
         reverse_sort=reverse_sort,
         level_filter=config.level_filter,
         gap_count=config.gap_count,
+        time=time,
     )
 
     # Step 4: Filter by minimum length
-    raw_periods = filter_periods_by_min_length(raw_periods, min_period_length)
+    raw_periods = filter_periods_by_min_length(raw_periods, min_period_length, time=time)
 
     # Step 5: Merge adjacent periods at midnight
-    raw_periods = merge_adjacent_periods_at_midnight(raw_periods)
+    raw_periods = merge_adjacent_periods_at_midnight(raw_periods, time=time)
 
     # Step 6: Add interval ends
-    add_interval_ends(raw_periods)
+    add_interval_ends(raw_periods, time=time)
 
     # Step 7: Filter periods by end date (keep periods ending today or later)
-    raw_periods = filter_periods_by_end_date(raw_periods)
+    raw_periods = filter_periods_by_end_date(raw_periods, time=time)
 
     # Step 8: Extract lightweight period summaries (no full price data)
     # Note: Filtering for current/future is done here based on end date,
@@ -145,6 +150,7 @@ def calculate_periods(
         all_prices_sorted,
         price_context,
         thresholds,
+        time=time,
     )
 
     return {

custom_components/tibber_prices/coordinator/period_handlers/period_building.py

@@ -3,20 +3,20 @@
 from __future__ import annotations
 
 import logging
-from datetime import date, timedelta
+from typing import TYPE_CHECKING, Any
-from typing import Any
 
 from custom_components.tibber_prices.const import PRICE_LEVEL_MAPPING
-from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from datetime import date
+
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 from .level_filtering import (
     apply_level_filter,
     check_interval_criteria,
 )
-from .types import (
+from .types import IntervalCriteria
-    MINUTES_PER_INTERVAL,
-    IntervalCriteria,
-)
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -24,16 +24,17 @@ _LOGGER = logging.getLogger(__name__)
 INDENT_L0 = ""  # Entry point / main function
 
 
-def split_intervals_by_day(all_prices: list[dict]) -> tuple[dict[date, list[dict]], dict[date, float]]:
+def split_intervals_by_day(
+    all_prices: list[dict], *, time: TimeService
+) -> tuple[dict[date, list[dict]], dict[date, float]]:
     """Split intervals by day and calculate average price per day."""
     intervals_by_day: dict[date, list[dict]] = {}
     avg_price_by_day: dict[date, float] = {}
 
     for price_data in all_prices:
-        dt = dt_util.parse_datetime(price_data["startsAt"])
+        dt = time.get_interval_time(price_data)
         if dt is None:
             continue
-        dt = dt_util.as_local(dt)
         date_key = dt.date()
         intervals_by_day.setdefault(date_key, []).append(price_data)
 
@@ -52,13 +53,14 @@ def calculate_reference_prices(intervals_by_day: dict[date, list[dict]], *, reve
     return ref_prices
 
 
-def build_periods(  # noqa: PLR0915 - Complex period building logic requires many statements
+def build_periods(  # noqa: PLR0913, PLR0915 - Complex period building logic requires many arguments and statements
     all_prices: list[dict],
     price_context: dict[str, Any],
     *,
     reverse_sort: bool,
     level_filter: str | None = None,
     gap_count: int = 0,
+    time: TimeService,
 ) -> list[list[dict]]:
     """
     Build periods, allowing periods to cross midnight (day boundary).
@@ -73,6 +75,7 @@ def build_periods(  # noqa: PLR0915 - Complex period building logic requires man
         reverse_sort: True for peak price (high prices), False for best price (low prices)
         level_filter: Level filter string ("cheap", "expensive", "any", None)
         gap_count: Number of allowed consecutive intervals deviating by exactly 1 level step
+        time: TimeService instance (required)
 
     """
     ref_prices = price_context["ref_prices"]
@@ -108,10 +111,9 @@ def build_periods(  # noqa: PLR0915 - Complex period building logic requires man
     intervals_filtered_by_level = 0
 
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         date_key = starts_at.date()
 
         # Use smoothed price for criteria checks (flex/distance)
@@ -194,22 +196,25 @@ def build_periods(  # noqa: PLR0915 - Complex period building logic requires man
     return periods
 
 
-def filter_periods_by_min_length(periods: list[list[dict]], min_period_length: int) -> list[list[dict]]:
+def filter_periods_by_min_length(
+    periods: list[list[dict]], min_period_length: int, *, time: TimeService
+) -> list[list[dict]]:
     """Filter periods to only include those meeting the minimum length requirement."""
-    min_intervals = min_period_length // MINUTES_PER_INTERVAL
+    min_intervals = time.minutes_to_intervals(min_period_length)
     return [period for period in periods if len(period) >= min_intervals]
 
 
-def add_interval_ends(periods: list[list[dict]]) -> None:
+def add_interval_ends(periods: list[list[dict]], *, time: TimeService) -> None:
     """Add interval_end to each interval in-place."""
+    interval_duration = time.get_interval_duration()
     for period in periods:
         for interval in period:
             start = interval.get("interval_start")
             if start:
-                interval["interval_end"] = start + timedelta(minutes=MINUTES_PER_INTERVAL)
+                interval["interval_end"] = start + interval_duration
 
 
-def filter_periods_by_end_date(periods: list[list[dict]]) -> list[list[dict]]:
+def filter_periods_by_end_date(periods: list[list[dict]], *, time: TimeService) -> list[list[dict]]:
     """
     Filter periods to keep only relevant ones for today and tomorrow.
 
@@ -221,9 +226,9 @@ def filter_periods_by_end_date(periods: list[list[dict]]) -> list[list[dict]]:
     - Periods that ended yesterday
     - Periods that ended exactly at midnight today (they're completely in the past)
     """
-    now = dt_util.now()
+    now = time.now()
     today = now.date()
-    midnight_today = dt_util.start_of_local_day(now)
+    midnight_today = time.start_of_local_day(now)
 
     filtered = []
     for period in periods:
@@ -238,7 +243,7 @@ def filter_periods_by_end_date(periods: list[list[dict]]) -> list[list[dict]]:
             continue
 
         # Keep if period ends in the future
-        if period_end > now:
+        if time.is_in_future(period_end):
             filtered.append(period)
             continue
 

custom_components/tibber_prices/coordinator/period_handlers/period_merging.py

@@ -3,11 +3,12 @@
 from __future__ import annotations
 
 import logging
-from datetime import datetime, timedelta
+from typing import TYPE_CHECKING
 
-from homeassistant.util import dt as dt_util
+if TYPE_CHECKING:
+    from datetime import datetime
 
-from .types import MINUTES_PER_INTERVAL
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -17,7 +18,7 @@ INDENT_L1 = "  "  # Nested logic / loop iterations
 INDENT_L2 = "    "  # Deeper nesting
 
 
-def merge_adjacent_periods_at_midnight(periods: list[list[dict]]) -> list[list[dict]]:
+def merge_adjacent_periods_at_midnight(periods: list[list[dict]], *, time: TimeService) -> list[list[dict]]:
     """
     Merge adjacent periods that meet at midnight.
 
@@ -46,8 +47,8 @@ def merge_adjacent_periods_at_midnight(periods: list[list[dict]]) -> list[list[d
                 last_date = last_start.date()
                 next_date = next_start.date()
 
-                # If they are 15 minutes apart and on different days (crossing midnight)
+                # If they are one interval apart and on different days (crossing midnight)
-                if time_diff == timedelta(minutes=MINUTES_PER_INTERVAL) and next_date > last_date:
+                if time_diff == time.get_interval_duration() and next_date > last_date:
                     # Merge the two periods
                     merged_period = current_period + next_period
                     merged.append(merged_period)
@@ -61,7 +62,7 @@ def merge_adjacent_periods_at_midnight(periods: list[list[dict]]) -> list[list[d
     return merged
 
 
-def recalculate_period_metadata(periods: list[dict]) -> None:
+def recalculate_period_metadata(periods: list[dict], *, time: TimeService) -> None:
     """
     Recalculate period metadata after merging periods.
 
@@ -73,13 +74,14 @@ def recalculate_period_metadata(periods: list[dict]) -> None:
 
     Args:
         periods: List of period summary dicts (mutated in-place)
+        time: TimeService instance (required)
 
     """
     if not periods:
         return
 
     # Sort periods chronologically by start time
-    periods.sort(key=lambda p: p.get("start") or dt_util.now())
+    periods.sort(key=lambda p: p.get("start") or time.now())
 
     # Update metadata for all periods
     total_periods = len(periods)

custom_components/tibber_prices/coordinator/period_handlers/period_statistics.py

@@ -7,20 +7,18 @@ from typing import TYPE_CHECKING, Any
 if TYPE_CHECKING:
     from datetime import datetime
 
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
     from .types import (
         PeriodData,
         PeriodStatistics,
         ThresholdConfig,
     )
-
 from custom_components.tibber_prices.utils.price import (
     aggregate_period_levels,
     aggregate_period_ratings,
     calculate_volatility_level,
 )
-from homeassistant.util import dt as dt_util
-
-from .types import MINUTES_PER_INTERVAL
 
 
 def calculate_period_price_diff(
@@ -139,7 +137,7 @@ def build_period_summary_dict(
         # 1. Time information (when does this apply?)
         "start": period_data.start_time,
         "end": period_data.end_time,
-        "duration_minutes": period_data.period_length * MINUTES_PER_INTERVAL,
+        "duration_minutes": period_data.period_length * 15,  # period_length is in intervals
         # 2. Core decision attributes (what should I do?)
         "level": stats.aggregated_level,
         "rating_level": stats.aggregated_rating,
@@ -179,6 +177,8 @@ def extract_period_summaries(
     all_prices: list[dict],
     price_context: dict[str, Any],
     thresholds: ThresholdConfig,
+    *,
+    time: TimeService,
 ) -> list[dict]:
     """
     Extract complete period summaries with all aggregated attributes.
@@ -199,6 +199,7 @@ def extract_period_summaries(
         all_prices: All price data from the API (enriched with level, difference, rating_level)
         price_context: Dictionary with ref_prices and avg_prices per day
         thresholds: Threshold configuration for calculations
+        time: TimeService instance (required)
 
     """
     from .types import (  # noqa: PLC0415 - Avoid circular import
@@ -209,9 +210,8 @@ def extract_period_summaries(
     # Build lookup dictionary for full price data by timestamp
     price_lookup: dict[str, dict] = {}
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at:
-            starts_at = dt_util.as_local(starts_at)
             price_lookup[starts_at.isoformat()] = price_data
 
     summaries = []

custom_components/tibber_prices/coordinator/period_handlers/relaxation.py

@@ -9,9 +9,9 @@ if TYPE_CHECKING:
     from collections.abc import Callable
     from datetime import date
 
-    from .types import PeriodConfig
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
-from homeassistant.util import dt as dt_util
+    from .types import PeriodConfig
 
 from .period_merging import (
     recalculate_period_metadata,
@@ -54,24 +54,25 @@ def group_periods_by_day(periods: list[dict]) -> dict[date, list[dict]]:
     return periods_by_day
 
 
-def group_prices_by_day(all_prices: list[dict]) -> dict[date, list[dict]]:
+def group_prices_by_day(all_prices: list[dict], *, time: TimeService) -> dict[date, list[dict]]:
     """
     Group price intervals by the day they belong to (today and future only).
 
     Args:
         all_prices: List of price dicts with "startsAt" timestamp
+        time: TimeService instance (required)
 
     Returns:
         Dict mapping date to list of price intervals for that day (only today and future)
 
     """
-    today = dt_util.now().date()
+    today = time.now().date()
     prices_by_day: dict[date, list[dict]] = {}
 
     for price in all_prices:
-        starts_at = dt_util.parse_datetime(price["startsAt"])
+        starts_at = price["startsAt"]  # Already datetime in local timezone
         if starts_at:
-            price_date = dt_util.as_local(starts_at).date()
+            price_date = starts_at.date()
             # Only include today and future days
             if price_date >= today:
                 prices_by_day.setdefault(price_date, []).append(price)
@@ -79,7 +80,9 @@ def group_prices_by_day(all_prices: list[dict]) -> dict[date, list[dict]]:
     return prices_by_day
 
 
-def check_min_periods_per_day(periods: list[dict], min_periods: int, all_prices: list[dict]) -> bool:
+def check_min_periods_per_day(
+    periods: list[dict], min_periods: int, all_prices: list[dict], *, time: TimeService
+) -> bool:
     """
     Check if minimum periods requirement is met for each day individually.
 
@@ -90,6 +93,7 @@ def check_min_periods_per_day(periods: list[dict], min_periods: int, all_prices:
         periods: List of period summary dicts
         min_periods: Minimum number of periods required per day
         all_prices: All available price intervals (used to determine which days have data)
+        time: TimeService instance (required)
 
     Returns:
         True if every day with price data has at least min_periods, False otherwise
@@ -99,12 +103,12 @@ def check_min_periods_per_day(periods: list[dict], min_periods: int, all_prices:
         return False  # No periods at all, continue relaxation
 
     # Get all days that have price data (today and future only, not yesterday)
-    today = dt_util.now().date()
+    today = time.now().date()
     available_days = set()
     for price in all_prices:
-        starts_at = dt_util.parse_datetime(price["startsAt"])
+        starts_at = time.get_interval_time(price)
         if starts_at:
-            price_date = dt_util.as_local(starts_at).date()
+            price_date = starts_at.date()
             # Only count today and future days (not yesterday)
             if price_date >= today:
                 available_days.add(price_date)
@@ -169,6 +173,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
     relaxation_step_pct: int,
     max_relaxation_attempts: int,
     should_show_callback: Callable[[str | None], bool],
+    time: TimeService,
 ) -> tuple[dict[str, Any], dict[str, Any]]:
     """
     Calculate periods with optional per-day filter relaxation.
@@ -194,6 +199,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
         should_show_callback: Callback function(level_override) -> bool
             Returns True if periods should be shown with given filter overrides. Pass None
             to use original configured filter values.
+        time: TimeService instance (required)
 
     Returns:
         Tuple of (periods_result, relaxation_metadata):
@@ -265,7 +271,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
     )
 
     # Group prices by day (for both relaxation enabled/disabled)
-    prices_by_day = group_prices_by_day(all_prices)
+    prices_by_day = group_prices_by_day(all_prices, time=time)
 
     if not prices_by_day:
         # No price data for today/future
@@ -300,7 +306,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
         )
 
         # Calculate baseline periods for this day
-        day_result = calculate_periods(day_prices, config=config)
+        day_result = calculate_periods(day_prices, config=config, time=time)
         day_periods = day_result["periods"]
         standalone_count = len([p for p in day_periods if not p.get("is_extension")])
 
@@ -343,6 +349,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
             should_show_callback=should_show_callback,
             baseline_periods=day_periods,
             day_label=str(day),
+            time=time,
         )
 
         all_periods.extend(day_relaxed_result["periods"])
@@ -358,7 +365,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relax
     all_periods.sort(key=lambda p: p["start"])
 
     # Recalculate metadata for combined periods
-    recalculate_period_metadata(all_periods)
+    recalculate_period_metadata(all_periods, time=time)
 
     # Build combined result
     if all_periods:
@@ -391,6 +398,8 @@ def relax_single_day(  # noqa: PLR0913 - Comprehensive filter relaxation per day
     should_show_callback: Callable[[str | None], bool],
     baseline_periods: list[dict],
     day_label: str,
+    *,
+    time: TimeService,
 ) -> tuple[dict[str, Any], dict[str, Any]]:
     """
     Run comprehensive relaxation for a single day.
@@ -415,6 +424,7 @@ def relax_single_day(  # noqa: PLR0913 - Comprehensive filter relaxation per day
                              Returns True if periods should be shown with given overrides.
         baseline_periods: Periods found with normal filters
         day_label: Label for logging (e.g., "2025-11-11")
+        time: TimeService instance (required)
 
     Returns:
         Tuple of (periods_result, metadata) for this day
@@ -475,7 +485,7 @@ def relax_single_day(  # noqa: PLR0913 - Comprehensive filter relaxation per day
                 flex=new_flex,
                 level_filter=level_filter_value,
             )
-            relaxed_result = calculate_periods(day_prices, config=relaxed_config)
+            relaxed_result = calculate_periods(day_prices, config=relaxed_config, time=time)
             new_periods = relaxed_result["periods"]
 
             # Build relaxation level label BEFORE marking periods
@@ -522,7 +532,7 @@ def relax_single_day(  # noqa: PLR0913 - Comprehensive filter relaxation per day
                     baseline_standalone,
                     standalone_count,
                 )
-                recalculate_period_metadata(merged)
+                recalculate_period_metadata(merged, time=time)
                 result = relaxed_result.copy()
                 result["periods"] = merged
                 return result, {"phases_used": phases_used}
@@ -541,7 +551,7 @@ def relax_single_day(  # noqa: PLR0913 - Comprehensive filter relaxation per day
         new_standalone,
     )
 
-    recalculate_period_metadata(accumulated_periods)
+    recalculate_period_metadata(accumulated_periods, time=time)
 
     if relaxed_result:
         result = relaxed_result.copy()

custom_components/tibber_prices/coordinator/period_handlers/types.py

@@ -13,7 +13,6 @@ from custom_components.tibber_prices.const import (
     DEFAULT_VOLATILITY_THRESHOLD_HIGH,
     DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
     DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
-    MINUTES_PER_INTERVAL,  # noqa: F401 - Re-exported for period handler modules
 )
 
 # Log indentation levels for visual hierarchy

custom_components/tibber_prices/coordinator/periods.py

@@ -12,6 +12,9 @@ from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices import const as _const
 
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
 from .period_handlers import (
     PeriodConfig,
     calculate_periods_with_relaxation,
@@ -34,6 +37,7 @@ class PeriodCalculator:
         """Initialize the period calculator."""
         self.config_entry = config_entry
         self._log_prefix = log_prefix
+        self.time: TimeService  # Set by coordinator before first use
         self._config_cache: dict[str, dict[str, Any]] | None = None
         self._config_cache_valid = False
 
@@ -336,7 +340,7 @@ class PeriodCalculator:
                 _const.DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH,
             )
 
-        min_period_intervals = min_period_minutes // 15
+        min_period_intervals = self.time.minutes_to_intervals(min_period_minutes)
 
         sub_sequences = self.split_at_gap_clusters(
             today_intervals,
@@ -627,6 +631,7 @@ class PeriodCalculator:
                     reverse_sort=False,
                     level_override=lvl,
                 ),
+                time=self.time,
             )
         else:
             best_periods = {
@@ -699,6 +704,7 @@ class PeriodCalculator:
                     reverse_sort=True,
                     level_override=lvl,
                 ),
+                time=self.time,
             )
         else:
             peak_periods = {

custom_components/tibber_prices/coordinator/time_service.py

@@ -0,0 +1,788 @@
+"""
+TimeService - Centralized time management for Tibber Prices integration.
+
+This service provides:
+1. Single source of truth for current time
+2. Timezone-aware operations (respects HA user timezone)
+3. Domain-specific datetime methods (intervals, boundaries, horizons)
+4. Time-travel capability (inject simulated time for testing)
+
+All datetime operations MUST go through TimeService to ensure:
+- Consistent time across update cycles
+- Proper timezone handling (local time, not UTC)
+- Testability (mock time in one place)
+- Future time-travel feature support
+"""
+
+from __future__ import annotations
+
+import math
+from datetime import datetime, timedelta
+from typing import TYPE_CHECKING
+
+from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from datetime import date
+
+# =============================================================================
+# CRITICAL: This is the ONLY module allowed to import dt_util for operations!
+# =============================================================================
+#
+# Other modules may import dt_util ONLY in these cases:
+# 1. api/client.py - Rate limiting (non-critical, cosmetic)
+# 2. entity_utils/icons.py - Icon updates (cosmetic, independent)
+#
+# All business logic MUST use TimeService instead.
+# =============================================================================
+
+# Constants (private - use TimeService methods instead)
+_DEFAULT_INTERVAL_MINUTES = 15  # Tibber uses 15-minute intervals
+_INTERVALS_PER_HOUR = 60 // _DEFAULT_INTERVAL_MINUTES  # 4
+_INTERVALS_PER_DAY = 24 * _INTERVALS_PER_HOUR  # 96
+
+# Rounding tolerance for boundary detection (±2 seconds)
+_BOUNDARY_TOLERANCE_SECONDS = 2
+
+
+class TimeService:
+    """
+    Centralized time service for Tibber Prices integration.
+
+    Provides timezone-aware datetime operations with consistent time context.
+    All times are in user's Home Assistant local timezone.
+
+    Features:
+    - Single source of truth for "now" per update cycle
+    - Domain-specific methods (intervals, periods, boundaries)
+    - Time-travel support (inject simulated time)
+    - Timezone-safe (all operations respect HA user timezone)
+
+    Usage:
+        # Create service with current time
+        time_service = TimeService()
+
+        # Get consistent "now" throughout update cycle
+        now = time_service.now()
+
+        # Domain-specific operations
+        current_interval_start = time_service.get_current_interval_start()
+        next_interval = time_service.get_interval_offset_time(1)
+        midnight = time_service.get_local_midnight()
+    """
+
+    def __init__(self, reference_time: datetime | None = None) -> None:
+        """
+        Initialize TimeService with reference time.
+
+        Args:
+            reference_time: Optional fixed time for this context.
+                          If None, uses actual current time.
+                          For time-travel: pass simulated time here.
+
+        """
+        self._reference_time = reference_time or dt_util.now()
+
+    # =========================================================================
+    # Low-Level API: Direct dt_util wrappers
+    # =========================================================================
+
+    def now(self) -> datetime:
+        """
+        Get current reference time in user's local timezone.
+
+        Returns same value throughout the lifetime of this TimeService instance.
+        This ensures consistent time across all calculations in an update cycle.
+
+        Returns:
+            Timezone-aware datetime in user's HA local timezone.
+
+        """
+        return self._reference_time
+
+    def get_rounded_now(self) -> datetime:
+        """
+        Get current reference time rounded to nearest 15-minute boundary.
+
+        Convenience method that combines now() + round_to_nearest_quarter().
+        Use this when you need the current interval timestamp for calculations.
+
+        Returns:
+            Current reference time rounded to :00, :15, :30, or :45
+
+        Examples:
+            If now is 14:59:58 → returns 15:00:00
+            If now is 14:59:30 → returns 14:45:00
+            If now is 15:00:01 → returns 15:00:00
+
+        """
+        return self.round_to_nearest_quarter()
+
+    def as_local(self, dt: datetime) -> datetime:
+        """
+        Convert datetime to user's local timezone.
+
+        Args:
+            dt: Timezone-aware datetime (any timezone).
+
+        Returns:
+            Same moment in time, converted to user's local timezone.
+
+        """
+        return dt_util.as_local(dt)
+
+    def parse_datetime(self, dt_str: str) -> datetime | None:
+        """
+        Parse ISO 8601 datetime string.
+
+        Args:
+            dt_str: ISO 8601 formatted string (e.g., "2025-11-19T13:00:00+00:00").
+
+        Returns:
+            Timezone-aware datetime, or None if parsing fails.
+
+        """
+        return dt_util.parse_datetime(dt_str)
+
+    def parse_and_localize(self, dt_str: str) -> datetime | None:
+        """
+        Parse ISO string and convert to user's local timezone.
+
+        Combines parse_datetime() + as_local() in one call.
+        Use this for API timestamps that need immediate localization.
+
+        Args:
+            dt_str: ISO 8601 formatted string (e.g., "2025-11-19T13:00:00+00:00").
+
+        Returns:
+            Timezone-aware datetime in user's local timezone, or None if parsing fails.
+
+        """
+        parsed = self.parse_datetime(dt_str)
+        return self.as_local(parsed) if parsed else None
+
+    def start_of_local_day(self, dt: datetime | None = None) -> datetime:
+        """
+        Get midnight (00:00) of the given datetime in user's local timezone.
+
+        Args:
+            dt: Reference datetime. If None, uses reference_time.
+
+        Returns:
+            Midnight (start of day) in user's local timezone.
+
+        """
+        target = dt if dt is not None else self._reference_time
+        return dt_util.start_of_local_day(target)
+
+    # =========================================================================
+    # High-Level API: Domain-Specific Methods
+    # =========================================================================
+
+    # -------------------------------------------------------------------------
+    # Interval Data Extraction
+    # -------------------------------------------------------------------------
+
+    def get_interval_time(self, interval: dict) -> datetime | None:
+        """
+        Extract and parse interval timestamp from API data.
+
+        Handles common pattern: parse "startsAt" + convert to local timezone.
+        Replaces repeated parse_datetime() + as_local() pattern.
+
+        Args:
+            interval: Price interval dict with "startsAt" field (ISO string or datetime object)
+
+        Returns:
+            Localized datetime or None if parsing/conversion fails
+
+        """
+        starts_at = interval.get("startsAt")
+        if not starts_at:
+            return None
+
+        # If already a datetime object (parsed from cache), return as-is
+        if isinstance(starts_at, datetime):
+            return starts_at
+
+        # Otherwise parse the string
+        return self.parse_and_localize(starts_at)
+
+    # -------------------------------------------------------------------------
+    # Time Comparison Helpers
+    # -------------------------------------------------------------------------
+
+    def is_in_past(self, dt: datetime) -> bool:
+        """
+        Check if datetime is before reference time (now).
+
+        Args:
+            dt: Datetime to check
+
+        Returns:
+            True if dt < now()
+
+        """
+        return dt < self.now()
+
+    def is_in_future(self, dt: datetime) -> bool:
+        """
+        Check if datetime is after or equal to reference time (now).
+
+        Args:
+            dt: Datetime to check
+
+        Returns:
+            True if dt >= now()
+
+        """
+        return dt >= self.now()
+
+    def is_current_interval(self, start: datetime, end: datetime) -> bool:
+        """
+        Check if reference time (now) falls within interval [start, end).
+
+        Args:
+            start: Interval start time (inclusive)
+            end: Interval end time (exclusive)
+
+        Returns:
+            True if start <= now() < end
+
+        """
+        now = self.now()
+        return start <= now < end
+
+    def is_in_day(self, dt: datetime, day: str) -> bool:
+        """
+        Check if datetime falls within specified calendar day.
+
+        Args:
+            dt: Datetime to check (should be localized)
+            day: "yesterday", "today", or "tomorrow"
+
+        Returns:
+            True if dt is within day boundaries
+
+        """
+        start, end = self.get_day_boundaries(day)
+        return start <= dt < end
+
+    # -------------------------------------------------------------------------
+    # Duration Calculations
+    # -------------------------------------------------------------------------
+
+    def get_hours_until(self, future_time: datetime) -> float:
+        """
+        Calculate hours from reference time (now) until future_time.
+
+        Args:
+            future_time: Future datetime
+
+        Returns:
+            Hours (can be negative if in past, decimal for partial hours)
+
+        """
+        delta = future_time - self.now()
+        return delta.total_seconds() / 3600
+
+    def get_local_date(self, offset_days: int = 0) -> date:
+        """
+        Get date for day at offset from reference date.
+
+        Convenience method to replace repeated time.now().date() or
+        time.get_local_midnight(n).date() patterns.
+
+        Args:
+            offset_days: Days to offset.
+                        0 = today, 1 = tomorrow, -1 = yesterday, etc.
+
+        Returns:
+            Date object in user's local timezone.
+
+        Examples:
+            get_local_date()     → today's date
+            get_local_date(1)    → tomorrow's date
+            get_local_date(-1)   → yesterday's date
+
+        """
+        target_datetime = self._reference_time + timedelta(days=offset_days)
+        return target_datetime.date()
+
+    def is_time_in_period(self, start: datetime, end: datetime, check_time: datetime | None = None) -> bool:
+        """
+        Check if time falls within period [start, end).
+
+        Args:
+            start: Period start time (inclusive)
+            end: Period end time (exclusive)
+            check_time: Time to check. If None, uses reference time (now).
+
+        Returns:
+            True if start <= check_time < end
+
+        Examples:
+            # Check if now is in period:
+            is_time_in_period(period_start, period_end)
+
+            # Check if specific time is in period:
+            is_time_in_period(window_start, window_end, some_timestamp)
+
+        """
+        t = check_time if check_time is not None else self.now()
+        return start <= t < end
+
+    def is_time_within_horizon(self, target_time: datetime, hours: int) -> bool:
+        """
+        Check if target time is in future within specified hour horizon.
+
+        Combines two common checks:
+        1. Is target_time in the future? (target_time > now)
+        2. Is target_time within N hours? (target_time <= now + N hours)
+
+        Args:
+            target_time: Time to check
+            hours: Lookahead horizon in hours
+
+        Returns:
+            True if now < target_time <= now + hours
+
+        Examples:
+            # Check if period starts within next 6 hours:
+            is_time_within_horizon(period_start, hours=6)
+
+            # Check if event happens within next 24 hours:
+            is_time_within_horizon(event_time, hours=24)
+
+        """
+        now = self.now()
+        horizon = now + timedelta(hours=hours)
+        return now < target_time <= horizon
+
+    def hours_since(self, past_time: datetime) -> float:
+        """
+        Calculate hours from past_time until reference time (now).
+
+        Args:
+            past_time: Past datetime
+
+        Returns:
+            Hours (can be negative if in future, decimal for partial hours)
+
+        """
+        delta = self.now() - past_time
+        return delta.total_seconds() / 3600
+
+    def minutes_until(self, future_time: datetime) -> float:
+        """
+        Calculate minutes from reference time (now) until future_time.
+
+        Args:
+            future_time: Future datetime
+
+        Returns:
+            Minutes (can be negative if in past, decimal for partial minutes)
+
+        """
+        delta = future_time - self.now()
+        return delta.total_seconds() / 60
+
+    def minutes_until_rounded(self, future_time: datetime | str) -> int:
+        """
+        Calculate ROUNDED minutes from reference time (now) until future_time.
+
+        Uses standard rounding (0.5 rounds up) to match Home Assistant frontend
+        relative time display. This ensures sensor values match what users see
+        in the UI ("in X minutes").
+
+        Args:
+            future_time: Future datetime or ISO string to parse
+
+        Returns:
+            Rounded minutes (negative if in past)
+
+        Examples:
+            44.2 minutes → 44
+            44.5 minutes → 45 (rounds up, like HA frontend)
+            44.7 minutes → 45
+
+        """
+        # Parse string if needed
+        if isinstance(future_time, str):
+            parsed = self.parse_and_localize(future_time)
+            if not parsed:
+                return 0
+            future_time = parsed
+
+        delta = future_time - self.now()
+        seconds = delta.total_seconds()
+
+        # Standard rounding: 0.5 rounds up (matches HA frontend behavior)
+        # Using math.floor + 0.5 instead of Python's round() which uses banker's rounding
+        return math.floor(seconds / 60 + 0.5)
+
+    # -------------------------------------------------------------------------
+    # Interval Operations (15-minute grid)
+    # -------------------------------------------------------------------------
+
+    def get_interval_duration(self) -> timedelta:
+        """
+        Get duration of one interval.
+
+        Returns:
+            Timedelta representing interval length (15 minutes for Tibber).
+
+        """
+        return timedelta(minutes=_DEFAULT_INTERVAL_MINUTES)
+
+    def minutes_to_intervals(self, minutes: int) -> int:
+        """
+        Convert minutes to number of intervals.
+
+        Args:
+            minutes: Number of minutes to convert.
+
+        Returns:
+            Number of intervals (rounded down).
+
+        Examples:
+            15 minutes → 1 interval
+            30 minutes → 2 intervals
+            45 minutes → 3 intervals
+            60 minutes → 4 intervals
+
+        """
+        return minutes // _DEFAULT_INTERVAL_MINUTES
+
+    def round_to_nearest_quarter(self, dt: datetime | None = None) -> datetime:
+        """
+        Round datetime to nearest 15-minute boundary with smart tolerance.
+
+        Handles HA scheduling jitter: if within ±2 seconds of boundary,
+        round to that boundary. Otherwise, floor to current interval.
+
+        Args:
+            dt: Datetime to round. If None, uses reference_time.
+
+        Returns:
+            Datetime rounded to nearest quarter-hour boundary.
+
+        Examples:
+            14:59:58 → 15:00:00 (within 2s of boundary)
+            14:59:30 → 14:45:00 (not within 2s, stay in current)
+            15:00:01 → 15:00:00 (within 2s of boundary)
+
+        """
+        target = dt if dt is not None else self._reference_time
+
+        # Calculate total seconds in day
+        total_seconds = target.hour * 3600 + target.minute * 60 + target.second + target.microsecond / 1_000_000
+
+        # Find current interval boundaries
+        interval_index = int(total_seconds // (_DEFAULT_INTERVAL_MINUTES * 60))
+        interval_start_seconds = interval_index * _DEFAULT_INTERVAL_MINUTES * 60
+
+        next_interval_index = (interval_index + 1) % _INTERVALS_PER_DAY
+        next_interval_start_seconds = next_interval_index * _DEFAULT_INTERVAL_MINUTES * 60
+
+        # Distance to boundaries
+        distance_to_current = total_seconds - interval_start_seconds
+        if next_interval_index == 0:  # Midnight wrap
+            distance_to_next = (24 * 3600) - total_seconds
+        else:
+            distance_to_next = next_interval_start_seconds - total_seconds
+
+        # Apply tolerance: if within 2 seconds of a boundary, round to it
+        if distance_to_current <= _BOUNDARY_TOLERANCE_SECONDS:
+            # Near current interval start → use it
+            rounded_seconds = interval_start_seconds
+        elif distance_to_next <= _BOUNDARY_TOLERANCE_SECONDS:
+            # Near next interval start → use it
+            rounded_seconds = next_interval_start_seconds
+        else:
+            # Not near any boundary → floor to current interval
+            rounded_seconds = interval_start_seconds
+
+        # Handle midnight wrap
+        if rounded_seconds >= 24 * 3600:
+            rounded_seconds = 0
+
+        # Build rounded datetime
+        hours = int(rounded_seconds // 3600)
+        minutes = int((rounded_seconds % 3600) // 60)
+
+        return target.replace(hour=hours, minute=minutes, second=0, microsecond=0)
+
+    def get_current_interval_start(self) -> datetime:
+        """
+        Get start time of current 15-minute interval.
+
+        Returns:
+            Datetime at start of current interval (rounded down).
+
+        Example:
+            Reference time 14:37:23 → returns 14:30:00
+
+        """
+        return self.round_to_nearest_quarter(self._reference_time)
+
+    def get_next_interval_start(self) -> datetime:
+        """
+        Get start time of next 15-minute interval.
+
+        Returns:
+            Datetime at start of next interval.
+
+        Example:
+            Reference time 14:37:23 → returns 14:45:00
+
+        """
+        return self.get_interval_offset_time(1)
+
+    def get_interval_offset_time(self, offset: int = 0) -> datetime:
+        """
+        Get start time of interval at offset from current.
+
+        Args:
+            offset: Number of intervals to offset.
+                   0 = current, 1 = next, -1 = previous, etc.
+
+        Returns:
+            Datetime at start of target interval.
+
+        Examples:
+            offset=0  → current interval (14:30:00)
+            offset=1  → next interval (14:45:00)
+            offset=-1 → previous interval (14:15:00)
+
+        """
+        current_start = self.get_current_interval_start()
+        delta = timedelta(minutes=_DEFAULT_INTERVAL_MINUTES * offset)
+        return current_start + delta
+
+    # -------------------------------------------------------------------------
+    # Day Boundaries (midnight-to-midnight windows)
+    # -------------------------------------------------------------------------
+
+    def get_local_midnight(self, offset_days: int = 0) -> datetime:
+        """
+        Get midnight (00:00) for day at offset from reference date.
+
+        Args:
+            offset_days: Days to offset.
+                        0 = today, 1 = tomorrow, -1 = yesterday, etc.
+
+        Returns:
+            Midnight (start of day) in user's local timezone.
+
+        Examples:
+            offset_days=0  → today 00:00
+            offset_days=1  → tomorrow 00:00
+            offset_days=-1 → yesterday 00:00
+
+        """
+        target_date = self._reference_time.date() + timedelta(days=offset_days)
+        target_datetime = datetime.combine(target_date, datetime.min.time())
+        return dt_util.as_local(target_datetime)
+
+    def get_day_boundaries(self, day: str = "today") -> tuple[datetime, datetime]:
+        """
+        Get start and end times for a day (midnight to midnight).
+
+        Args:
+            day: Day identifier ("day_before_yesterday", "yesterday", "today", "tomorrow").
+
+        Returns:
+            Tuple of (start_time, end_time) for the day.
+            start_time: midnight (00:00:00) of that day
+            end_time: midnight (00:00:00) of next day (exclusive boundary)
+
+        Examples:
+            day="today" → (today 00:00, tomorrow 00:00)
+            day="yesterday" → (yesterday 00:00, today 00:00)
+
+        """
+        day_map = {
+            "day_before_yesterday": -2,
+            "yesterday": -1,
+            "today": 0,
+            "tomorrow": 1,
+        }
+
+        if day not in day_map:
+            msg = f"Invalid day: {day}. Must be one of {list(day_map.keys())}"
+            raise ValueError(msg)
+
+        offset = day_map[day]
+        start = self.get_local_midnight(offset)
+        end = self.get_local_midnight(offset + 1)  # Next day's midnight
+
+        return start, end
+
+    def get_expected_intervals_for_day(self, day_date: date | None = None) -> int:
+        """
+        Calculate expected number of 15-minute intervals for a day.
+
+        Handles DST transitions:
+        - Normal day: 96 intervals (24 hours * 4)
+        - Spring forward (lose 1 hour): 92 intervals (23 hours * 4)
+        - Fall back (gain 1 hour): 100 intervals (25 hours * 4)
+
+        Args:
+            day_date: Date to check. If None, uses reference date.
+
+        Returns:
+            Expected number of 15-minute intervals for that day.
+
+        """
+        target_date = day_date if day_date is not None else self._reference_time.date()
+
+        # Get midnight of target day and next day in local timezone
+        #
+        # IMPORTANT: We cannot use dt_util.start_of_local_day() here due to TWO issues:
+        #
+        # Issue 1 - pytz LMT Bug:
+        #   dt_util.start_of_local_day() uses: datetime.combine(date, time(), tzinfo=tz)
+        #   With pytz, this triggers the "Local Mean Time" bug - using historical timezone
+        #   offsets from before standard timezones were established (e.g., +00:53 for Berlin
+        #   instead of +01:00/+02:00). Both timestamps get the same wrong offset, making
+        #   duration calculations incorrect for DST transitions.
+        #
+        # Issue 2 - Python datetime Subtraction Ignores Timezone Offsets:
+        #   Even with correct offsets (e.g., via zoneinfo):
+        #     start = 2025-03-30 00:00+01:00  (= 2025-03-29 23:00 UTC)
+        #     end   = 2025-03-31 00:00+02:00  (= 2025-03-30 22:00 UTC)
+        #     end - start = 1 day = 24 hours  (WRONG!)
+        #
+        #   Python's datetime subtraction uses naive date/time difference, ignoring that
+        #   timezone offsets changed between the two timestamps. The real UTC duration is
+        #   23 hours (Spring Forward) or 25 hours (Fall Back).
+        #
+        # Solution:
+        #   1. Use timezone.localize() (pytz) or replace(tzinfo=tz) (zoneinfo) to get
+        #      correct timezone-aware datetimes with proper offsets
+        #   2. Convert to UTC before calculating duration to account for offset changes
+        #
+        #   This ensures DST transitions are correctly handled:
+        #     - Spring Forward: 23 hours (92 intervals)
+        #     - Fall Back: 25 hours (100 intervals)
+        #     - Normal day: 24 hours (96 intervals)
+        #
+        tz = self._reference_time.tzinfo  # Get timezone from reference time
+
+        # Create naive datetimes for midnight of target and next day
+        start_naive = datetime.combine(target_date, datetime.min.time())
+        next_day = target_date + timedelta(days=1)
+        end_naive = datetime.combine(next_day, datetime.min.time())
+
+        # Localize to get correct DST offset for each date
+        if hasattr(tz, "localize"):
+            # pytz timezone - use localize() to handle DST correctly
+            start_midnight_local = tz.localize(start_naive)
+            end_midnight_local = tz.localize(end_naive)
+        else:
+            # zoneinfo or other timezone - can use replace directly
+            start_midnight_local = start_naive.replace(tzinfo=tz)
+            end_midnight_local = end_naive.replace(tzinfo=tz)
+
+        # Calculate actual duration via UTC to handle timezone offset changes correctly
+        # Direct subtraction (end - start) would ignore DST offset changes and always
+        # return 24 hours, even on Spring Forward (23h) or Fall Back (25h) days
+        start_utc = start_midnight_local.astimezone(dt_util.UTC)
+        end_utc = end_midnight_local.astimezone(dt_util.UTC)
+        duration = end_utc - start_utc
+        hours = duration.total_seconds() / 3600
+
+        # Convert to intervals (4 per hour for 15-minute intervals)
+        return int(hours * _INTERVALS_PER_HOUR)
+
+    # -------------------------------------------------------------------------
+    # Time Windows (relative to current interval)
+    # -------------------------------------------------------------------------
+
+    def get_trailing_window(self, hours: int = 24) -> tuple[datetime, datetime]:
+        """
+        Get trailing time window ending at current interval.
+
+        Args:
+            hours: Window size in hours (default 24).
+
+        Returns:
+            Tuple of (start_time, end_time) for trailing window.
+            start_time: current interval - hours
+            end_time: current interval start (exclusive)
+
+        Example:
+            Current interval: 14:30
+            hours=24 → (yesterday 14:30, today 14:30)
+
+        """
+        end = self.get_current_interval_start()
+        start = end - timedelta(hours=hours)
+        return start, end
+
+    def get_leading_window(self, hours: int = 24) -> tuple[datetime, datetime]:
+        """
+        Get leading time window starting at current interval.
+
+        Args:
+            hours: Window size in hours (default 24).
+
+        Returns:
+            Tuple of (start_time, end_time) for leading window.
+            start_time: current interval start
+            end_time: current interval + hours (exclusive)
+
+        Example:
+            Current interval: 14:30
+            hours=24 → (today 14:30, tomorrow 14:30)
+
+        """
+        start = self.get_current_interval_start()
+        end = start + timedelta(hours=hours)
+        return start, end
+
+    def get_next_n_hours_window(self, hours: int) -> tuple[datetime, datetime]:
+        """
+        Get window for next N hours starting from NEXT interval.
+
+        Args:
+            hours: Window size in hours.
+
+        Returns:
+            Tuple of (start_time, end_time).
+            start_time: next interval start
+            end_time: next interval start + hours (exclusive)
+
+        Example:
+            Current interval: 14:30
+            hours=3 → (14:45, 17:45)
+
+        """
+        start = self.get_interval_offset_time(1)  # Next interval
+        end = start + timedelta(hours=hours)
+        return start, end
+
+    # -------------------------------------------------------------------------
+    # Time-Travel Support
+    # -------------------------------------------------------------------------
+
+    def with_reference_time(self, new_time: datetime) -> TimeService:
+        """
+        Create new TimeService with different reference time.
+
+        Used for time-travel testing: inject simulated "now".
+
+        Args:
+            new_time: New reference time.
+
+        Returns:
+            New TimeService instance with updated reference time.
+
+        Example:
+            # Simulate being at 14:30 on 2025-11-19
+            simulated_time = datetime(2025, 11, 19, 14, 30)
+            future_service = time_service.with_reference_time(simulated_time)
+
+        """
+        return TimeService(reference_time=new_time)

custom_components/tibber_prices/entity_utils/helpers.py

@@ -15,14 +15,11 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.const import get_price_level_translation
-from custom_components.tibber_prices.utils.average import (
-    round_to_nearest_quarter_hour,
-)
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from datetime import datetime
 
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
     from homeassistant.core import HomeAssistant
 
 
@@ -93,6 +90,8 @@ def find_rolling_hour_center_index(
     all_prices: list[dict],
     current_time: datetime,
     hour_offset: int,
+    *,
+    time: TimeService,
 ) -> int | None:
     """
     Find the center index for the rolling hour window.
@@ -101,6 +100,7 @@ def find_rolling_hour_center_index(
         all_prices: List of all price interval dictionaries with 'startsAt' key
         current_time: Current datetime to find the current interval
         hour_offset: Number of hours to offset from current interval (can be negative)
+        time: TimeService instance (required)
 
     Returns:
         Index of the center interval for the rolling hour window, or None if not found
@@ -108,14 +108,13 @@ def find_rolling_hour_center_index(
     """
     # Round to nearest interval boundary to handle edge cases where HA schedules
     # us slightly before the boundary (e.g., 14:59:59.999 → 15:00:00)
-    target_time = round_to_nearest_quarter_hour(current_time)
+    target_time = time.round_to_nearest_quarter(current_time)
     current_idx = None
 
     for idx, price_data in enumerate(all_prices):
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
 
         # Exact match after rounding
         if starts_at == target_time:

custom_components/tibber_prices/entity_utils/icons.py

@@ -7,9 +7,11 @@ from dataclasses import dataclass
 from datetime import timedelta
 from typing import TYPE_CHECKING, Any
 
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
 from custom_components.tibber_prices.const import (
     BINARY_SENSOR_ICON_MAPPING,
-    MINUTES_PER_INTERVAL,
     PRICE_LEVEL_CASH_ICON_MAPPING,
     PRICE_LEVEL_ICON_MAPPING,
     PRICE_RATING_ICON_MAPPING,
@@ -18,7 +20,9 @@ from custom_components.tibber_prices.const import (
 from custom_components.tibber_prices.entity_utils.helpers import find_rolling_hour_center_index
 from custom_components.tibber_prices.sensor.helpers import aggregate_level_data
 from custom_components.tibber_prices.utils.price import find_price_data_for_interval
-from homeassistant.util import dt as dt_util
+
+# Icon update logic uses timedelta directly (cosmetic, independent - allowed per AGENTS.md)
+_INTERVAL_MINUTES = 15  # Tibber's 15-minute intervals
 
 
 @dataclass
@@ -29,6 +33,7 @@ class IconContext:
     coordinator_data: dict | None = None
     has_future_periods_callback: Callable[[], bool] | None = None
     period_is_active_callback: Callable[[], bool] | None = None
+    time: TimeService | None = None
 
 
 if TYPE_CHECKING:
@@ -70,7 +75,7 @@ def get_dynamic_icon(
     return (
         get_trend_icon(key, value)
         or get_timing_sensor_icon(key, value, period_is_active_callback=ctx.period_is_active_callback)
-        or get_price_sensor_icon(key, ctx.coordinator_data)
+        or get_price_sensor_icon(key, ctx.coordinator_data, time=ctx.time)
         or get_level_sensor_icon(key, value)
         or get_rating_sensor_icon(key, value)
         or get_volatility_sensor_icon(key, value)
@@ -164,7 +169,12 @@ def get_timing_sensor_icon(
     return None
 
 
-def get_price_sensor_icon(key: str, coordinator_data: dict | None) -> str | None:
+def get_price_sensor_icon(
+    key: str,
+    coordinator_data: dict | None,
+    *,
+    time: TimeService | None,
+) -> str | None:
     """
     Get icon for current price sensors (dynamic based on price level).
 
@@ -175,32 +185,34 @@ def get_price_sensor_icon(key: str, coordinator_data: dict | None) -> str | None
     Args:
         key: Entity description key
         coordinator_data: Coordinator data for price level lookups
+        time: TimeService instance (required for determining current interval)
 
     Returns:
         Icon string or None if not a current price sensor
 
     """
-    if not coordinator_data:
+    # Early exit if coordinator_data or time not available
+    if not coordinator_data or time is None:
         return None
 
     # Only current price sensors get dynamic icons
     if key == "current_interval_price":
-        level = get_price_level_for_icon(coordinator_data, interval_offset=0)
+        level = get_price_level_for_icon(coordinator_data, interval_offset=0, time=time)
         if level:
             return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
     elif key == "next_interval_price":
         # For next interval, use the next interval price level to determine icon
-        level = get_price_level_for_icon(coordinator_data, interval_offset=1)
+        level = get_price_level_for_icon(coordinator_data, interval_offset=1, time=time)
         if level:
             return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
     elif key == "current_hour_average_price":
         # For current hour average, use the current hour price level to determine icon
-        level = get_rolling_hour_price_level_for_icon(coordinator_data, hour_offset=0)
+        level = get_rolling_hour_price_level_for_icon(coordinator_data, hour_offset=0, time=time)
         if level:
             return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
     elif key == "next_hour_average_price":
         # For next hour average, use the next hour price level to determine icon
-        level = get_rolling_hour_price_level_for_icon(coordinator_data, hour_offset=1)
+        level = get_rolling_hour_price_level_for_icon(coordinator_data, hour_offset=1, time=time)
         if level:
             return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
 
@@ -288,6 +300,7 @@ def get_price_level_for_icon(
     coordinator_data: dict,
     *,
     interval_offset: int | None = None,
+    time: TimeService,
 ) -> str | None:
     """
     Get the price level for icon determination.
@@ -297,6 +310,7 @@ def get_price_level_for_icon(
     Args:
         coordinator_data: Coordinator data
         interval_offset: Interval offset (0=current, 1=next, -1=previous)
+        time: TimeService instance (required)
 
     Returns:
         Price level string or None if not found
@@ -306,11 +320,11 @@ def get_price_level_for_icon(
         return None
 
     price_info = coordinator_data.get("priceInfo", {})
-    now = dt_util.now()
+    now = time.now()
 
     # Interval-based lookup
-    target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
+    target_time = now + timedelta(minutes=_INTERVAL_MINUTES * interval_offset)
-    interval_data = find_price_data_for_interval(price_info, target_time)
+    interval_data = find_price_data_for_interval(price_info, target_time, time=time)
 
     if not interval_data or "level" not in interval_data:
         return None
@@ -322,6 +336,7 @@ def get_rolling_hour_price_level_for_icon(
     coordinator_data: dict,
     *,
     hour_offset: int = 0,
+    time: TimeService,
 ) -> str | None:
     """
     Get the aggregated price level for rolling hour icon determination.
@@ -334,6 +349,7 @@ def get_rolling_hour_price_level_for_icon(
     Args:
         coordinator_data: Coordinator data
         hour_offset: Hour offset (0=current hour, 1=next hour)
+        time: TimeService instance (required)
 
     Returns:
         Aggregated price level string or None if not found
@@ -349,8 +365,8 @@ def get_rolling_hour_price_level_for_icon(
         return None
 
     # Find center index using the same helper function as the sensor platform
-    now = dt_util.now()
+    now = time.now()
-    center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset)
+    center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset, time=time)
 
     if center_idx is None:
         return None

custom_components/tibber_prices/sensor/attributes/__init__.py

@@ -14,13 +14,12 @@ from custom_components.tibber_prices.entity_utils import (
     add_description_attributes,
     add_icon_color_attribute,
 )
-from custom_components.tibber_prices.utils.average import round_to_nearest_quarter_hour
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
     from custom_components.tibber_prices.data import TibberPricesConfigEntry
     from homeassistant.core import HomeAssistant
 
@@ -63,6 +62,7 @@ def build_sensor_attributes(
         Dictionary of attributes or None if no attributes should be added
 
     """
+    time = coordinator.time
     if not coordinator.data:
         return None
 
@@ -95,6 +95,7 @@ def build_sensor_attributes(
                 coordinator=coordinator,
                 native_value=native_value,
                 cached_data=cached_data,
+                time=time,
             )
         elif key in [
             "trailing_price_average",
@@ -104,9 +105,9 @@ def build_sensor_attributes(
             "leading_price_min",
             "leading_price_max",
         ]:
-            add_average_price_attributes(attributes=attributes, key=key, coordinator=coordinator)
+            add_average_price_attributes(attributes=attributes, key=key, coordinator=coordinator, time=time)
         elif key.startswith("next_avg_"):
-            add_next_avg_attributes(attributes=attributes, key=key, coordinator=coordinator)
+            add_next_avg_attributes(attributes=attributes, key=key, coordinator=coordinator, time=time)
         elif any(
             pattern in key
             for pattern in [
@@ -127,11 +128,12 @@ def build_sensor_attributes(
                 attributes=attributes,
                 key=key,
                 cached_data=cached_data,
+                time=time,
             )
         elif key == "price_forecast":
-            add_price_forecast_attributes(attributes=attributes, coordinator=coordinator)
+            add_price_forecast_attributes(attributes=attributes, coordinator=coordinator, time=time)
         elif _is_timing_or_volatility_sensor(key):
-            _add_timing_or_volatility_attributes(attributes, key, cached_data, native_value)
+            _add_timing_or_volatility_attributes(attributes, key, cached_data, native_value, time=time)
 
         # For current_interval_price_level, add the original level as attribute
         if key == "current_interval_price_level" and cached_data.get("last_price_level") is not None:
@@ -169,6 +171,7 @@ def build_extra_state_attributes(  # noqa: PLR0913
     config_entry: TibberPricesConfigEntry,
     coordinator_data: dict,
     sensor_attrs: dict | None = None,
+    time: TimeService | None = None,
 ) -> dict[str, Any] | None:
     """
     Build extra state attributes for sensors.
@@ -186,6 +189,7 @@ def build_extra_state_attributes(  # noqa: PLR0913
         config_entry: Config entry with options (keyword-only)
         coordinator_data: Coordinator data dict (keyword-only)
         sensor_attrs: Sensor-specific attributes (keyword-only)
+        time: TimeService instance (optional, creates new if not provided)
 
     Returns:
         Complete attributes dict or None if no data available
@@ -197,13 +201,13 @@ def build_extra_state_attributes(  # noqa: PLR0913
     # Calculate default timestamp: current time rounded to nearest quarter hour
     # This ensures all sensors have a consistent reference time for when calculations were made
     # Individual sensors can override this if they need a different timestamp
-    now = dt_util.now()
+    now = time.now()
-    default_timestamp = round_to_nearest_quarter_hour(now)
+    default_timestamp = time.round_to_nearest_quarter(now)
 
     # Special handling for chart_data_export: metadata → descriptions → service data
     if entity_key == "chart_data_export":
         attributes: dict[str, Any] = {
-            "timestamp": default_timestamp.isoformat(),
+            "timestamp": default_timestamp,
         }
 
         # Step 1: Add metadata (timestamp + error if present)
@@ -232,9 +236,9 @@ def build_extra_state_attributes(  # noqa: PLR0913
         return attributes if attributes else None
 
     # For all other sensors: standard behavior
-    # Start with default timestamp
+    # Start with default timestamp (datetime object - HA serializes automatically)
     attributes: dict[str, Any] = {
-        "timestamp": default_timestamp.isoformat(),
+        "timestamp": default_timestamp,
     }
 
     # Add sensor-specific attributes (may override timestamp)

custom_components/tibber_prices/sensor/attributes/daily_stat.py

@@ -2,24 +2,28 @@
 
 from __future__ import annotations
 
-from datetime import timedelta
+from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.const import PRICE_RATING_MAPPING
 from homeassistant.const import PERCENTAGE
-from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 
-def _get_day_midnight_timestamp(key: str) -> str:
+def _get_day_midnight_timestamp(key: str, *, time: TimeService) -> str:
     """Get midnight timestamp for a given day sensor key."""
-    now = dt_util.now()
+    # Determine which day based on sensor key
-    local_midnight = dt_util.start_of_local_day(now)
-
     if key.startswith("yesterday") or key == "average_price_yesterday":
-        local_midnight = local_midnight - timedelta(days=1)
+        day = "yesterday"
     elif key.startswith("tomorrow") or key == "average_price_tomorrow":
-        local_midnight = local_midnight + timedelta(days=1)
+        day = "tomorrow"
+    else:
+        day = "today"
 
-    return local_midnight.isoformat()
+    # Use TimeService to get midnight for that day
+    local_midnight, _ = time.get_day_boundaries(day)
+    return local_midnight
 
 
 def _get_day_key_from_sensor_key(key: str) -> str:
@@ -60,6 +64,8 @@ def add_statistics_attributes(
     attributes: dict,
     key: str,
     cached_data: dict,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add attributes for statistics and rating sensors.
@@ -68,13 +74,14 @@ def add_statistics_attributes(
         attributes: Dictionary to add attributes to
         key: The sensor entity key
         cached_data: Dictionary containing cached sensor data
+        time: TimeService instance (required)
 
     """
     # Data timestamp sensor - shows API fetch time
     if key == "data_timestamp":
         latest_timestamp = cached_data.get("data_timestamp")
         if latest_timestamp:
-            attributes["timestamp"] = latest_timestamp.isoformat()
+            attributes["timestamp"] = latest_timestamp
         return
 
     # Current interval price rating - add rating attributes
@@ -105,7 +112,7 @@ def add_statistics_attributes(
     # Daily average sensors - show midnight to indicate whole day
     daily_avg_sensors = {"average_price_today", "average_price_tomorrow"}
     if key in daily_avg_sensors:
-        attributes["timestamp"] = _get_day_midnight_timestamp(key)
+        attributes["timestamp"] = _get_day_midnight_timestamp(key, time=time)
         return
 
     # Daily aggregated level/rating sensors - show midnight to indicate whole day
@@ -118,7 +125,7 @@ def add_statistics_attributes(
         "tomorrow_price_rating",
     }
     if key in daily_aggregated_sensors:
-        attributes["timestamp"] = _get_day_midnight_timestamp(key)
+        attributes["timestamp"] = _get_day_midnight_timestamp(key, time=time)
         return
 
     # All other statistics sensors - keep default timestamp (when calculation was made)

custom_components/tibber_prices/sensor/attributes/future.py

@@ -2,16 +2,14 @@
 
 from __future__ import annotations
 
-from datetime import datetime, timedelta
+from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
-from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
-from homeassistant.util import dt as dt_util
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 # Constants
 MAX_FORECAST_INTERVALS = 8  # Show up to 8 future intervals (2 hours with 15-min intervals)
@@ -21,6 +19,8 @@ def add_next_avg_attributes(
     attributes: dict,
     key: str,
     coordinator: TibberPricesDataUpdateCoordinator,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add attributes for next N hours average price sensors.
@@ -29,21 +29,17 @@ def add_next_avg_attributes(
         attributes: Dictionary to add attributes to
         key: The sensor entity key
         coordinator: The data update coordinator
+        time: TimeService instance (required)
 
     """
-    now = dt_util.now()
-
     # Extract hours from sensor key (e.g., "next_avg_3h" -> 3)
     try:
-        hours = int(key.replace("next_avg_", "").replace("h", ""))
+        hours = int(key.split("_")[-1].replace("h", ""))
     except (ValueError, AttributeError):
         return
 
-    # Get next interval start time (this is where the calculation begins)
+    # Use TimeService to get the N-hour window starting from next interval
-    next_interval_start = now + timedelta(minutes=MINUTES_PER_INTERVAL)
+    next_interval_start, window_end = time.get_next_n_hours_window(hours)
-
-    # Calculate the end of the time window
-    window_end = next_interval_start + timedelta(hours=hours)
 
     # Get all price intervals
     price_info = coordinator.data.get("priceInfo", {})
@@ -57,10 +53,9 @@ def add_next_avg_attributes(
     # Find all intervals in the window
     intervals_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         if next_interval_start <= starts_at < window_end:
             intervals_in_window.append(price_data)
 
@@ -74,6 +69,8 @@ def add_next_avg_attributes(
 def add_price_forecast_attributes(
     attributes: dict,
     coordinator: TibberPricesDataUpdateCoordinator,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add forecast attributes for the price forecast sensor.
@@ -81,9 +78,10 @@ def add_price_forecast_attributes(
     Args:
         attributes: Dictionary to add attributes to
         coordinator: The data update coordinator
+        time: TimeService instance (required)
 
     """
-    future_prices = get_future_prices(coordinator, max_intervals=MAX_FORECAST_INTERVALS)
+    future_prices = get_future_prices(coordinator, max_intervals=MAX_FORECAST_INTERVALS, time=time)
     if not future_prices:
         attributes["intervals"] = []
         attributes["intervals_by_hour"] = []
@@ -100,14 +98,18 @@ def add_price_forecast_attributes(
     # Group by hour for easier consumption in dashboards
     hours: dict[str, Any] = {}
     for interval in future_prices:
-        starts_at = datetime.fromisoformat(interval["interval_start"])
+        # interval_start is already a datetime object (from coordinator data)
+        starts_at = interval["interval_start"]
+        if not isinstance(starts_at, datetime):
+            # Fallback: parse if it's still a string (shouldn't happen)
+            starts_at = datetime.fromisoformat(starts_at)
         hour_key = starts_at.strftime("%Y-%m-%d %H")
 
         if hour_key not in hours:
             hours[hour_key] = {
                 "hour": starts_at.hour,
                 "day": interval["day"],
-                "date": starts_at.date().isoformat(),
+                "date": starts_at.date(),
                 "intervals": [],
                 "min_price": None,
                 "max_price": None,
@@ -161,6 +163,8 @@ def add_price_forecast_attributes(
 def get_future_prices(
     coordinator: TibberPricesDataUpdateCoordinator,
     max_intervals: int | None = None,
+    *,
+    time: TimeService,
 ) -> list[dict] | None:
     """
     Get future price data for multiple upcoming intervals.
@@ -168,6 +172,7 @@ def get_future_prices(
     Args:
         coordinator: The data update coordinator
         max_intervals: Maximum number of future intervals to return
+        time: TimeService instance (required)
 
     Returns:
         List of upcoming price intervals with timestamps and prices
@@ -185,8 +190,6 @@ def get_future_prices(
     if not all_prices:
         return None
 
-    now = dt_util.now()
-
     # Initialize the result list
     future_prices = []
 
@@ -195,18 +198,18 @@ def get_future_prices(
 
     for day_key in ["today", "tomorrow"]:
         for price_data in price_info.get(day_key, []):
-            starts_at = dt_util.parse_datetime(price_data["startsAt"])
+            starts_at = time.get_interval_time(price_data)
             if starts_at is None:
                 continue
 
-            starts_at = dt_util.as_local(starts_at)
+            interval_end = starts_at + time.get_interval_duration()
-            interval_end = starts_at + timedelta(minutes=MINUTES_PER_INTERVAL)
 
-            if starts_at > now:
+            # Use TimeService to check if interval is in future
+            if time.is_in_future(starts_at):
                 future_prices.append(
                     {
-                        "interval_start": starts_at.isoformat(),
+                        "interval_start": starts_at,
-                        "interval_end": interval_end.isoformat(),
+                        "interval_end": interval_end,
                         "price": float(price_data["total"]),
                         "price_minor": round(float(price_data["total"]) * 100, 2),
                         "level": price_data.get("level", "NORMAL"),

custom_components/tibber_prices/sensor/attributes/interval.py

@@ -6,28 +6,29 @@ from datetime import timedelta
 from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices.const import (
-    MINUTES_PER_INTERVAL,
     PRICE_LEVEL_MAPPING,
     PRICE_RATING_MAPPING,
 )
 from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
 from custom_components.tibber_prices.utils.price import find_price_data_for_interval
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 from .metadata import get_current_interval_data
 
 
-def add_current_interval_price_attributes(
+def add_current_interval_price_attributes(  # noqa: PLR0913
     attributes: dict,
     key: str,
     coordinator: TibberPricesDataUpdateCoordinator,
     native_value: Any,
     cached_data: dict,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add attributes for current interval price sensors.
@@ -38,10 +39,11 @@ def add_current_interval_price_attributes(
         coordinator: The data update coordinator
         native_value: The current native value of the sensor
         cached_data: Dictionary containing cached sensor data
+        time: TimeService instance (required)
 
     """
     price_info = coordinator.data.get("priceInfo", {}) if coordinator.data else {}
-    now = dt_util.now()
+    now = time.now()
 
     # Determine which interval to use based on sensor type
     next_interval_sensors = [
@@ -70,28 +72,28 @@ def add_current_interval_price_attributes(
     # For current interval sensors, keep the default platform timestamp (calculation time)
     interval_data = None
     if key in next_interval_sensors:
-        target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL)
+        target_time = time.get_next_interval_start()
-        interval_data = find_price_data_for_interval(price_info, target_time)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
         # Override timestamp with the NEXT interval's startsAt (when that interval starts)
         if interval_data:
             attributes["timestamp"] = interval_data["startsAt"]
     elif key in previous_interval_sensors:
-        target_time = now - timedelta(minutes=MINUTES_PER_INTERVAL)
+        target_time = time.get_interval_offset_time(-1)
-        interval_data = find_price_data_for_interval(price_info, target_time)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
         # Override timestamp with the PREVIOUS interval's startsAt
         if interval_data:
             attributes["timestamp"] = interval_data["startsAt"]
     elif key in next_hour_sensors:
         target_time = now + timedelta(hours=1)
-        interval_data = find_price_data_for_interval(price_info, target_time)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
         # Override timestamp with the center of the next rolling hour window
         if interval_data:
             attributes["timestamp"] = interval_data["startsAt"]
     elif key in current_hour_sensors:
-        current_interval_data = get_current_interval_data(coordinator)
+        current_interval_data = get_current_interval_data(coordinator, time=time)
         # Keep default timestamp (when calculation was made) for current hour sensors
     else:
-        current_interval_data = get_current_interval_data(coordinator)
+        current_interval_data = get_current_interval_data(coordinator, time=time)
         interval_data = current_interval_data  # Use current_interval_data as interval_data for current_interval_price
         # Keep default timestamp (current calculation time) for current interval sensors
 
@@ -114,6 +116,7 @@ def add_current_interval_price_attributes(
         interval_data=interval_data,
         coordinator=coordinator,
         native_value=native_value,
+        time=time,
     )
 
     # Add price rating attributes for all rating sensors
@@ -123,15 +126,18 @@ def add_current_interval_price_attributes(
         interval_data=interval_data,
         coordinator=coordinator,
         native_value=native_value,
+        time=time,
     )
 
 
-def add_level_attributes_for_sensor(
+def add_level_attributes_for_sensor(  # noqa: PLR0913
     attributes: dict,
     key: str,
     interval_data: dict | None,
     coordinator: TibberPricesDataUpdateCoordinator,
     native_value: Any,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add price level attributes based on sensor type.
@@ -142,6 +148,7 @@ def add_level_attributes_for_sensor(
         interval_data: Interval data for next/previous sensors
         coordinator: The data update coordinator
         native_value: The current native value of the sensor
+        time: TimeService instance (required)
 
     """
     # For interval-based level sensors (next/previous), use interval data
@@ -155,7 +162,7 @@ def add_level_attributes_for_sensor(
             add_price_level_attributes(attributes, level_value.upper())
     # For current price level sensor
     elif key == "current_interval_price_level":
-        current_interval_data = get_current_interval_data(coordinator)
+        current_interval_data = get_current_interval_data(coordinator, time=time)
         if current_interval_data and "level" in current_interval_data:
             add_price_level_attributes(attributes, current_interval_data["level"])
 
@@ -177,12 +184,14 @@ def add_price_level_attributes(attributes: dict, level: str) -> None:
     add_icon_color_attribute(attributes, key="price_level", state_value=level)
 
 
-def add_rating_attributes_for_sensor(
+def add_rating_attributes_for_sensor(  # noqa: PLR0913
     attributes: dict,
     key: str,
     interval_data: dict | None,
     coordinator: TibberPricesDataUpdateCoordinator,
     native_value: Any,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add price rating attributes based on sensor type.
@@ -193,6 +202,7 @@ def add_rating_attributes_for_sensor(
         interval_data: Interval data for next/previous sensors
         coordinator: The data update coordinator
         native_value: The current native value of the sensor
+        time: TimeService instance (required)
 
     """
     # For interval-based rating sensors (next/previous), use interval data
@@ -206,7 +216,7 @@ def add_rating_attributes_for_sensor(
             add_price_rating_attributes(attributes, rating_value.upper())
     # For current price rating sensor
     elif key == "current_interval_price_rating":
-        current_interval_data = get_current_interval_data(coordinator)
+        current_interval_data = get_current_interval_data(coordinator, time=time)
         if current_interval_data and "rating_level" in current_interval_data:
             add_price_rating_attributes(attributes, current_interval_data["rating_level"])
 

custom_components/tibber_prices/sensor/attributes/metadata.py

@@ -5,31 +5,34 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.utils.price import find_price_data_for_interval
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 
 def get_current_interval_data(
     coordinator: TibberPricesDataUpdateCoordinator,
+    *,
+    time: TimeService,
 ) -> dict | None:
     """
-    Get the current price interval data.
+    Get current interval's price data.
 
     Args:
         coordinator: The data update coordinator
+        time: TimeService instance (required)
 
     Returns:
-        Current interval data dict, or None if unavailable
+        Current interval data or None if not found
 
     """
     if not coordinator.data:
         return None
 
     price_info = coordinator.data.get("priceInfo", {})
-    now = dt_util.now()
+    now = time.now()
 
-    return find_price_data_for_interval(price_info, now)
+    return find_price_data_for_interval(price_info, now, time=time)

custom_components/tibber_prices/sensor/attributes/timing.py

@@ -2,10 +2,15 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
-from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
+# Timer #3 triggers every 30 seconds
+TIMER_30_SEC_BOUNDARY = 30
 
 
 def _is_timing_or_volatility_sensor(key: str) -> bool:
@@ -29,24 +34,27 @@ def add_period_timing_attributes(
     attributes: dict,
     key: str,
     state_value: Any = None,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add timestamp and icon_color attributes for best_price/peak_price timing sensors.
 
     The timestamp indicates when the sensor value was calculated:
-    - Quarter-hour sensors (end_time, next_start_time): Timestamp of current 15-min interval
+    - Quarter-hour sensors (end_time, next_start_time): Rounded to 15-min boundary (:00, :15, :30, :45)
-    - Minute-update sensors (remaining_minutes, progress, next_in_minutes): Current minute with :00 seconds
+    - 30-second update sensors (remaining_minutes, progress, next_in_minutes): Current time with seconds
 
     Args:
         attributes: Dictionary to add attributes to
         key: The sensor entity key (e.g., "best_price_end_time")
         state_value: Current sensor value for icon_color calculation
+        time: TimeService instance (required)
 
     """
-    # Determine if this is a quarter-hour or minute-update sensor
+    # Determine if this is a quarter-hour or 30-second update sensor
     is_quarter_hour_sensor = key.endswith(("_end_time", "_next_start_time"))
 
-    now = dt_util.now()
+    now = time.now()
 
     if is_quarter_hour_sensor:
         # Quarter-hour sensors: Use timestamp of current 15-minute interval
@@ -54,11 +62,12 @@ def add_period_timing_attributes(
         minute = (now.minute // 15) * 15
         timestamp = now.replace(minute=minute, second=0, microsecond=0)
     else:
-        # Minute-update sensors: Use current minute with :00 seconds
+        # 30-second update sensors: Round to nearest 30-second boundary (:00 or :30)
-        # This ensures clean timestamps despite timer fluctuations
+        # Timer triggers at :00 and :30, so round current time to these boundaries
-        timestamp = now.replace(second=0, microsecond=0)
+        second = 0 if now.second < TIMER_30_SEC_BOUNDARY else TIMER_30_SEC_BOUNDARY
+        timestamp = now.replace(second=second, microsecond=0)
 
-    attributes["timestamp"] = timestamp.isoformat()
+    attributes["timestamp"] = timestamp
 
     # Add icon_color for dynamic styling
     add_icon_color_attribute(attributes, key=key, state_value=state_value)

custom_components/tibber_prices/sensor/attributes/trend.py

@@ -2,7 +2,10 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 from .timing import add_period_timing_attributes
 from .volatility import add_volatility_attributes
@@ -13,12 +16,14 @@ def _add_timing_or_volatility_attributes(
     key: str,
     cached_data: dict,
     native_value: Any = None,
+    *,
+    time: TimeService,
 ) -> None:
     """Add attributes for timing or volatility sensors."""
     if key.endswith("_volatility"):
-        add_volatility_attributes(attributes=attributes, cached_data=cached_data)
+        add_volatility_attributes(attributes=attributes, cached_data=cached_data, time=time)
     else:
-        add_period_timing_attributes(attributes=attributes, key=key, state_value=native_value)
+        add_period_timing_attributes(attributes=attributes, key=key, state_value=native_value, time=time)
 
 
 def _add_cached_trend_attributes(attributes: dict, key: str, cached_data: dict) -> None:

custom_components/tibber_prices/sensor/attributes/volatility.py

@@ -3,14 +3,19 @@
 from __future__ import annotations
 
 from datetime import timedelta
+from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.utils.price import calculate_volatility_level
-from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 
 def add_volatility_attributes(
     attributes: dict,
     cached_data: dict,
+    *,
+    time: TimeService,  # noqa: ARG001
 ) -> None:
     """
     Add attributes for volatility sensors.
@@ -18,6 +23,7 @@ def add_volatility_attributes(
     Args:
         attributes: Dictionary to add attributes to
         cached_data: Dictionary containing cached sensor data
+        time: TimeService instance (required)
 
     """
     if cached_data.get("volatility_attributes"):
@@ -27,6 +33,8 @@ def add_volatility_attributes(
 def get_prices_for_volatility(
     volatility_type: str,
     price_info: dict,
+    *,
+    time: TimeService,
 ) -> list[float]:
     """
     Get price list for volatility calculation based on type.
@@ -34,6 +42,7 @@ def get_prices_for_volatility(
     Args:
         volatility_type: One of "today", "tomorrow", "next_24h", "today_tomorrow"
         price_info: Price information dictionary from coordinator data
+        time: TimeService instance (required)
 
     Returns:
         List of prices to analyze
@@ -47,18 +56,17 @@ def get_prices_for_volatility(
 
     if volatility_type == "next_24h":
         # Rolling 24h from now
-        now = dt_util.now()
+        now = time.now()
         end_time = now + timedelta(hours=24)
         prices = []
 
         for day_key in ["today", "tomorrow"]:
             for price_data in price_info.get(day_key, []):
-                starts_at = dt_util.parse_datetime(price_data.get("startsAt"))
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
                 if starts_at is None:
                     continue
-                starts_at = dt_util.as_local(starts_at)
 
-                if now <= starts_at < end_time and "total" in price_data:
+                if time.is_in_future(starts_at) and starts_at < end_time and "total" in price_data:
                     prices.append(float(price_data["total"]))
         return prices
 
@@ -79,6 +87,8 @@ def add_volatility_type_attributes(
     volatility_type: str,
     price_info: dict,
     thresholds: dict,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add type-specific attributes for volatility sensors.
@@ -88,6 +98,7 @@ def add_volatility_type_attributes(
         volatility_type: Type of volatility calculation
         price_info: Price information dictionary from coordinator data
         thresholds: Volatility thresholds configuration
+        time: TimeService instance (required)
 
     """
     # Add timestamp for calendar day volatility sensors (midnight of the day)
@@ -124,5 +135,5 @@ def add_volatility_type_attributes(
             volatility_attributes["interval_count_tomorrow"] = len(tomorrow_prices)
     elif volatility_type == "next_24h":
         # Add time window info
-        now = dt_util.now()
+        now = time.now()
-        volatility_attributes["timestamp"] = now.isoformat()
+        volatility_attributes["timestamp"] = now

custom_components/tibber_prices/sensor/attributes/window_24h.py

@@ -2,15 +2,13 @@
 
 from __future__ import annotations
 
-from datetime import timedelta
 from typing import TYPE_CHECKING
 
-from homeassistant.util import dt as dt_util
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 
 def _update_extreme_interval(extreme_interval: dict | None, price_data: dict, key: str) -> dict:
@@ -44,6 +42,8 @@ def add_average_price_attributes(
     attributes: dict,
     key: str,
     coordinator: TibberPricesDataUpdateCoordinator,
+    *,
+    time: TimeService,
 ) -> None:
     """
     Add attributes for trailing and leading average/min/max price sensors.
@@ -52,10 +52,9 @@ def add_average_price_attributes(
         attributes: Dictionary to add attributes to
         key: The sensor entity key
         coordinator: The data update coordinator
+        time: TimeService instance (required)
 
     """
-    now = dt_util.now()
-
     # Determine if this is trailing or leading
     is_trailing = "trailing" in key
 
@@ -69,13 +68,11 @@ def add_average_price_attributes(
     if not all_prices:
         return
 
-    # Calculate the time window
+    # Calculate the time window using TimeService
     if is_trailing:
-        window_start = now - timedelta(hours=24)
+        window_start, window_end = time.get_trailing_window(hours=24)
-        window_end = now
     else:
-        window_start = now
+        window_start, window_end = time.get_leading_window(hours=24)
-        window_end = now + timedelta(hours=24)
 
     # Find all intervals in the window
     intervals_in_window = []
@@ -83,10 +80,9 @@ def add_average_price_attributes(
     is_min_max_sensor = "min" in key or "max" in key
 
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         if window_start <= starts_at < window_end:
             intervals_in_window.append(price_data)
 

custom_components/tibber_prices/sensor/calculators/daily_stat.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-from datetime import timedelta
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.const import (
@@ -16,7 +15,6 @@ from custom_components.tibber_prices.sensor.helpers import (
     aggregate_level_data,
     aggregate_rating_data,
 )
-from homeassistant.util import dt as dt_util
 
 from .base import BaseCalculator
 
@@ -72,28 +70,19 @@ class DailyStatCalculator(BaseCalculator):
 
         price_info = self.price_info
 
-        # Get local midnight boundaries based on the requested day
+        # Get local midnight boundaries based on the requested day using TimeService
-        local_midnight = dt_util.as_local(dt_util.start_of_local_day(dt_util.now()))
+        time = self.coordinator.time
-        if day == "tomorrow":
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
-            local_midnight = local_midnight + timedelta(days=1)
-        local_midnight_next_day = local_midnight + timedelta(days=1)
 
         # Collect all prices and their intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         price_intervals = []
         for day_key in ["today", "tomorrow"]:
             for price_data in price_info.get(day_key, []):
-                starts_at_str = price_data.get("startsAt")
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                if not starts_at_str:
+                if not starts_at:
                     continue
 
-                starts_at = dt_util.parse_datetime(starts_at_str)
-                if starts_at is None:
-                    continue
-
-                # Convert to local timezone for comparison
-                starts_at = dt_util.as_local(starts_at)
-
                 # Include price if it starts within the target day's local date boundaries
                 if local_midnight <= starts_at < local_midnight_next_day:
                     total_price = price_data.get("total")
@@ -147,30 +136,19 @@ class DailyStatCalculator(BaseCalculator):
 
         price_info = self.price_info
 
-        # Get local midnight boundaries based on the requested day
+        # Get local midnight boundaries based on the requested day using TimeService
-        local_midnight = dt_util.as_local(dt_util.start_of_local_day(dt_util.now()))
+        time = self.coordinator.time
-        if day == "tomorrow":
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
-            local_midnight = local_midnight + timedelta(days=1)
-        elif day == "yesterday":
-            local_midnight = local_midnight - timedelta(days=1)
-        local_midnight_next_day = local_midnight + timedelta(days=1)
 
         # Collect all intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         day_intervals = []
         for day_key in ["yesterday", "today", "tomorrow"]:
             for price_data in price_info.get(day_key, []):
-                starts_at_str = price_data.get("startsAt")
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                if not starts_at_str:
+                if not starts_at:
                     continue
 
-                starts_at = dt_util.parse_datetime(starts_at_str)
-                if starts_at is None:
-                    continue
-
-                # Convert to local timezone for comparison
-                starts_at = dt_util.as_local(starts_at)
-
                 # Include interval if it starts within the target day's local date boundaries
                 if local_midnight <= starts_at < local_midnight_next_day:
                     day_intervals.append(price_data)

custom_components/tibber_prices/sensor/calculators/interval.py

@@ -2,12 +2,9 @@
 
 from __future__ import annotations
 
-from datetime import timedelta
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
 from custom_components.tibber_prices.utils.price import find_price_data_for_interval
-from homeassistant.util import dt as dt_util
 
 from .base import BaseCalculator
 
@@ -64,10 +61,11 @@ class IntervalCalculator(BaseCalculator):
             return None
 
         price_info = self.price_info
-        now = dt_util.now()
+        time = self.coordinator.time
-        target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
+        # Use TimeService to get interval offset time
+        target_time = time.get_interval_offset_time(interval_offset)
 
-        interval_data = find_price_data_for_interval(price_info, target_time)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
         if not interval_data:
             return None
 
@@ -124,9 +122,10 @@ class IntervalCalculator(BaseCalculator):
             self._last_rating_level = None
             return None
 
-        now = dt_util.now()
+        time = self.coordinator.time
+        now = time.now()
         price_info = self.price_info
-        current_interval = find_price_data_for_interval(price_info, now)
+        current_interval = find_price_data_for_interval(price_info, now, time=time)
 
         if current_interval:
             rating_level = current_interval.get("rating_level")

custom_components/tibber_prices/sensor/calculators/rolling_hour.py

@@ -14,7 +14,6 @@ from custom_components.tibber_prices.sensor.helpers import (
     aggregate_price_data,
     aggregate_rating_data,
 )
-from homeassistant.util import dt as dt_util
 
 from .base import BaseCalculator
 
@@ -60,8 +59,9 @@ class RollingHourCalculator(BaseCalculator):
             return None
 
         # Find center index for the rolling window
-        now = dt_util.now()
+        time = self.coordinator.time
-        center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset)
+        now = time.now()
+        center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset, time=time)
         if center_idx is None:
             return None
 

custom_components/tibber_prices/sensor/calculators/timing.py

@@ -16,11 +16,8 @@ The calculator provides smart defaults:
 
 from datetime import datetime
 
-from homeassistant.util import dt as dt_util
+from .base import BaseCalculator  # Constants
 
-from .base import BaseCalculator
-
-# Constants
 PROGRESS_GRACE_PERIOD_SECONDS = 60  # Show 100% for 1 minute after period ends
 
 
@@ -80,12 +77,13 @@ class TimingCalculator(BaseCalculator):
             return 0 if value_type in ("remaining_minutes", "progress", "next_in_minutes") else None
 
         period_summaries = period_data["periods"]
-        now = dt_util.now()
+        time = self.coordinator.time
+        now = time.now()
 
         # Find current, previous and next periods
-        current_period = self._find_active_period(period_summaries, now)
+        current_period = self._find_active_period(period_summaries)
-        previous_period = self._find_previous_period(period_summaries, now)
+        previous_period = self._find_previous_period(period_summaries)
-        next_period = self._find_next_period(period_summaries, now, skip_current=bool(current_period))
+        next_period = self._find_next_period(period_summaries, skip_current=bool(current_period))
 
         # Delegate to specific calculators
         return self._calculate_timing_value(value_type, current_period, previous_period, next_period, now)
@@ -106,26 +104,46 @@ class TimingCalculator(BaseCalculator):
             ),
             "period_duration": lambda: self._calc_period_duration(current_period, next_period),
             "next_start_time": lambda: next_period.get("start") if next_period else None,
-            "remaining_minutes": lambda: (self._calc_remaining_minutes(current_period, now) if current_period else 0),
+            "remaining_minutes": lambda: (self._calc_remaining_minutes(current_period) if current_period else 0),
             "progress": lambda: self._calc_progress_with_grace_period(current_period, previous_period, now),
-            "next_in_minutes": lambda: (self._calc_next_in_minutes(next_period, now) if next_period else None),
+            "next_in_minutes": lambda: (self._calc_next_in_minutes(next_period) if next_period else None),
         }
 
         calculator = calculators.get(value_type)
         return calculator() if calculator else None
 
-    def _find_active_period(self, periods: list, now: datetime) -> dict | None:
+    def _find_active_period(self, periods: list) -> dict | None:
-        """Find currently active period."""
+        """
+        Find currently active period.
+
+        Args:
+            periods: List of period dictionaries
+
+        Returns:
+            Currently active period or None
+
+        """
+        time = self.coordinator.time
         for period in periods:
             start = period.get("start")
             end = period.get("end")
-            if start and end and start <= now < end:
+            if start and end and time.is_current_interval(start, end):
                 return period
         return None
 
-    def _find_previous_period(self, periods: list, now: datetime) -> dict | None:
+    def _find_previous_period(self, periods: list) -> dict | None:
-        """Find the most recent period that has already ended."""
+        """
-        past_periods = [p for p in periods if p.get("end") and p.get("end") <= now]
+        Find the most recent period that has already ended.
+
+        Args:
+            periods: List of period dictionaries
+
+        Returns:
+            Most recent past period or None
+
+        """
+        time = self.coordinator.time
+        past_periods = [p for p in periods if p.get("end") and time.is_in_past(p["end"])]
 
         if not past_periods:
             return None
@@ -134,20 +152,20 @@ class TimingCalculator(BaseCalculator):
         past_periods.sort(key=lambda p: p["end"], reverse=True)
         return past_periods[0]
 
-    def _find_next_period(self, periods: list, now: datetime, *, skip_current: bool = False) -> dict | None:
+    def _find_next_period(self, periods: list, *, skip_current: bool = False) -> dict | None:
         """
         Find next future period.
 
         Args:
             periods: List of period dictionaries
-            now: Current time
             skip_current: If True, skip the first future period (to get next-next)
 
         Returns:
             Next period dict or None if no future periods
 
         """
-        future_periods = [p for p in periods if p.get("start") and p.get("start") > now]
+        time = self.coordinator.time
+        future_periods = [p for p in periods if p.get("start") and time.is_in_future(p["start"])]
 
         if not future_periods:
             return None
@@ -163,21 +181,47 @@ class TimingCalculator(BaseCalculator):
 
         return None
 
-    def _calc_remaining_minutes(self, period: dict, now: datetime) -> float:
+    def _calc_remaining_minutes(self, period: dict) -> int:
-        """Calculate minutes until period ends."""
+        """
+        Calculate ROUNDED minutes until period ends.
+
+        Uses standard rounding (0.5 rounds up) to match Home Assistant frontend
+        relative time display. This ensures sensor values match what users see
+        in the UI ("in X minutes").
+
+        Args:
+            period: Period dictionary
+
+        Returns:
+            Rounded minutes until period ends (matches HA frontend display)
+
+        """
+        time = self.coordinator.time
         end = period.get("end")
         if not end:
             return 0
-        delta = end - now
+        return time.minutes_until_rounded(end)
-        return max(0, delta.total_seconds() / 60)
 
-    def _calc_next_in_minutes(self, period: dict, now: datetime) -> float:
+    def _calc_next_in_minutes(self, period: dict) -> int:
-        """Calculate minutes until period starts."""
+        """
+        Calculate ROUNDED minutes until next period starts.
+
+        Uses standard rounding (0.5 rounds up) to match Home Assistant frontend
+        relative time display. This ensures sensor values match what users see
+        in the UI ("in X minutes").
+
+        Args:
+            period: Period dictionary
+
+        Returns:
+            Rounded minutes until period starts (matches HA frontend display)
+
+        """
+        time = self.coordinator.time
         start = period.get("start")
         if not start:
             return 0
-        delta = start - now
+        return time.minutes_until_rounded(start)
-        return max(0, delta.total_seconds() / 60)
 
     def _calc_period_duration(self, current_period: dict | None, next_period: dict | None) -> float | None:
         """

custom_components/tibber_prices/sensor/calculators/trend.py

@@ -12,16 +12,14 @@ Caching strategy:
 - Current trend + next change: Cached centrally for 60s to avoid duplicate calculations
 """
 
-from datetime import datetime, timedelta
+from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
-from custom_components.tibber_prices.const import MINUTES_PER_INTERVAL
 from custom_components.tibber_prices.utils.average import calculate_next_n_hours_avg
 from custom_components.tibber_prices.utils.price import (
     calculate_price_trend,
     find_price_data_for_interval,
 )
-from homeassistant.util import dt as dt_util
 
 from .base import BaseCalculator
 
@@ -89,16 +87,16 @@ class TrendCalculator(BaseCalculator):
             return None
 
         current_interval_price = float(current_interval["total"])
-        current_starts_at = dt_util.parse_datetime(current_interval["startsAt"])
+        time = self.coordinator.time
+        current_starts_at = time.get_interval_time(current_interval)
         if current_starts_at is None:
             return None
-        current_starts_at = dt_util.as_local(current_starts_at)
 
         # Get next interval timestamp (basis for calculation)
-        next_interval_start = current_starts_at + timedelta(minutes=MINUTES_PER_INTERVAL)
+        next_interval_start = time.get_next_interval_start()
 
         # Get future average price
-        future_avg = calculate_next_n_hours_avg(self.coordinator.data, hours)
+        future_avg = calculate_next_n_hours_avg(self.coordinator.data, hours, time=self.coordinator.time)
         if future_avg is None:
             return None
 
@@ -113,7 +111,7 @@ class TrendCalculator(BaseCalculator):
         today_prices = price_info.get("today", [])
         tomorrow_prices = price_info.get("tomorrow", [])
         all_intervals = today_prices + tomorrow_prices
-        lookahead_intervals = hours * 4  # Convert hours to 15-minute intervals
+        lookahead_intervals = self.coordinator.time.minutes_to_intervals(hours * 60)
 
         # Calculate trend with volatility-adaptive thresholds
         trend_state, diff_pct = calculate_price_trend(
@@ -137,10 +135,10 @@ class TrendCalculator(BaseCalculator):
 
         # Store attributes in sensor-specific dictionary AND cache the trend value
         self._trend_attributes = {
-            "timestamp": next_interval_start.isoformat(),
+            "timestamp": next_interval_start,
             f"trend_{hours}h_%": round(diff_pct, 1),
             f"next_{hours}h_avg": round(future_avg * 100, 2),
-            "interval_count": hours * 4,
+            "interval_count": lookahead_intervals,
             "threshold_rising": threshold_rising,
             "threshold_falling": threshold_falling,
             "icon_color": icon_color,
@@ -259,18 +257,19 @@ class TrendCalculator(BaseCalculator):
             return None
 
         # Calculate which intervals belong to the later half
-        total_intervals = hours * 4
+        time = self.coordinator.time
+        total_intervals = time.minutes_to_intervals(hours * 60)
         first_half_intervals = total_intervals // 2
-        later_half_start = next_interval_start + timedelta(minutes=MINUTES_PER_INTERVAL * first_half_intervals)
+        interval_duration = time.get_interval_duration()
-        later_half_end = next_interval_start + timedelta(minutes=MINUTES_PER_INTERVAL * total_intervals)
+        later_half_start = next_interval_start + (interval_duration * first_half_intervals)
+        later_half_end = next_interval_start + (interval_duration * total_intervals)
 
         # Collect prices in the later half
         later_prices = []
         for price_data in all_prices:
-            starts_at = dt_util.parse_datetime(price_data["startsAt"])
+            starts_at = time.get_interval_time(price_data)
             if starts_at is None:
                 continue
-            starts_at = dt_util.as_local(starts_at)
 
             if later_half_start <= starts_at < later_half_end:
                 price = price_data.get("total")
@@ -296,7 +295,8 @@ class TrendCalculator(BaseCalculator):
         trend_cache_duration_seconds = 60  # Cache for 1 minute
 
         # Check if we have a valid cache
-        now = dt_util.now()
+        time = self.coordinator.time
+        now = time.now()
         if (
             self._trend_calculation_cache is not None
             and self._trend_calculation_timestamp is not None
@@ -310,13 +310,12 @@ class TrendCalculator(BaseCalculator):
 
         price_info = self.coordinator.data.get("priceInfo", {})
         all_intervals = price_info.get("today", []) + price_info.get("tomorrow", [])
-        current_interval = find_price_data_for_interval(price_info, now)
+        current_interval = find_price_data_for_interval(price_info, now, time=time)
 
         if not all_intervals or not current_interval:
             return None
 
-        current_interval_start = dt_util.parse_datetime(current_interval["startsAt"])
+        current_interval_start = time.get_interval_time(current_interval)
-        current_interval_start = dt_util.as_local(current_interval_start) if current_interval_start else None
 
         if not current_interval_start:
             return None
@@ -380,14 +379,15 @@ class TrendCalculator(BaseCalculator):
         # Calculate duration of current trend
         trend_duration_minutes = None
         if trend_start_time:
-            duration = now - trend_start_time
+            time = self.coordinator.time
-            trend_duration_minutes = int(duration.total_seconds() / 60)
+            # Duration is negative of minutes_until (time in the past)
+            trend_duration_minutes = -int(time.minutes_until(trend_start_time))
 
         # Calculate minutes until change
         minutes_until_change = None
         if next_change_time:
-            time_diff = next_change_time - now
+            time = self.coordinator.time
-            minutes_until_change = int(time_diff.total_seconds() / 60)
+            minutes_until_change = int(time.minutes_until(next_change_time))
 
         result = {
             "current_trend_state": current_trend_state,
@@ -546,9 +546,10 @@ class TrendCalculator(BaseCalculator):
 
     def _find_current_interval_index(self, all_intervals: list, current_interval_start: datetime) -> int | None:
         """Find the index of current interval in all_intervals list."""
+        time = self.coordinator.time
         for idx, interval in enumerate(all_intervals):
-            interval_start = dt_util.parse_datetime(interval["startsAt"])
+            interval_start = time.get_interval_time(interval)
-            if interval_start and dt_util.as_local(interval_start) == current_interval_start:
+            if interval_start and interval_start == current_interval_start:
                 return idx
         return None
 
@@ -577,15 +578,15 @@ class TrendCalculator(BaseCalculator):
         intervals_in_3h = 12  # 3 hours = 12 intervals @ 15min each
 
         # Scan backward to find when trend changed TO current state
+        time = self.coordinator.time
         for i in range(current_index - 1, max(-1, current_index - 97), -1):
             if i < 0:
                 break
 
             interval = all_intervals[i]
-            interval_start = dt_util.parse_datetime(interval["startsAt"])
+            interval_start = time.get_interval_time(interval)
             if not interval_start:
                 continue
-            interval_start = dt_util.as_local(interval_start)
 
             # Calculate trend at this past interval
             future_intervals = all_intervals[i + 1 : i + intervals_in_3h + 1]
@@ -617,9 +618,9 @@ class TrendCalculator(BaseCalculator):
             if trend_state != current_trend_state:
                 # Found the change point - the NEXT interval is where current trend started
                 next_interval = all_intervals[i + 1]
-                trend_start = dt_util.parse_datetime(next_interval["startsAt"])
+                trend_start = time.get_interval_time(next_interval)
                 if trend_start:
-                    return dt_util.as_local(trend_start), trend_state
+                    return trend_start, trend_state
 
         # Reached data boundary - current trend extends beyond available data
         return None, None
@@ -642,6 +643,7 @@ class TrendCalculator(BaseCalculator):
             Timestamp of next trend change, or None if no change in next 24h
 
         """
+        time = self.coordinator.time
         intervals_in_3h = 12  # 3 hours = 12 intervals @ 15min each
         current_index = scan_params["current_index"]
         current_trend_state = scan_params["current_trend_state"]
@@ -650,10 +652,9 @@ class TrendCalculator(BaseCalculator):
 
         for i in range(current_index + 1, min(current_index + 97, len(all_intervals))):
             interval = all_intervals[i]
-            interval_start = dt_util.parse_datetime(interval["startsAt"])
+            interval_start = time.get_interval_time(interval)
             if not interval_start:
                 continue
-            interval_start = dt_util.as_local(interval_start)
 
             # Skip if this interval is in the past
             if interval_start <= now:
@@ -689,8 +690,8 @@ class TrendCalculator(BaseCalculator):
             # We want to find ANY change from current state, including changes to/from stable
             if trend_state != current_trend_state:
                 # Store details for attributes
-                time_diff = interval_start - now
+                time = self.coordinator.time
-                minutes_until = int(time_diff.total_seconds() / 60)
+                minutes_until = int(time.minutes_until(interval_start))
 
                 self._trend_change_attributes = {
                     "direction": trend_state,

custom_components/tibber_prices/sensor/calculators/volatility.py

@@ -64,7 +64,7 @@ class VolatilityCalculator(BaseCalculator):
         }
 
         # Get prices based on volatility type
-        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info)
+        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info, time=self.coordinator.time)
 
         if not prices_to_analyze:
             return None
@@ -95,7 +95,9 @@ class VolatilityCalculator(BaseCalculator):
         add_icon_color_attribute(self._last_volatility_attributes, key="volatility", state_value=volatility)
 
         # Add type-specific attributes
-        add_volatility_type_attributes(self._last_volatility_attributes, volatility_type, price_info, thresholds)
+        add_volatility_type_attributes(
+            self._last_volatility_attributes, volatility_type, price_info, thresholds, time=self.coordinator.time
+        )
 
         # Return lowercase for ENUM device class
         return volatility.lower()

custom_components/tibber_prices/sensor/calculators/window_24h.py

@@ -42,7 +42,7 @@ class Window24hCalculator(BaseCalculator):
         if not self.coordinator_data:
             return None
 
-        value = stat_func(self.coordinator_data)
+        value = stat_func(self.coordinator_data, time=self.coordinator.time)
 
         if value is None:
             return None

custom_components/tibber_prices/sensor/chart_data.py

@@ -118,7 +118,7 @@ def build_chart_data_attributes(
     """
     # Build base attributes with metadata FIRST
     attributes: dict[str, object] = {
-        "timestamp": chart_data_last_update.isoformat() if chart_data_last_update else None,
+        "timestamp": chart_data_last_update,
     }
 
     # Add error message if service call failed

custom_components/tibber_prices/sensor/core.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from datetime import datetime, timedelta
+from datetime import datetime  # noqa: TC003 - Used at runtime for _get_data_timestamp()
 from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices.binary_sensor.attributes import (
@@ -25,10 +25,9 @@ from custom_components.tibber_prices.entity import TibberPricesEntity
 from custom_components.tibber_prices.entity_utils import (
     add_icon_color_attribute,
     find_rolling_hour_center_index,
-    get_dynamic_icon,
     get_price_value,
 )
-from custom_components.tibber_prices.entity_utils.icons import IconContext
+from custom_components.tibber_prices.entity_utils.icons import IconContext, get_dynamic_icon
 from custom_components.tibber_prices.utils.average import (
     calculate_next_n_hours_avg,
 )
@@ -42,7 +41,6 @@ from homeassistant.components.sensor import (
 )
 from homeassistant.const import EntityCategory
 from homeassistant.core import callback
-from homeassistant.util import dt as dt_util
 
 from .attributes import (
     add_volatility_type_attributes,
@@ -75,10 +73,10 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator import (
         TibberPricesDataUpdateCoordinator,
     )
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 HOURS_IN_DAY = 24
 LAST_HOUR_OF_DAY = 23
-INTERVALS_PER_HOUR = 4  # 15-minute intervals
 MAX_FORECAST_INTERVALS = 8  # Show up to 8 future intervals (2 hours with 15-min intervals)
 MIN_HOURS_FOR_LATER_HALF = 3  # Minimum hours needed to calculate later half average
 
@@ -148,8 +146,17 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             self._minute_update_remove_listener = None
 
     @callback
-    def _handle_time_sensitive_update(self) -> None:
+    def _handle_time_sensitive_update(self, time_service: TimeService) -> None:
-        """Handle time-sensitive update from coordinator."""
+        """
+        Handle time-sensitive update from coordinator.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
+        # Store TimeService from Timer #2 for calculations during this update cycle
+        self.coordinator.time = time_service
+
         # Clear cached trend values on time-sensitive updates
         if self.entity_description.key.startswith("price_trend_"):
             self._trend_calculator.clear_trend_cache()
@@ -159,8 +166,17 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
         self.async_write_ha_state()
 
     @callback
-    def _handle_minute_update(self) -> None:
+    def _handle_minute_update(self, time_service: TimeService) -> None:
-        """Handle minute-by-minute update from coordinator."""
+        """
+        Handle minute-by-minute update from coordinator.
+
+        Args:
+            time_service: TimeService instance with reference time for this update cycle
+
+        """
+        # Store TimeService from Timer #3 for calculations during this update cycle
+        self.coordinator.time = time_service
+
         self.async_write_ha_state()
 
     @callback
@@ -235,8 +251,9 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             return None
 
         # Find center index for the rolling window
-        now = dt_util.now()
+        time = self.coordinator.time
-        center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset)
+        now = time.now()
+        center_idx = find_rolling_hour_center_index(all_prices, now, hour_offset, time=time)
         if center_idx is None:
             return None
 
@@ -288,28 +305,21 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
 
         price_info = self.coordinator.data.get("priceInfo", {})
 
-        # Get local midnight boundaries based on the requested day
+        # Get TimeService from coordinator
-        local_midnight = dt_util.as_local(dt_util.start_of_local_day(dt_util.now()))
+        time = self.coordinator.time
-        if day == "tomorrow":
+
-            local_midnight = local_midnight + timedelta(days=1)
+        # Get local midnight boundaries based on the requested day using TimeService
-        local_midnight_next_day = local_midnight + timedelta(days=1)
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
 
         # Collect all prices and their intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         price_intervals = []
         for day_key in ["today", "tomorrow"]:
             for price_data in price_info.get(day_key, []):
-                starts_at_str = price_data.get("startsAt")
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                if not starts_at_str:
+                if not starts_at:
                     continue
 
-                starts_at = dt_util.parse_datetime(starts_at_str)
-                if starts_at is None:
-                    continue
-
-                # Convert to local timezone for comparison
-                starts_at = dt_util.as_local(starts_at)
-
                 # Include price if it starts within the target day's local date boundaries
                 if local_midnight <= starts_at < local_midnight_next_day:
                     total_price = price_data.get("total")
@@ -363,30 +373,19 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
 
         price_info = self.coordinator.data.get("priceInfo", {})
 
-        # Get local midnight boundaries based on the requested day
+        # Get local midnight boundaries based on the requested day using TimeService
-        local_midnight = dt_util.as_local(dt_util.start_of_local_day(dt_util.now()))
+        time = self.coordinator.time
-        if day == "tomorrow":
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
-            local_midnight = local_midnight + timedelta(days=1)
-        elif day == "yesterday":
-            local_midnight = local_midnight - timedelta(days=1)
-        local_midnight_next_day = local_midnight + timedelta(days=1)
 
         # Collect all intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         day_intervals = []
         for day_key in ["yesterday", "today", "tomorrow"]:
             for price_data in price_info.get(day_key, []):
-                starts_at_str = price_data.get("startsAt")
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                if not starts_at_str:
+                if not starts_at:
                     continue
 
-                starts_at = dt_util.parse_datetime(starts_at_str)
-                if starts_at is None:
-                    continue
-
-                # Convert to local timezone for comparison
-                starts_at = dt_util.as_local(starts_at)
-
                 # Include interval if it starts within the target day's local date boundaries
                 if local_midnight <= starts_at < local_midnight_next_day:
                     day_intervals.append(price_data)
@@ -482,7 +481,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             Average price in minor currency units (e.g., cents), or None if unavailable
 
         """
-        avg_price = calculate_next_n_hours_avg(self.coordinator.data, hours)
+        avg_price = calculate_next_n_hours_avg(self.coordinator.data, hours, time=self.coordinator.time)
         if avg_price is None:
             return None
 
@@ -490,7 +489,16 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
         return round(avg_price * 100, 2)
 
     def _get_data_timestamp(self) -> datetime | None:
-        """Get the latest data timestamp."""
+        """
+        Get the latest data timestamp from price data.
+
+        Returns timezone-aware datetime of the most recent price interval.
+        Home Assistant automatically displays TIMESTAMP sensors in user's timezone.
+
+        Returns:
+            Latest interval timestamp (timezone-aware), or None if no data available.
+
+        """
         if not self.coordinator.data:
             return None
 
@@ -499,11 +507,14 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
 
         for day in ["today", "tomorrow"]:
             for price_data in price_info.get(day, []):
-                timestamp = datetime.fromisoformat(price_data["startsAt"])
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                if not latest_timestamp or timestamp > latest_timestamp:
+                if not starts_at:
-                    latest_timestamp = timestamp
+                    continue
+                if not latest_timestamp or starts_at > latest_timestamp:
+                    latest_timestamp = starts_at
 
-        return dt_util.as_utc(latest_timestamp) if latest_timestamp else None
+        # Return timezone-aware datetime (HA handles timezone display automatically)
+        return latest_timestamp
 
     def _get_volatility_value(self, *, volatility_type: str) -> str | None:
         """
@@ -529,7 +540,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
         }
 
         # Get prices based on volatility type
-        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info)
+        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info, time=self.coordinator.time)
 
         if not prices_to_analyze:
             return None
@@ -560,7 +571,9 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
         add_icon_color_attribute(self._last_volatility_attributes, key="volatility", state_value=volatility)
 
         # Add type-specific attributes
-        add_volatility_type_attributes(self._last_volatility_attributes, volatility_type, price_info, thresholds)
+        add_volatility_type_attributes(
+            self._last_volatility_attributes, volatility_type, price_info, thresholds, time=self.coordinator.time
+        )
 
         # Return lowercase for ENUM device class
         return volatility.lower()
@@ -572,7 +585,9 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
     # Add method to get future price intervals
     def _get_price_forecast_value(self) -> str | None:
         """Get the highest or lowest price status for the price forecast entity."""
-        future_prices = get_future_prices(self.coordinator, max_intervals=MAX_FORECAST_INTERVALS)
+        future_prices = get_future_prices(
+            self.coordinator, max_intervals=MAX_FORECAST_INTERVALS, time=self.coordinator.time
+        )
         if not future_prices:
             return "No forecast data available"
 
@@ -726,29 +741,30 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
         """Check if the current time is within a best price period."""
         if not self.coordinator.data:
             return False
-        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False)
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
         if not attrs:
             return False
         start = attrs.get("start")
         end = attrs.get("end")
         if not start or not end:
             return False
-        now = dt_util.now()
+        time = self.coordinator.time
+        now = time.now()
         return start <= now < end
 
     def _is_peak_price_period_active(self) -> bool:
         """Check if the current time is within a peak price period."""
         if not self.coordinator.data:
             return False
-        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True)
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
         if not attrs:
             return False
         start = attrs.get("start")
         end = attrs.get("end")
         if not start or not end:
             return False
-        now = dt_util.now()
+        time = self.coordinator.time
-        return start <= now < end
+        return time.is_current_interval(start, end)
 
     @property
     def icon(self) -> str | None:
@@ -790,6 +806,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             context=IconContext(
                 coordinator_data=self.coordinator.data,
                 period_is_active_callback=period_is_active_callback,
+                time=self.coordinator.time,
             ),
         )
 
@@ -806,6 +823,8 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             # Get sensor-specific attributes
             sensor_attrs = self._get_sensor_attributes()
 
+            time = self.coordinator.time
+
             # Build complete attributes using unified builder
             return build_extra_state_attributes(
                 entity_key=self.entity_description.key,
@@ -814,6 +833,7 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
                 config_entry=self.coordinator.config_entry,
                 coordinator_data=self.coordinator.data,
                 sensor_attrs=sensor_attrs,
+                time=time,
             )
 
         except (KeyError, ValueError, TypeError) as ex:
@@ -891,7 +911,8 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
             config_entry=self.coordinator.config_entry,
         )
         self._chart_data_response = response
-        self._chart_data_last_update = dt_util.now()
+        time = self.coordinator.time
+        self._chart_data_last_update = time.now()
         self._chart_data_error = error
         # Trigger state update after refresh
         self.async_write_ha_state()

custom_components/tibber_prices/sensor/helpers.py

@@ -21,12 +21,14 @@ from __future__ import annotations
 from datetime import timedelta
 from typing import TYPE_CHECKING
 
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
+
 from custom_components.tibber_prices.entity_utils.helpers import get_price_value
 from custom_components.tibber_prices.utils.price import (
     aggregate_price_levels,
     aggregate_price_rating,
 )
-from homeassistant.util import dt as dt_util
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -132,6 +134,7 @@ def get_hourly_price_value(
     *,
     hour_offset: int,
     in_euro: bool,
+    time: TimeService,
 ) -> float | None:
     """
     Get price for current hour or with offset.
@@ -143,13 +146,14 @@ def get_hourly_price_value(
         price_info: Price information dict with 'today' and 'tomorrow' keys
         hour_offset: Hour offset from current time (positive=future, negative=past)
         in_euro: If True, return price in major currency (EUR), else minor (cents/øre)
+        time: TimeService instance (required)
 
     Returns:
         Price value, or None if not found
 
     """
-    # Use HomeAssistant's dt_util to get the current time in the user's timezone
+    # Use TimeService to get the current time in the user's timezone
-    now = dt_util.now()
+    now = time.now()
 
     # Calculate the exact target datetime (not just the hour)
     # This properly handles day boundaries
@@ -162,12 +166,11 @@ def get_hourly_price_value(
 
     for price_data in price_info.get(day_key, []):
         # Parse the timestamp and convert to local time
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
 
         # Make sure it's in the local timezone for proper comparison
-        starts_at = dt_util.as_local(starts_at)
 
         # Compare using both hour and date for accuracy
         if starts_at.hour == target_hour and starts_at.date() == target_date:
@@ -177,11 +180,10 @@ def get_hourly_price_value(
     # This is a fallback for potential edge cases
     other_day_key = "today" if day_key == "tomorrow" else "tomorrow"
     for price_data in price_info.get(other_day_key, []):
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
 
-        starts_at = dt_util.as_local(starts_at)
         if starts_at.hour == target_hour and starts_at.date() == target_date:
             return get_price_value(float(price_data["total"]), in_euro=in_euro)
 

custom_components/tibber_prices/services/chartdata.py

@@ -43,7 +43,6 @@ from custom_components.tibber_prices.const import (
     PRICE_RATING_NORMAL,
 )
 from homeassistant.exceptions import ServiceValidationError
-from homeassistant.util import dt as dt_util
 
 from .formatters import aggregate_hourly_exact, get_period_data, normalize_level_filter, normalize_rating_level_filter
 from .helpers import get_entry_and_data
@@ -227,7 +226,7 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
                     current = start
                     while current < end:
                         period_timestamps.add(current.isoformat())
-                        current = current + timedelta(minutes=15)
+                        current = current + coordinator.time.get_interval_duration()
 
     # Collect all timestamps if insert_nulls='all' (needed to insert NULLs for missing filter matches)
     all_timestamps = set()
@@ -374,10 +373,9 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
                     last_value = last_interval.get(filter_field)
 
                     if last_start_time and last_price is not None and last_value in filter_values:
-                        # Parse timestamp and calculate midnight of next day
+                        # Timestamp is already datetime in local timezone
-                        last_dt = dt_util.parse_datetime(last_start_time)
+                        last_dt = last_start_time  # Already datetime object
                         if last_dt:
-                            last_dt = dt_util.as_local(last_dt)
                             # Calculate next day at 00:00
                             next_day = last_dt.replace(hour=0, minute=0, second=0, microsecond=0)
                             next_day = next_day + timedelta(days=1)
@@ -483,6 +481,7 @@ async def handle_chartdata(call: ServiceCall) -> dict[str, Any]:  # noqa: PLR091
                     day_prices,
                     start_time_field,
                     price_field,
+                    coordinator=coordinator,
                     use_minor_currency=minor_currency,
                     round_decimals=round_decimals,
                     include_level=include_level,

custom_components/tibber_prices/services/formatters.py

@@ -28,7 +28,6 @@ from custom_components.tibber_prices.const import (
     get_translation,
 )
 from custom_components.tibber_prices.sensor.helpers import aggregate_level_data, aggregate_rating_data
-from homeassistant.util import dt as dt_util
 
 
 def normalize_level_filter(value: list[str] | None) -> list[str] | None:
@@ -50,6 +49,7 @@ def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
     start_time_field: str,
     price_field: str,
     *,
+    coordinator: Any,
     use_minor_currency: bool = False,
     round_decimals: int | None = None,
     include_level: bool = False,
@@ -75,6 +75,7 @@ def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
         intervals: List of 15-minute price intervals
         start_time_field: Custom name for start time field
         price_field: Custom name for price field
+        coordinator: Data update coordinator instance (required)
         use_minor_currency: Convert to minor currency units (cents/øre)
         round_decimals: Optional decimal rounding
         include_level: Include aggregated level field
@@ -108,8 +109,9 @@ def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
             i += 1
             continue
 
-        # Parse the timestamp
+        # Get timestamp (already datetime in local timezone)
-        start_time = dt_util.parse_datetime(start_time_str)
+        time = coordinator.time
+        start_time = start_time_str  # Already datetime object
         if not start_time:
             i += 1
             continue
@@ -119,10 +121,11 @@ def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
             i += 1
             continue
 
-        # Collect 4 intervals for this hour (with optional filtering)
+        # Collect intervals for this hour (with optional filtering)
+        intervals_per_hour = time.minutes_to_intervals(60)
         hour_intervals = []
         hour_interval_data = []  # Complete interval data for aggregation functions
-        for j in range(4):
+        for j in range(intervals_per_hour):
             if i + j < len(intervals):
                 interval = intervals[i + j]
 
@@ -180,8 +183,8 @@ def aggregate_hourly_exact(  # noqa: PLR0913, PLR0912, PLR0915
 
             hourly_data.append(data_point)
 
-        # Move to next hour (skip 4 intervals)
+        # Move to next hour (skip intervals_per_hour)
-        i += 4
+        i += time.minutes_to_intervals(60)
 
     return hourly_data
 
@@ -260,14 +263,11 @@ def get_period_data(  # noqa: PLR0913, PLR0912, PLR0915
             price_info = coordinator.data.get("priceInfo", {})
             day_prices = price_info.get(day, [])
             if day_prices:
-                # Extract date from first interval
+                # Extract date from first interval (already datetime in local timezone)
                 first_interval = day_prices[0]
-                starts_at = first_interval.get("startsAt")
+                starts_at = first_interval.get("startsAt")  # Already datetime object
                 if starts_at:
-                    dt = dt_util.parse_datetime(starts_at)
+                    allowed_dates.add(starts_at.date())
-                    if dt:
-                        dt = dt_util.as_local(dt)
-                        allowed_dates.add(dt.date())
 
         # Filter periods to those within allowed dates
         for period in period_summaries:

custom_components/tibber_prices/utils/__init__.py

@@ -24,7 +24,6 @@ from .average import (
     calculate_current_trailing_max,
     calculate_current_trailing_min,
     calculate_next_n_hours_avg,
-    round_to_nearest_quarter_hour,
 )
 from .price import (
     aggregate_period_levels,
@@ -59,5 +58,4 @@ __all__ = [
     "calculate_volatility_level",
     "enrich_price_info_with_differences",
     "find_price_data_for_interval",
-    "round_to_nearest_quarter_hour",
 ]

custom_components/tibber_prices/utils/average.py

@@ -3,73 +3,10 @@
 from __future__ import annotations
 
 from datetime import datetime, timedelta
+from typing import TYPE_CHECKING
 
-from homeassistant.util import dt as dt_util
+if TYPE_CHECKING:
-
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
-# Constants
-INTERVALS_PER_DAY = 96  # 24 hours * 4 intervals per hour
-
-
-def round_to_nearest_quarter_hour(dt: datetime) -> datetime:
-    """
-    Round datetime to nearest 15-minute boundary with smart tolerance.
-
-    This handles edge cases where HA schedules us slightly before the boundary
-    (e.g., 14:59:59.500), while avoiding premature rounding during normal operation.
-
-    Strategy:
-    - If within ±2 seconds of a boundary → round to that boundary
-    - Otherwise → floor to current interval start
-
-    Examples:
-    - 14:59:57.999 → 15:00:00 (within 2s of boundary)
-    - 14:59:59.999 → 15:00:00 (within 2s of boundary)
-    - 14:59:30.000 → 14:45:00 (NOT within 2s, stay in current)
-    - 15:00:00.000 → 15:00:00 (exact boundary)
-    - 15:00:01.500 → 15:00:00 (within 2s of boundary)
-
-    Args:
-        dt: Datetime to round
-
-    Returns:
-        Datetime rounded to appropriate 15-minute boundary
-
-    """
-    # Calculate current interval start (floor)
-    total_seconds = dt.hour * 3600 + dt.minute * 60 + dt.second + dt.microsecond / 1_000_000
-    interval_index = int(total_seconds // (15 * 60))  # Floor division
-    interval_start_seconds = interval_index * 15 * 60
-
-    # Calculate next interval start
-    next_interval_index = (interval_index + 1) % INTERVALS_PER_DAY
-    next_interval_start_seconds = next_interval_index * 15 * 60
-
-    # Distance to current interval start and next interval start
-    distance_to_current = total_seconds - interval_start_seconds
-    if next_interval_index == 0:  # Midnight wrap
-        distance_to_next = (24 * 3600) - total_seconds
-    else:
-        distance_to_next = next_interval_start_seconds - total_seconds
-
-    # Tolerance: If within 2 seconds of a boundary, snap to it
-    boundary_tolerance_seconds = 2.0
-
-    if distance_to_next <= boundary_tolerance_seconds:
-        # Very close to next boundary → use next interval
-        target_interval_index = next_interval_index
-    elif distance_to_current <= boundary_tolerance_seconds:
-        # Very close to current boundary (shouldn't happen in practice, but handle it)
-        target_interval_index = interval_index
-    else:
-        # Normal case: stay in current interval
-        target_interval_index = interval_index
-
-    # Convert back to time
-    target_minutes = target_interval_index * 15
-    target_hour = int(target_minutes // 60)
-    target_minute = int(target_minutes % 60)
-
-    return dt.replace(hour=target_hour, minute=target_minute, second=0, microsecond=0)
 
 
 def calculate_trailing_24h_avg(all_prices: list[dict], interval_start: datetime) -> float:
@@ -79,6 +16,7 @@ def calculate_trailing_24h_avg(all_prices: list[dict], interval_start: datetime)
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate average for
+        time: TimeService instance (required)
 
     Returns:
         Average price for the 24 hours preceding the interval (not including the interval itself)
@@ -91,10 +29,9 @@ def calculate_trailing_24h_avg(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = price_data["startsAt"]  # Already datetime object in local timezone
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window (not including the current interval's end)
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -112,6 +49,7 @@ def calculate_leading_24h_avg(all_prices: list[dict], interval_start: datetime)
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate average for
+        time: TimeService instance (required)
 
     Returns:
         Average price for up to 24 hours following the interval (including the interval itself)
@@ -124,10 +62,9 @@ def calculate_leading_24h_avg(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = price_data["startsAt"]  # Already datetime object in local timezone
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -138,12 +75,17 @@ def calculate_leading_24h_avg(all_prices: list[dict], interval_start: datetime)
     return 0.0
 
 
-def calculate_current_trailing_avg(coordinator_data: dict) -> float | None:
+def calculate_current_trailing_avg(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the trailing 24-hour average for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current trailing 24-hour average price, or None if unavailable
@@ -161,16 +103,21 @@ def calculate_current_trailing_avg(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
     return calculate_trailing_24h_avg(all_prices, now)
 
 
-def calculate_current_leading_avg(coordinator_data: dict) -> float | None:
+def calculate_current_leading_avg(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the leading 24-hour average for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current leading 24-hour average price, or None if unavailable
@@ -188,17 +135,23 @@ def calculate_current_leading_avg(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
     return calculate_leading_24h_avg(all_prices, now)
 
 
-def calculate_trailing_24h_min(all_prices: list[dict], interval_start: datetime) -> float:
+def calculate_trailing_24h_min(
+    all_prices: list[dict],
+    interval_start: datetime,
+    *,
+    time: TimeService,
+) -> float:
     """
     Calculate trailing 24-hour minimum price for a given interval.
 
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate minimum for
+        time: TimeService instance (required)
 
     Returns:
         Minimum price for the 24 hours preceding the interval (not including the interval itself)
@@ -211,10 +164,9 @@ def calculate_trailing_24h_min(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window (not including the current interval's end)
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -225,13 +177,19 @@ def calculate_trailing_24h_min(all_prices: list[dict], interval_start: datetime)
     return 0.0
 
 
-def calculate_trailing_24h_max(all_prices: list[dict], interval_start: datetime) -> float:
+def calculate_trailing_24h_max(
+    all_prices: list[dict],
+    interval_start: datetime,
+    *,
+    time: TimeService,
+) -> float:
     """
     Calculate trailing 24-hour maximum price for a given interval.
 
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate maximum for
+        time: TimeService instance (required)
 
     Returns:
         Maximum price for the 24 hours preceding the interval (not including the interval itself)
@@ -244,10 +202,9 @@ def calculate_trailing_24h_max(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window (not including the current interval's end)
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -258,13 +215,19 @@ def calculate_trailing_24h_max(all_prices: list[dict], interval_start: datetime)
     return 0.0
 
 
-def calculate_leading_24h_min(all_prices: list[dict], interval_start: datetime) -> float:
+def calculate_leading_24h_min(
+    all_prices: list[dict],
+    interval_start: datetime,
+    *,
+    time: TimeService,
+) -> float:
     """
     Calculate leading 24-hour minimum price for a given interval.
 
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate minimum for
+        time: TimeService instance (required)
 
     Returns:
         Minimum price for up to 24 hours following the interval (including the interval itself)
@@ -277,10 +240,9 @@ def calculate_leading_24h_min(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -291,13 +253,19 @@ def calculate_leading_24h_min(all_prices: list[dict], interval_start: datetime)
     return 0.0
 
 
-def calculate_leading_24h_max(all_prices: list[dict], interval_start: datetime) -> float:
+def calculate_leading_24h_max(
+    all_prices: list[dict],
+    interval_start: datetime,
+    *,
+    time: TimeService,
+) -> float:
     """
     Calculate leading 24-hour maximum price for a given interval.
 
     Args:
         all_prices: List of all price data (yesterday, today, tomorrow combined)
         interval_start: Start time of the interval to calculate maximum for
+        time: TimeService instance (required)
 
     Returns:
         Maximum price for up to 24 hours following the interval (including the interval itself)
@@ -310,10 +278,9 @@ def calculate_leading_24h_max(all_prices: list[dict], interval_start: datetime)
     # Filter prices within the 24-hour window
     prices_in_window = []
     for price_data in all_prices:
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
         # Include intervals that start within the window
         if window_start <= starts_at < window_end:
             prices_in_window.append(float(price_data["total"]))
@@ -324,12 +291,17 @@ def calculate_leading_24h_max(all_prices: list[dict], interval_start: datetime)
     return 0.0
 
 
-def calculate_current_trailing_min(coordinator_data: dict) -> float | None:
+def calculate_current_trailing_min(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the trailing 24-hour minimum for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current trailing 24-hour minimum price, or None if unavailable
@@ -347,16 +319,21 @@ def calculate_current_trailing_min(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
-    return calculate_trailing_24h_min(all_prices, now)
+    return calculate_trailing_24h_min(all_prices, now, time=time)
 
 
-def calculate_current_trailing_max(coordinator_data: dict) -> float | None:
+def calculate_current_trailing_max(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the trailing 24-hour maximum for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current trailing 24-hour maximum price, or None if unavailable
@@ -374,16 +351,21 @@ def calculate_current_trailing_max(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
-    return calculate_trailing_24h_max(all_prices, now)
+    return calculate_trailing_24h_max(all_prices, now, time=time)
 
 
-def calculate_current_leading_min(coordinator_data: dict) -> float | None:
+def calculate_current_leading_min(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the leading 24-hour minimum for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current leading 24-hour minimum price, or None if unavailable
@@ -401,16 +383,21 @@ def calculate_current_leading_min(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
-    return calculate_leading_24h_min(all_prices, now)
+    return calculate_leading_24h_min(all_prices, now, time=time)
 
 
-def calculate_current_leading_max(coordinator_data: dict) -> float | None:
+def calculate_current_leading_max(
+    coordinator_data: dict,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate the leading 24-hour maximum for the current time.
 
     Args:
         coordinator_data: The coordinator data containing priceInfo
+        time: TimeService instance (required)
 
     Returns:
         Current leading 24-hour maximum price, or None if unavailable
@@ -428,11 +415,16 @@ def calculate_current_leading_max(coordinator_data: dict) -> float | None:
     if not all_prices:
         return None
 
-    now = dt_util.now()
+    now = time.now()
-    return calculate_leading_24h_max(all_prices, now)
+    return calculate_leading_24h_max(all_prices, now, time=time)
 
 
-def calculate_next_n_hours_avg(coordinator_data: dict, hours: int) -> float | None:
+def calculate_next_n_hours_avg(
+    coordinator_data: dict,
+    hours: int,
+    *,
+    time: TimeService,
+) -> float | None:
     """
     Calculate average price for the next N hours starting from the next interval.
 
@@ -442,6 +434,7 @@ def calculate_next_n_hours_avg(coordinator_data: dict, hours: int) -> float | No
     Args:
         coordinator_data: The coordinator data containing priceInfo
         hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12, etc.)
+        time: TimeService instance (required)
 
     Returns:
         Average price for the next N hours, or None if insufficient data
@@ -459,46 +452,38 @@ def calculate_next_n_hours_avg(coordinator_data: dict, hours: int) -> float | No
     if not all_prices:
         return None
 
-    now = dt_util.now()
-
     # Find the current interval index
     current_idx = None
     for idx, price_data in enumerate(all_prices):
-        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
-        starts_at = dt_util.as_local(starts_at)
+        interval_end = starts_at + time.get_interval_duration()
-        interval_end = starts_at + timedelta(minutes=15)
 
-        if starts_at <= now < interval_end:
+        if time.is_current_interval(starts_at, interval_end):
             current_idx = idx
             break
 
     if current_idx is None:
         return None
 
-    # Calculate how many 15-minute intervals are in N hours
+    # Calculate how many intervals are in N hours
-    intervals_needed = hours * 4  # 4 intervals per hour
+    intervals_needed = time.minutes_to_intervals(hours * 60)
 
     # Collect prices starting from NEXT interval (current_idx + 1)
     prices_in_window = []
     for offset in range(1, intervals_needed + 1):
         idx = current_idx + offset
-        if idx < len(all_prices):
+        if idx >= len(all_prices):
+            # Not enough future data available
+            break
         price = all_prices[idx].get("total")
         if price is not None:
             prices_in_window.append(float(price))
-        else:
-            # Not enough future data available
-            break
-
-    # Only return average if we have data for the full requested period
-    if len(prices_in_window) >= intervals_needed:
-        return sum(prices_in_window) / len(prices_in_window)
-
-    # If we don't have enough data for full period, return what we have
-    # (allows graceful degradation when tomorrow's data isn't available yet)
-    if prices_in_window:
-        return sum(prices_in_window) / len(prices_in_window)
 
+    # Return None if no data at all
+    if not prices_in_window:
         return None
+
+    # Return average (prefer full period, but allow graceful degradation)
+    return sum(prices_in_window) / len(prices_in_window)

custom_components/tibber_prices/utils/price.py

@@ -5,7 +5,10 @@ from __future__ import annotations
 import logging
 import statistics
 from datetime import datetime, timedelta
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator.time_service import TimeService
 
 from custom_components.tibber_prices.const import (
     DEFAULT_VOLATILITY_THRESHOLD_HIGH,
@@ -19,9 +22,6 @@ from custom_components.tibber_prices.const import (
     VOLATILITY_MODERATE,
     VOLATILITY_VERY_HIGH,
 )
-from homeassistant.util import dt as dt_util
-
-from .average import round_to_nearest_quarter_hour
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -131,18 +131,10 @@ def calculate_trailing_average_for_interval(
     matching_prices = []
 
     for price_data in all_prices:
-        starts_at_str = price_data.get("startsAt")
+        price_time = price_data.get("startsAt")  # Already datetime object in local timezone
-        if not starts_at_str:
+        if not price_time:
             continue
 
-        # Parse the timestamp
-        price_time = dt_util.parse_datetime(starts_at_str)
-        if price_time is None:
-            continue
-
-        # Convert to local timezone for comparison
-        price_time = dt_util.as_local(price_time)
-
         # Check if this price falls within our lookback window
         # Include prices that start >= lookback_start and start < interval_start
         if lookback_start <= price_time < interval_start:
@@ -244,15 +236,9 @@ def _process_price_interval(
         day_label: Label for logging ("today" or "tomorrow")
 
     """
-    starts_at_str = price_interval.get("startsAt")
+    starts_at = price_interval.get("startsAt")  # Already datetime object in local timezone
-    if not starts_at_str:
+    if not starts_at:
         return
-
-    starts_at = dt_util.parse_datetime(starts_at_str)
-    if starts_at is None:
-        return
-
-    starts_at = dt_util.as_local(starts_at)
     current_interval_price = price_interval.get("total")
 
     if current_interval_price is None:
@@ -295,6 +281,7 @@ def enrich_price_info_with_differences(
         price_info: Dictionary with 'yesterday', 'today', 'tomorrow' keys
         threshold_low: Low threshold percentage for rating_level (defaults to -10)
         threshold_high: High threshold percentage for rating_level (defaults to 10)
+        time: TimeService instance (required)
 
     Returns:
         Updated price_info dict with 'difference' and 'rating_level' added
@@ -333,13 +320,19 @@ def enrich_price_info_with_differences(
     return price_info
 
 
-def find_price_data_for_interval(price_info: Any, target_time: datetime) -> dict | None:
+def find_price_data_for_interval(
+    price_info: Any,
+    target_time: datetime,
+    *,
+    time: TimeService,
+) -> dict | None:
     """
     Find the price data for a specific 15-minute interval timestamp.
 
     Args:
         price_info: The price info dictionary from Tibber API
         target_time: The target timestamp to find price data for
+        time: TimeService instance (required)
 
     Returns:
         Price data dict if found, None otherwise
@@ -347,9 +340,9 @@ def find_price_data_for_interval(price_info: Any, target_time: datetime) -> dict
     """
     # Round to nearest quarter-hour to handle edge cases where we're called
     # slightly before the boundary (e.g., 14:59:59.999 → 15:00:00)
-    rounded_time = round_to_nearest_quarter_hour(target_time)
+    rounded_time = time.round_to_nearest_quarter(target_time)
 
-    day_key = "tomorrow" if rounded_time.date() > dt_util.now().date() else "today"
+    day_key = "tomorrow" if rounded_time.date() > time.now().date() else "today"
     search_days = [day_key, "tomorrow" if day_key == "today" else "today"]
 
     for search_day in search_days:
@@ -358,11 +351,10 @@ def find_price_data_for_interval(price_info: Any, target_time: datetime) -> dict
             continue
 
         for price_data in day_prices:
-            starts_at = dt_util.parse_datetime(price_data["startsAt"])
+            starts_at = time.get_interval_time(price_data)
             if starts_at is None:
                 continue
 
-            starts_at = dt_util.as_local(starts_at)
             # Exact match after rounding
             if starts_at == rounded_time and starts_at.date() == rounded_time.date():
                 return price_data