diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
index 1471b48..1fbebb0 100644
--- a/RELEASE_NOTES.md
+++ b/RELEASE_NOTES.md
@@ -10,7 +10,7 @@
 
 ## New Features
 
-<!-- Here goes the main new features and examples or instructions on how to use them -->
+* Add cumulative_energy calculation to read out energy consumption and production over a specified time range.
 
 ## Bug Fixes
 
diff --git a/pyproject.toml b/pyproject.toml
index 5e90299..f0c1d5f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,9 @@ classifiers = [
 ]
 requires-python = ">= 3.11, < 4"
 dependencies = [
-  "typing-extensions >= 4.5.0, < 5",
+  "typing-extensions >= 4.6.1, < 5",
+  "frequenz-client-reporting>=0.8.0, < 1",
+  "frequenz-client-common >= 0.2.0, < 0.3",
 ]
 dynamic = ["version"]
 
diff --git a/src/frequenz/reporting/_reporting.py b/src/frequenz/reporting/_reporting.py
new file mode 100644
index 0000000..1948957
--- /dev/null
+++ b/src/frequenz/reporting/_reporting.py
@@ -0,0 +1,124 @@
+# License: MIT
+# Copyright © 2024 Frequenz Energy-as-a-Service GmbH
+
+"""A highlevel interface for the reporting API."""
+
+from collections import namedtuple
+from datetime import datetime
+
+from frequenz.client.common.metric import Metric
+from frequenz.client.reporting import ReportingApiClient
+
+CumulativeEnergy = namedtuple(
+    "CumulativeEnergy", ["start_time", "end_time", "consumption", "production"]
+)
+"""Type for cumulative energy consumption and production over a specified time."""
+
+
+# pylint: disable-next=too-many-arguments
+async def cumulative_energy(
+    client: ReportingApiClient,
+    microgrid_id: int,
+    component_id: int,
+    start_time: datetime,
+    end_time: datetime,
+    use_active_power: bool,
+    resolution: int | None = None,
+) -> CumulativeEnergy:
+    """
+    Calculate the cumulative energy consumption and production over a specified time range.
+
+    Args:
+        client: The client used to fetch the metric samples from the Reporting API.
+        microgrid_id: The ID of the microgrid.
+        component_id: The ID of the component within the microgrid.
+        start_time: The start date and time for the period.
+        end_time: The end date and time for the period.
+        use_active_power: If True, use the 'AC_ACTIVE_POWER' metric.
+                          If False, use the 'AC_ACTIVE_ENERGY' metric.
+        resolution: The resampling resolution for the data, represented in seconds.
+                    If None, no resampling is applied.
+    Returns:
+        EnergyMetric: A named tuple with start_time, end_time, consumption, and production
+        in Wh. Consumption has a positive sign, production has a negative sign.
+    """
+    metric = Metric.AC_ACTIVE_POWER if use_active_power else Metric.AC_ACTIVE_ENERGY
+
+    metric_samples = [
+        sample
+        async for sample in client.list_microgrid_components_data(
+            microgrid_components=[(microgrid_id, [component_id])],
+            metrics=metric,
+            start_dt=start_time,
+            end_dt=end_time,
+            resolution=resolution,
+        )
+    ]
+
+    if metric_samples:
+        if use_active_power:
+            # Convert power to energy if using AC_ACTIVE_POWER
+            consumption = (
+                sum(
+                    m1.value * (m2.timestamp - m1.timestamp).total_seconds()
+                    for m1, m2 in zip(metric_samples, metric_samples[1:])
+                    if m1.value > 0
+                )
+                / 3600.0
+            )  # Convert seconds to hours
+
+            last_value_consumption = (
+                metric_samples[-1].value
+                * (end_time - metric_samples[-1].timestamp).total_seconds()
+                if metric_samples[-1].value > 0
+                else 0
+            ) / 3600.0
+
+            consumption += last_value_consumption
+
+            production = (
+                sum(
+                    m1.value * (m2.timestamp - m1.timestamp).total_seconds()
+                    for m1, m2 in zip(metric_samples, metric_samples[1:])
+                    if m1.value < 0
+                )
+                / 3600.0
+            )
+
+            last_value_production = (
+                metric_samples[-1].value
+                * (end_time - metric_samples[-1].timestamp).total_seconds()
+                if metric_samples[-1].value < 0
+                else 0
+            ) / 3600.0
+
+            production += last_value_production
+
+        else:
+            # Directly use energy values if using AC_ACTIVE_ENERGY
+            consumption = sum(
+                m2.value - m1.value
+                for m1, m2 in zip(metric_samples, metric_samples[1:])
+                if m2.value - m1.value > 0
+            )
+            production = sum(
+                m2.value - m1.value
+                for m1, m2 in zip(metric_samples, metric_samples[1:])
+                if m2.value - m1.value < 0
+            )
+
+            if len(metric_samples) > 1:
+                last_value_diff = metric_samples[-1].value - metric_samples[-2].value
+                if last_value_diff > 0:
+                    consumption += last_value_diff
+                elif last_value_diff < 0:
+                    production += last_value_diff
+    else:
+        consumption = production = 0.0
+
+    return CumulativeEnergy(
+        start_time=start_time,
+        end_time=end_time,
+        consumption=consumption,
+        production=production,
+    )